deliverable d7.1: kpi definition 1_kpi definition_v1 2_final.pdf · the ide4l kpi set has been...

IDE4L is a project co-funded by the European Commission

Project no: 608860

Project acronym: IDE4L

Project title: IDEAL GRID FOR ALL

Deliverable D7.1: KPI Definition

Due date of deliverable: 30.11.2014

Actual submission date: 02.12.2014

Start date of project: 01.09.2013 Duration: 36 months

Lead beneficial name: Tampere University of Technology, Finland

Writers/authors: Fernando Salazar (UFD); Fernando Martín (UFD); Maite Hormigo (GNF)

Dissemination level: PU

Track Changes

Version Date Description Author Reviewer

V0.0 01/04/2014 Table of Contents Maite Hormigo

V 0.1 21/04/2014 A2A comments Alessio Dede

V0.2 22/04/2014 WP7 meeting comments Maite Hormigo

V0.3 29/04/2014 Content update (state of the art) Maite Hormigo

V0.4 21/05/2014 A2A comments Alessio Dede

V0.5 27/05/2014 Document update Maite Hormigo Alessio Dede

V0.6 22/07/2014 Inserted chapter 8 summary Maite Hormigo

IDE4L Deliverable D7.1

2 IDE4L is a project co-funded by the European Commission

V0.7 01/08/2014 Inserted chapter 8 content Maite Hormigo Amelia Álvarez Alessio Dede

Per Routh

V0.8 10/10/2014 Insertion of KPIs, Abstract, ToC update, Acronyms

Maite Hormigo

V0.9 21/10/2014 IDE4L and EEGI KPI correspondence review

Maite Hormigo Juan Luis Garrote Miquel Cruz Alessio Dede

V1.0 24/11/2014 Deliverable review Maite Hormigo Fernando Salazar Fernando Martín Alessio Dede Per Routh Lukas Verheggen Juan Luis Garrote Andrea Angioni Antonino Riccobono Sami Repo Stefano Zanini Antti Mutanen Zaid Al-Jassim Jasmin Mehmedalic Hossein Hooshyar Miquel Cruz Julio Usaola



1. EXECUTIVE SUMMARY

This document provides a comprehensive context and description of the KPI definition and

selection procedure, in order to assess the performance of the IDE4L architecture and use cases.

The IDE4L KPI set has been developed using the European Electricity Grid Initiative (EEGI)

methodology as the reference framework for this task. Therefore, the EEGI KPI structure is

introduced and given an ample depiction at the beginning of the deliverable (Chapter 4).

The IDE4L KPIs are going to be used as a fundamental tool for evaluating the Use Cases to be

tested within the project, so a deep analysis of the Use Cases has been performed, and a specific

set of KPIs has been designed for each one.

In the process of crafting KPIs for the IDE4L use cases, two templates have been used:

1. a short one for a first approach of the selection, and

2. a detailed one for a complete definition of the selected ones

The correspondence between Use Cases and KPIs is shown in Chapter 5, whereas their whole

description can be found in Chapter 6.

In Chapter 7, some guidelines for the KPI calculation are given, in order to explain how to use and

make the most of them. This process is defined as a flow chart, starting with the validation of all

the inputs in the different demonstration scenarios, and then continuing with the evaluation of the

KPI. Moreover, a template for the results is designed, in order to standardize and facilitate this

task.

Finally, in order to determine the impact on the DSO business performance of IDE4L project, a map

from IDE4L to EEGI KPIs is drawn.



TABLE OF CONTENTS:

1. EXECUTIVE SUMMARY .................................................................................................................................. 3

2. ACRONYMS ................................................................................................................................................... 6

3. INTRODUCTION .......................................................................................................................................... 10

4. REFERENCE FRAMEWORK FOR KPI DEFINITION ......................................................................................... 11

4.1 EEGI methodology ........................................................................................................................... 11

4.2 Overarching KPIs (EEGI Level 1) ....................................................................................................... 12

4.3 Specific KPIs (EEGI Level 2) .............................................................................................................. 14

4.4 Project KPIs (EEGI Level 3) ............................................................................................................... 19

4.5 EEGI Framework contextualization for IDE4L: IDE4L methodology ................................................ 20

5. KPIS AND USE CASES CORRESPONDANCE .................................................................................................. 24

6. KPI DEFINITION ........................................................................................................................................... 26

6.1 UC1: MV & LV Real-time monitoring ............................................................................................... 26

6.2 UC2: MV & LV State estimation ....................................................................................................... 41

6.3 UC3: Dynamic Monitoring for TSO .................................................................................................. 45

6.4 UC4: Communication devices for transmitting signals considering long distances within the

electrical distribution system ...................................................................................................................... 48

6.5 UC5: PLC communications for the active distribution grid ............................................................. 50

6.6 UC6: MV&LV Load Forecast ............................................................................................................. 52

6.7 UC7: MV&LV State Forecast ............................................................................................................ 55

6.8 UC8: Network Description Update .................................................................................................. 60

6.9 UC9: Protection Configuration Update ........................................................................................... 63

6.10 UC10: Control Center Network Power Control (Tertiary Control) .................................................. 65

6.11 UC11: LV Network Power Control (Secondary control)................................................................... 74

6.12 UC12: MV Network Power Control (Primary Control) ..................................................................... 83

6.13 UC13: Decentralized FLISR ............................................................................................................... 92

6.14 UC14: Power quality control ........................................................................................................... 99

6.15 UC15: Expansion Planning ............................................................................................................. 103

6.16 UC16: Operational Planning .......................................................................................................... 110

6.17 UC17: Target network Planning ..................................................................................................... 113

6.18 UC18 and UC19: Load Areas Configuration; Flexible Table ........................................................... 115

6.19 UC20: Off-Line Validation .............................................................................................................. 116

6.20 UC21: Real-time Validation ........................................................................................................... 118

6.21 UC22: SRP and CRP Day-Ahead and Intra-Day Market Procurement ........................................... 120



6.22 UC23: Conditional re-profiling activation (CRP activation) ........................................................... 122

6.23 UC24: Day-ahead Demand Response ............................................................................................ 123

6.24 UC25: Day-ahead Dynamic Tariff................................................................................................... 125

7. GUIDELINES FOR KPI EVALUATION ........................................................................................................... 129

7.1 Introduction to the methodology .................................................................................................. 129

7.2 IDE4L to EEGI KPIs mapping ........................................................................................................... 129

7.3 Step-by-step approach for KPI evaluation ..................................................................................... 132

7.4 Results template description ......................................................................................................... 133

8 REFERENCES ............................................................................................................................................. 136



2. ACRONYMS

AMI Advanced Metering Infrastructure

AQR Automatic Reactive power Regulator

AVC Automatic Voltage Controller

AVR Automatic Voltage Regulator

BaU Business as Usual

BEO Breaker Energyzed Operations

C Cost

C Total number of monitored currents

𝐶𝑐𝑜𝑚𝑝 Cost of network components

𝐶𝑙𝑜𝑠𝑠 Cost of Network loses per kWh

𝐶𝑜𝑝 Cost of Operation of network components

CAPEX Capital Expenditure

CBR_OP Number of operations done on the breaker during the

fault condition

CCPC Control Center Tertiary Power Control

CD Total data of monitored currents

CMG Current Monitoring Granularity

CMVD Current Monitoring Data Volume

CO2 Reduction in CO2 emissions

COST Reduction of energy cost

Dmax/min Maximum/minimum hourly demand

DADT Day Ahead Dynamic Tariff

DCS Difference in Cost of Serv

DER Distributed Energy Resource

DMS Distribution Management System

DNO Distribution Network Operator

DR Demand Response



DSO Distribution System Operator

E_curl RES curtailment

E_not-injected total energy not injected in network

EEGI European Electricity Grid Initiative

EHC Enhanced Hosting Capacity

EPSE Expansion Planning Scenario Evaluation

EV Electric Vehicle

Flick_M Flicker mitigation MV/LV active grid

FLISR Fault Location, Isolation and Service Restoration

GB Number of branches of the network

GN Number of nodes

HC Hosting Capacity

HC_EV Hosting Capacity of Electric Vehicles

HHI Herfindahl-Hirschman Index

HP Heat Pump

I Voltage stability of the electricity system

Iavg Average amplitude of the complex current in line

Inom Nominal current rating of line

IS Interconnection switch

ILA Improved Life-time of Assets

KPI Key Performance Indicator

LV Low Voltage

LVLGF LV Load/generation forecaster

LVPC LV Network Power Control

LVSE Real-time LV Network State Estimator

LVSF LV State forecaster

MMDR Ratio between minimum and maximum electricity

demand within a day



MO Market Operator

MV Medium Voltage

MVLGF MV Load/generation forecaster

MVPC MV Network Power Control

MVSE Real-time MV Network State Estimator

MVSF MV State forecaster

NC Increase in Network Capacity

NCAC Network Capacity at an Affordable Cost

NDU Network Description Update

OLTC On-load Tap Changer

OLV Off-line Validation

OPEX Operational Expenditure

P Total number of monitored active powers

Pcur Curtailed Production for generation unit

Pdr Curtailed/moved Load of load unit

Pload Active Power consumed by the load

Pprod Active Power of production unit

PRL Peak Load Reduction

Pt Active Power going through Transformer

P/QMG Power Monitoring Granularity

PCU Protection Configuration Update

PD Total data of monitored active powers

PD Peak demand reduction ratio

PER Packet Error Rate

PLC Power Line Communication

PMDV Power Monitoring Data Volume

Q Total number of monitored reactive powers

QD Total data of monitored reactive power



RC Replacement Cost

RES Renewable Energy Source

RTV Real-time Validation

Savg Average loading of transformer

Snom Nominal rating of transformer

SAIDI System Average Interruption Duration Index

SAIFI System Average Interruption Frequency Index

SF System Flexibility

SFAC System Flexibility at Affordable Cost

SI Service Interruption

SIMR Success Index in Meter Reading

SP Number of samples

Tfault Average time required for fault awareness,

localization and isolation

TL Reduction of technical network losses

TNP Target Network Planning

TSO Transmission System Operator

U Percentage utilization of electricity network

components

UC Use Case

UR TSO´s visibility of distribution network

V Total number of monitored voltages

VD Total data of monitored voltages

VMDV Voltage Monitoring Data Volume

VMG Voltage Monitoring Granularity

Wp Wrong received or non-received packets

WP Work Package



3. INTRODUCTION

In IDE4L project, 22 Use Cases are going to be tested in different environments: development

laboratories, demonstration laboratories, and field demonstrators. In order to evaluate the

solutions, algorithms and procedures to be tested, a proper methodology has to be defined. This

document aims at deeply describing the methodology defined in IDE4L for the KPIs selection and

definition, and also establishing a set of guidelines to help the calculations.



4. REFERENCE FRAMEWORK FOR KPI DEFINITION

4.1 EEGI methodology

After the analysis of the different frameworks for KPIs using the judgement and experience of the

project participants, the EEGI framework has been adopted as a reference for the IDE4L KPI study,

due to its simplicity and level of development.

EEGI has analysed the energy targets for the future electricity sector decarbonisation, and it has

developed a complete roadmap for 2022, where a set of Key Performance Indicators (KPI) are

defined in order to quantify the proposed goals for a low carbon economy at affordable costs

[EEGI_roadmap].

The EEGI framework is developed under the GRID+ project [grid+], oriented to support the

development of EEGI initiative. With this purpose, three levels of KPIs have been introduced, each

level having a specific management goal of the Research and Innovation Roadmap. These KPIs are

not oriented to evaluate the results of R&I projects, but to estimate their contribution to achieve

the EU goals.

In the following figure, the three EEGI KPI levels are shown:

Figure 1 EEGI KPI levels

Source: EEGI Grid+ project

Level 1: “Overarching KPIs” consist of a set of indicators which trace clear progress brought

by EEGI activities towards its overarching goal

Level 2: “Specific KPIs” include those indicators oriented to quantify the expected impacts

of a group of R&I activities in view of meeting the R&I roadmap overarching goal

Level 3: “Project KPIs” are a set of indicators proposed by each R&I project in view of

detailing further the contribution of each R&I project to level 2 KPIs

In the study of KPIs, some scenarios shall be taken into consideration. First of all, the current

situation must be analyzed as a reference, and then two possible future situations:



Figure 2 Scenarios to be measured


Business as Usual (BAU) case: this scenario shows what would be the situation if

conventional solutions are applied (it reflects the normal evolution that the network would

have)

R&I case: this scenario corresponds to a future situation where innovative solutions

provided by R&I project results are implemented. This impact can be technical, economical,

or both of them

Baseline case: reference scenario

Once the scenarios are defined, EEGI proposes a step-by-step methodology to measure the KPIs. It

is a six-step process, defined as it follows:

1 STEP 1: Determination of the reference scenario or initial situation, the problems to

solve, needs to satisfy, and the drivers that trigger a network/system improvement

2 STEP 2: Analysis of the future situation when the conventional evolution of the

network happens (BAU situation)

3 STEP 3: Calculation of the correspondent KPI to evaluate the BAU situation

4 STEP 4: Analysis of the future situation when smart grid solutions are deployed in

the network (R&I situation)

5 STEP 5: Calculation of the correspondent KPI to evaluate the R&I situation

6 STEP 6: Comparison of both scenarios, and calculation of the final indicator

applying the proposed formula

4.2 Overarching KPIs (EEGI Level 1)

The EEGI Roadmap has identified an overarching goal of allowing European electricity networks

continuously deliver effective flexible capacities to integrate actions of grid users at affordable



costs, keeping the system reliability at levels compatible with societal needs. In order to evaluate

the approach of R&I activities to this goal, two KPIs are defined to be applied to clusters of projects:

1. A.1 Increased network capacity at affordable cost

This indicator evaluates the increase/decrease of network capacity at an affordable cost

(NCAC) for all grid-users (generation and demand), wherever and whenever it is necessary,

maintaining an affordable cost, i.e. maximising the added network capacity per euro of

investment. Two different calculations are provided:

a) Variation of the amount of network capacity per euro of cost:

∆𝑁𝐶𝐴𝐶 = 𝑁𝐶𝐴𝐶𝑅&𝐼 − 𝑁𝐶𝐴𝐶𝐵𝐴𝑈 [unit: W/€]

Being 𝑁𝐶𝐴𝐶𝑅&𝐼 = 𝑁𝐶𝑅&𝐼/𝐶𝑅&𝐼 ; and 𝑁𝐶𝐴𝐶𝐵𝐴𝑈 = 𝑁𝐶𝐵𝐴𝑈/𝐶𝐵𝐴𝑈

It can also be expressed in terms of percentage:

∆𝑁𝐶𝐴𝐶% = ((𝑁𝐶𝐴𝐶𝑅&𝐼 − 𝑁𝐶𝐴𝐶𝐵𝐴𝑈)/𝑁𝐶𝐴𝐶𝐵𝐴𝑈) × 100

b) There is the possibility that the R&I project does not provide an improvement of the

network capacity, but a reduction of the cost. In this case, the interesting indicator is the

one proposed:

∆𝐶 = 𝐶𝑅&𝐼 − 𝐶𝐵𝐴𝑈 [𝑢𝑛𝑖𝑡: €]

∆𝐶% = ((𝐶𝑅&𝐼 − 𝐶𝐵𝐴𝑈)/𝐶𝐵𝐴𝑈) × 100 [𝑢𝑛𝑖𝑡: %]

Where:

NC is the Increase in Network Capacity (through BAU or R&I) (increase in the amount of

electrical power that can be transmitted or distributed in the selected frame) [W]

C is the Cost (OPEX and/or CAPEX) [€]

NCAC is the Increase of Network Capacity at Affordable Cost [W/€]

2. A.2 Increased system flexibility at affordable cost

This indicator evaluates the increase/decrease of system flexibility for all grid-users

(generation, loads, and network operators), while avoiding potential instability and

blackouts, keeping an affordable cost. The following general indicator is provided:

∆𝑆𝐹𝐴𝐶 = 𝑆𝐹𝐴𝐶𝑅&𝐼 − 𝑆𝐹𝐴𝐶𝐵𝐴𝑈 [𝑢𝑛𝑖𝑡: 𝑊 𝑜𝑟 𝑀𝑉𝐴𝑟/€]

being 𝑆𝐹𝐴𝐶𝑅&𝐼 = 𝑆𝐹𝑅&𝐼/𝐶𝑅&𝐼

and 𝑆𝐹𝐴𝐶𝐵𝐴𝑈 = 𝑆𝐹𝐵𝐴𝑈/𝐶𝐵𝐴𝑈



It can also be expressed in terms of percentage:

∆𝑆𝐹𝐴𝐶% = ((𝑆𝐹𝐴𝐶𝑅&𝐼 − 𝑆𝐹𝐴𝐶𝐵𝐴𝑈)/𝑆𝐹𝐴𝐶𝐵𝐴𝑈) × 100

Two more indicators are provided, for the following special cases:

a) If the R&I project does not know information about the cost of the innovative solutions

they are testing, the indicator will only take the system flexibility into account:

∆𝑆𝐹 = 𝑆𝐹𝑅&𝐼 − 𝑆𝐹𝐵𝐴𝑈 [𝑢𝑛𝑖𝑡: 𝑊 𝑜𝑟 𝑀𝑉𝐴𝑟]

∆𝑆𝐹% = ((𝑆𝐹𝑅&𝐼 − 𝑆𝐹𝐵𝐴𝑈)/𝑆𝐹𝐵𝐴𝑈) × 100 [𝑢𝑛𝑖𝑡: %]

b) If the R&I project does not provide an improvement of the network capacity, but a

reduction of the cost:

∆𝐶 = 𝐶𝑅&𝐼 − 𝐶𝐵𝐴𝑈 [𝑢𝑛𝑖𝑡: €]

∆𝐶% = ((𝐶𝑅&𝐼 − 𝐶𝐵𝐴𝑈)/𝐶𝐵𝐴𝑈) × 100 [𝑢𝑛𝑖𝑡: %]

Where:

SF = System Flexibility: amount of electrical power (generation and load) that can be

modulated to the needs of the system operation within a specified unit of time [W or

MVAr]

SFAC = System flexibility at affordable cost [W or MVAr/€]

C = OPEX and CAPEX estimated to manage the flexibility of the network [€]

4.3 Specific KPIs (EEGI Level 2)

The overarching goals aforementioned (increasing network capacity and system flexibility) can be

further quantified and monitored through seven specific KPIs; six of them are common for DSOs

and TSOs, and the last one is specific for DSOs only.

Common TSO and DSO Specific KPIs

B.1 Increased RES and DER hosting capacity. B.2 Reduced energy curtailment of RES and DER. B.3 Power quality and quality of supply. B.4 Extended asset life time. B.5 Increased flexibility from energy players. B.6 Improved competitiveness of the electricity

market.

DSO Specific KPI B.7 Increased hosting capacity for electric vehicles (EVs) and other new loads.



These KPIs also cover the functional objectives defined in EEGI Roadmap. They are explained in the

following list:

1 B.1 Increased RES and DER hosting capacity (DSO+TSO)

This KPI is intended to give a statement about the additional RES/DER that can be installed

in the network, when R&I solutions are applied, and compared to the BAU scenario. This

improvement can be quantified by means of the following percentage:

𝐸𝐻𝐶% =𝐻𝐶𝑅&𝐼 − 𝐻𝐶𝐵𝐴𝑈

𝐻𝐶𝐵𝐴𝑈× 100 [𝑢𝑛𝑖𝑡: %]

The formula is applicable for TSO with RES integration and for DSO with DER integration.

Where:

EHC % is the enhanced hosting capacity of RES/DER when R&I solutions are applied with

respect to BAU scenario.

HCR&I is the additional hosting capacity of RES/DER when R&I solutions are applied with

respect to currently connected generation (GW or MW)

HCBAU is the additional hosting capacity of RES/DER in BAU scenario applied with respect to

currently connected generation (GW or MW).

2 B.2 Reduced energy curtailment of RES and DER (DSO+TSO)

The objective of this KPI is the reduction of energy curtailment due to technical and

operational problems, such as over voltage, over frequency, local congestion, etc. In the

case of the DSO, the increased presence of monitoring will have an impact on producers, as

the time for curtailment will be reduced, and the operative range will be wider. This

indicator can be measured as the percentage of GWh electricity curtailment from DER

reduction of R&I solution compared to BAU for a period of time, i.e. a year.

𝐸𝑛𝑜𝑡−𝑖𝑛𝑗𝑒𝑐𝑡𝑒𝑑 =𝐸𝑛𝑜𝑡−𝑖𝑛𝑗𝑒𝑐𝑡𝑒𝑑

𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒 − 𝐸𝑛𝑜𝑡−𝑖𝑛𝑗𝑒𝑐𝑡𝑒𝑑𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

𝐸𝑛𝑜𝑡−𝑖𝑛𝑗𝑒𝑐𝑡𝑒𝑑𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒

Where:

Enot-injected is the percentage reduction in energy not injected in network due to MV/LV

network conditions [MWh].

E_not-injected, baseline is the total energy not injected in network due to MV/LV network

conditions under baseline condition [MWh].

E_not-injected, measured is the total energy not injected in network due to MV/LV network

conditions under new measured condition [MWh].



3 B.3 Power Quality and Quality of Supply (DSO+TSO)

This KPI is based on the goal of maintaining high quality of power supply to end users via

preventive actions with early warning system, advanced and optimal system development

and operation tools, and high availability of assets via optimal maintenance strategies and

planning tools. For the DSO, three indicators are proposed:

a) SAIDI and SAIFI indicators:

∆𝑆𝐴𝐼𝐷𝐼% =𝑆𝐴𝐼𝐷𝐼𝑆𝐺 − 𝑆𝐴𝐼𝐷𝐼𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒

𝑆𝐴𝐼𝐷𝐼𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒× 100 [𝑢𝑛𝑖𝑡: %]

∆𝑆𝐴𝐼𝐹𝐼% =𝑆𝐴𝐼𝐹𝐼𝑆𝐺 − 𝑆𝐴𝐼𝐹𝐼𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒

𝑆𝐴𝐼𝐹𝐼𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒× 100 [𝑢𝑛𝑖𝑡: %]

Where:

SAIDI is the System Average Interruption Duration Index

SAIFI is the System Average Interruption Frequency Index

b) Line voltage profiles fulfilling grid nominal voltage requirements, as defined in EN 50160

standard [EN 50160]:

𝑉% =𝑉𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒 − 𝑉𝑆𝐺

𝑉𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒× 100 [𝑢𝑛𝑖𝑡: %]

Where:

V% is the evolution of the line voltage profiles

VSG is defined as the line voltage profiles with SG solutions

Vbaseline is the line voltage profiles in baseline situation

The definition of voltage profile can vary, including different options:

- Vmax: maximum reached line voltage during defined monitoring period

- V95%: value for which 95% of all voltage line measurements fall below

- Total number of voltage line violations

c) Average time needed for awareness, localization and isolation of grid fault:

∆𝑇𝑓𝑎𝑢𝑙𝑡[%] =𝑇𝑓𝑎𝑢𝑙𝑡,𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒 − 𝑇𝑓𝑎𝑢𝑙𝑡,𝑆𝐺

𝑇𝑓𝑎𝑢𝑙𝑡,𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒× 100 [𝑢𝑛𝑖𝑡: %]

Where:

∆Tfault [%] is the percentage reduction in time required for fault awareness, localization and

isolation

Tfault,SG is the average time required for fault awareness, localization and isolation with

Smart Grid Solutions

Tfault,Baseline is the average time required for fault awareness, localization and isolation in

Baseline situation



4 B.4 Extended asset lifetime (DSO+TSO)

The base of this KPI is that, thanks to R&I solutions, network assets can last longer while

maintaining the system reliability, so the replacement costs are reduced. There are two

alternatives to define this indicator:

a) Improved Life-time of Assets takes into account the total cost of exploiting a given

group of assets, including the capital expenditure and the operational expenditure:

𝐼𝐿𝐴 =(𝐶𝐴𝑃𝐸𝑋𝐵𝐴𝑈 + 𝑂𝑃𝐸𝑋𝐵𝐴𝑈) − (𝐶𝐴𝑃𝐸𝑋𝑅&𝐷 + 𝑂𝑃𝐸𝑋𝑅&𝐷)

(𝐶𝐴𝑃𝐸𝑋𝐵𝐴𝑈 + 𝑂𝑃𝐸𝑋𝐵𝐴𝑈)

b) Improved Life-time of Assets looking only at the replacement costs (RC):

𝐼𝐿𝐴 = (𝑅𝐶𝐵𝐴𝑈 − 𝑅𝐶𝑅&𝐷)/(𝐶𝐴𝑃𝐸𝑋𝐵𝐴𝑈)

Where:

CAPEX is the Capital Expenditure

OPEX is the Operational Expenditure

RC is the Replacement cost

5 B.5 Increased flexibility from energy players (DSO+TSO)

The flexibility is an indication of the ability of the electricity system to respond and balance

supply and demand in real-time. To this matter, dispatchable generation technologies help

to adjust output on demand serve.

