30/06/2019 · accessibility ☒public ☐ consortium + ec ☐ restricted to a specific group + ec...

D8.7 Raw demonstration results based on the KPI measurements

Version 1.0

Deliverable D8.7

30/06/2019

Ref. Ares(2019)4140682 - 30/06/2019


InterFlex – GA n°731289 of 45

Disclaimer: This report reflects only the author's view and the Agency is not responsible for

any use that may be made of the information it contains.

ID & Title: D8.7 Raw demonstration results based on the KPI measurements

Version: 1.0 Number of pages:

45

Short Description

D8.7 Raw demonstration of results based on the KPI measurements from five different Use Cases. All the Use Cases are based in Sweden and his version is the first demonstration of the results. The purpose of the KPIs within this deliverable are to show the potential contribution that new distributed steerable assets could have within energy systems.

Revision history

Version Date Modifications’ nature Author

V0.1 02-05-2019 Initialisation Annie Bengtsson

V0.2 03-05-2019 Report structure finalization Sebastian Jansson, Pauline Ahlgren, Jörgen Rosvall

V0.3 29-05-2019 First review of results

Helen Carlström, Karolina Ekerlund, Sebastian Jansson, Jörgen Rosvall

V1.0 28-06-2019 Revisited and updated

Helen Carlström, Karolina Ekerlund, Sebastian Jansson, Jörgen Rosvall

Accessibility

☒Public ☐ Consortium + EC ☐ Restricted to a specific group + EC

☐ Confidential + EC

Owner/Main responsible

Name Function Company Visa

Peder Kjellén WPL E.ON

Author(s)/contributor(s): company name(s)

Sebastian Jansson, Helen Carlström, Karolina Ekerlund, Jörgen Rosvall, Annie Bengtsson, Pauline Ahlgren

Reviewer: company name

Company Name

CEZ Distribuce Stanislav Hes

Approver(s): company name(s)

Company Name(s)

Enedis Christian Dumbs

Work Package ID WP 8 Task ID T8.8, T8.9, T8.10, T8.11

https://extranet.enexis.nl/sites/H2020/Gedeelde%20%20documenten/H2020%20documenten/Deliverables/D7.4%20Customer%20recruitment%20and%20contractual%20procedures%20for%20the%20demonstrations.docx



TABLE OF CONTENT

1. INTRODUCTION .........................................................................................4

1.1. Scope of the document ...........................................................................4

1.2. Notations, abbreviations and acronyms .......................................................4

2. SUMMARY OF KPI’S .....................................................................................5

2.1. KPI Relationships and Summary List............................................................5

3. KPI DETAILS..............................................................................................6

3.1. WP8_KPI_1: DSR Economic and Operational Impact on Distribution Network (District

Heating/Cooling) ...........................................................................................6

3.1.1. Major Results From Use Cases............................................................8

3.2. WP8_KPI_2: System Peak Load Reduction (District Cooling Grid) .......................9

3.2.1. Major Results From Use Cases.......................................................... 10

3.3. WP8_KPI_3: DSR Dispatch Quality ............................................................ 13


3.4. WP8_KPI_4: Observability of microgrid performance .................................... 18


3.5. WP8_KPI_5: Increase of Renewable Penetration .......................................... 22

3.5.1. Major results from the Use Case....................................................... 23

3.6. WP8_KPI_6: DSR technical availability ...................................................... 24


3.7. WP8_KPI_7: DSR Flexibility Response Time................................................. 28


3.8. WP8_KPI_8: DSR Potential...................................................................... 32


3.9. WP8_KPI_9: Customer energy awareness ................................................... 38


3.10. WP8_KPI_10: Customer Satisfaction Index.................................................. 40


3.11. WP8_KPI_11: Customer Recruitment ........................................................ 42


3.12. WP8