Regarding DSO operations, power flexibility is mainly measured through

demand/generation response capabilities, so both indexes will be defined:

Demand:

𝑃𝐷𝑆𝑀 =(𝑃𝐷𝑆𝑀)𝑆𝐺 − (𝑃𝐷𝑆𝑀)𝐵𝐴𝑈

𝑃𝑝𝑒𝑎𝑘

∆𝑃𝑅𝐿% =𝑃𝐿𝑅𝑆𝐺

𝑃𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒,𝑝𝑒𝑎𝑘

Generation:

𝑃𝐷𝐸𝑅 =(𝑃𝐷𝐸𝑅)𝑆𝐺

∑ 𝑃𝑅 𝑆𝐺−

(𝑃𝐷𝐸𝑅)𝐵𝐴𝑈

∑ 𝑃𝑅 𝐵𝐴𝑈

Where

PRL is the Peak Load Reduction

PDSM represents the amount of load capacity participating in DSM in the BAU and SG

scenario



Ppeak represents the maximum electricity demand in area under evaluation

PDER represents the amount of flexible generation in the in the BAU and SG scenario

PR represents the installed capacity of each generation and/or storage unit on the system

6 B.6 Improved competitiveness of the electricity market (DSO+TSO)

R&I solutions facilitate the high penetration of DER/RES. In order to integrate these

technologies (demand-side management, energy storage ...) by improving the

competitiveness in the electricity market, efficient market mechanisms need to be put in

place, providing energy and ancillary services to the grid at affordable costs.

Three different definitions are provided:

a) Cost of a Given service:

Evaluated in N different national/regional markets:

𝐷𝐶𝑆 = ∑ (𝐶𝑆𝑖 − 𝐶𝑆𝑗)2

𝑁

𝑖,𝑗=1𝑖≠𝑗

Comparing DCS between BAU and R&D scenarios:

∆𝐷𝐶𝑆 = 𝐷𝐶𝑆𝐵𝐴𝑈 − 𝐷𝐶𝑆𝑅&𝐷 = ∑ (𝐶𝑆𝑖𝐵𝐴𝑈 − 𝐶𝑆𝑗

𝐵𝐴𝑈)2

𝑁


− ∑ (𝐶𝑆𝑖𝑅&𝐷 − 𝐶𝑆𝑗

𝑅&𝐷)2

𝑁


Where:

DCS is the difference in Cost of Service

N is the number of regional markets

b) Number of market players

∆𝑀𝑂𝑁 = ∑ 𝑀𝑂𝑖

𝑁𝑅&𝐷

𝑖=1

− ∑ 𝑀𝑂𝑖

𝑁𝐵𝐴𝑈

𝑖=1

Where:

MO is the market operator

NR&I is the number of market operators participating in a given market following

deployment of smart grid solution

NBAU is the number of market operators participating in a given market following BAU

system evolution

c) Size of individual market players

𝐻𝐻𝐼 = ∑ 𝑠𝑖2

𝑁

𝑖=1



Where:

HHI (Herfindahl-Hirschman Index or simply Herfindal Index) is a measure of the size of firms

in relation to the industry and an indicator of the amount of competition among them.

HHI is defined as the sum of the squares of the market shares (s, expressed as fraction or as

percentage) of the different firms (i = firm number i, from a total of N) within a given

industry. HHI ranges from 1/N to 1. An increase in the HHI generally indicates a decrease in

competition and an increase of market power

7 B.7 Increased hosting capacity for Electric Vehicles and other new loads (DSO)

This KPI is intended to measure the capacity of the network to host the new load that

emergent large fleets of electric vehicles (EV) represent. A proposal for this calculation is

provided:

𝐻𝐶_𝐸𝑉% =𝐻𝐶_𝐸𝑉𝑆𝐺 − 𝐻𝐶_𝐸𝑉𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒

𝐻𝐶_𝐸𝑉𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒× 100

Where:

HC_EVSG is the Hosting Capacity of Electric Vehicles in MV and LV network with Smart Grid

solutions

HC_EVbaseline is the Hosting Capacity of Electric Vehicles in MV and LV network under a

normal baseline situation (no intelligent solutions)

HC_EV% is the percentage of increase in Hosting Capacity of Electric Vehicles in MV and LV

network with Smart Grid solutions

4.4 Project KPIs (EEGI Level 3)

EEGI establishes this level to include indicators explicitly designed for specific R&I projects under

the EEGI Roadmap, in order to test the impact of the solutions implemented in the demonstration

fields. However, they are expected to be similar to level 2 EEGI KPIs, or at least to contribute to

them in any case, so the following mapping can be identified:



Figure 3 Level 3 to level 2 EEGI KPI mapping


However, level 3 KPIs are out of the scope of GRID+ project, so no specific directions are defined.

The calculation methodology of level 2 KPIs can be used as an assessment to a possible calculation

methodology for level 3 KPIs.

4.5 EEGI Framework contextualization for IDE4L: IDE4L methodology

Once this EEGI framework is analysed, it must be adapted to IDE4L requirements and needs. The

way to proceed is to define different KPIs for each Use Case, focused on measuring the

improvement that they provide to the system: these are EEGI level 3 KPIs. Once these KPIs are

selected and defined, their contribution to EEGI level 2 KPIs will be analysed. Overarching KPIs are

not considered, due to their relation with clusters of projects.

In order to define IDE4L KPIs properly, a methodology has been defined. It consists of six main

steps:

1. Use Case Definition

In order to start defining the Project Key Performance Indicators, a detailed definition of the

Use Cases must be provided.

2. KPI proposal (general definition)

3. Agreement among WP leaders

A general template will be provided for a first approximation to the KPI list. This list will be

reviewed by project partners until a definitive list of KPIs is achieved.

4. Specific KPI definition, scenario identification, required data and step-by-step procedure

A complete template should be filled out for each one of the KPIs, following these main

sections:

• General and specific description of the magnitude to be measured



• Main objective

• Connection with other KPIs and Use Cases

• Magnitude formula

• Formula of the KPI as a difference (improvement or worsening) between scenarios

• Step-by-step methodology for the evaluation

• Required data and granularity

• Definition of scenarios (Baseline, BaU, Smart Grid) where the magnitude must be

measured

5. Data Collection

Depending on the type of data required and the frequency of data acquisition, proper

templates will be prepared in order to collect the information needed to calculate the KPI.

6. KPI Calculation

KPI is calculated using a data compilation tool, e.g., an Excel file. The evaluations will include

the calculation of the magnitude in the specific scenarios, and their comparison using the KPI

formula.

This procedure can be summarized in the following diagram:

Figure 4 KPI definition, selection and calculation procedure

In order to select and define these KPIs, two templates have been created:

Use Case Definition

KPI proposal

Agreement?

Specific KPI definition, scenario identification,

data gathering templates and step-by-step

procedure

Data Collection and KPI calculation

Yes

No



- General template: it consists of basic information about the KPI and the scenarios to be applied.

Figure 5 KPI General template

- Detailed template: it consists of specific information, step-by-step methodology, data collection

and scenarios to be applied.



Figure 6 KPI Detailed template

Once all these steps are completed, a definitive list of IDE4L KPIs will be available.



5. KPIS AND USE CASES CORRESPONDANCE

In order to define IDE4L KPIs, an analysis of each Use Case is performed, so that the parameters

necessary to evaluate the Use Case are determined. Hence, the chosen methodology is the

definition of a set of KPIs for each Use Case. This assignation is shown in the following table:

Use Case KPIs

LV Real-time Monitoring

Current Monitoring Data Volume

Current Monitoring Granularity

Powers Monitoring Data Volume

MV Real-time Monitoring

Powers Monitoring Granularity

Voltage Monitoring Data Volume

Voltage Monitoring Granularity

LV State Estimation Real-time LV Network State estimator

MV State Estimation Real-time MV Network State estimator

Dynamic Monitoring for TSO Voltage stability of the electricity system

TSO’s visibility of distribution network

Communication devices for transmitting signals considering long distances within the electrical distribution system

Evaluation of IEC 61850-90-5 library

PLC communications for the active distribution grid Success index in meter reading

LV Load and State Forecast LV load/generation forecaster

LV state forecaster

MV Load and State Forecast MV load/generation forecaster

MV state forecaster

Network Description Update Network Description Update

Protection Configuration Update Protection Configuration Update

Control Center Network Power Control

Control Centre Tertiary Power Control Technical and Economic Parameters

Control Center Tertiary Power Control - Operational Parameters

Control Center Tertiary Power Control - Technical Safety Parameters

LV Network Power Control

LV Network Power Control - Technical and Economic Parameters

LV Network Power Control - Operational Parameters

LV Network Power Control - Technical Safety Parameters

MV Network Power Control

MV Network Power Control - Technical and Economic Parameters

MV Network Power Control - Operational Parameters

MV Network Power Control - Technical Safety Parameters

Decentralized FLISR

SAIDI

SAIFI

Breaker energyzed operations

Interconnection Switch



Power Quality Control Flicker mitigation MV/LV active grid

Expansion Planning Expansion Planning Scenario Evaluation

Operational Planning

Reduction of energy cost

Ratio between minimum and maximum electricity demand within a day

Target Network Planning Target Network Planning

Load Areas Configuration Reduction of technical network losses

Flexibility table

Off-Line Validation Peak demand reduction ratio

Real-time Validation Percentage utilization of electricity network components

SRP and CRP Day-Ahead and Intra-Day Market Procurement

Reduction in CO2 emissions

Conditional re-profiling activation (CRP Activation) RES curtailment

Day-Ahead Demand Response Demand Response (DR)

Day-Ahead Dynamic Tariff Day Ahead Dynamic Tariff (DADT)



6. KPI DEFINITION

6.1 UC1: MV & LV Real-time monitoring Six KPIs have been defined to evaluate this Use Case:

Current Monitoring Data Volume (CMDV)

BASIC KPI INFORMATION

KPI Name Current Monitoring Data-Volume KPI ID CMDV

Main Objective Evaluation of the increase of data amount for new monitored currents in PS or SS

KPI Description This KPI evaluates the total increment of data, for all branches of the grid grouped by PS or SS, of monitored currents from baseline to IDE4L scenario

KPI Formula

𝑪𝑴𝑫𝑽𝑺𝒕𝒂𝒕𝒊𝒐𝒏 = ∑(𝑪𝑫𝒊𝑰𝑫𝑬𝟒𝑳 − 𝑪𝑫𝒊

𝑩𝑳)

#𝑮𝑩

𝒊=𝟏

Where:

#GB: Number of branches of the network fed by the considered PS or SS

CDiIDE4L: Total data of monitored currents in a single branch i in IDE4L

scenario in the X time slot

CDiBL: Total data of monitored currents in a single branch i in baseline


This KPI provides:

the increase of costs (in terms of data amount to handle) for the monitoring system to support new IDE4L functionalities (KPI direct proportionality)

Unit of measurement kBytes/X_time_slot (i.e. kBytes/Hour)

Connection / Link with other relevant defined KPIs and Use Cases

CMG (Current Monitoring Granularity KPI) - The increment of data volume related to the monitored currents is directly connected to the increment of monitoring granularity required to perform IDE4L algorithms. So that CMDV must be considered together with CMG KPI in order to understand the cost of the monitoring improvements Both KPIs are linked to ‘LV&MV Real-time Monitoring’ Use Case

Project sites to be calculated

Development laboratory

Integration laboratory

Demonstration laboratory

Field demonstrator

KPI CALCULATION METHODOLOGY

KPI Step Methodology ID

[KPI ID #] Step Responsible

CMDV_01 Collect the total number of monitored currents (#Ci

BL) in base line scenario for each considered i branch

Responsible of each field demo

CMDV_02 Collect the number of samples (SPj

BL) in the X time slot and the data dimension for each j monitored current (CDj

BL) in baseline scenario for each considered i branch


CMDV_03 Evaluate the total data of monitored currents for all considered branches in baseline scenario in the X time slot




𝑪𝑫𝑩𝑳 = ∑ ∑ (𝑺𝑷𝒋𝑩𝑳 ∗ 𝑪𝑫𝒋

𝑩𝑳)

#𝑪𝒊𝑩𝑳

𝒋=𝟏

#𝑮𝑩

𝒊=𝟏

CMDV_04 Collect the total number of monitored currents (#Ci

IDE4L) in IDE4L scenario for each considered i branch


CMDV_05 Collect the number of samples (SPj

IDE4L) in the X time slot and the data dimension for each j monitored current (CDj

IDE4L) in IDE4L scenario for each considered branch


CMDV_06

Evaluate the total data of monitored currents for all considered branches in IDE4L scenario in the X time slot

𝑪𝑫𝑰𝑫𝑬𝟒𝑳 = ∑ ∑ (𝑺𝑷𝒋𝑰𝑫𝑬𝟒𝑳 ∗ 𝑪𝑫𝒋

𝑰𝑫𝑬𝟒𝑳)

#𝑪𝒊𝑰𝑫𝑬𝟒𝑳

𝒋=𝟏

#𝑮𝑩

𝒊=𝟏


CMDV_07

Calculate the CMDV KPI as:

𝑪𝑴𝑫𝑽𝑺𝒕𝒂𝒕𝒊𝒐𝒏 = 𝑪𝑫𝑰𝑫𝑬𝟒𝑳 − 𝑪𝑫𝑩𝑳


KPI SCENARIOS

Scenarios to be measured

BASELINE

BUSINESS AS USUAL (BaU)

SMART GRID

KPI DATA COLLECTION

Data Data ID Methodolog

y for data collection

Source/Tools/Instruments

for Data collection

Location of Data

collection

Frequency of data collection

Minimum monitoring period

Data collection

responsible

Number of considered branches

#GB

Provided by each

field demonstra

tor

Excel Excel Once //

Responsible of each

field demo

Number of monitored

currents for each branch in baseline scenario

#CiBL

Provided by each

field demonstra

tor

Excel Excel Once //

Responsible of each

field demo

Number of samples for each current in X time slot in baseline

scenario

SPjBL

Provided by each

field demonstra

tor

Excel Excel Once //

Responsible of each

field demo

Data volume for each monitored

current in baseline scenario

CDjBL

Provided by each

field demonstra

tor

Excel Excel Once //

Responsible of each

field demo



Number of monitored

currents for each branch in IDE4L

scenario

#CiIDE4L

Provided by each

field demonstra

tor

Excel Excel Once //

Responsible of each

field demo

Number of samples for each current in X time

slot in IDE4L scenario

SPjIDE4L

Provided by each

field demonstra

tor

Excel Excel Once //

Responsible of each

field demo

Data volume for each monitored current in IDE4L

scenario

CDjIDE4L

Provided by each

field demonstra

tor

Excel Excel Once //

Responsible of each

field demo

KPI BASELINE

Source of Baseline Condition

LITERATURE VALUES

COMPANY HISTORICAL VALUES

VALUES MEASURED AT START OF PROJECT

Details of Baseline Data needed to evaluate the KPI can be collected in a “single-shot”.

Responsible (Name, Company) for Baseline

This KPI will be calculated only by DSOs because inputs are about real network

GENERAL COMMENTS

The goals is to evaluate the increase of data amount for a fixed time slot X (X = hour, day, month, year, …) – in kBytes/X – due to the improvement of current monitoring granularity to support the new IDE4L functionalities. This increment directly impacts both on the data layer of the Substation Automation Unit (SAU) and on the communication infrastructures.

The evaluation is performed calculating the total increase over all branches of the considered PS or SS.

Current Monitoring Granularity (CMG)


KPI Name Current Monitoring Granularity KPI ID CMG

Main Objective Evaluation of the increase of monitored currents in MV and LV grid

KPI Description This KPI evaluates the average increment, for all branches of the grid, of monitored currents from baseline to IDE4L scenario

KPI Formula

𝑪𝑴𝑮𝑮𝒓𝒊𝒅 =∑ (#𝑪𝒊

𝑰𝑫𝑬𝟒𝑳 − #𝑪𝒊𝑩𝑳)#𝑮𝑩

𝒊=𝟏

#𝑮𝑩

Where:

#GB: Number of branches in the considered grid

#CiIDE4L: Total number of monitored currents in a single branch i in IDE4L

scenario

#CiBL: Total number of monitored currents in a single branch i in baseline



scenario

This KPI provides:

the increase of the current monitoring granularity (KPI direct proportionality)

Unit of measurement %/Branch


CMDV (Current Monitoring Data-Volume KPI) - The increment of current monitoring granularity entails also the increment of data volume related to the monitored currents. So that CMG must be considered together with CMDV KPI in order to understand the cost of the monitoring improvements Both KPIs are linked to ‘LV&MV Real-time Monitoring’ Use Case





Field demonstrator




CMG_01 Collect the total number of monitored currents (#Ci

BL) in base line scenario for each considered i branch


CMG_02

Evaluate the total number of monitored currents for all considered branches in baseline scenario

#𝑪𝑩𝑳 = ∑ #𝑪𝒊𝑩𝑳

#𝑮𝑩

𝒊=𝟏


CMG_03 Collect the total number of monitored currents (#Ci

IDE4L) in IDE4L scenario for each considered i branch


CMG_04

Evaluate the total number of monitored currents for all considered branches in IDE4L scenario

#𝑪𝑰𝑫𝑬𝟒𝑳 = ∑ #𝑪𝒊𝑰𝑫𝑬𝟒𝑳

#𝑮𝑩

𝒊=𝟏


CMG_05

Calculate the CMG KPI as:

𝑪𝑴𝑮𝑮𝒓𝒊𝒅 =#𝑪𝑰𝑫𝑬𝟒𝑳 − #𝑪𝑩𝑳

#𝑮𝑩


KPI SCENARIOS


BASELINE


SMART GRID

KPI DATA COLLECTION




for Data collection

Location of Data

collection



Data collection

responsible




#GB

Provided by each

field demonstra

tor

Excel Excel File Once //

Responsible of each

field demo

Number of monitored

currents for each branch in baseline scenario

#CiBL

Provided by each

field demonstra

tor

Excel Excel File Once //

Responsible of each

field demo

Number of monitored

currents for each branch in IDE4L

scenario

#CiIDE4L

Provided by each

field demonstra

tor

Excel Excel Once //

Responsible of each

field demo

KPI BASELINE


LITERATURE VALUES






GENERAL COMMENTS

The goals is to demonstrate the improvements of the current monitoring granularity in MV and LV grids thanks to new automation systems installed in PS and SS to support new IDE4L functionalities, such as State Estimation, Optimal Power Flow, …

The evaluation is performed calculating the average increase over all branches of the considered grid.

Power Monitoring Data Volume (PMDV)


KPI Name Powers Monitoring Data-Volume KPI ID PMDV

Main Objective Evaluation of the increase of data amount for new monitored powers in PS or SS

KPI Description This KPI evaluates the total data increment, for all branches of the grid grouped by PS or SS, of monitored powers (active and reactive) from baseline to IDE4L scenario

KPI Formula

𝑷𝑴𝑫𝑽𝑺𝒕𝒂𝒕𝒊𝒐𝒏 = ∑(𝑷𝑫𝒊𝑰𝑫𝑬𝟒𝑳 − 𝑷𝑫𝒊

𝑩𝑳)

#𝑮𝑩

𝒊=𝟏

+ ∑(𝑸𝑫𝒊𝑰𝑫𝑬𝟒𝑳 − 𝑸𝑫𝒊

𝑩𝑳)

#𝑮𝑩

𝒊=𝟏

Where:

#GB: Number of branches of the network fed by the considered PS or SS

PDiIDE4L: Total data of monitored active powers in a single branch i in

IDE4L scenario in the X time slot

PDiBL: Total data of monitored active powers in a single branch i in



baseline scenario in the X time slot

QDiIDE4L: Total data of monitored reactive powers in a single branch i in

IDE4L scenario in the X time slot

QDiBL: Total data of monitored reactive powers in a single branch i in

baseline scenario in the X time slot

This KPI provides:




P/QMG (P/Q Powers Monitoring Granularity KPI) - The increment of data volume related to the monitored powers is directly connected to the increment of monitoring granularity required to perform IDE4L algorithms. So that PMDV must be considered together with P/QMG KPI in order to understand the cost of the monitoring improvements Both KPIs are linked to ‘LV&MV Real-time Monitoring’ Use Case





Field demonstrator




PMDV_01 Collect the total number of monitored active (#Pi

BL) and reactive (#Qi

BL) powers in baseline scenario for each considered i branch Responsible of

each field demo

PMDV_02

Collect the number of samples (SPPjBL and SPQj

BL) in the X time slot and the data dimension for each j monitored active (PDj

BL) and reactive (QDj

BL) power in baseline scenario for each considered i branch


PMDV_03

Evaluate the total data of monitored active and reactive powers for all considered branches in baseline scenario in the X time slot

𝑷𝑫𝑽𝑩𝑳 = ∑ ( ∑ (𝑺𝑷𝑷𝒋𝑩𝑳 ∗ 𝑷𝑫𝒋

𝑩𝑳)

#𝑷𝒊𝑩𝑳

𝒋=𝟏

+ ∑ (𝑺𝑷𝑸𝒋𝑩𝑳 ∗ 𝑸𝑫𝒋

𝑩𝑳)

#𝑸𝒊𝑩𝑳

𝒋=𝟏

)

#𝑮𝑩

𝒊=𝟏


PMDV_04 Collect the total number of monitored active (#Pi

IDE4L) and reactive (#Qi

IDE4L) powers in IDE4L scenario for each considered i branch Responsible of

each field demo

PMDV_05

Collect the number of samples (SPPjIDE4L and SPQj

IDE4L) in the X time slot and the data dimension for each j monitored active (PDj

IDE4L) and reactive (QDj

IDE4L) power in the IDE4L scenario for each considered i branch


PMDV_06 Evaluate the total data of monitored active and reactive powers for all considered branches in IDE4L scenario in the X time slot




𝑷𝑫𝑽𝑰𝑫𝑬𝟒𝑳 = ∑ ( ∑ (𝑺𝑷𝑷𝒋𝑰𝑫𝑬𝟒𝑳 ∗ 𝑷𝑫𝒋


#𝑷𝒊𝑰𝑫𝑬𝟒𝑳

𝒋=𝟏

+ ∑ (𝑺𝑷𝑸𝒋𝑰𝑫𝑬𝟒𝑳 ∗ 𝑸𝑫𝒋


#𝑸𝒊𝑰𝑫𝑬𝟒𝑳

𝒋=𝟏

)

#𝑮𝑩

𝒊=𝟏

PMDV_07

Calculate the PMDV KPI as:

𝑷𝑴𝑫𝑽𝑺𝒕𝒂𝒕𝒊𝒐𝒏 = 𝑷𝑫𝑽𝑰𝑫𝑬𝟒𝑳 − 𝑷𝑫𝑽𝑩𝑳


KPI SCENARIOS


BASELINE


SMART GRID

KPI DATA COLLECTION




for Data collection

Location of Data

collection



Data collection

responsible


#GN

Provided by each

field demonstra

tor

Excel Excel Once //

Responsible of each

field demo

Number of monitored

active powers for each branch

in baseline scenario

#PiBL

Provided by each

field demonstra

tor

Excel Excel Once //

Responsible of each

field demo

Number of monitored

reactive powers for each branch


#QiBL

Provided by each

field demonstra

tor

Excel Excel Once //

Responsible of each

field demo

Number of samples for each active power in

X time slot in baseline scenario

SPPjBL

Provided by each

field demonstra

tor

Excel Excel Once //

Responsible of each

field demo

Number of samples for each reactive power in X time slot in

baseline scenario

SPQjBL

Provided by each

field demonstra

tor

Excel Excel Once //

Responsible of each

field demo

Data volume for each monitored active power in

baseline scenario

PDjBL

Provided by each

field demonstra

tor

Excel Excel Once //

Responsible of each

field demo



Data volume for each monitored reactive power


QDjBL

Provided by each

field demonstra

tor

Excel Excel Once //

Responsible of each

field demo

Number of monitored


in IDE4L scenario

#PiIDE4L

Provided by each

field demonstra

tor

Excel Excel Once //

Responsible of each

field demo

Number of monitored


in IDE4L scenario

#QiIDE4L

Provided by each

field demonstra

tor

Excel Excel Once //

Responsible of each

field demo

Number of samples for each active power in

X time slot in IDE4L scenario

SPPjIDE4L

Provided by each

field demonstra

tor

Excel Excel Once //

Responsible of each

field demo

Number of samples for each reactive power in X time slot in IDE4L scenario

SPQjIDE4L

Provided by each

field demonstra

tor

Excel Excel Once //

Responsible of each

field demo

Data volume for each monitored active power in IDE4L scenario

PDjIDE4L

Provided by each

field demonstra

tor

Excel Excel Once //

Responsible of each

field demo

Data volume for each monitored reactive power

in IDE4L scenario

QDjIDE4L

Provided by each

field demonstra

tor

Excel Excel Once //

Responsible of each

field demo

Number of monitored active powers for each branch in baseline scenario


LITERATURE VALUES






GENERAL COMMENTS

The goals is to evaluate the increase of data amount for a fixed time slot X (X = hour, day, month, year, …) – in kBytes/X – related to the improvement of voltage monitoring granularity to support the new IDE4L functionalities. This increment directly impacts both on the data layer of the Substation Automation Unit (SAU) and on the communication infrastructures.

The evaluation is performed calculating the total increase over all nodes of the considered PS or SS.



Power Monitoring Granularity (P/QMG)


KPI Name P/Q Powers Monitoring Granularity KPI ID P/QMG

Main Objective Evaluation of the increase of monitored P/Q powers in MV and LV grid

KPI Description This KPI evaluates the average increment, for all branches of the grid, of monitored powers (active and reactive) from baseline to IDE4L scenario

KPI Formula

𝑷𝑴𝑮𝑮𝒓𝒊𝒅 =∑ (#𝑷𝒊

𝑰𝑫𝑬𝟒𝑳 − #𝑷𝒊𝑩𝑳)#𝑮𝑩

𝒊=𝟏

#𝑮𝑩

𝑸𝑴𝑮𝑮𝒓𝒊𝒅 =∑ (#𝑸𝒊

𝑰𝑫𝑬𝟒𝑳 − #𝑸𝒊𝑩𝑳)#𝑮𝑩

𝒊=𝟏

#𝑮𝑩

Where:

#GB: Number of branches in the considered grid

#PiIDE4L: Total number of monitored active powers in a single branch i in

IDE4L scenario

#PiBL: Total number of monitored active powers in a single branch i in

baseline scenario

#QiIDE4L: Total number of monitored reactive powers in a single branch i in

IDE4L scenario

#QiBL: Total number of monitored reactive powers in a single branch i in

baseline scenario

This KPI provides:

the increase of the P/Q powers monitoring granularity (KPI direct proportionality)

Unit of measurement PMGGrid (%/Branch) QMGGrid (%/Branch)


PMDV (Powers Monitoring Data-Volume KPI) - The increment of powers (active and reactive) monitoring granularity entails also the increment of data volume related to the monitored powers. So that P/QMG must be considered together with PMDV KPI in order to understand the cost of the monitoring improvements Both KPIs are linked to ‘LV&MV Real-time Monitoring’ Use Case





Field demonstrator




P/QMG_01 Collect the total number of monitored active (#Pi

BL) and reactive (#Qi

BL) in baseline scenario for each considered i branch Responsible of

each field demo

P/QMG_02 Evaluate the total number of monitored active and reactive powers for all considered branches in baseline scenario




#𝑷𝑩𝑳 = ∑ #𝑷𝒊𝑩𝑳

#𝑮𝑩

𝒊=𝟏

#𝑸𝑩𝑳 = ∑ #𝑸𝒊𝑩𝑳

#𝑮𝑩

𝒊=𝟏

P/QMG_03 Collect the total number of monitored active (#Pi

IDE4L) and reactive (#Qi

IDE4L) in IDE4L scenario for each considered i branch Responsible of

each field demo

P/QMG_04

Evaluate the total number of monitored active and reactive powers for all considered branches in the IDE4L scenario

#𝑷𝑰𝑫𝑬𝟒𝑳 = ∑ #𝑷𝒊𝑰𝑫𝑬𝟒𝑳

#𝑮𝑩

𝒊=𝟏

#𝑸𝑰𝑫𝑬𝟒𝑳 = ∑ #𝑸𝒊𝑰𝑫𝑬𝟒𝑳

#𝑮𝑩

𝒊=𝟏


P/QMG_05

Calculate the P/QMG KPI as:

𝑷𝑴𝑮𝑮𝒓𝒊𝒅 =#𝑷𝑰𝑫𝑬𝟒𝑳 − #𝑷𝑩𝑳

#𝑮𝑩

𝑸𝑴𝑮𝑮𝒓𝒊𝒅 =#𝑸𝑰𝑫𝑬𝟒𝑳 − #𝑸𝑩𝑳

#𝑮𝑩


KPI SCENARIOS


BASELINE


SMART GRID

KPI DATA COLLECTION




for Data collection

Location of Data

collection



Data collection

responsible


#GB

Provided by each

field demonstra

tor

Excel Excel Once //

Responsible of each

field demo

Number of monitored



#PiBL

Provided by each

field demonstra

tor

Excel Excel Once //

Responsible of each

field demo

Number of monitored

reactive powers

#QiBL

Provided by each

field Excel Excel Once //

Responsible of each

field



for each branch in baseline

scenario

demonstrator

demo

Number of monitored


in IDE4L scenario

#PiIDE4L

Provided by each

field demonstra

tor

Excel Excel Once //

Responsible of each

field demo

Number of monitored


in IDE4L scenario

#QiIDE4L

Provided by each

field demonstra

tor

Excel Excel Once //

Responsible of each

field demo

KPI BASELINE


LITERATURE VALUES






GENERAL COMMENTS

The goals is to demonstrate the improvements of the active and reactive powers monitoring granularity in MV and LV grids thanks to new automation systems installed in PS and SS to support new IDE4L functionalities, such as State Estimation, Optimal Power Flow, …

The evaluation is performed calculating the average increase over all branches of the considered grid.

Voltage Monitoring Data Volume (VMDV)


KPI Name Voltage Monitoring Data-Volume KPI ID VMDV

Main Objective Evaluation of the increase of data amount for new monitored voltages in PS or SS

KPI Description This KPI evaluates the total data increment, for all nodes of the grid grouped by PS or SS, of monitored voltages from baseline to IDE4L scenario

KPI Formula

𝑽𝑴𝑫𝑽𝑺𝒕𝒂𝒕𝒊𝒐𝒏 = ∑(𝑽𝑫𝒊𝑰𝑫𝑬𝟒𝑳 − 𝑽𝑫𝒊

𝑩𝑳)

#𝑮𝑵

𝒊=𝟏

Where:

#GN: Number of nodes in the considered PS or SS

VDiIDE4L: Total data of monitored voltages in a single node i in IDE4L


VDiBL: Total data of monitored voltages in a single node i in baseline


This KPI provides:






VMG (Voltage Monitoring Granularity KPI) - The increment of data volume related to the monitored voltages is directly connected to the increment of monitoring granularity required to perform IDE4L algorithms. So that VMDV must be considered together with VMG KPI in order to understand the cost of the monitoring improvements Both KPIs are linked to ‘LV&MV Real-time Monitoring’ Use Case





Field demonstrator




VMDV_01 Collect the total number of monitored voltages (#Vi

BL) in base line scenario for each considered i node


VMDV_02 Collect the number of samples (SPj

BL) in the X time slot and the data dimension for each j monitored voltage (VDj

BL) in baseline scenario for each considered i node


VMDV_03

Evaluate the total data of monitored voltages for all considered nodes in baseline scenario in the X time slot

𝑽𝑫𝑩𝑳 = ∑ ∑ (𝑺𝑷𝒋𝑩𝑳 ∗ 𝑽𝑫𝒋

𝑩𝑳)

#𝑽𝒊𝑩𝑳

𝒋=𝟏

#𝑮𝑵

𝒊=𝟏


VMDV_04 Collect the total number of monitored voltages (#Vi

IDE4L) in IDE4L scenario for each considered i node


VMDV_05 Collect the number of samples (SPj

IDE4L) in the X time slot and the data dimension for each j monitored voltage (VDj



VMDV_06

Evaluate the total data of monitored voltages for all considered nodes in IDE4L scenario in the X time slot

𝑽𝑫𝑰𝑫𝑬𝟒𝑳 = ∑ ∑ (𝑺𝑷𝒋𝑰𝑫𝑬𝟒𝑳 ∗ 𝑽𝑫𝒋


#𝑽𝒊𝑰𝑫𝑬𝟒𝑳

𝒋=𝟏

#𝑮𝑵

𝒊=𝟏


VMDV_07

Calculate the VMDV KPI as:

𝑽𝑴𝑫𝑽𝑺𝒕𝒂𝒕𝒊𝒐𝒏 = 𝑽𝑫𝑰𝑫𝑬𝟒𝑳 − 𝑽𝑫𝑩𝑳


KPI SCENARIOS


BASELINE BUSINESS AS USUAL (BaU) SMART GRID



KPI DATA COLLECTION




for Data collection

Location of Data

collection



Data collection

responsible


#GN

Provided by each

field demonstra

tor

Excel Excel Once //

Responsible of each

field demo

Number of monitored

voltages for each node in baseline

scenario

#ViBL

Provided by each

field demonstra

tor

Excel Excel Once //

Responsible of each

field demo

Number of samples for each voltage in X time slot in baseline

scenario

SPjBL

Provided by each

field demonstra

tor

Excel Excel Once //

Responsible of each

field demo

Data volume for each monitored

voltage in baseline scenario

VDjBL

Provided by each

field demonstra

tor

Excel Excel Once //

Responsible of each

field demo

Number of monitored

voltages for each node in IDE4L

scenario

#CiIDE4L

Provided by each

field demonstra

tor

Excel Excel Once //

Responsible of each

field demo

Number of samples for each voltage in X time

slot in IDE4L scenario

SPjIDE4L

Provided by each

field demonstra

tor

Excel Excel Once //

Responsible of each

field demo

Data volume for each monitored voltage in IDE4L

scenario

VDjIDE4L

Provided by each

field demonstra

tor

Excel Excel Once //

Responsible of each

field demo

KPI BASELINE


LITERATURE VALUES



Details of Baseline Data needed to evaluate the KPI can be collected in a “single-shot”


This KPI will be calculated only by DSOs because inputs are about real network.

GENERAL COMMENTS



The goals is to evaluate the increase of data amount for a fixed time slot X (X = hour, day, month, year, …) – in kBytes/X – related to the improvement of voltage monitoring granularity to support the new IDE4L functionalities. This increment directly impacts both on the data layer of the Substation Automation Unit (SAU) and on the communication infrastructures.

The evaluation is performed calculating the total increase over all nodes of the considered PS or SS.

Voltage Monitoring Granularity (VMG)


KPI Name Voltage Monitoring Granularity KPI ID VMG

Main Objective Evaluation of the increase of monitored voltages in MV and LV grid

KPI Description This KPI evaluates the average increment, for all nodes of the grid, of monitored voltages from baseline to IDE4L scenario

KPI Formula

𝑽𝑴𝑮𝑮𝒓𝒊𝒅 =∑ (#𝑽𝒊

𝑰𝑫𝑬𝟒𝑳 − #𝑽𝒊𝑩𝑳)#𝑮𝑵

𝒊=𝟏

#𝑮𝑵

Where:

#GN: Number of nodes in the considered grid

#ViIDE4L: Total number of monitored voltages in a single node i in IDE4L

scenario

#ViBL: Total number of monitored voltages in a single node i in baseline

scenario

This KPI provides:

the increase of the voltage monitoring granularity in the considered grid (KPI direct proportionality)

Unit of measurement %/Branch


CMDV (Current Monitoring Data-Volume KPI) - The increment of current monitoring granularity entails also the increment of data volume related to the monitored currents. So that CMG must be considered together with CMDV KPI in order to understand the cost of the monitoring improvements Both KPIs are linked to ‘LV&MV Real-time Monitoring’ Use Case





Field demonstrator




VMG_01 Collect the total number of monitored voltages (#Vi

BL) in base line scenario for each considered i node


VMG_02 Evaluate the total number of monitored voltages for all considered nodes in baseline scenario




#𝑽𝑩𝑳 = ∑ #𝑽𝒊𝑩𝑳

#𝑮𝑵

𝒊=𝟏

VMG_03 Collect the total number of monitored voltages (#Vi



VMG_04

Evaluate the total number of monitored voltages for all considered nodes in IDE4L scenario

#𝑽𝑰𝑫𝑬𝟒𝑳 = ∑ #𝑽𝒊𝑰𝑫𝑬𝟒𝑳

#𝑮𝑵

𝒊=𝟏


VMG_05

Calculate the VMG KPI as:

𝑽𝑴𝑮𝑮𝒓𝒊𝒅 =#𝑽𝑰𝑫𝑬𝟒𝑳 − #𝑽𝑩𝑳

#𝑮𝑵


KPI SCENARIOS


BASELINE


SMART GRID

KPI DATA COLLECTION




for Data collection

Location of Data

collection



Data collection

responsible

Number of considered

nodes

#GN

Provided by each

field demonstra

tor

Excel Excel Once //

Responsible of each

field demo

Number of monitored

voltages for each node in baseline

scenario

#ViBL

Provided by each

field demonstra

tor

Excel Excel Once //

Responsible of each

field demo

Number of monitored

voltages for each node in IDE4L

scenario

#ViIDE4L

Provided by each

field demonstra

tor

Excel Excel Once //

Responsible of each

field demo

KPI BASELINE


LITERATURE VALUES






GENERAL COMMENTS



The goals is to demonstrate the improvements of the voltage monitoring granularity in MV and LV grids thanks to new automation systems installed in PS and SS to support new IDE4L functionalities, such as State Estimation, Optimal Power Flow, …

The evaluation is performed calculating the average increase over all nodes of the considered grid.

6.2 UC2: MV & LV State estimation In this Use Case, two KPIs have been defined:

Real-time LV Network State Estimator (LVSE)


KPI Name Real-time LV network State Estimator KPI ID LVSE

Main Objective Evaluate the accuracy of the state estimates

KPI Description This KPI evaluates the accuracy of the real-time LV network state estimates by comparing the estimated values with actual simulated values.

KPI Formula

𝐿𝑉𝑆𝐸 =1

𝑁∑ √

1

𝑇∑(�̃�(𝑡)𝑛 − 𝑥(𝑡)𝑛)2

𝑇

𝑡=1

𝑁

𝑛=1

where:

𝑁 : number of studied state variables

𝑇 : number of time intervals under study

�̃�(𝑡)𝑛 : real instantaneous value for the state variable at time t

𝑥(𝑡)𝑛 : estimated value for the state variable n with at time t.

Unit of measurement

KPI LVSE can be calculated for several different variables. Units of measurements used are as follows:

Injected active power (kW)

Injected reactive power (kVAr)

Line current (A)

Node voltage (V)


LVSE KPI formula is identical with MVSE KPI formula




Field demonstrator




LVSE_01

The first step is the collection of real-time measurements. The real time measurements are collected either form the simulated network (laboratory demos) or from the real distribution network (field demos). Part of the real-time measurements is used as inputs to the state estimator and the others are used for KPI calculation. The

Research organism (TUT)



measurements are stored into DXP.

LVSE_02 LV network state estimator (LVSE function) uses (part of the) real-time measurements and load models to calculate state estimates for the LV network.

LVSE (TUT)

LVSE_03 LVSE KPI is calculated according to the formula above Research

organism (TUT)

KPI SCENARIOS


BASELINE


SMART GRID

KPI DATA COLLECTION

Data Data ID Methodology

for data collection


for Data collection

Location of Data

collection



Data collection

responsible

Real-time measurements reserved for KPI

calculation

�̃� measureme

nt

RTDS (lab demo)/

The whole measurem

ent infrastructure (field

demo)

local (lab demo)/

DXP (field demo)

10 s (lab)/ 1-15 min (demo)

1 day

Lab and field

demos (TUT,

UFD, OST, A2A)

LV network state estimates

𝑥 calculation LVSE

function DXP 1 min 1 day

Research organism

(TUT)

LVSE KPI values LVSE calculation LVSE KPI

calculation script

local once - Research organism

(TUT)

KPI BASELINE


LITERATURE VALUES



Details of Baseline LVSE values from RTDS simulations done in TUT in an earlier INTEGRIS project will be used as a baseline (results have also been published).


Antti Mutanen, TUT

GENERAL COMMENTS

This KPI provides a performance index for state estimation accuracy. Demonstration laboratory: - The estimated states are compared with actual network states read directly from RSCAD/RTDS. - Provides a much more comprehensive view on state estimation accuracy than the field demonstrations since true values for all variables can be read from the simulator.



- The LVSE KPI will be calculated with the highest possible frequency (e.g. once every 10 seconds) so that even the effects that temporary fluctuations in electricity consumption have on LV network state are taken into account. Field demonstrations: - Some of the real-time measurements are reserved for KPI calculation and only a part of real-time measurements are used as inputs to the LVSE. At the moment (2014), the DNOs participating to this project do not have LV network state estimation capabilities. Also, in general, calculation of LV network state estimates does not belong to current DNO practices. Therefore, the KPI’s for business-as-usual scenarios are not calculated. However, comparisons with different measurement setups are made. In addition to the above defined KPI, another important performance metric for LV network state estimation is the state estimation function execution time which is measured separately.

Real-time MV Network State Estimator (MVSE)


KPI Name Real-time MV network state Estimator KPI ID MVSE

Main Objective Evaluate the accuracy of the state estimation

KPI Description This KPI evaluates the accuracy of the real-time MV network state estimator by comparing the estimated values with actual simulated values.

KPI Formula

𝑀𝑉𝑆𝐸 =1

𝑁∑ √

1

𝑇∑(�̃�(𝑡)𝑛 − 𝑥(𝑡)𝑛)2

𝑇

𝑡=1

𝑁

𝑛=1

where:



�̃�(𝑡)𝑛 : real instantaneous value for the state variable at time t

𝑥(𝑡)𝑛 : estimated value for the state variable n with at time t.

Unit of measurement

KPI MVSE can be calculated for several different variables. Units of measurements used are as follows:

Injected active power (kW)

Injected reactive power (kVAr)

Line current (A)

Node voltage (V)


MVSE KPI formula is identical with LVSE KPI formula




Field demonstrator






MVSE_01

The first step is the collection of real-time measurements. The real time measurements are collected either form the simulated network (laboratory demos) or from the real distribution network (field demos). Part of the real-time measurements is used as inputs to the state estimator and the others are used for KPI calculation. The measurements are stored into DXP.

Lab and field demos (TUT,

A2A)

MVSE_02 MV network state estimator (MVSE function) uses real-time measurements and load models to calculate state estimates for the MV network.

MVSE (TUT)

MVSE_03 KPI is calculated according to the MVSE KPI formula

Research organism (TUT)

KPI SCENARIOS


BASELINE


SMART GRID

KPI DATA COLLECTION


for data collection


for Data collection

Location of Data

collection



Data collection

responsible

Real-time measurements reserved for KPI

calculation

�̃� measureme

nt

RTDS (lab demo)/

The whole measurem

ent infrastructure (field

demo)

local (lab demo)/

DXP (field demo)

10 s (lab)/ 1-15 min (demo)

1 day (TUT)

MV network state estimates

𝑥 calculation MVSE

function DXP 1 min 1 day (TUT)

MVSE KPI values

𝑀𝑉𝑆𝐸 calculation MVSE KPI

calculation script

local once - (TUT)

KPI BASELINE


LITERATURE VALUES



Details of Baseline

Distribution network calculation software and practices vary from country to country and utility to utility. Therefore it is impossible to define one universal baseline case for state estimation. Some network management programs are capable of calculating MV network state estimates. The DNOs participating in field demonstrations do not have (yet) this possibility and therefore baseline performance indices are not calculated for MV network state estimation.


GENERAL COMMENTS



This KPI provides a performance index for state estimation accuracy. Demonstration laboratory: - The estimated states are compared with actual network states read directly from RSCAD/RTDS. - Provides a much more comprehensive view on state estimation accuracy than the field demonstrations since true values for all variables can be read from the simulator. - The MVSE KPI will be calculated with the highest possible frequency (e.g. once every 10 seconds) so that even the effects that temporary fluctuations in electricity consumption have on MV network state are taken into account. Field demonstrations: - Some of the real-time measurements are reserved for KPI calculation and only a part of real-time measurements are used as inputs to the MVSE. Since, at the moment (2014), the DNOs participating in this project do not have the software required for MV network state estimation, the MVSE business-as-usual values will be calculated only during laboratory demonstrations using state estimation methods and practices that are known to be used in Finland. In addition to the above defined MVSE KPI, another important performance metric for MV network state estimation is the state estimation function execution time which is measured separately.

6.3 UC3: Dynamic Monitoring for TSO Two KPIs are defined to evaluate this Use Case:

Voltage stability of the electricity system (I)


KPI Name Voltage stability of the electricity system KPI ID I

Main Objective This KPI measures improvement in TSO’s awareness of voltage stability issues in their downstream distribution grids.

KPI Description

This KPI measures improvement achieved in IDE4L project in TSO’s awareness of voltage instabilities occurring in its downstream distribution systems.

The KPI can be evaluated by comparing a stability index, calculated for the distribution feeder based on the distribution system conventional data at TSO, with a similarly defined stability index, calculated based on real-time measurements and equivalent models of the distribution system at DSO.

KPI Formula

∆𝐼[%] = |𝐼𝑟𝑡−𝐼𝑜𝑓𝑓

𝐼𝑜𝑓𝑓| × 100

Where Irt is the stability index calculated based on real-time measurements and equivalent models of the distribution system at DSO, and Ioff is the stability index calculated based on distribution system conventional data at TSO.

The stability index (I) itself has to be defined during the project.

Unit of measurement %




This KPI is defined in conjunction with UC “Dynamic Monitoring for TSO”.




Field demonstrator




I_01 Definition of stability index. Research

organism (KTH)

I_02 Definition of scenario(s) under which the KPI will be measured. Research

organism (KTH)

I_03 Calculate the KPI for the reference grid. Research

organism (KTH)

KPI SCENARIOS


BASELINE


SMART GRID

The calculation of this KPI will be done totally based on simulation data. This means that there will be two main simulations:

1. A simulation including the development that we are going to achieve (Smart grid scenario)

2. A simulation that does not include that development (BaU scenario)

The Ioff will be calculated based on a reduced model of the reference grid that is assumed to be available to TSO, i.e., a model not containing detailed data about DG units – typical situation for DSOs –.

KPI DATA COLLECTION

Data Data ID

Methodology for data

collection


for Data collection

Location of Data

collection



Data collection

responsible

Stability index calculated based on distribution system conventional data

Ioff Calculation --- SmarTS Lab

(KTH) --- ---

Research organism

(KTH)

Stability index calculated based on real-time measurements and equivalent models of the distribution system

Irt HIL

Simualtion

PMU, RT-Simulator,

Data mediator

SmarTS Lab (KTH)

As required As

required

Research organism

(KTH)



KPI BASELINE


LITERATURE VALUES



Details of Baseline


GENERAL COMMENTS

TSO´s visibility of distribution network (UR)


KPI Name TSO’s visibility of distribution network KPI ID UR

Main Objective This KPI measures improvement in models that a TSO has for its downstream distribution grids.

KPI Description

It is defined to study the impact of the increased monitoring in models that a TSO maintain for its downstream distribution grids.

The KPI can be evaluated by comparing the updating rate of the conventional models of the distribution system at TSO with those of the steady-state and dynamic model synthesis for distribution systems calculated based on real-time model reduction techniques.

KPI Formula

∆𝑈𝑅[%] = |𝑈𝑅𝑟𝑡−𝑈𝑅𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒

𝑈𝑅𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒| × 100

Where URrt is updating rate of the steady-state and dynamic model synthesis for distribution systems calculated based on real-time model reduction techniques, and URbaseline is the updating rate of the conventional models of the distribution system at TSO.



This KPI is defined in conjunction with UC “Distribution grid dynamic monitoring for providing “dynamics” information to TSOs”.




Field demonstrator




UR_01 Determination of URrt. Research

organism (KTH)

UR_02 Specification of URbaseline. Research

organism (KTH)



UR_03 Calculate the KPI for the reference grid. Research

organism (KTH)

KPI SCENARIOS


BASELINE


SMART GRID

KPI DATA COLLECTION

Data Data ID


collection


for Data collection

Location of Data

collection



Data collection

responsible

Updating rate of the steady-state and dynamic model synthesis for distribution systems calculated based on real-time model reduction techniques

URrt

Calculation and HIL

Simulation

PMU, RT-Simulator,

Data mediator, National

Instrument cRIO

SmarTS Lab (KTH)

As required As

required

Research organism

(KTH)

KPI BASELINE


LITERATURE VALUES



Details of Baseline URbaseline will be specified by the help of TSOs as Energinet.dk (Danish national TSO).


Hossein Hooshyar (KTH) with the help of Per Lund (Energinet.dk)

GENERAL COMMENTS

6.4 UC4: Communication devices for transmitting signals considering long

distances within the electrical distribution system In this Use Case, the following KPI has been defined:

Evaluation of IEC 61850-90-5 library (PMU_eval)


KPI Name Evaluation of IEC 61850-90-5 library KPI ID PMU_eval

Main Objective This KPI evaluates the performance of the IEC 61850-90-5 library that will be developed.

KPI Description Scenario: a PMU sends information to a PDC according to IEC 61850-90-5; both of



them use the library developed by IREC:

The KPI evaluates if the implementation of Sample Value protocol and GOOSE protocol is as defined in the standard. In particular, it will be evaluated if the PMU correctly generates Sample Value and GOOSE messages To do so, it will be used Wireshark, which has support for Sample Value and GOOSE messages:

KPI Formula

100 different messages will be sent from the PMU and parsed by Wireshark.

PMU_eval = number_of_messages_well_parsed_by_Wireshark / 100



Use Case: Communication devices for transmitting signals considering long distances within the electrical distribution system.




Field demonstrator




PMU_eval_01 The PMU generates a message (according to IEC 61850-90-5). Research

organism (IREC)

PMU_eval_02 Wireshark captures the message. Research

organism (IREC)

PMU_eval_03 Wireshark “tries” to parse the message. Research

organism (IREC)

KPI SCENARIOS


BASELINE


SMART GRID

KPI DATA COLLECTION

Data Data ID


collection

Source/Tools/Instruments for Data collection

Location of Data

collection



Data collection

responsible



Sample Value messages

SVM

Generate and send different Sample Value

messages and

capturing them

Ethernet packet capture

software, PMU (IEC

61850-90-5 library

installed)

Laboratory

Research organism

(IREC)

Goose messages GM

Generate and send different GOOSE

messages

Ethernet packet capture

software, PMU (IEC

61850-90-5 library

installed)

Laboratory

Research organism

(IREC)

KPI BASELINE


LITERATURE VALUES



Details of Baseline Not available


Research organism (IREC)

GENERAL COMMENTS

6.5 UC5: PLC communications for the active distribution grid One KPI is defined to evaluate this Use Case:

Success index in meter reading (SIMR)


KPI Name Success index in meter reading KPI ID SIMR

Main Objective Evaluation of the impact of low-cost power converters to the performance of PLC-based communications for metering applications in LV distribution networks

KPI Description

(Primary Use Case: PLC communications for the active distribution grid) This KPI evaluates the impact of different degrees of penetration for low-cost power converters applied mainly in the integration of renewable generation in the distribution grid, on the correct reception of PLC data packets for metering purposes.

KPI Formula 𝑃𝑎𝑐𝑘𝑒𝑡 𝐸𝑟𝑟𝑜𝑟 𝑅𝑎𝑡𝑒 (𝑃𝐸𝑅) can be defined according to:



𝑃𝐸𝑅 =𝑎𝑚𝑜𝑢𝑛𝑡 𝑜𝑓 𝑖𝑛𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑙𝑦 𝑟𝑒𝑐𝑒𝑖𝑣𝑒𝑑 𝑑𝑎𝑡𝑎 𝑝𝑎𝑐𝑘𝑒𝑡𝑠 𝑖𝑛 ′Δ𝑡′

𝑡𝑜𝑡𝑎𝑙 𝑎𝑚𝑜𝑢𝑛𝑡 𝑜𝑓 𝑡𝑟𝑎𝑛𝑠𝑚𝑖𝑡𝑡𝑒𝑑 𝑑𝑎𝑡𝑎 𝑝𝑎𝑐𝑘𝑒𝑡𝑠 𝑖𝑛 ′Δ𝑡′

So it will be evaluated by considering:

1. Number of packets that are being sent by the transmitter during a time interval ‘Δt’.

2. Number of packets that are being received during the same time interval ‘Δt’, and

3. The ones that are being received incorrectly The evaluation of this KPI is performed previously calculating the packet error rate (PER) of the communication between the PLC transmitter and receiver, according to the expression:

𝑆𝐼𝑀𝑅𝑡 =𝑃𝐸𝑅𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒 − 𝑃𝐸𝑅𝐵𝑎𝑈

𝑃𝐸𝑅𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒

Where: 𝑆𝐼𝑀𝑅𝑡 is the success index in meter reading during a given period 𝑡 𝑃𝐸𝑅𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒 is the packet error rate in the baseline scenario 𝑃𝐸𝑅𝐵𝑎𝑈 is the packet error rate in the business as usual scenario



- Current monitoring data weight - Voltage monitoring data weight - Monitoring level in the network state estimation




Field demonstrator




SIMR_01 Measurement of the quantity of information packets being sent by the AMI transmitter


SIMR_02 Measurement of the quantity of information packets being received correctly by the Concentrator receiver


SIMR_03 Measurement of the communication failures (wrong received and non-received packets)


SIMR_04 Number of times that the test is being performed Research

organism (IREC)

KPI SCENARIOS


BASELINE


SMART GRID

KPI DATA COLLECTION

Data Data ID


collection


for Data collection

Location of Data

collection



Data collection

responsible

Wrong received or non-received

Wp SIMR1, SIMR3

Oscilloscope, data

laboratory Every time a

packet is 20*SIM

R4 Research organism



packets logger being transmitted

(20 is just an estimated value for the messag

es)

(IREC)

Correct received packets

Cp SIMR1, SIMR2

Oscilloscope, data logger

laboratory

Every time a packet is

being transmitted

20*SIMR4

(20 is just an estimated value for the messag

es)

Research organism

(IREC)

KPI BASELINE


LITERATURE VALUES






GENERAL COMMENTS

Two scenarios are taken into consideration. In the baseline scenario, a low degree of renewable penetration in LV networks is considered -thus with low penetration of low-cost power converters-, whereas in the BaU scenario a higher degree of renewable penetration is assumed, considering that no innovations are implemented to assure sufficient frequency of meter readings. There will be a demonstration in IREC laboratory facilities, where an experimental set-up with both the power (converter, line impedances, load) and the PLC-based communications infrastructures (PLC transmitter, PLC receiver...) will be used at lab scale to measure the frequency of meter readings in both scenarios.

6.6 UC6: MV&LV Load Forecast Two KPIs have been defined in this case:

LV Load/generation forecaster (LVLGF)


KPI Name LV Load/Generation Forecaster KPI ID LVLGF

Main Objective Evaluate the accuracy of the load/generation forecasts for the predefined prediction horizons on the LV network.

KPI Description

The KPI evaluates the deviation between the forecasted values and the corresponding measurements for the load/generation when becoming available. This is a measure for the reliability of the applied forecasting method as a function of the considered forecast horizons. This provides an error measure for the variance of the considered load/generation prediction horizons in the field



test, for the particular load/generation in node. This indicates that the large prediction errors have the largest effect in the evaluation.

KPI Formula

LVLGF(𝑘) = √1

𝑁∑(𝑃(𝑡 + 𝑘)𝑛 − �̂�(𝑡 + 𝑘|𝑡)𝑛)

2𝑁

𝑛=1

where:

𝑘 : look-ahead time

𝑁 : number of load/generation nodes in the MV network

𝑃(𝑡 + 𝑘)𝑛 : Observed load/generation at node 𝑛 at time 𝑡 + 𝑘

�̂�(𝑡 + 𝑘|𝑡)𝑛 : Forecasted load/generation at node n for time 𝑡 + 𝑘, issued at time 𝑡.

Unit of measurement Load: kilowatt [kW] – Generation: kilowatt [kW]


This KPI is linked to ‘LV Load and State Forecast’ Use Case




Field demonstrator




LVLGF_01 Get forecasts for the 𝑁 load/production nodes in a LV network,

generated at time instant 𝑡, for the 𝑘 time steps – �̂�(𝑡 + 𝑘|𝑡). The origin of these forecasts is the LV network forecaster (LVF).

LVF (UC3M/DE)

LVLGF_02 Get the available measurements from the Data eXchange Platform (DXP) that correspond to the forecasts in the LVLGF-1 – 𝑃(𝑡 + 𝑘).

(UC3M/DE)

LVLGF_03 Calculate the 𝐿𝐺𝐹𝐿𝑉 for the 𝑘-th forecast horizon as the root mean square error of the total load/generation in the LV network.

(UC3M/DE)

KPI SCENARIOS


BASELINE


SMART GRID

KPI DATA COLLECTION

Data Data ID


collection


for Data collection

Location of Data

collection



Data collection

responsible

Forecasted load/generation

�̂� Obtain from

LVF algorithm

Directly from LVF or from

DXP

LV To be

determined (1-15min)

24h (UFD, OST)

Measured load/generation

𝑃 Measurements stored in a data base

Meters/ DXP

LV (1-15min) 24h (UFD, OST)

KPI BASELINE


LITERATURE VALUES





Details of Baseline Baselines for this KPI cannot be found in the literature or calculated using the information available in the existing distribution network monitoring system.


GENERAL COMMENTS

The aim is to evaluate the prediction error for the prediction horizons that are considered in the IDE4L field tests. With such an approach the reliability of the load/generation forecast method can be verified for subsequent actions depending on the forecast outputs. However, the KPI should be initially applied to a case in a demonstration laboratory, to construct references for the KPI’s applied to the field demonstration.

MV Load/generation forecaster (MVLGF)


KPI Name MV Load/Generation Forecaster KPI ID MVLGF

Main Objective Evaluate the accuracy of the load/generation forecasts for the predefined prediction horizons on the MV network.

KPI Description

The KPI evaluates the deviation between the forecasted values and the corresponding measurements for the load/generation when becoming available. This is a measure for the reliability of the applied forecasting method as a function of the considered forecast horizons. This provides an error measure for the variance of the considered load/generation prediction horizons in the field test, for the particular load/generation in node. This indicates that the large prediction errors have the largest effect in the evaluation.

KPI Formula

MVLGF(𝑘) = √1

𝑁∑(𝑃(𝑡 + 𝑘)𝑛 − �̂�(𝑡 + 𝑘|𝑡)𝑛)

2𝑁

𝑛=1

where:


𝑁 : number of load/generation nodes in the MV network

𝑃(𝑡 + 𝑘)𝑛 : Observed load/generation at node 𝑛 at time 𝑡 + 𝑘

�̂�(𝑡 + 𝑘|𝑡)𝑛 : Forecasted load/generation at node n for time 𝑡 + 𝑘, issued at time 𝑡.

Unit of measurement Load: kilowatt [kW] – Generation: kilowatt [kW]


This KPI is linked to ‘MV Load and State Forecast’ Use Case.




Field demonstrator




MVLGF_01

Get forecasts for the 𝑁 load/production nodes in a MV network,

generated at time instant 𝑡, for the 𝑘 time steps – �̂�(𝑡 + 𝑘|𝑡). The origin of these forecasts is the MV network Forecaster (MVF).

MVF(UC3M/DE)



MVLGF_02 Get the available measurements from the Data eXchange Platform (DXP) that correspond to the forecasts in the MVLGF-1 – 𝑃(𝑡 + 𝑘).

(UC3M/DE)

MVLGF_03 Calculate the 𝑀𝑉𝐿𝐺𝐹 for the 𝑘-th forecast horizon as the root mean square error of the total load/generation in the MV network.

(UC3M/DE)

KPI SCENARIOS


BASELINE


SMART GRID

KPI DATA COLLECTION

Data Data ID


collection


for Data collection

Location of Data

collection



Data collection

responsible

Forecasted load/generation

�̂� Obtain from

MVF algorithm

Directly from MVF

or from DXP

MV To be

determined (1-15min)

24h

Measured load/generation

𝑃 Measurements stored in a data base

Meters/ DXP

MV (1-15min) 24h

KPI BASELINE


LITERATURE VALUES





GENERAL COMMENTS

The aim is to evaluate the prediction error for the prediction horizons that are considered in the IDE4L field tests. With such an approach the reliability of the load/generation forecast method can be verified for subsequent actions depending on the forecast outputs. However, the KPI should be initially applied to a case in a demonstration laboratory, to construct references for the KPI’s applied to the field demonstration.

6.7 UC7: MV&LV State Forecast In order to evaluate this Use Case, two KPIs are defined:

LV State forecaster (LVSF)


KPI Name LV state forecaster KPI ID LVSF

Main Objective Evaluate the accuracy of the state forecasts for the predetermined prediction horizons on the LV network.

KPI Description The KPI evaluates the performance of the short-term state forecasts by considering the deviation between the estimated states and corresponding state forecasts.

KPI Formula



𝐿𝑉𝑆𝐹(𝑘) = √1

𝑁𝑇 (∑ ∑ (

1

𝑀∑ (𝑥(𝑡 + 𝑘)𝑛.𝑚)

𝑀

𝑚=1

− 𝑥(𝑡 + 𝑘|𝑡)𝑛)

𝑇

𝑡=1

2𝑁

𝑛=1

)

where:




𝑀 : number of times the state estimation is run during the time period when state forecast for time t+k is valid

𝑥(𝑡 + 𝑘)𝑛,𝑚 : mth estimated value for the state n during the time period when state forecast for time t+k is valid

𝑥(𝑡 + 𝑘|𝑡)𝑛 : forecasts for the state n with look-ahead time k, issued at time t.

Unit of measurement

KPI LVSF can be calculated for several different variables. Units of measurements are as follows:

Active power (kW)

Reactive power (kVAr)

Current (A)

Voltage (V)


KPI formula is identical with MVSF




Field demonstrator




LVSF_01

The LV network State Forecaster (LVSF) uses load forecasts from the LV network load forecaster (LVF) to calculate state forecasts 𝑥(𝑡 + 𝑘|𝑡) for the whole LV network (𝑁 number of states), 1 to 𝑘 time-steps ahead.

LVSF (TUT)

LVSF_02

The LV network state estimator (LVSE) calculates the corresponding state estimates 𝑥(𝑡 + 𝑘) for the LV network (this happens k hours after the step LVSF-1). The frequency of state estimation is higher than the frequency of state forecasting, thereby state estimation is calculated 𝑀 times during the time period when state forecasts for time 𝑡 + 𝑘 are valid. The value of 𝑀 depends on the forecasting horizon 𝑘.

LVSE (TUT)

LVSF_03 In order to make the state estimates calculated in step LVSF-2 temporally comparable with the state forecasts calculated in step LVSF-1, averages over the 𝑀 state estimates are calculated.

(TUT)

LVSF_04 Steps 1–3 are repeated 𝑇 times during the monitoring period. (TUT)

LVSF_05 KPI is calculated according to the LVSF formula for the 𝑘-th forecast horizon.

(TUT)

KPI SCENARIOS


BASELINE BUSINESS AS USUAL (BaU) SMART GRID



KPI DATA COLLECTION

Data Data

ID


collection


for Data collection

Location of Data

collection



Data collection

responsible

LV network state estimates

𝑥 Calculation LVSE

function LV DXP 1 min 1 week

(TUT, UFD, OST)

LV network state forecasts

𝑥 Calculation LVSF

function LV DXP 10 min 1 week

(TUT, UFD, OST)

KPI values 𝐿𝑉𝑆𝐹 Calculation LVSF script local once - (TUT,

UFD, OST)

KPI BASELINE


LITERATURE VALUES





GENERAL COMMENTS

This KPI provides a performance index for state forecasting accuracy. The performance index is calculated as a function of look-ahead time. The forecasted states are compared with estimated states instead of actual measured states. This is because in field demonstrations all states are not measured and measurement data can contain errors. Measurement errors are detected and filtered during the state estimation process. Therefore, the estimated state values are the best possible estimates we have on our disposal. During laboratory demonstration it would also be possible to use real measured values but then the LVSF’s would not be comparable with LVSF’s calculated during field demonstrations. LVSF can be calculated for single (N=1) or multiple (N>1) state variables and for different types of state variables. During IDE4L project, LVSF will be calculated for the following cases with multiple state variables:

1) customer connection point voltages 2) customer connection point power flows 3) line currents (all lines within LV network).

Three cases with different types of variables are calculated because the results of LVSF are utilized for a variety of purposes, and although these variables are interconnected, separate accuracy indices for different variables help us to evaluate the effect of forecasting errors on specific control functions (e.g. voltage control). LVSFs for individual state variables will be calculated only if a special need to study them arises during simulations or demonstrations. The forecasting horizon can be as long as 48 hours and is divided into time intervals of unequal length. For the first couple of hours the forecasts are made with high resolution (e.g. 10 minutes) but after that the forecasts are made on an hourly basis. The state estimation is run several times during one hour (e.g. once a minute). Therefore, average values over the forecasting window (e.g. 10 min or 1 h) must be calculated so that the state estimates can be compared with the state forecasts. Distribution network calculation software and practices vary from country to country and utility to utility.



Therefore it is impossible to define one universal business-as-usual case for state forecasting. To our knowledge there is no automatic state forecasting in existing network calculation or management software. However, Finnish DNOs for example have possibility to initiate manually LV network load flow calculation for any given hour in the future. This calculation utilises customer class load profiles. This could be considered as rudimental state forecasting. The DNOs participating in field demonstrations do not have this possibility and therefore the baseline and business-as-usual performance indices are not calculated for LV state forecasting. In addition to the above defined LVSF index, another important performance metric for LV network state forecasting is the state forecasting function execution time which is measured separately.

MV State forecaster (MVSF)


KPI Name MV state forecaster KPI ID MVSF

Main Objective Evaluate the accuracy of the state forecasts for the predetermined prediction horizons on the MV network.

KPI Description The KPI evaluates the performance of the short-term state forecasts by considering the deviation between the estimated states and corresponding state forecasts.

KPI Formula

𝑀𝑉𝑆𝐹(𝑘) = √1

𝑁𝑇 (∑ ∑ (

1

𝑀∑ (𝑥(𝑡 + 𝑘)𝑛.𝑚)

𝑀

𝑚=1

− 𝑥(𝑡 + 𝑘|𝑡)𝑛)

𝑇

𝑡=1

2𝑁

𝑛=1

)

where:




𝑀 : number of times the state estimation is run during the time period when state forecast for time t+k is valid

𝑥(𝑡 + 𝑘)𝑛,𝑚 : mth estimated value for the state n during the time period when state forecast for time t+k is valid

𝑥(𝑡 + 𝑘|𝑡)𝑛 : forecasts for the state n with look-ahead time k, issued at time t.

Unit of measurement

KPI MVSF can be calculated for several different variables. Units of measurements are as follows:

Active power (kW)

Reactive power (kVAr)

Current (A)

Voltage (V)


KPI formula is identical with LVSF This KPI is linked to ‘MV Load and State Forecast’ Use Case



Demonstration laboratory Field demonstrator






MVSF_01

The MV network State Forecaster (MVSF) uses load forecasts from the MV network load forecaster (MVF) to calculate state forecasts 𝑥(𝑡 + 𝑘|𝑡) for the whole MV network (𝑁 number of states), 1 to 𝑘 time-steps ahead.

MVSF (TUT)

MVSF_02

The MV network state estimator (MVSE) calculates the corresponding state estimates 𝑥(𝑡 + 𝑘) for the MV network (this happens k hours after the step MVSF-1). The frequency of state estimation is higher than the frequency of state forecasting, thereby state estimation is calculated 𝑀 times during the time period when state forecasts for time 𝑡 + 𝑘 are valid. The value of 𝑀 depends on the forecasting horizon 𝑘.

MVSE (TUT)

MVSF_03 In order to make the state estimates calculated in step MVSF-2 temporally comparable with the state forecasts calculated in step MVSF-1, averages over the 𝑀 state estimates are calculated.

(TUT)

MVSF_04 Steps 1–3 are repeated 𝑇 times during the monitoring period. (TUT)

MVSF_05 KPI is calculated according to the MVSF formula for the 𝑘-th forecast horizon.

(TUT)

KPI SCENARIOS


BASELINE


SMART GRID

KPI DATA COLLECTION

Data Data ID


collection


for Data collection

Location of Data

collection



Data collection

responsible

MV network load forecasts

𝑀𝑉𝐹 calculation MVF

function MV DXP 10 min 1 week (DE)

MV network state estimates

𝑥 calculation MVSE

function MV DXP 1 min 1 week (TUT)

MV network state forecasts

𝑥 calculation MVSF

function MV DXP 10 min 1 week (TUT)

KPI values 𝑀𝑉𝑆𝐹 calculation MVSF script

local once - (TUT)

KPI BASELINE


LITERATURE VALUES





Research organism (DE)

GENERAL COMMENTS



This KPI provides a performance index for state forecasting accuracy. The performance index is calculated as a function of look-ahead time. The forecasted states are compared with estimated states instead of actual measured states. This is because in field demonstrations all states are not measured and measurement data can contain errors. Measurement errors are detected and filtered during the state estimation process. Therefore, the estimated state values are the best possible estimates we have on our disposal. During laboratory demonstration it would also be possible to use real measured values but then the MVSF’s would not be comparable with MVSF’s calculated during field demonstrations. MVSF can be calculated for single (N=1) or multiple (N>1) state variables and for different types of state variables. During IDE4L project, MVSF will be calculated for the following cases with multiple state variables:

1) customer connection point voltages 2) customer connection point power flows 3) line currents (all lines within LV network).

Three cases with different types of variables are calculated because the results of MVSF are utilized for a variety of purposes, and although these variables are interconnected, separate accuracy indices for different variables help us to evaluate the effect of forecasting errors on specific control functions (e.g. voltage control). MVSFs for individual state variables will be calculated only if a special need to study them arises during simulations or demonstrations. The forecasting horizon can be as long as 48 hours and is divided into time intervals of unequal length. For the first couple of hours the forecasts are made with high resolution (e.g. 10 minutes) but after that the forecasts are made on an hourly basis. The state estimation is run several times during one hour (e.g. once a minute). Therefore, average values over the forecasting window (e.g. 10 min or 1 h) must be calculated so that the state estimates can be compared with the state forecasts. Distribution network calculation software and practices vary from country to country and utility to utility. Therefore it is impossible to define one universal business-as-usual case for state forecasting. To our knowledge there is no automatic state forecasting in existing network calculation or management software. However, Finnish DNOs for example have possibility to manually initiate MV network load flow calculation for any given hour in the future. This calculation utilises customer class load profiles. This could be considered as rudimental state forecasting. The DNOs participating in field demonstrations do not have this possibility and therefore baseline and business-as-usual performance indices are not calculated for MV state forecasting. In addition to the above defined MVSF index, another important performance metric for MV network state forecasting is the state forecasting function execution time which is measured separately.

6.8 UC8: Network Description Update One KPI has been defined to evaluate this Use Case:

Network Description Update (NDU)


KPI Name Network Description Update KPI ID NDU



Main Objective Evaluation of the percentage of the grid which can be actually managed by using WP5-like algorithms

KPI Description

Assuming that there isn’t any tool to align the central database which contains overall description of the network and the distributed DBs located in PSs and SSs. Each change in the network (new customer connected, changing of cables, new substation, new PV, etc.) implies that an entire area cannot be managed by WP5-like algorithms (State Estimation, Power Flow Control, etc.) which rely on the accurate description of the network topology. Summing the average number of changes per year per type, we can estimate the percentage of the grid which can be actually managed by IDE4L features.

KPI Formula

This KPI is evaluated by using historical data about changes on the network description.

As an example, the impact due to changes related to new customers in one year can be determined in the following way:

𝑵𝑫𝑼 =𝒁

𝑲∗ 𝟏𝟎𝟎

Where:

Z: Average number of feeders which have new customers in one year

K: Total number of feeders present in the network

And Z can be calculated as:

𝒁 =𝑿

𝒀

Where:

X: Number of new customers in one year

Y: Average number of new customers per feeder in one year

This KPI provides:

the average percentage of LV feeder which cannot be managed by WP5-

like algorithms due to new customers activated in one year



This KPI is linked to ‘Network Description Update’ Use Case





Field demonstrator




NDU_01 Collect the average number of new customers in one year (X) and the average number of new customers per each feeder in one year (Y)




NDU_02

Calculate the average number of feeders which have new customers in one year (Z)

𝒁 =𝑿

𝒀


NDU_03

Calculate the NDU KPI due to new customers as:

𝑵𝑫𝑼 =𝒁

𝑲∗ 𝟏𝟎𝟎


KPI SCENARIOS


BASELINE


SMART GRID

KPI DATA COLLECTION




for Data collection

Location of Data

collection



Data collection

responsible

Number of new customers in

one year

X

Provided by each

field demonstra

tor

Excel Excel Once //

Responsible of each

field demo

Average number of new

customers per feeder in one

year

Y

Provided by each

field demonstra

tor

Excel Excel Once //

Responsible of each

field demo

Total number of feeders present in the network

K

Provided by each

field demonstra

tor

Excel Excel Once //

Responsible of each

field demo

KPI BASELINE


LITERATURE VALUES





DSO (A2A)

GENERAL COMMENTS

Further considerations (not included in the KPI itself) could be:

- If we invest E € per LV feeder to setup WP5-like algorithms - And we have Y LV feeders - The over-cost for the installation is

𝑬 ∗ 𝒀 €

- Because of the mismatch about the topology description we are wasting:



𝑾 = 𝑬 ∗ 𝒀 ∗ 𝑷 €𝒚𝒆𝒂𝒓⁄

6.9 UC9: Protection Configuration Update One KPI has been defined to evaluate this Use Case:

Protection Configuration Update (PCU)


KPI Name Protection Configuration Update KPI ID PCU

Main Objective Evaluation the percentage of the grid which can be actually managed by using WP4-like algorithms

KPI Description

Assuming that there isn’t any tool to align the central database which contains overall description of the automation system DBs located in PSs and SSs. Each change in the network configuration (e.g. after a fault) implies that an entire area cannot be managed by WP4-like algorithms (FLISR, etc.) which rely on the accurate description of the network configuration, maximum current calculation, and a coordination amongst the IEDs. Summing the average number of changes per year, we can estimate the percentage of the grid which can be actually managed by IDE4L features

KPI Formula

This KPI is evaluated by using historical data about changing on the network description.

As an example, the impact due to changes related to MV network configuration in one year can be determined in the following way:

𝑷𝑪𝑼 =𝟐 ∗ 𝑿

𝒀∗ 𝟏𝟎𝟎

Where:

X: Average number of changes in the MV network configuration per year

Y: Total number of MV feeders present in the network

Hyp:

Consider that when the MV network configuration changes in average 2

feeders are involved, so 2*X is the number of MV feeders involved in one

year in MV network reconfigurations

This KPI provides:

The average percentage of MV feeders which cannot be managed by

WP4-like algorithms due to network reconfigurations in one year



This KPI is linked to ‘Protection Configuration Update’ Use Case

Project sites to be Development Integration Demonstration Field



calculated laboratory

laboratory

laboratory

demonstrator




PCU_01 Collect the average number of changes in the MV network configuration per year (X) and the total number of MV feeders present in the network (Y)


PCU_02

Calculate the PCU KPI due to MV network reconfiguration:

𝑷𝑪𝑼 =𝟐 ∗ 𝑿

𝒀∗ 𝟏𝟎𝟎


KPI SCENARIOS


BASELINE


SMART GRID

KPI DATA COLLECTION




for Data collection

Location of Data

collection



Data collection

responsible

Number of changings in the

MV network configuration

per year

X

Provided by each

field demonstra

tor

Excel Excel Once //

Responsible of each

field demo

Total number of MV feeders

present in the network

Y

Provided by each

field demonstra

tor

Excel Excel Once //

Responsible of each

field demo

KPI BASELINE


LITERATURE VALUES





DSO (A2A)

GENERAL COMMENTS

Further considerations (not included in the KPI itself) could be:

- If we invest E € per MV feeder to setup WP4-like algorithms - And we have Y MV feeders - The over-cost for the installation is

𝑬 ∗ 𝒀 €

- Because of the mismatch about the topology description we are wasting:

𝑾 = 𝑬 ∗ 𝒀 ∗ 𝑷 €𝒚𝒆𝒂𝒓⁄



6.10 UC10: Control Center Network Power Control (Tertiary Control) The KPIs related to this Use case are divided in three different categories:

Control Center Tertiary Power Control - Technical and Economic Parameters (CCPC-E)


KPI Name Control Center network Power Control (CCPC) Technical and Economic Parameters

KPI ID CCPC-E

Main Objective

The objective of the KPI is to evaluate the benefits and performance of the CCPC from a technical and economic point of view.

KPI Description

This KPI looks at a set of parameters relating to both the MV network and the CCPC itself in order to evaluate the performance of the CCPC from a technical and economic aspect and quantify the benefits of the CCPC in a period of time. The parameters are as follows:

1) Curtailed production [kWh]: Sum of curtailed production in the

evaluated period of time.

2) Curtailed/moved load [kWh]: Sum of curtailed/moved load in the


3) Network losses [kWh]: Sum of all the real power generated in the

network and transferred through the substation transformers and

then subtracting all real power consumed by the loads in the

network, in the evaluated period of time. Losses of transformers are

not included.

4) Target function value [€]: Sum of cost function of tertiary controller.

KPI Formula

CCPC-E1: 𝑃𝑐𝑢𝑟 = ∑𝑖

𝑃𝑐𝑢𝑟, 𝑖

CCPC-E2: 𝑃𝑑𝑟 = ∑𝑖

𝑃𝑑𝑟, 𝑖

CCPC-E3: 𝑃𝑙𝑜𝑠𝑠 = ∑𝑖

𝑃𝑡, 𝑖 + ∑𝑖

𝑃𝑝𝑟𝑜𝑑, 𝑖 − ∑𝑖

𝑃𝑙𝑜𝑎𝑑, 𝑖

where:

Pcur,i is the curtailed production for generation unit i

Pdr,i is the curtailed/moved load of load unit i

Pt,i is the active power going through transformer i



Pprod,i is the active power of production unit i

Pload,i is the active power consumed by the load i

Unit of measurement Kilowatt hour [kWh], Euro [€]


State estimation & forecasting, day ahead dynamic grid tariff.




Field demonstrator




CCPC-E_01 1. Obtain curtailment of each production unit from algorithm. 2. Calculate the total curtailment as sum of curtailment of each

production unit.

CCPC-E_02 1. Obtain curtailment of each load unit from algorithm. 2. Calculate the total curtailment as sum of curtailment of each

load unit.

CCPC-E_03

1. Obtain active power of all load units from state estimator. 2. Calculate total power consumed (Pload) as sum of active

power of each load unit. 3. Obtain active power going through transformer from state

estimator. 4. Obtain active power produced by all production units from

state estimator. 5. Calculate total power produced (Pprod) as sum of active

power of each production unit and power going through transformer.

6. Calculate network loss (Ploss) by subtracting power consumed from power produced (Pprod-Pload).

CCPC-E_04 1. Obtain cost function from tertiary controller.

KPI SCENARIOS


BASELINE


SMART GRID

KPI DATA COLLECTION

Data Data ID


collection


for Data collection

Location of Data

collection



Data collection

responsible

Target function value

Obtain from algorithm (and store

in DXP)

Directly from

algorithm or

historical data in DXP

Control Center

Not available

Lab: Not availabl

e Field: 2 weeks

Research organism

(DE)

Active power of each

transformer

Pt,i Obtain from

SE and Automation

SE and Automation System

Control Center

Not available Lab: Not availabl

e

Research organism

(DE)



System (through

DXP)

(through DXP)

Field: 2 weeks

Active power of each node

Pi

Obtain from SE and

Automation System

(through DXP)

SE and Automation System (through

DXP)

Control Center

Not available

Lab: Not availabl

e Field: 2 weeks

Research organism

(DE)

KPI BASELINE


LITERATURE VALUES



Details of Baseline



GENERAL COMMENTS

Control schemes used in tests: 1. Base case: No control. 2. Non-market case: Tertiary control without market integration (directly controlled DER only). In this

case, DER is either owned by DSO or under a bilateral contract that allows the DSO to control it directly.

3. Market case: Tertiary control with market integration (dynamic grid tariff, indirectly controlled DER).

Test setup:

Network reconfiguration: Tertiary controller uses network reconfiguration algorithm to find the optimal configuration for the MV network.

Network reconfiguration with directly controlled DER: Tertiary controller uses network reconfiguration algorithm to find the optimal configuration for the MV network and then applies power limits and voltage references to the secondary controllers (MVPC/LVPC).

Dynamic tariff: Tertiary controller applies a dynamic grid tariff to avoid network congestion. This test setup goes together with DADT KPI, where the setup is described in more detail.

DSO asking to buy flexibility services in the market The 4 parameters described before are calculated for the purpose of comparing results of the different test

sequences.

Control Center Tertiary Power Control - Operational Parameters (CCPC-O)


KPI Name Control Center network Power Control (CCPC) Operational Parameters

KPI ID CCPC-O

Main Objective

The objective of the KPI is to evaluate the operational performance of the CCPC.

KPI Description



This KPI looks at a set of technical parameters relating to both the MV network and the CCPC itself in order to evaluate the performance of the CCPC from an operational aspect and quantify the benefits of the CCPC in a period of time. The operational parameters are:

1) Average algorithm execution time [s]: Average of tertiary controller

execution time.

2) Alerts [pcs]: Alerts (maximum iterations reached, not converged, etc.) of

tertiary control algorithm.

3) OLTC steps taken [pcs]: Number of OLTC step actions.

4) P set point changes [pcs]: Number of active power set point changes.

5) Q set point changes [pcs]: Number of reactive power set point changes.

6) V set point changes [pcs]: Number of voltage set point changes.

7) Switch activations [pcs]: Number of times a switch is closed or opened.

8) Capacity utilization [%]: Average loading of each electrical component (line/transformer).

KPI Formula

Capacity utilization of each line/transformer can be obtained through:

CCPC-O8:

For transformers: 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦 𝑢𝑡𝑖𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛𝑖 =𝑆𝑎𝑣𝑔,𝑖

𝑆𝑛𝑜𝑚,𝑖⋅ 100%

For lines: 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦 𝑢𝑡𝑖𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛𝑖 =𝐼𝑎𝑣𝑔,𝑖𝑗

𝐼𝑛𝑜𝑚,𝑖𝑗⋅ 100%

where

Savg,i is the average loading of transformer i

Snom,i is the nominal rating of transformer i

Iavg,ij is the average amplitude of the complex current in line i

Inom,ij is the nominal current rating of line i

Unit of measurement Seconds [s], pieces/count/number of [pcs], Percent [%]






Field demonstrator




CCPC-O_01 1. Obtain the execution time of algorithm at each time point

from algorithm. 2. Calculate the average execution time.

CCPC-O_02 1. Count number of alerts.

CCPC-O_03

1. Obtain state of tap changer at each time point. 2. Find the number of OLTC step actions. 3. ALT: Count number of OLTC step actions (e.g. obtain it from

algorithm or measurement).



CCPC-O_04

1. Obtain active power setpoint at each time point. 2. Find number of active power setpoint changes. 3. ALT: Count number of active power setpoint changes (e.g.

obtain it from algorithm or measurement).

CCPC-O_05

1. Obtain reactive power setpoint at each time point. 2. Find number of reactive power setpoint changes. 3. ALT: Count number of reactive power setpoint changes (e.g.


CCPC-O_06

1. Obtain voltage setpoint at each time point. 2. Find number of voltage setpoint changes. 3. ALT: Count number of voltage setpoint changes (e.g. obtain

it from algorithm or measurement).

CCPC-O_07

1. Obtain switch states at each time point. 2. Find number of times a switch is closed or opened. 3. ALT: Count number of times a switch is closed or opened

(e.g. obtain it from algorithm or measurement).

CCPC-O_08

1. Obtain loading of each transformer in kVA at each time point.

2. Calculate average loading (Savg) of each transformer in kVA. 3. Find relative average loading of each transformer (in %) by

dividing the average loading with nominal rating and multiplying by 100 (Savg/Snom*100).

4. Obtain loading of each line in A at each time point. 5. Calculate average loading (Iavg) of each line in A. 6. Find relative average loading of each line (in %) by dividing

the average loading with nominal rating and multiplying by 100 (Iavg/Inom*100).

KPI SCENARIOS


BASELINE


SMART GRID

KPI DATA COLLECTION

Data Data ID


collection


for Data collection

Location of Data

collection



Data collection

responsible

Execution time

Measure in algorithm (and store

in DXP)

Directly from

algorithm or


Control Center

Every timestep

Lab: Not availabl

e Field: 2 weeks

Alerts

Store alerts in DXP or

count alerts in algorithm

Historical data in DXP or directly

from algorithm

Control Center

Not available

Lab: Not availabl

e Field: 2 weeks

OLTC steps

Obtain from measureme

nt system/logs

or count

Historical data from measurem

ents or directly

Control Center

Every timestep

Lab: Not availabl

e Field: 2 weeks



steps in algorithm

from algorithm

Active/reactive power setpoint

Store setpoints in

DXP or count

changes in algorithm


from algorithm

Control Center

Every timestep

Lab: Not availabl

e Field: 2 weeks

Voltage setpoint

Store setpoints in

DXP or count



from algorithm

Control Center

Every timestep

Lab: Not availabl

e Field: 2 weeks

Switch activations


nt system/logs

or count activations

in algorithm


ents or directly

from algorithm

Control Center

Every timestep

Lab: Not availabl

e Field: 2 weeks

Apparent power of each

transformer

St,i

Obtain from SE and

Automation System

(through DXP)


DXP)

Control Center

Every timestep

Lab: Not availabl

e Field: 2 weeks

Amplitude of complex current

of each line

Iij

Obtain from SE and

Automation System

(through DXP)


DXP)

Control Center

Every timestep

Lab: Not availabl

e Field: 2 weeks

KPI BASELINE


LITERATURE VALUES



Details of Baseline


GENERAL COMMENTS






Test setup:

Network reconfiguration: Tertiary controller uses network reconfiguration algorithm to find the optimal configuration for the MV network.


Dynamic tariff: Tertiary controller applies a dynamic grid tariff to avoid network congestion. This test setup goes together with WP5-DADT KPI, where the setup is described in more detail.


sequences.

Control Center Tertiary Power Control - Technical Safety Parameters (CCPC-S)


KPI Name Control Center network Power Control (CCPC) Technical Safety Parameters

KPI ID CCPC-S

Main Objective

The objective of the KPI is to evaluate the benefits and performance of the CCPC in regard to the voltage and current limits of the network .

KPI Description

This KPI looks at a set of parameters relating to the MV network in order to evaluate the performance of the CCPC from a technical aspect and quantify the benefits of the CCPC in a period of time. The technical safety parameters are:

1) Over-voltage volume [pu * s]: The over-voltage volumes are calculated

using trapezoidal integration. These volumes are formed between the

maximum voltage plane and the network voltage profile in a three

dimensional coordinate system. In this system x-axis is the node

numbering, y-axis is the time (s) and z-axis is the node voltage (pu), which

means that the unit of the volume is (pu*s).

2) Over-current volume [pu * s]: The over-current volumes are calculated


maximum currents and the network currents in a three dimensional

coordinate system. In this system x-axis is the branch numbering, y-axis is

the time (s) and z-axis is the branch current (pu), which means that the

unit of the volume is (pu*s).

3) Under-voltage volume [pu * s]: This is calculated similarly than over-

voltage volume from minimum voltage plane and the voltage profile.

4) Duration the voltage/current is out of bounds [s]: It is the duration when

voltage/current exceeds allowed range.

5) Number of over-voltage events [pcs]: The number of times an over-

voltage event has occurred.

6) Number of under-voltage events [pcs]: The number of times an under-



voltage event has occurred.

7) Number of over-current events [pcs]: The number of times an over-current

event has occurred.

KPI Formula

CCPC-S1: ∑𝑖

∫ max (0, 𝑈𝑖 − 𝑈𝑚𝑎𝑥)

CCPC-S2: ∑𝑖𝑗

∫ max (0, 𝐼𝑖𝑗 − 𝐼𝑚𝑎𝑥, 𝑖𝑗)

CCPC-S3: ∑𝑖

∫ max (0, 𝑈𝑚𝑖𝑛 − 𝑈𝑖)

Unit of measurement Seconds [s], per unit times seconds [pu * s]






Field demonstrator




CCPC-S_01

1. Obtain voltage at each node (Ui) from state estimator. 2. If voltage is higher than Umax, obtain voltage violation (Uvi)

by subtracting Umax from the node voltage (Ui-Umax). 3. Find the overvoltage area of each node by integrating the

voltage violation of each node (Uvi) with respect to time. 4. Calculate the overvoltage volume as the sum of all

overvoltage areas.

CCPC-S_02

1. Obtain current in each line (Iij) from state estimator. 2. If the current in a line (Iij) is higher than the max allowed

current in that line (Imax,ij), obtain current violation (Ivij) by subtracting max allowed current from line current (Iij-Imax,ij).

3. Find the overcurrent area of each line by integrating the current violation of each line (Ivij) with respect to time.

4. Calculate the overcurrent volume as the sum of all overcurrent areas.

CCPC-S_03

1. Obtain voltage at each node (Ui) from state estimator. 2. If voltage is lower than Umin, obtain voltage violation (Uvi)

by subtracting the node voltage from Umin (Umin-Ui). 3. Find the undervoltage area of each node by integrating the

voltage violation of each node (Uvi) with respect to time. 4. Calculate the undervoltage volume as the sum of all

undervoltage areas.

CCPC-S_04

1. Obtain all time points in which voltage or current is out of bounds (all time instances with over/undervoltage or overcurrent).

2. Calculate the total duration as the number of time points multiplied with the time resolution in seconds.

CCPC-S_05 1. Obtain all time points in which overvoltage was detected.



2. Find the number of overvoltage events. 3. ALT: Count number of overvoltage events (e.g. obtain it

from algorithm or measurement).

CCPC-S_06

1. Obtain all time points in which undervoltage was detected. 2. Find the number of undervoltage events. 3. ALT: Count number of undervoltage events (e.g. obtain it


CCPC-S_07

1. Obtain all time points in which overcurrent was detected. 2. Find the number of overcurrent events. 3. ALT: Count number of overcurrent events (e.g. obtain it


KPI SCENARIOS


BASELINE


SMART GRID

KPI DATA COLLECTION

Data Data

ID


collection


for Data collection

Location of Data

collection



Data collection

responsible

Voltage of each node

Ui

Obtain from SE and

Automation System

(through DXP)


DXP)

Control Center

Not available

Lab: Not availabl

e Field: 2 weeks

Research organism

(DE)

Current of each line

Iij

Obtain from SE and

Automation System

(through DXP)


DXP)

Control Center

Not available

Lab: Not availabl

e Field: 2 weeks

Research organism

(DE)

KPI BASELINE


LITERATURE VALUES



Details of Baseline



GENERAL COMMENTS




Test setup:

Network reconfiguration: Tertiary controller uses network reconfiguration algorithm to find the



optimal configuration for the MV network.


Dynamic tariff: Tertiary controller applies a dynamic grid tariff to avoid network congestion. This test setup goes together with WP5-DADT KPI, where the setup is described in more detail.


sequences.

6.11 UC11: LV Network Power Control (Secondary control) The KPIs related to this Use case are divided in three different categories:

LV Network Power Control - Technical and Economic Parameters (LVPC-E)


KPI Name Low Voltage network Power Control (LVPC) Technical and Economic Parameters

KPI ID LVPC-E

Main Objective

The objective of the KPI is to evaluate the benefits and performance of the LVPC from a technical and economic point of view.

KPI Description

This KPI looks at a set of parameters relating to the LV network in order to

evaluate the performance of the LVPC from a technical and economic aspect and

quantify the benefits of the LVPC in a period of time. The parameters are as

follows:

1) Curtailed production [kWh]: Sum of curtailed production in the evaluated

period of time.


network and transferred through the substation transformer and then

subtracting all real power consumed by the loads in the network in the

evaluated period of time. Losses of transformer are not included.

3) Target function value [€]: Sum of cost function of secondary controller.

KPI Formula

LVPC-E1: 𝑃𝑐𝑢𝑟 = ∑𝑖


LVPC-E2: 𝑃𝑙𝑜𝑠𝑠 = 𝑃𝑡 + ∑𝑖



where:

Pcur,i is the curtailed production for generation unit i Pt is the active power going through the transformer







FLISR, State estimation & forecasting




Field demonstrator




LVPC-E_01 1. Obtain curtailment of each production unit from algorithm. 2. Calculate the total curtailment as sum of curtailment of each

production unit.


LVPC-E_02


power of each load unit. 3. Obtain active power going through transformer (Pt) from

state estimator. 4. Obtain active power produced by all production units from





LVPC-E_03 1. Obtain cost function from Secondary controller. Research

organism (DE)

KPI SCENARIOS


BASELINE


SMART GRID

KPI DATA COLLECTION

Data Data ID


collection


for Data collection

Location of Data

collection



Data collection

responsible



in DXP)

Directly from

algorithm or


MV/LV transforme

r station

Every timestep (<= 10 minutes)

Lab: Not availabl

e Field: 2 weeks

Research organism

(DE)

Active power of transformer

Pt

Obtain from SE and

Automation System

(through local DXP)


local DXP)

MV/LV transforme

r station

Every timestep

Lab: Not availabl

e Field: 2 weeks

Research organism

(DE)




Pi

Obtain from SE and

Automation System

(through local DXP)


local DXP)

MV/LV transforme

r station

Every timestep

Lab: Not availabl

e Field: 2 weeks

Research organism

(DE)

KPI BASELINE


LITERATURE VALUES



Details of Baseline



GENERAL COMMENTS

Control schemes used in tests:

– Base case; no control

– Primary control schemes:

a. Single type of controllers: AVC control, AQR control, AVR control, AVR control with a deadband

b. Combination of two independent primary controllers: AVC and AQR, AVC and AVR, AVC and AVR

with a deadband

– Secondary control schemes

a. CVC control with AQR

b. CVC control with AVR

Controllers:

1. Automatic Voltage Controller (AVC) of the OLTC

2. Automatic Voltage Regulator (AVR) of the DER unit

3. Automatic Reactive power Regulator (AQR) of the DER unit

4. Production curtailment of the DER unit

5. Direct load control (load shedding) of the DER unit

Test setup:

AVC control: The AVC control is realized using an AVC-relay connected to the substation transformer,

which maintains the constant secondary voltage of transformer as close to the reference value as

possible.

AQR control: DER units control the power factor (or reactive power) of their connection points.

AVR control: DER units control the voltage of their connection points.

AVR control with a deadband: AVR control with a deadband the PID controller only changes reactive

power generation or consumption when the connection point voltage is outside of the allowed range

of voltage.

AVC control and AVR control: Combination of two control schemes.

AVC control and AVR control with a deadband: Combination of two control schemes. AVC relay



controlling the OLTC has a deadband to prevent changes of tap position unless the voltage differs

enough from the reference value.

CVC control with AQR: Secondary controller (OPF algorithm) controls the reference values of AVC and

DERs (reactive and real power).

CVC control with AVR: Similarly like previous control scheme, except that AVRs of DER units are

operated in voltage control mode during the delay of OLTC when it is activated by the secondary

controller and for a safety period after the tap change. During this time period the secondary

controller provides the voltage reference for AVR instead of reactive power reference.

The 3 parameters described before are calculated for the purpose of comparing results of the different test

sequences.

LV Network Power Control - Operational Parameters (LVPC-O)


KPI Name Low Voltage network Power Control (LVPC) Operational Parameters

KPI ID LVPC-O

Main Objective

The objective of the KPI is to evaluate the operational performance of the LVPC.

KPI Description

This KPI looks at a set of operational parameters relating to the LVPC in order to

evaluate the performance of the LVPC from a technical aspect. The KPI

parameters are as follows:

1) Average algorithm execution time [s]: Average of secondary controller

execution time.

2) Alerts [pcs]: Alerts (maximum iterations reached, not converged, etc.)

of secondary control algorithm.



5) Q set point changes [pcs]: Number of reactive power set point

changes.


KPI Formula See general comments

Unit of measurement Seconds [s], pieces/count/number of [pcs]






Field demonstrator


KPI Step Step Responsible



Methodology ID [KPI ID #]

LVPC-O_01 1. Obtain the execution time of algorithm at each time point



LVPC-O_02 1. Count number of alerts. Research

organism (DE)

LVPC-O_03




LVPC-O_04




LVPC-O_05




LVPC-O_06




KPI SCENARIOS


BASELINE


SMART GRID

KPI DATA COLLECTION

Data Data

ID


collection


for Data collection

Location of Data

collection



Data collection

responsible

Execution time


in DXP)

Directly from

algorithm or


MV/LV transforme

r station


Lab: Not availabl

e Field: 2 weeks

Research organism

(DE)

Alerts




from algorithm

MV/LV transforme

r station Not available

Lab: Not availabl

e Field: 2 weeks

Research organism

(DE)

OLTC steps


nt system/logs

or count steps in

algorithm


ents or directly

from algorithm

MV/LV transforme

r station

Every timestep

Lab: Not availabl

e Field: 2 weeks

Research organism

(DE)


Store setpoints in

Historical data in DXP

MV/LV transforme

Every timestep

Lab: Not availabl

Research organism



DXP or count


or directly from

algorithm

r station e Field: 2 weeks

(DE)

Voltage setpoint

Store setpoints in

DXP or count



from algorithm

MV/LV transforme

r station

Every timestep

Lab: Not availabl

e Field: 2 weeks

Research organism

(DE)

KPI BASELINE


LITERATURE VALUES



Details of Baseline



GENERAL COMMENTS






with a deadband




Controllers:






Test setup:



possible.







of voltage.











The 6 operational parameters are calculated for the purpose of comparing results of the different test

sequences.

LV Network Power Control - Technical Safety Parameters (LVPC-S)


KPI Name Low Voltage network Power Control (LVPC) Technical Safety Parameters

KPI ID LVPC-S

Main Objective

The objective of the KPI is to evaluate the benefits and performance of the LVPC in regard to the voltage and current limits of the network.

KPI Description

This KPI looks at a set technical safety parameters relating to the LV network in

order to evaluate the performance of the LVPC from a technical aspect and

quantify the benefits of the LVPC in a period of time. The voltage/current safety

parameters are:

















4) Duration the voltage/current is out of bounds [s]: It is the duration when voltage/current exceeds allowed range.

KPI Formula

LVPC-S1: ∑𝑖


LVPC-S2: ∑𝑖𝑗


LVPC-S3: ∑𝑖








Field demonstrator




LVPC-S_01




overvoltage areas.


LVPC-S_02






LVPC-S_03

1. Obtain voltage at each node (Ui) from state estimator. 2. If voltage is lower than Umin, obtain voltage violation (Uvi)

by subtracting the node voltage from Umin (Umin-Ui). 3. Find the undervoltage area of each node by integrating the

voltage violation of each node (Uvi) with respect to time. 4. Calculate the undervoltage volume as the sum of all

undervoltage areas.


LVPC-S_04




KPI SCENARIOS




BASELINE


SMART GRID

KPI DATA COLLECTION

Data Data ID


collection


for Data collection

Location of Data

collection



Data collection

responsible


Ui

Obtain from SE and

Automation System

(through local DXP)


local DXP)

MV/LV transforme


Lab: Not availabl

e Field: 2 weeks

Research organism

(DE)


Iij

Obtain from SE and

Automation System

(through local DXP)


local DXP)

MV/LV transforme


Lab: Not availabl

e Field: 2 weeks

Research organism

(DE)

KPI BASELINE


LITERATURE VALUES



Details of Baseline



GENERAL COMMENTS






with a deadband




Controllers:








Test setup:



possible.





of voltage.












sequences.

6.12 UC12: MV Network Power Control (Primary Control) The KPIs related to this Use case are divided in three different categories:

MV Network Power Control - Technical and Economic Parameters (MVPC-E)


KPI Name Medium Voltage network Power Control (MVPC) Technical and Economic Parameters

KPI ID MVPC-E

Main Objective

The objective of the KPI is to evaluate the benefits and performance of the MVPC from a technical and economic point of view.

KPI Description

This KPI looks at a set of parameters relating to the MV network in order to

evaluate the performance of the MVPC from a technical and economic aspect and

quantify the benefits of the MVPC in a period of time. The parameters are as

follows:

5) Curtailed production [kWh]: Sum of curtailed production in the



network and transferred through the substation transformer and then

subtracting all real power consumed by the loads in the network, in



the evaluated period of time. Losses of transformer are not included.

7) Target function value [€]: Sum of cost function of secondary controller.

KPI Formula

MVPC-E1: 𝑃𝑐𝑢𝑟 = ∑𝑖


MVPC-E2: 𝑃𝑙𝑜𝑠𝑠 = 𝑃𝑡 + ∑𝑖



where:

Pcur,i is the curtailed production for generation unit i Pt is the active power going through the substation transformer









Field demonstrator




MVPC-E_01 1. Obtain curtailment of each production unit from algorithm. 2. Calculate the total curtailment as sum of curtailment of each

production unit.


MVPC-E_02


power of each load unit. 3. Obtain active power going through transformer (Pt) from

state estimator. 4. Obtain active power produced by all production units from





MVPC-E_03 1. Obtain cost function from Secondary controller. Research

organism (DE)

KPI SCENARIOS


BASELINE


SMART GRID

KPI DATA COLLECTION

Data Data ID


collection


for Data collection

Location of Data

collection



Data collection

responsible





in DXP)

Directly from

algorithm or


MV/LV transforme

r station


Lab: Not availabl

e Field: 2 weeks

Research organism

(DE)

Active power of transformer

Pt

Obtain from SE and

Automation System

(through local DXP)


local DXP)

MV/LV transforme

r station

Every timestep

Lab: Not availabl

e Field: 2 weeks

Research organism

(DE)


Pi

Obtain from SE and

Automation System

(through local DXP)


local DXP)

MV/LV transforme

r station

Every timestep

Lab: Not availabl

e Field: 2 weeks

Research organism

(DE)

KPI BASELINE


LITERATURE VALUES



Details of Baseline



GENERAL COMMENTS






with a deadband




Controllers:






Test setup:





possible.





of voltage.












sequences.

MV Network Power Control - Operational Parameters (MVPC-O)


KPI Name Medium Voltage network Power Control (MVPC) Operational Parameters

KPI ID MVPC-O

Main Objective

The objective of the KPI is to evaluate the benefits and performance of the MVPC from an operational point of view.

KPI Description

This KPI looks at a set of technical parameters relating to the MVPC in order to

evaluate the performance of the MVPC from an operational aspect. The


1) Average algorithm execution time [s]: Average of secondary

controller execution time.

2) Alerts [pcs]: Alerts (maximum iterations reached, not converged,

etc.) of secondary control algorithm.



5) Q set point changes [pcs]: Number of reactive power set point

changes.





Unit of measurement Seconds [s], pieces/count/number of [pcs]






Field demonstrator




MVPC-O_01 1. Obtain the execution time of algorithm at each time point



MVPC-O_02 1. Count number of alerts. Research

organism (DE)

MVPC-O_03




MVPC-O_04




MVPC-O_05




MVPC-O_06




KPI SCENARIOS


BASELINE


SMART GRID

KPI DATA COLLECTION

Data Data ID


collection


for Data collection

Location of Data

collection



Data collection

responsible

Execution time


in DXP)

Directly from

algorithm or


HV/MV transforme

r station


Lab: Not availabl

e Field: 2 weeks

Research organism

(DE)



Alerts




from algorithm

HV/MV transforme


Lab: Not availabl

e Field: 2 weeks

Research organism

(DE)

OLTC steps


nt system/logs

or count steps in

algorithm


ents or directly

from algorithm

HV/MV transforme

r station

Every timestep

Lab: Not availabl

e Field: 2 weeks

Research organism

(DE)


Store setpoints in

DXP or count



from algorithm

HV/MV transforme

r station

Every timestep

Lab: Not availabl

e Field: 2 weeks

Research organism

(DE)

Voltage setpoint

Store setpoints in

DXP or count



from algorithm

HV/MV transforme

r station

Every timestep

Lab: Not availabl

e Field: 2 weeks

Research organism

(DE)

KPI BASELINE


LITERATURE VALUES



Details of Baseline



GENERAL COMMENTS






with a deadband




Controllers:








Test setup:



possible.





of voltage.











The 6 operational parameters described before are calculated for the purpose of comparing results of the

different test sequences.

MV Network Power Control - Technical Safety Parameters (MVPC-S)


KPI Name Medium Voltage network Power Control (MVPC) Technical Safety Parameters

KPI ID MVPC-S

Main Objective

The objective of the KPI is to evaluate the benefits and performance of the MVPC in regard to the voltage and current limits of the network.

KPI Description

This KPI looks at a set of technical safety parameters relating to the MV network

in order to evaluate the performance of the MVPC from a technical aspect and

quantify the benefits of the MVPC in a period of time. The voltage/current safety


















4) Duration the voltage/current is out of bounds [s]: It is the duration when

voltage/current exceeds allowed range.

KPI Formula

MVPC-S1: ∑𝑖


MVPC-S2: ∑𝑖𝑗


MVPC-S3: ∑𝑖








Field demonstrator




MVPC-S_01




overvoltage areas.


MVPC-S_02






MVPC-S_03 1. Obtain voltage at each node (Ui) from state estimator. Research



2. If voltage is lower than Umin, obtain voltage violation (Uvi) by subtracting the node voltage from Umin (Umin-Ui).

3. Find the undervoltage area of each node by integrating the voltage violation of each node (Uvi) with respect to time.

4. Calculate the undervoltage volume as the sum of all undervoltage areas.

organism (DE)

MVPC-S_04




KPI SCENARIOS


BASELINE


SMART GRID

KPI DATA COLLECTION

Data Data

ID


collection


for Data collection

Location of Data

collection



Data collection

responsible


Ui

Obtain from SE and

Automation System

(through local DXP)


local DXP)

HV/MV transforme


Lab: Not availabl

e Field: 2 weeks

Research organism

(DE)


Iij

Obtain from SE and

Automation System

(through local DXP)


local DXP)

HV/MV transforme


Lab: Not availabl

e Field: 2 weeks

Research organism

(DE)

KPI BASELINE


LITERATURE VALUES



Details of Baseline



GENERAL COMMENTS






with a deadband






Controllers:






Test setup:



possible.





of voltage.












sequences.

6.13 UC13: Decentralized FLISR Four different KPIs are defined for this Use Case:

SAIDI


KPI Name SAIDI KPI ID SAIDI

Main Objective Estimate the average interruption duration



KPI Description

This KPI will estimate the average interruption duration, which leads to disturbance for network users and maintenance costs. It can be calculated using the outage time for every track and the total number of users on it (or averaged number of users per track)

KPI Formula

𝑆𝐴𝐼𝐷𝐼𝐵𝐿 − 𝑆𝐴𝐼𝐷𝐼𝑆𝐺

𝑆𝐴𝐼𝐷𝐼𝐵𝐿

SAIDI is measured according to Std. IEEE 1366-1998.

𝑆𝐴𝐼𝐷𝐼 =∑ 𝑟𝑖𝑁𝑖

𝑁𝑡

ri Restoration time for each interruption event; Ni Number of interrupted customers for each interruption event during reporting period; Nt Total number of customers served for the area being indexed;

𝑟𝑖 = 𝑆𝐼𝑒𝑛𝑑 − 𝑆𝐼𝑠𝑡𝑎𝑟𝑡 SI Service Interruption



Related to Decentralized FLISR UC.




Field demonstrator




SAIDI_01 Detect number and duration of interruptions DSO

SAIDI_02 Detect or estimate the number of affected customers DSO

SAIDI_03 Calculate SAIDI in smart grid scenario DMS

SAIDI_04 Compare to baseline scenario DMS

KPI SCENARIOS


BASELINE


SMART GRID

KPI DATA COLLECTION

Data Data ID


collection


for Data collection

Location of Data

collection



Data collection

responsible

Service interruption

event timestamp

SIstart Sequence of

events logging

RTUs managing switches

and breakers

Once at the end of the monitoring

period

Complete test phase.

(Typically one year)

DSO



Restoration command timestamp

SIend Sequence of

events logging

RTUs managing switches

and breakers


period



DSO

Number of interrupted customers

Ni

Adding the number of customers

in the affected

areas

DMS


period



DMS

KPI BASELINE


LITERATURE VALUES



Details of Baseline Previous SAIDI values in the same area.


DSOs

GENERAL COMMENTS

This KPI has been defined for UC “Decentralized FLISR”

SAIFI


KPI Name SAIFI KPI ID SAIFI

Main Objective Measure the number of service interruptions suffered by a average user.

KPI Description This KPI will estimate the average number of service interruptions detected by a typical end user in the network during a defined time t (typically one year)

KPI Formula

𝑆𝐴𝐼𝐹𝐼𝐵𝐿 − 𝑆𝐴𝐼𝐹𝐼𝑆𝐺

𝑆𝐴𝐼𝐹𝐼𝐵𝐿

SAIFI is measured according to IEEE 1366-1998:

𝑆𝐴𝐼𝐹𝐼 =∑ 𝑁𝑖

𝑁𝑡

Where :

Ni Number of interrupted customers for each interruption event during reporting period: Nt Total number of customers served for the area being indexed;



Related to Decentralized FLISR UC.






Field demonstrator

×




SAIFI_01 Detect or estimate the number of affected customers DSO

SAIFI_02 Calculate SAIFI in smart grid scenario DMS

SAIFI_03 Compare to baseline scenario DMS

KPI SCENARIOS


BASELINE


SMART GRID

KPI DATA COLLECTION

Data Data

ID


collection


for Data collection

Location of Data

collection



Data collection

responsible

Number of interrupted customers

Ni

Adding the number of customers

in the affected

areas

DMS


period



DMS

Total number of customers

Nt

Adding the number of customers in test area

DMS


period



DMS

KPI BASELINE


LITERATURE VALUES


×


Details of Baseline Previous SAIFI values in the same area.


DSOs

GENERAL COMMENTS


Breaker energized operations (BEO)


KPI Name Breaker energized operations KPI ID BEO



Main Objective Measure the load for breakers due to the number of energized operations in terms of cost.

KPI Description This KPI will estimate the maintenance cost due to breaker energized operations. This could be measured by counting the average total number of breaker energized operations in a fault isolation activity.

KPI Formula

BEO = ∑ 𝐶𝐵𝑅_𝑂𝑃𝑖

𝑁−1

𝑖=0

Where: N is the number of breakers in the test network CBR_OPi is the number of operation done on the breaker i during the fault condition



Related to Fault detection, isolation and location UC.




Field demonstrator




BEO_01 Detect CBs involved and collect the number of operations DSO

BEO_02 Collect the number of available CBs DSO

BEO_03 Calculate BEO in the smart grid scenario DMS

BEO_04 Compare to baseline scenario DMS

KPI SCENARIOS


BASELINE


SMART GRID

KPI DATA COLLECTION

Data Data ID


collection


for Data collection

Location of Data

collection



Data collection

responsible

Breaker operation counter

Sequence of events

logging or breaker

operation counter

RTUs

managing breakers

Just after every fault

event.

Depending on

the number

of events. At least

30 events.

DSO

Number of available CBs in

the test network

N

Count the CBs

declared in active

topology

DMS Just after

every fault event

Depending on

the number

of events. At least

DSO



30 events.

KPI BASELINE


LITERATURE VALUES



Details of Baseline Historical data of breaker maintenance and operation.


DSOs

GENERAL COMMENTS


Interconnection switch (IS)


KPI Name Interconnection Switch KPI ID IS

Main Objective This KPI evaluates the performance of the interconnection switch

KPI Description It measures the capability of the Interconnection Switch of isolating a microgrid under different contingencies in the distribution grid.

KPI Formula

KPI = 1/(1 + ct/α + rt/β), the higher the better. (KPI = 1 ideal behavior)

Where ct (Clearing Time) is the time from fault occurrence to microgrid isolation in milliseconds and α is the typical closing time (α = 50); rt (Reconnection Time) is the time from reconnection trigger signal to reconnection, in milliseconds, and β is the typical reconnection time (β = 5000). Notice that reconnection trigger may be issued by FLISR or DSO and send to the microgrid central controller, which is responsible to synchronize the microgrid with the distribution grid. Thus the performance of rt no only depends on the IS, but also on the microgrid implementation as well. For this reason, ct has more weight in the KPI formula.

The proposed KPI formula reflects that the complexity to obtain high values of KPI is non-linear with respect to ct and rt; i.e., the slope of the KPI function is much higher from 0 to 50 ms, than from 100 to 150. This is because with the ”classical” technology (mechanical switch), is easy to get ct around 100 ms. However, to obtain ct lower than 50 ms, newer technology, such as static switch, would be required. See Figure 1 for typical values of KPI depending on ct and rt.



Figure 7, illustration of KPI values for different ct and rt times.

Unit of measurement ct: milliseconds rt: milliseconds KPI in per unit (adimensional)


This KPI is linked to the Use Case: Decentralized FLISR




Field demonstrator




IS_01 Measure current at the distribution side of interconnection switch Research

organism (IREC)

IS_02 Trigger a distribution side fault. Research

organism (IREC)

IS_03 Time from fault occurrence to instant when current measured is zero.


IS_04 Clear the distribution fault, and signal IS to reconnect. Research

organism (IREC)

IS_05 Time from reconnection trigger to reconnection of the microgrid with the distribution grid. (i.e. current starts flowing again)


KPI SCENARIOS


BASELINE


SMART GRID

KPI DATA COLLECTION

Data Data ID


collection


Location of Data

collection



Data collection

responsible

Clearing time ct

Step_1,2,3 Oscilloscope laboratory punctual 3 min Research organism



(IREC)

Reconnection time

rt Step_1,4,5 Oscilloscope laboratory punctual 3 min

Research organism

(IREC)

KPI BASELINE


LITERATURE VALUES






GENERAL COMMENTS

Good performance is indicated by a KPI bigger than 0.3 (i.e. ct is at least faster than 125ms). KPI values below 0.2 might not be acceptable. The rt parameter should be repeatedly measured, as its value will depend on the initial phase difference between the microgrid and the distribution grid voltage. The lowest value should be taken, since we are evaluating the IS performance, not the microgrid synchronization algorithm.

6.14 UC14: Power quality control One KPI is defined to evaluate this Use Case:

Flicker mitigation MV/LV active grid (Flick_M)


KPI Name Flicker mitigation in MV/LV lines KPI ID Flick_M

Main Objective This KPI evaluates the performance of some sort of variable DER units connected to the distribution power grid (wind, PV), that can cause some disturbance in the power quality signal such as flicker noise.

KPI Description

It measures the capability of improving power quality at certain points of medium voltage / low voltage electrical networks for protection of the connected loads and / or other equipment or installations. Power quality is a generic term concerning several aspects affecting the constancy and waveforms of current and voltage in the electrical systems. Power quality can be affected, for instance, by the stochastic nature of the power flows in networks with high penetration of renewable generators, which are connected to the network through power electronics. This KPI addresses a specific aspect of power quality as named in the following:

- Voltage flicker levels.

KPI Formula

Flicker is a subjective sensation of visual instability provoked by fast fluctuations in light stimulus. It can cause annoying luminance changes in lamps in the frequency spectrum from 0.05 Hz to 35 Hz. According to experimental analyses, flicker sensation is maximum at 8.8 Hz and it depends on two major variables: the square of the magnitude of voltage fluctuations and the time length of these disturbances. Flicker emission severity can be calculated according to the international



standard IEC 61000-4-15. This standard describes the so-called flicker meter. This device provides two flicker indices: short and long term flicker severity, from the measurement and processing of voltage signal at the required connection point. Also, it can provide a third index, the instantaneous flicker sensation. This instantaneous flicker index is intended to represent the instantaneous sensation experienced by humans while subjected to a fluctuating light stimulus. Short-term flicker severity index requires precise measurement of RMS voltage level for at least 10 minutes, while continuous measurement of voltage for up to 2 hours is needed for determining long-term flicker index. According to IEC 61000-4-15, the flicker meter is composed by the 5 major modules represented in the following figure.

Figure 8, Flicker meter representation

As shown, the determination of the instantaneous flicker severity index, 𝑃𝑖𝑛𝑠𝑡(𝑡), is required to compute both short-term and long-term indices (𝑃𝑠𝑡(𝑡) and 𝑃𝑙𝑡(𝑡) respectively). In order to compute 𝑃𝑖𝑛𝑠𝑡(𝑡), first voltage measurement has to be scaled to a 230 V RMS signal in Module 1, then the square of this signal is obtained in Module 2 and all serve to represent the equivalent voltage fluctuation of a domestic lamp. The obtained signal at this point is double-filtered in Module 3 to obtain the instantaneous sensation of flicker just prior being processed by the brain. To this aim, Module 3 firstly applies a first-order Butterworth high-pass filter with cut-off frequency 0.05 Hz. Then, the resultant signal is further modified by a high-pass filter (sixth order, Butterworth type, with a cut-off frequency 35 Hz, thus bounding at this frequency voltage fluctuations associated to flicker). Finally, Module 3 applies a third transfer function in order to emulate the response or the effect of voltage fluctuations of a domestic lamp in human eyes. All these filters and transfer functions serves to obtain a fluctuating signal which is further processed and low-pass filtered in Module 4 of flickermeter, emulating the instantaneous response of human brain to such fluctuating light stimulus, so giving the instantaneous sensation of flicker index 𝑃𝑖𝑛𝑠𝑡(𝑡). As previously noted, the instantaneous flicker index serves to compute 𝑃𝑠𝑡(𝑡) and 𝑃𝑙𝑡(𝑡) and these are calculated applying statistic treatment to 𝑃𝑖𝑛𝑠𝑡(𝑡). Detailed description around these statistics will be offered when convenient. To sum up, and for the sake of clarity, the mathematical expressions for 𝑃𝑠𝑡(𝑡) is calculated as

𝑃𝑠𝑡(𝑡) = √0.314𝑃0.1 + 0.0525𝑃1𝑠 + 0.0657𝑃3𝑠 + 0.28𝑃10𝑠 + 0.08𝑃50𝑠,

Module 3

Signal filtering:

Module 1 Module 2 Module 4 Module 5

ADC module

Signalprocessing:

u2

Signalfiltering

and processing

Compu-tation of

short and long

termindices

u(t)

Pst (t)

Plt (t)

Pinst (t)



where 𝑃0.1, 𝑃1𝑠, 𝑃3𝑠, 𝑃10𝑠, 𝑃50𝑠 are related to the measures or instantaneous flicker indexes above threshold limits during 0.1%, 1%, 3%, 10% and 50% of the time interval concerned for analysis (i.e. the last 10 minutes). From a sequence of short-term flicker indexes (the last 12 indexes determined each 10 minutes), it is easy to compute the long-term flicker index by

𝑃𝑙𝑡(𝑡) = √∑ 𝑃𝑠𝑡𝑖

3𝑁𝑖=1

𝑁

3

The flickermeter is the standardized device to measure flicker noise and as such should be properly implemented in the active flicker filter to be developed in this use case, for addressing power quality in electrical networks. The comparison between the previously described indexes provided by the flickermeter prior and after the implementation of the active flicker filter determines the KPI to evaluate the performance of the designed solution. The step-by-step procedure for KPI calculation is offered in following sections of this document.



This KPI is defined in conjunction with UC “Power quality control”.




Field demonstrator




Flick_M_01 Real-time data collection for at least 10 minutes by an acquisition system / oscilloscope


Flick_M_02 Computation of instantaneous flicker severity index Research

organism (IREC)

Flick_M_03 Computation of short-term flicker severity index Research

organism (IREC)

Flick_M_04 Computation of long-term flicker severity index Research

organism (IREC)

KPI SCENARIOS


BASELINE


SMART GRID

KPI DATA COLLECTION

Data Data ID


collection


for Data collection

Location of Data

collection



Data collection

responsible

RMS phase-to-neutral voltage

at the connection point of flickermeter

Data collected to be directly processed

by a

Oscilloscope / ADC

modules of DSP

microproce

Laboratory Milliseconds 10

minutes

Research organism

(IREC)



microprocessor of the

active filter to be

designed in the project

or to be saved for further

analyses.

ssor building up the active

filter based on power

electronics to be

developed in the

project. The sample rate should

be in the range of no more than 20 ms so as

to represent

phenomena with a

frequency lower than 50 Hz. This continuous measurement serves

to continuously update

the so called

instantaneous flicker index. This

index is averaged

(considering the last

10 minutes of

measurements) to

calculate the so-called

short-term flicker

index (see the

description in KPI

formula



section).

KPI BASELINE


LITERATURE VALUES



Details of Baseline Proposed concepts are to be tested in laboratory, so this provides us with flexibility to perform the baseline scenario from which it would be easy to evaluate the proposed KPI.



GENERAL COMMENTS

6.15 UC15: Expansion Planning In this Use Case, one KPI has been defined:

Expansion planning scenario evaluation (EPSE)


KPI Name Expansion Planning Scenario Evaluation KPI ID EPSE

Main Objective To evaluate different planning strategies’ pros and cons.

KPI Description The KPI evaluates the quality of planning solution in comparison to other possible development sequences.

KPI Formula

Nomenclature

Vn, h nodal voltage on node n at hour h

Nnodes Number of system nodes

Ncust Total number of customers

Toutage,ann,x Annual outage time at location x

Ncust,x Number of customers at location x

λx Annual average fault rate at location x

Ncomp Number of network components

CMaint,i(t) Annual maintenance costs of component i at year t

Vul Highest permissible voltage (pu)

Vll Lowest permissible voltage (pu)

Irating, i Current rating for component i

Icomp,i,h Current of component i at hour h

CCIC Total cost of interruption



Cintr,s,i Cost of short interruption for customer i

Cintr,i(T) Cost of interruption of T hours for customer i

Pgen Total nominal generation in the system

Pgen,cap System hosting capacity

Pload Total nominal load in the system

Pload,cap System loading capacity

CDFV,n,h(V) Cumulative distribution function of nodal voltage at hour h on node n

Pdist,i per kWh price of distribution for customer i.

Edemand, i Average hourly demand of energy for customer i

Toutage, i Average annual outage time for customer i

Nintr,s,i Average annual number of short interruptions for customer i

tbeg first year of development step

tend last year of development step

Cinv,k(t) Annual investments at year t, for transition k

tref Reference year for capitalization

r interest rate

Nk Number of transitions in development scenario

Closs Total cost of network losses during one development step

closs(t) Average cost of loss energy

Eloss,i(t) Annual energy loss of component i

Cact,i(t) Cost of active services for customer i

CAct Total cost of active services

NAct Total number of active control actions

Nact,i(t) Number of active control actions for customer i

NAct,cust Total number of customers providing active control services

Following formulas are calculated for every development step of the expansion planning scenario. This gives a sequence of values for every variable so that the evolution of a single variable can be observed trough the planning scenario.

NETWORK CAPACITY

Network hosting capacity for generation and load is defined as a difference



between theoretical maximum capacity and existing capacity at existing generator/load topology.

hosting

Pgen,cap = max( 𝑃gen ) − Pgen

loading

Pload,cap = max( 𝑃load ) − Pload

so that

Vn,h < Vul and Vn,h > Vll

∀ Vn,h | h = 1 … 8760 n = 1 … Nnodes

and

Icomp,i,h < Irating,i

Icomp,i,h | h = 1 … 8760 i = 1 … Ncomp

These indicators are represented as plain power values or as a percentage of theoretical maximum capacity.

SAIDI

SAIDI = ∑ Ncust,xToutage,ann,x

Ncust

SAIFI

SAIFI =∑ Ncust,xλx

Ncust

Network reliability (regulation)

CCIC = ∑ Cintr,s,iNintr,s,i +

Ncust

i=1

Cintr,i(Toutage,i)

Cost of energy not supplied

Cens = ∑ Pdist,iEdemand,iToutage,i

Ncust

i=1

Voltage

max ( 𝑉n,h) min ( 𝑉n,h)



𝑉n,h = {Vn,h} | h = 1 … 8760 n = 1 … Nnodes

This is a direct output of power flow calculation

Probability of under/overvoltage

povervoltage = 1 − CDFV,n,h(Vul)

where n and h are the node and hour of highest system voltage respectively

pundervoltage = CDFV,n,h(Vll)

where n and h are the node and hour of lowest system voltage

Costs of losses

Closs = ∑ ∑ closs(t)Eloss,i(t)

Ncomp

i=1

tend

t=tbeg

Maintenance costs

CMaint = ∑ ∑ CMaint,i(t)

Ncomp

i=1

tend

t=tbeg

Investment costs

Cinvestments = ∑ ∑1

(1 + r100⁄ )

t−tref

tend

t=tbeg

k=Nk

k=1

Cinv,k(t)

Usage of active control services

CAct = ∑ ∑ Cact,i(t)

Ncust

i=1

tend

t=tbeg

NAct = ∑ ∑ Nact,i(t)

Ncust

i=1

tend

t=tbeg

NAct,cust = ∑ Ni

Ncust

i=1

Ni , customer i is active (1,0)

Unit of measurement €, kW, pu, %, interruptions per customer, hours or unitless


This KPI is linked to ‘Expansion Planning’ Use Case Also connection with KPI Target Network Planning






Field demonstrator




EPSE_01 Expansion planning of distribution network with business as usual solutions from existing network to target network.

Research organism (Antti Supponen, TUT)

EPSE_02 Expansion planning of distribution network with smart grid solutions from existing network to target network.

Research organism (Antti Supponen, TUT)

EPSE_03 Cost - benefits analysis of smart grid solutions Research

organism (Antti Supponen, TUT)

KPI SCENARIOS


BASELINE


SMART GRID

KPI DATA COLLECTION

Data Data

ID


collection


for Data collection

Location of Data

collection



Data collection

responsible

Network Data (DSO)

Supplied by DSO

CIS, NIS, etc…

(DSO’s information systems in general)

Not available

once n/a DSO

DER scenario (Planner)

Devised by planner

literature, national

forecasts, etc…

Not available

once n/a DSO/Plan

ner

KPI BASELINE


LITERATURE VALUES





Not available

GENERAL COMMENTS

The network expansion planning consists of defining possible development steps and then defining the best sequence of steps in order to reach the predefined target network (illustrated on figure 9). The purpose of this KPI is to help planners in decision making when selecting the development sequence.



Current network

Target Network(s)

Possible developement steps

One possible developement scenario

Figure 9: Expansion planning

Assumptions of calculations:

Temporal information from city planning is available.

DER scenario provides information about changes of loads and production in general.

Planning will consider only MV network. HV network is assumed to be ideal. Primary and secondary

substation sitting is not considered i.e. substations are located in given places. LV customers are

divided between neighboring secondary substations based on “electrical distance” principle.

o LV feeders are not modeled. Distribution transformer is included in the model.

o Aggregated loads (statistical sum of load profiles) and production (time series of DG unit

types, correlation within DG unit type is one)

o Replacement costs are considered (aging of network (possible maximum service lifetime) or

component rating).

o Protection requirements (short-circuit current) determine the size of cables and over-

loading of cables is not possible with hourly average values. Therefore congestion

management is needed only for a secondary transformer.

Outage costs:

o Fictive cost utilized in network regulation (profit regulation) to optimize network

investment cost and inconvenience caused by outage from societal viewpoint.

Variable cost component dependent on outage duration: very short < 1s and short

< 3 min (low value) and long (high value)

o Direct cost of energy not supplied only for faults of long duration.



Assumptions of reliability analysis:

o Substation and HV network failures are not considered.

o Failure rate of MV line is dependent on type of line (cable, covered conductor overhead line

or bare overhead line) and terrain of overhead line (field, beside a road or in forest).

o Failure rate of components is dependent on age of component after nominal lifetime (<

possible maximum service lifetime).

o Outage duration: switching time (automation functionality (fast < 1s, slow < 3 min, very

slow ASAP) and type of switches), repair time of permanent faults (statistical variable), and

number of simultaneous faults.

o Island operation is not considered.

Investment costs:

o Electrical component costs are fixed. They don’t change over time.

o Investment has an initial cost. For example if two investments from the same area may be

combined together, they have also a single initial cost.

o Rates of inflation, interest, etc. are constants.

Parameters of automation/ICT: Initial cost, operational cost (maintenance/service cost), size of

system, and number of actions (amount of data). Three scenarios for automation/ICT: i) DSO owns

and operates everything, ii) DSO owns controls of DER but purchase communication services, and

iii) DSO purchase only services.

Costs of active controls:

o Direct control of DSO’s resources has fixed value based on average maintenance cost of

resource.

o Direct control of DERs has fixed compensation per action (e.g. €/kWh/h) and annual

compensation (compensation may have different value for each DER)

o Indirect control has variable compensation per action based on day-ahead market price.

Energy consumed by network losses:

o The cost of losses is the average of day-ahead market price over several years.

o The cost itself may be part of DSO planning optimization or not (depending who is paying

losses in national regulation model).

Modeling of operational decisions is simplified:

o Energy management of day-ahead market does not include forecasting of price, load or

production (price, load and generation has a given scenario), bidding strategies, etc. The

demand response of DERs is based on cost optimization of energy price (consumed and

produced) and variable grid tariff (consumed and produced).

o Intra-hour behavior of DERs is not considered.

o Availabilities of DERs, communication, active controls, etc. are based on normal behavior,

i.e. failures of them are not considered. Alternative scenario may consider different kind of

availabilities.

The progress of following network performance variables are considered through the planning horizon:

Network capacity

- loading and hosting

Reliability



- indexes (SAIDI, SAIFI, …)

- cost of reliability (outage costs)

Voltage level

- Lowest and highest voltage levels in network

- Probability of under or overvoltage

- Costs of losses

Maintenance cost

- The age of network components impact on maintenance cost.

Investment costs

- Costs of transition from one development step to another

Usage of active control methods (DR, AVC, Congestion management, …)

- Cost of usage (production curtailment, ancillary services, …)

The KPI shall indicate if a strategy of using active network management functionalities would lead to lower total expenses for DSO over the planning horizon than a strategy of using passive reinforcement methods.

6.16 UC16: Operational Planning Two KPIs are defined in this case:

Reduction of energy cost (COST)


KPI Name Reduction of energy cost KPI ID COST

Main Objective Assess the economic benefits of a scheduling strategy for prosumers coordinated by an aggregator.

KPI Description The KPI will measure the cost of the energy traded by an aggregator in the organized markets (day-ahead and intraday) when following different optimization strategies, and will compare them.

KPI Formula COST = COSTSmartGrid – COSTBaseline/BAU

Unit of measurement Euro per year (or other period of time).


The KPI is for the use case “Operational Planning”. Similar to WP5-DADR




Field demonstrator




COST_01 Baseline scenario definition Grid

operator/evaluator

COST_02 Simulation baseline scenario Evaluator

COST_03 Cost evaluation of baseline scenario Evaluator

COST_04 BaU scenario definition Grid

operator/evaluator



COST_05 Simulation BaU scenario Evaluator

COST_06 Cost evaluation of BaU scenario Evaluator

COST_07 Smart grid scenario definition Grid

operator/evaluator

COST_08 Simulation smart grid scenario Evaluator

COST_09 Cost evaluation of smart grid scenario Evaluator

COST_10 Comparison of costs. KPI evaluation. Evaluator

KPI SCENARIOS

Scenarios to be measured BASELINE


SMART GRID

KPI DATA COLLECTION


for data collection

Source/Tools/Instruments for

Data collection

Location of Data

collection

Frequency of data

collection

Minimum monitoring

period

Data collection

responsible

Cost of the energy in baseline situation

KPI21.1 Result of simulation

Simulation

Evaluator database.

Once. One year Evaluator

Cost of the energy in BAU scenario


Simulation

Evaluator database.


Cost of the energy in Smart Grid

scenario


Simulation

Evaluator database.


KPI BASELINE


LITERATURE VALUES



Details of Baseline


Grid operator

GENERAL COMMENTS

The KPI will determine which strategy is the best for the consumers under given regulatory assumptions (role of the aggregator, energy market architecture) and different price levels. The aggregator will have the role also of retailer and will coordinate inflexible and flexible demand, RES and other generation resources, as well as storage devices. The price levels will be taken from existing markets, but they will also be modified to take into account different price patterns that will arise if the future implementation of smart grids flattens the demand curve shape. The regulatory framework will be assumed as flexible and designed to allow taking advantage of all the technical possibilities offered by existing and envisaged smart grid technology.

Ratio between minimum and maximum electricity demand within a day (MMDR)


KPI Name Ratio between minimum and maximum electricity demand within a day.

KPI ID MMDR

Main Objective To assess the benefits of prosumer scheduling from the point of view of the



utilisation of the grid elements. The KPI is for the use case “Operational Planning”.

KPI Description

The ratios between maximum and minimum load for the daily consumption will be compared under different optimized scheduled, to see what is the most favourable for the system, allowing a better utilisation of existing assets and reducing the need of grid updates.

KPI Formula MMDR =

Dmax

Dmin

æ

èçö

ø÷jj=1

N

å

N N, number of days; Dmax/min maximum/minimum hourly

demand; j, day.

Unit of measurement Per unit/adimensional.


The KPI is for the use case “Operational Planning”.




Field demonstrator




MMDR_01 Baseline scenario definition Grid

operator/evaluator

MMDR_02 Simulation baseline scenario Evaluator

MMDR_03 Cost evaluation of baseline scenario Evaluator

MMDR_04 BaU scenario definition Grid

operator/evaluator

MMDR_05 Simulation BaU scenario Evaluator

MMDR_06 Cost evaluation of BaU scenario Evaluator

MMDR_07 Smart grid scenario definition Grid

operator/evaluator

MMDR_08 Simulation smart grid scenario Evaluator

MMDR_09 Cost evaluation of smart grid scenario Evaluator

MMDR_10 Comparison of costs. KPI evaluation. Evaluator

KPI SCENARIOS



SMART GRID

KPI DATA COLLECTION

Data Data ID Methodology for data collection


Location of Data

collection

Frequency of data

collection

Minimum monitoring

period

Data collection

responsible

Maximum hourly demand for every

day in a sample year


Simulation

Evaluator database.

Once One year Evaluator

Minimum hourly demand for every

day in a sample year


Simulation

Evaluator database.

Once One year Evaluator



KPI BASELINE


LITERATURE VALUES



Details of Baseline


Grid operator

GENERAL COMMENTS

The use of scheduling strategies leads to the flattening of load curve, enabling a better utilisation of grid elements. This is possible thanks to optimized schedules that increase the consumption in the valley hours and reduce it in the peak hours. The reduction of this ratio leads to a better use of grid components. Different conditions, such as electrical heating, weather conditions, shares of industrial and domestic load will be taken into account, if there are data available. This KPI has been proposed by the EC Task Force for Smart Grids (KPI no. 21). [Giordano]

6.17 UC17: Target network Planning One KPI is defined to evaluate this Use Case:

Target network planning (TNP)


KPI Name Target Network Planning KPI ID TNP

Main Objective Planning future network under consideration of smart grid technologies

KPI Description This KPI plans long-term future and cost-optimal network for Medium and Low Voltage Level. In the long-term planning the benefits of considering smart-grid components could be evaluated.

KPI Formula

𝑇𝑁𝑃 = 𝐶𝑜𝑠𝑡𝑠𝐵𝑎𝑈 − 𝐶𝑜𝑠𝑡𝑠𝑆𝐺 or 𝑇𝑁𝑃 =𝐶𝑜𝑠𝑡𝑠𝐵𝑎𝑈−𝐶𝑜𝑠𝑡𝑠𝑆𝐺

𝐶𝑜𝑠𝑡𝑠𝐵𝑎𝑈⋅ 100%

With:

𝐶𝑜𝑠𝑡𝑠𝑥 = ∑(𝐶𝑐𝑜𝑚𝑝,𝑖,𝑥 + 𝐶𝑜𝑝,𝑖,𝑥) + 𝑃𝑙𝑜𝑠𝑠,𝑥 ⋅

𝑁

𝑖=0

𝐶𝑙𝑜𝑠𝑠

Unit of measurement Euro or %


This KPI is linked to ‘Target Network Planning’ Use Case




Field demonstrator




TNP_01 Business as usual planning of distribution network and evaluation of network costs 𝐶𝑜𝑠𝑡𝑠𝐵𝑎𝑈

Research organism



(RWTH)

TNP_02 Planning of distribution network under consideration of smart grid components and evaluation of network costs 𝐶𝑜𝑠𝑡𝑠𝑆𝐺

Research organism (RWTH)

TNP_03 Evaluation of the Benefits Research organism (RWTH)

KPI SCENARIOS



SMART GRID

KPI DATA COLLECTION




for Data collection

Location of Data

collection



Data collection

responsible

Cost of Network 𝐶𝑜𝑠𝑡𝑠𝑥 Planning

tool Excel Excel

Once per Calculation

Not availabl

e


Network losses 𝑃𝑙𝑜𝑠𝑠 Planning tool

Excel Excel Once per

Calculation

Not availabl

e


Number of components

𝑁 Planning tool

Excel Excel Once per

Calculation

Not availabl

e


Cost of network components

𝐶𝑐𝑜𝑚𝑝 DSO Excel Excel Once

Not availabl

e


Cost of Network loses per kWh

𝐶𝑙𝑜𝑠𝑠 DSO Excel Excel once

Not availabl

e


Cost of Operation of

network components

𝐶𝑜𝑝 DSO Excel Excel once

Not availabl

e


KPI BASELINE


LITERATURE VALUES



Details of Baseline


GENERAL COMMENTS

The goal is to minimize the network cost of the MV and LV Network. The benefits of considering smart grid components in the MV an LV Network Planning could be shown by lower network costs.

𝑇𝑁𝑃 = 𝐶𝑜𝑠𝑡𝑠𝐵𝑎𝑈 − 𝐶𝑜𝑠𝑡𝑠𝑆𝐺 or 𝑇𝑁𝑃 =𝐶𝑜𝑠𝑡𝑠𝐵𝑎𝑈−𝐶𝑜𝑠𝑡𝑠𝑆𝐺

𝐶𝑜𝑠𝑡𝑠𝐵𝑎𝑈⋅ 100%

The used approach for long-term network planning is a “green field” approach. Thus, in this approach is not



considering the present network. This KPI is defined in conjunction with UC “Target Network Planning”.

6.18 UC18 and UC19: Load Areas Configuration; Flexible Table One KPI is defined to evaluate these Use Cases:Reduction of technical network losses (TL)


KPI Name Reduction of technical network losses KPI ID TL

Main Objective Measure the reduction of technical losses due to different optimization strategies

KPI Description This KPI will measure the impact of the aggregator concept implementation on active power losses by means of the off-line validation (OLV) and the real-time validation (RTV) procedures.

KPI Formula

This KPI will be computed by means of comparing the technical losses of the BAU scenario against the ones from the smart grid scenario for a period of time, i.e. a day.

𝑇𝐿𝑟𝑒𝑑𝑢𝑐𝑡𝑖𝑜𝑛 =𝑇𝐿𝑆𝑚𝑎𝑟𝑡𝐺𝑟𝑖𝑑 − 𝑇𝐿𝐵𝐴𝑈

𝑇𝐿𝐵𝐴𝑈

where:

𝑇𝐿𝑟𝑒𝑑𝑢𝑐𝑡𝑖𝑜𝑛: Percentage reduction in technical losses comparing Smart Grid and BAU scenarios [%]

𝑇𝐿𝑆𝑚𝑎𝑟𝑡𝐺𝑟𝑖𝑑 : Total technical losses under Smart Grid scenario [kWh]

𝑇𝐿𝐵𝐴𝑈 : Total technical losses under BAU [kWh]

Unit of measurement Percentage reduction in technical losses comparing Smart Grid and BAU scenarios [%]


This KPI has been defined for the High Level Use Cases “Load Areas Configuration; Flexible Table”




Field demonstrator




TL_01 Perform data calculation of technical losses in the test grid on the

BAU scenario 𝑇𝐿𝐵𝐴𝑈

DSO (IREC)

TL_02 Perform data calculation of technical losses in the test grid on the Smart Grid scenario 𝑇𝐿𝑟𝑒𝑑𝑢𝑐𝑡𝑖𝑜𝑛

DSO (IREC)

TL_03 Calculate the percentage reduction in technical losses comparing Smart Grid and BAU scenarios 𝑇𝐿𝑟𝑒𝑑𝑢𝑐𝑡𝑖𝑜𝑛

DSO (IREC)

KPI SCENARIOS


BASELINE


SMART GRID



KPI DATA COLLECTION

Data Data ID


collection


Data collection

Location of Data

collection



Data collection

responsible

Total technical losses

Output data from the emulated

grid (PSS/E output file

used for soft PHIL).

PSS/E output file by means of the Phyton

script

Labora-tory

3 seconds 24

hours DSO

(IREC)

KPI BASELINE


LITERATURE VALUES



Details of Baseline

The baseline scenario will be built up in collaboration with WP6 partners in order to ensure a realistic demand curve and distribution network utilization network. After the aggregator implementation (Smart Grid scenario), the amount of technical losses of the distribution network are expected to decrease due to the closer energy generation to the final demand point.


IREC with the collaboration of project utilities (A2A, UFD and OST)

GENERAL COMMENTS

6.19 UC20: Off-Line Validation One KPI is defined to evaluate these Use Cases:

Peak demand reduction ratio (PD)


KPI Name Peak demand reduction ratio KPI ID PD

Main Objective Measure peak demand reduction ratio as a % of total demand

KPI Description Compare the peak demand before the aggregator implementation (baseline) with the peak demand after the aggregator implementation (per final consumer, per feeder, per network)

KPI Formula

This KPI will be computed by means of comparing the the peak demand before the aggregator implementation (BAU scenario) with the peak demand after the aggregator implementation (Smart Grid scenario) at distribution network level during a period of time, i.e. a day.

𝑃𝐷𝑟𝑒𝑑𝑢𝑐𝑡𝑖𝑜𝑛 =𝑃𝐷𝑆𝑚𝑎𝑟𝑡𝐺𝑟𝑖𝑑 − 𝑃𝐷𝐵𝐴𝑈

𝑃𝐷𝐵𝐴𝑈

where:

𝑃𝐷𝑟𝑒𝑑𝑢𝑐𝑡𝑖𝑜𝑛: Percentage reduction in peak demand comparing Smart



Grid and BAU scenarios [%]

𝑃𝐷𝑆𝑚𝑎𝑟𝑡𝐺𝑟𝑖𝑑 : Total peak demand under Smart Grid scenario [kW]

𝑃𝐷𝐵𝐴𝑈 : Total peak demand under BAU [kW]

Unit of measurement Percentage reduction in peak demand comparing Smart Grid and BAU scenarios [%]


This KPI has been defined for the High Level Use Cases “Off-Line Validation”




Field demonstrator




PD_01 Perform data calculation of distribution network peak demand in the

test grid on the BAU scenario 𝑃𝐷𝐵𝐴𝑈

DSO (IREC)

PD_02 Perform data calculation of distribution network peak demand in the test grid on the Smart Grid scenario 𝑃𝐷𝑟𝑒𝑑𝑢𝑐𝑡𝑖𝑜𝑛

DSO (IREC)

PD_03 Calculate the percentage reduction distribution network peak demand comparing Smart Grid and BAU scenarios 𝑃𝐷𝑟𝑒𝑑𝑢𝑐𝑡𝑖𝑜𝑛

DSO (IREC)

KPI SCENARIOS


BASELINE


SMART GRID

KPI DATA COLLECTION

Data Data ID


collection


Data collection

Location of Data

collection



Data collection

responsible

Peak Demand





script

Labora-tory

3 seconds 24

hours DSO

(IREC)

KPI BASELINE


LITERATURE VALUES



Details of Baseline

The total load and generation within the distribution network on the baseline scenario will be defined in order to create a realistic test environment. The peak demand on the DSO/TSO point of common coupling will measured for defining the peak load for the BAU scenario.



GENERAL COMMENTS



6.20 UC21: Real-time Validation One KPI is defined to evaluate this Use Case:

Percentage utilization of electricity network components (U)


KPI Name Percentage utilization of electricity network components

KPI ID U

Main Objective Calculate the percentage utilization of the components of the distribution network under analysis

KPI Description This KPI captures the relative improvement of capacity utilization in the SG scenario compared to the BaU scenario, normalized by means of the nominal capacity of the component weighted with the capital cost of the component

KPI Formula

This KPI will be computed by means of comparing the weighted capacity utilization of the electricity network components on the BAU scenario against the ones from the smart grid scenario for a period of time, i.e. a day.

𝑈𝑖𝑛𝑐𝑟𝑒𝑎𝑠𝑒 =𝑈𝑆𝑚𝑎𝑟𝑡𝐺𝑟𝑖𝑑 − 𝑈𝐵𝐴𝑈

𝑈𝐵𝐴𝑈

where:

𝑈𝑖𝑛𝑐𝑟𝑒𝑎𝑠𝑒: Increase of the weighted capacity utilization of the electricity network components [%]

𝑈𝑆𝑚𝑎𝑟𝑡𝐺𝑟𝑖𝑑 : weighted capacity utilization of the electricity network components for the smart grid scenario [%]

𝑈𝐵𝐴𝑈 : weighted capacity utilization of the electricity network components for the business as usual scenario [%]

The calculation of the weighted capacity utilization of the electricity network components is described below:

𝑈𝑆 = ∑ 𝑈𝑖 ·𝐶𝑖

𝐶𝑇

𝑖=𝑁

𝑖=1

𝑈𝑖 =𝑃𝑖

𝑎𝑣𝑔

𝑃𝑖𝑛𝑜𝑚 · 100

𝐶𝑇 = ∑ 𝐶𝑖

𝑖=𝑁

𝑖=1

where:

𝑖 : index for the distribution network components (line or transformer)

𝑁 : total number of distribution network components

𝑈𝑆: weighted capacity utilization of the electricity distribution network



components for scenario S [%]

𝑈𝑖: relative average loading of distribution network component i (line or transformer) [%]

𝐶𝑖: capital cost of the distribution network component i [€]

𝐶𝑇: total capital cost of the distribution network [€]

𝑃𝑖𝑎𝑣𝑔

: average loading of a distribution network component i (line or

transformer) [kW]

𝑃𝑖𝑛𝑜𝑚: nominal rating of a distribution network component i (line or

transformer) [kW]

Unit of measurement Increase of the weighted capacity utilization of the electricity network components [%]


This KPI has been defined for the High Level Use Cases “Real-time Validation”




Field demonstrator




U_01 Obtain loading of each line and transformer in kW at each time step DSO

(IREC)

U_02 Calculate average loading (𝑃𝑖

𝑎𝑣𝑔) of each line and transformer in kW DSO

(IREC)

U_03 Find relative average loading of each line and transformer 𝑈𝑖 DSO

(IREC)

U_04 Find the weighted capacity utilization of the electricity distribution network components for scenario S (𝑈𝑆)

DSO (IREC)

U_05 Compute the increase of the weighted capacity utilization of the electricity network components 𝑈𝑖𝑛𝑐𝑟𝑒𝑎𝑠𝑒

DSO (IREC)

KPI SCENARIOS


BASELINE


SMART GRID

KPI DATA COLLECTION

Data Data ID


collection


Data collection

Location of Data

collection



Data collection

responsible

Loading of each line and

transformer





script

Labora-tory

3 seconds 24

hours DSO

(IREC)

Nominal rating of each network

component

From the

components specification

- - - - DSO

(IREC)



Capital cost of distribution

network components

From the

components specification

- - - - DSO

(IREC)

KPI BASELINE


LITERATURE VALUES



Details of Baseline

The baseline scenario will be built up in collaboration with WP6 partners (mainly with utilities) in order to ensure that realistic values are considered for the utilization level of distribution network components under the BaU scenario. The costs of the distribution network components will be also provided by WP6 partners.



GENERAL COMMENTS

6.21 UC22: SRP and CRP Day-Ahead and Intra-Day Market Procurement One KPI is defined to evaluate this Use Case:

Reduction in CO2 emissions (CO2)


KPI Name Reduction in CO2 emissions KPI ID CO2

Main Objective Measure the reduction in CO2 emissions enabled by the aggregator concept implementation

KPI Description This KPI will be derived from the amount of CO2 kg reduction due to substitution of fossil power generation by additional RES units inside the distribution network under analysis (enabled by Smart Grid solution).

KPI Formula

This KPI is measured in percentage using the amount of CO2 kg emitted due to the energy consumption within the distribution network in the BAU scenario comparing to the smart grid scenario for a period of time, i.e. a day.

𝐶𝑂2𝑟𝑒𝑑𝑢𝑐𝑡𝑖𝑜𝑛 =𝐶𝑂2𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛

𝑆𝑚𝑎𝑟𝑡𝐺𝑟𝑖𝑑 − 𝐶𝑂2𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝐵𝐴𝑈

𝐶𝑂2𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝐵𝐴𝑈

where:

𝐶𝑂2𝑟𝑒𝑑𝑢𝑐𝑡𝑖𝑜𝑛: Percentage reduction in CO2 emissions comparing Smart Grid and BAU scenarios [%]

𝐶𝑂2𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑆𝑚𝑎𝑟𝑡𝐺𝑟𝑖𝑑 : Total CO2 emissions under Smart Grid scenario [kg]

𝐶𝑂2𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝐵𝐴𝑈 : Total CO2 emissions under BAU [kg]

Unit of measurement Percentage reduction in CO2 emissions comparing Smart Grid and BAU scenarios [%]

Connection / Link with This KPI has been defined for the High Level Use Cases “SRP and CRP Day-Ahead



other relevant defined KPIs and Use Cases

and Intra-Day Market Procurement”




Field demonstrator




CO2_01 Perform data calculation of CO2 kg emission in the test grid on the

BAU scenario 𝐶𝑂2𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝐵𝐴𝑈

DSO (IREC)

CO2_02 Perform data calculation of CO2 kg emission in the test grid on the

Smart Grid scenario 𝐶𝑂2𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑆𝑚𝑎𝑟𝑡𝐺𝑟𝑖𝑑

DSO (IREC)

CO2_03 Calculate the percentage reduction in CO2 emissions comparing Smart Grid and BAU scenarios 𝐶𝑂2𝑟𝑒𝑑𝑢𝑐𝑡𝑖𝑜𝑛

DSO (IREC)

KPI SCENARIOS


BASELINE


SMART GRID

KPI DATA COLLECTION

Data Data ID


collection


Data collection

Location of Data

collection



Data collection

responsible

CO2 emission estimation

Output data from the emulated microgrid

(emulation cabinet), or

from the emulated



Microgrid cabinet meter

/ PSS/E output file by means of the Phyton script

Utilization emission

factors of the different energy

sources. Estimation of

the C02 emissions per

kWh consumed from the

transmission grid

Labora-tory

3 seconds 24

hours DSO

(IREC)

KPI BASELINE


LITERATURE VALUES



Details of Baseline The main CO2 emissions will come from the energy consumed from the transmission system through the point of common coupling between the DSO



and the TSO. In order to estimate this amount of embedded CO2, the hourly energy mix for the country under analysis (Spain, Italy and/or Denmark) will be used. Due to the expected increase of the RES hosting capacity of the distribution network after the aggregator concept implementation (Smart Grid scenario), the amount of energy consumed from the transmission grid is expected to be reduced.



GENERAL COMMENTS

6.22 UC23: Conditional re-profiling activation (CRP activation) One KPI is defined to evaluate this Use Case:

RES curtailment (E_curl)


KPI Name RES curtailment KPI ID E_curl

Main Objective Estimate the RES curtailment in the test grid

KPI Description This KPI compares the amount of renewable energy injected that had to be curtailed because of grid congestion between BaU and SG scenarios.

KPI Formula

This KPI is measured in percentage of the amount of energy output from RES that should be reduced due to technical reasons in the BAU scenario comparing to the smart grid scenario for a period of time, i.e. a day.

𝐸𝑐𝑢𝑟𝑡𝑎𝑖𝑙𝑚𝑒𝑛𝑡 =𝐸𝑐𝑢𝑟𝑡𝑎𝑖𝑙𝑚𝑒𝑛𝑡

𝑆𝑚𝑎𝑟𝑡𝐺𝑟𝑖𝑑 − 𝐸𝑐𝑢𝑟𝑡𝑎𝑖𝑙𝑚𝑒𝑛𝑡𝐵𝐴𝑈

𝐸𝑐𝑢𝑟𝑡𝑎𝑖𝑙𝑚𝑒𝑛𝑡𝐵𝐴𝑈

where:

𝐸𝑐𝑢𝑟𝑡𝑎𝑖𝑙𝑚𝑒𝑛𝑡 : Percentage reduction in energy curtailment from RES due to network conditions comparing Smart Grid and BAU scenarios [%]

𝐸𝑐𝑢𝑟𝑡𝑎𝑖𝑙𝑚𝑒𝑛𝑡𝑆𝑚𝑎𝑟𝑡𝐺𝑟𝑖𝑑 : Total energy not injected in network or curtailment from

RES under Smart Grid scenario [GWh]

𝐸𝑐𝑢𝑟𝑡𝑎𝑖𝑙𝑚𝑒𝑛𝑡𝐵𝐴𝑈 : Total energy not injected in network or curtailment from

RES under BaU [GWh]

Unit of measurement Percentage reduction in energy curtailment from RES due to network conditions comparing Smart Grid and BaU scenarios [%]


This KPI has been defined for the High Level Use Cases “Conditional re-profiling activation (CRP activation)”




Field demonstrator


KPI Step Step Responsible



Methodology ID [KPI ID #]

E_curl_01 Perform data calculation of RES energy production values in the test

grid on the BAU scenario 𝐸𝑐𝑢𝑟𝑡𝑎𝑖𝑙𝑚𝑒𝑛𝑡𝐵𝐴𝑈

DSO (IREC)

E_curl_02 Perform data calculation of RES energy production values in the test

grid on the Smart Grid scenario 𝐸𝑐𝑢𝑟𝑡𝑎𝑖𝑙𝑚𝑒𝑛𝑡𝑆𝑚𝑎𝑟𝑡𝐺𝑟𝑖𝑑

DSO (IREC)

E_curl_03 Calculate the percentage reduction in energy curtailment from RES due to network conditions comparing Smart Grid and BAU scenarios 𝐸𝑐𝑢𝑟𝑡𝑎𝑖𝑙𝑚𝑒𝑛𝑡

DSO (IREC)

KPI SCENARIOS


BASELINE


SMART GRID

KPI DATA COLLECTION

Data Data ID


collection


for Data collection

Location of Data

collection



Data collection

responsible

RES energy output

Output data from the emulated microgrid

(emulation cabinet), or

from the emulated



Microgrid cabinet meter / PSS/E

output file by means

of the Phyton script

Laboratory 3 seconds 24

hours DSO

(IREC)

KPI BASELINE


LITERATURE VALUES



Details of Baseline

The amount of RES to be considered in the grid scenario for the aggregator emulations in IREC’s lab will be defined in order to ensure that during some time steps a certain amount of RES production should be curtailed due to technical reasons (overvoltage or grid congestion). After the aggregator implementation (Smart Grid scenario), the amount of RES to be curtailed should be reduced.



GENERAL COMMENTS

6.23 UC24: Day-ahead Demand Response One KPI is defined in this case:



Demand Response (DR)


KPI Name Demand Response (DR) KPI ID DR

Main Objective

The objective of the Day Ahead Demand Response (DADR) is to represent the

energy plan of flexible demands such as electric vehicles (EVs) and heat pumps (HPs) with the electricity cost minimized according to the predicted day-ahead electricity prices.

KPI Description

This KPI evaluates the electricity cost per kWh which is to check the optimization of the energy plan of flexible demands.


Unit of measurement €/kWh


Day-ahead dynamic tariff, Forecasting, aggregator




Field demonstrator




DR_01 Obtain the day-ahead forecast of system energy prices Aggregators

DR_02

Obtain the day-ahead dynamic grid tariff from DSO

Aggregators

Determine the energy plan of flexible demand with both system energy prices and day-ahead dynamic grid tariff

Aggregators

Determine the energy plan of flexible demand without considering the prices (base case)

Aggregators

DR_03

Obtain the average energy cost for both cases, i.e. with and without considering prices in the energy planning stage. For the base case, it is assumed that both the day-ahead energy price and DADT will be included for calculating the cost.

Aggregators

KPI SCENARIOS


BASELINE


SMART GRID

KPI DATA COLLECTION

Data Data ID


collection


for Data collection

Location of Data

collection



Data collection

responsible

total energy cost D1 Obtain from

commercial aggregators

commercial

aggregator optimizatio

n

Load points Once a day

Lab: not availabl

e

Aggregators



total energy consumption

D2 Obtain from commercial aggregators

commercial

aggregator optimizatio

n

Load points Once a day

Lab: not availabl

e

Aggregators

KPI BASELINE


LITERATURE VALUES



Details of Baseline


GENERAL COMMENTS

Scenarios used in tests:

– Base case; without considering prices

– Optimization with system prices and day ahead dynamic grid tariffs

Controllers:

– Commercial aggregators determine the energy plan of flexible demands by minimizing the energy

cost

Test setup:

DR: commercial aggregators determine the energy plan of flexible demands by minimizing the energy

cost.

KPIs are calculated for the purpose of comparing results of the two test cases with and without DADT.

1. Economic KPIs

8) Average energy cost of EVs and HPs: €/kWh. KPI=total cost

total energy consumption

6.24 UC25: Day-ahead Dynamic Tariff In this case, one KPI has been defined:

Day Ahead Dynamic Tariff (DADT)


KPI Name Day Ahead Dynamic Tariff (DADT) KPI ID DADT

Main Objective The objective of the Day Ahead Dynamic Tariff (DADT) is to alleviate predicted



overloading at the medium voltage (MV) grid in the day-ahead electricity market time frame by sending proper hourly varying grid tariff on top of the fixed grid tariff to commercial aggregators.

KPI Description

This KPI evaluates the loading of the power components of the MV grid with and without the DADT. Time series power flow studies shall be carried out with inputs of the demand profile of inflexible demands and flexible demand, production of distributed generation, and the grid model to evaluate the DADT KPI.




Forecasting, aggregator, CCPC




Field demonstrator




DADT_01 Obtain the day-ahead forecast of inflexible demand, production of DGs, flexible demands and system energy prices(through DXP at the Control Centre)

DSO

DADT_02

DSO run the one day time series low flow study with the MV distribution grid model to check if there is congestion for the next day

DSO and

commercial

aggregators

If there is congestion, an initial congestion grid tariff will be determined and sent to the commercial aggregators

Commercial aggregators determine a new energy plan of all flexible demands according to the day-ahead system energy prices and the congestion grid tariff.

DSO run the one day time series load flow study with the MV distribution grid model with the updated energy plan of flexible demand to check if there is congestion for the next day. If there is congestion, go to Step 5, otherwise, go to Step 8.

Increase the congestion grid tariff with a certain amount and send the congestion grid tariff to commercial aggregators.

Commercial aggregators determine a new energy plan of all flexible demands according to the day-ahead system energy prices and the congestion grid tariff.

Go back to Step 4 (in 1b).

Dynamic tariff is determined.

DADT_03 Extract the highest loading with and without day-ahead dynamic tariff

DSO

KPI SCENARIOS


BASELINE


SMART GRID



KPI DATA COLLECTION

Data Data

ID


collection


for Data collection

Location of Data

collection



Data collection

responsible

Scheduled DR capacity and energy

D1 Obtain from step 1b.4

aggregator report

aggregators

Once a day Lab: ?

DSO

loading of each line and transformer

D2

Obtain from DSO time

series load flow studies

DSO time series load

flow studies

MV lines and MV/LV transformer stations

Once a day Lab: ?

DSO

KPI BASELINE


LITERATURE VALUES



Details of Baseline


GENERAL COMMENTS

Scenarios used in tests:

– Base case; no DADT

– With DADT

Controllers:

– DADT is used to influence the day-ahead energy plan of flexible demands such as EVs and HPs.

Test setup:

DADT: The day-ahead energy plan adjustment of flexible demands is realized by an interactive

process between the DSO and commercial aggregators.

KPIs are calculated for the purpose of comparing results of the two test cases with and without DADT.

Loading KPIs

– Line and transformer loading [%]: loading of all lines and MV/LV transformers. KPI =

100%line or transformer loading

line or transformer loading limit

– Reduction of peak load: kW. KPI = peak load of base case - peak load of DADT case

– Variation of load profile at different level of network (primary substation, MV feeders,

secondary substations): Average distance of load profile to mean value of load. KPI =

2

1

1 N

tt

line or transformer loading - average loadingN

– Scheduled DR capacity and energy: kW and kWh



– Increase of pay-back demand: kW and kWh



7. GUIDELINES FOR KPI EVALUATION

7.1 Introduction to the methodology

This chapter is intended to give a detailed picture of the KPI evaluation methodology,

indicating the process to be followed depending on the type of KPI to be calculated. IDE4L

KPIs are not only practical, but also theoretical, so the EEGI methodology has to be modified

to cover these different alternatives.

7.2 IDE4L to EEGI KPIs mapping

The main aim of IDE4L automation architecture is the improvement of the distribution

network business, reducing costs and future investments to manage the grid. This goal is

achieved by means of efficiency measures supported by ICT instruments, which involve

planning processes, considering DERs and REs, fault location isolation and restoration and, in

general, all the processes involved in network management and control. The functional units

developed in the architecture and identified in the project Use Cases have this business

improvement as basic and general characteristic. However, some of these units have been

defined to enable functionalities provided in other units and, for this reason, they have no

direct improvements on the network management. For example, the monitoring Use Cases

do not provide a direct enhancement on the network business but they allow the execution

of other algorithms, such as the state estimation or the optimal power flow, which allow

improving the grid management and planning processes.

Figure 10: Mapping Schema for IDE4L KPIs and EEGI Level 2 KPIs

Taking into account these considerations, it is easy to understand that some of the IDE4L

KPIs, those defined to evaluate Use Cases related to the general smart grid business, can be

mapped to EEGI Level 2 KPIs – Table 1 –. In contrast, some others match no EEGI definition,

because they are related to functionalities internal to the IDE4L architecture.

IDE4L KPIs EEGI Level 2 KPIs

IDE4LInternal

Use Cases

IDE4LSmart Grid Business

Use Cases

ENA

BLE



Table 1: EEGI Level 2 KPIs

EEGI Level 2 KPIs

B.1 Increased RES&DER hosting capacity (DSO+TSO)

B.2 Reduced energy curtailment of RES and DER (DSO+TSO)

B.3 - Power quality and quality of supply (TSO+DSO)

B.3.1 SAIDI

B.3.2 SAIFI

B.3.3 Line voltage profiles fulfilling grid nominal voltage requirements

B.3.4 Average time needed for awareness, localization and isolation of grid fault

B.4 Extended asset lifetime (DSO+TSO)

B.5 - Increased flexibility from energy players (DSO + TSO)

B.5.1 Demand response capability

B.5.2 Generation response capability

B.6 - Improved competitiveness of the electricity market (DSO + TSO)

B.6.1 Cost of a given service

B.6.2 Number of market players

B.6.3 Size of individual market players

B.7 Increased hosting capacity for Electric Vehicles and other new loads (DSO)

In order to facilitate the interpretation of IDE4L results outside the consortium a map from

IDE4L KPIs to EEGI KPIs is provided here below. Results reported in Table 2 can be used to

understand, through a high level evaluation framework, in which area of the distribution

network business IDE4L project has its major contributions.

Table 2: Mapping table for IDE4L KPIs and EEGI Level 2 KPIs

IDE4L KPIs

B.1

B.2

B.3

.1

B.3

.2

B.3

.3

B.3

.4

B.4

B.5

.1 B

.5.2

B.6

.1

B.6

.2

B.6

.3

B.7

Note

Current Monitoring Data Volume No direct match with EEGI KPIs. Monitoring system has not a direct impact on a Business Case but it is required because it enables other Use Cases. So that also the related KPIs can't be classified as index of improved performances in terms of direct business

Current Monitoring Granularity

Powers Monitoring Data Volume

Powers Monitoring Granularity

Voltage Monitoring Data Volume

Voltage Monitoring Granularity

Real-time LV Network State estimation X X X

Real-time MV Network State estimation

X

X



Voltage stability of the electricity system

No direct match with EEGI level 2 KPIs

TSO´s visibility of distribution network X X No direct match with EEGI level 2 KPIs

Evaluation of IEC 61850-90-5 library


Success index in meter reading No direct match with EEGI level 2 KPIs

LV load/generation forecaster X X

MV load/generation forecaster X X

LV state forecaster X X X

MV state forecaster X X X

Network Description Update No direct match with EEGI level 2 KPIs. To update the system does not provide a direct impact on the main business but it is needed to maximize the performance of other use cases.

Protection Configuration Update

Control Centre Tertiary Power Control - Technical and Economic Parameters

X X X X

Control Center Tertiary Power Control - Operational Parameters

X X

X

X

Control Center Tertiary Power Control - Technical Safety Parameters

X X

X

X

LV Network Power Control - Technical and Economic Parameters

X X X X

LV Network Power Control - Operational Parameters

X X

X

X

LV Network Power Control - Technical Safety Parameters

X X

X

X

MV Network Power Control - Technical and Economic Parameters

X X X X

MV Network Power Control - Operational Parameters

X X

X

X

MV Network Power Control - Technical Safety Parameters

X X

X

X

SAIDI X

X

They do not follow the common definition of SAIDI but they provide a similar evaluation (B3.1). Time needed for FLI is related to averaged interruption time.

SAIFI X X

They do not follow the common definition of SAIFI but they provide a similar evaluation (B3.2). Number of voltage line violations is related to averaged interruption frequency.

Breaker energized operations X

Reduction of breaker operations in fault condition extends asset lifetime

Interconnection Switch No direct match with EEGI level 2 KPIs

Flicker mitigation MV/LV active grid X Flicker mitigation improves the fulfill of the nominal voltages



requirements

Expansion Planning Scenario Evaluation

X X X X X X X X X

Expansion planning will consider the usage of FLISR and congestion management as an alternative solution to network investments and to postpone these investments.

Target network Planning No direct match with EEGI level 2 KPIs

Reduction of technical network losses No direct match with EEGI level 2 KPIs

Peak demand reduction ratio


Percentage utilization of electricity network components


Reduction in CO2 emissions


RES curtailment X

They look similar according the definition. Detailed description of KPI is needed to evaluate the complete or partial equivalence

Demand Response

X

Day Ahead Dynamic Tariff

X

7.3 Step-by-step approach for KPI evaluation

As it has been defined in previous chapters, there are two types of KPIs:

- Measured KPIs: based on the empirical results obtained from the tests carried out in

demo sites or laboratories, and

- Theoretical KPIs: based on calculations and simulations

Given this fact, there would be two different procedures to face IDE4L KPI evaluation.

Measured KPIs

Step 1. The scenario must be determined and defined. Also, the parameter that will

characterize the Use Case validation must be analyzed and the needed data have to be

clearly selected.

Step 2. The measurements for the selected reference scenario must be collected. If baseline

scenario is chosen, the measurements must have been collected before the test cases, or

historical data must be provided. In case that not all the measurements are available, a state

estimator algorithm can be used to get all the values. If BaU scenario is the reference, these

BaU values related to the future must be calculated.

Step 3. The measurements for the smart scenario must be collected at the end of the test

cases. Just in case, a state estimator algorithm can be used to get all the values.

Step 4. The KPI is the comparison between values obtained in steps 2 and 3.



Step 5. The result must be analysed and well explained.

Theoretical KPIs

Step 1. The scenarios must be determined and defined. Also, the parameter that will

characterize the Use Case validation must be analyzed and the needed data have to be

clearly selected.

Step 2. The values for the selected reference scenario (baseline or BaU) must be calculated

from simulations or theoretical calculations.

Step 3. The measurements for the smart scenario must be calculated from simulations or

theoretical calculations.

Step 4. The KPI is the comparison between values obtained in steps 2 and 3.

Step 5. The result must be analysed and well explained.

7.4 Results template description

The numerical results of the evaluation of each KPI, as well as test conditions and conclusions

will be collected in a result template form.

The result of each KPI will be a numerical comparison of a particular feature of the use case

evaluated in two different scenarios.

To obtain these results, three different types of method can be applied:

- Based on demo measurements: Preferably, this type of method will be applied as

it will provide more accurate and realistic results.

It will be necessary to analyse if for the specific KPI to be evaluated and the demo

deployed for the particular Use Case, tools and/or measurement equipment can be

applied to determine the value of the KPI under certain operation conditions that

will be recreate on the demo site. Both operation conditions and the mechanisms

used to determine the value of the KPI will be introduced in the form under the

field “KPI Conditions Where Calculated” and “Evaluation”, respectively.

- Based on historical data: In case specific demo measurements cannot be obtained,

due to demo constraints, and historic data are available in the operation conditions

defined to obtain the KPIs, these could be used to determine the KPI in a particular

scenario.

This type of methods could be applied only for Baseline Scenarios, so it is important

to highlight that, in order to compare values with other scenarios; the same

conditions have to be considered. For instance, in case of KPI related to FLISR Use

Case, Fault Conditions has to be considered in the same points of the grid for both

scenarios.

- Based on theoretical computations: This type of method will be applied only when

none of the methods above can be used. They are based on the use of technical

data and mathematical equations for particular operation configurations of the



scenario under study. The result template should collect the relations and

explanations of the applied equations to obtain the KPI values.

For each scenario and each KPI may be applied different class of method depending of the

demo conditions and available tools. For this reason, in case of using different methods, the

result template should include a justification that values obtained with both methods are

equivalent.

In case none of the methods can be applied, KPI will be considered as not evaluable.

Figure 11 shows the steps and verifications explained above for methods selection and KPI

evaluation:

Figure 11 KPI Calculation Diagram

Figure 12 shows the result template form to be completed for each KPI. The type of method

applied for each scenario will be indicated in the field “Evaluation and Interpretation”.



Figure 12 Results template



8 REFERENCES

[EEGI_roadmap] http://www.gridplus.eu/Documents/20130228_EEGI%20Roadmap%202013-

2022_to%20print.pdf

[EN 50160]

http://www.aenor.es/aenor/normas/normas/fichanorma.asp?tipo=N&codigo=N0046945&PDF=Si

[Giordano] V. Giordano, et al. "Guidelines for conducting a cost-benefit analysis of Smart Grid

projects” Report EUR 25246 EN. Joint Research Centre, Institute for Energy and Transport (2012).

[GRID+] http://www.gridplus.eu/

http://www.gridplus.eu/Documents/20130228_EEGI%20Roadmap%202013-2022_to%20print.pdf

http://www.gridplus.eu/Documents/20130228_EEGI%20Roadmap%202013-2022_to%20print.pdf

http://www.aenor.es/aenor/normas/normas/fichanorma.asp?tipo=N&codigo=N0046945&PDF=Si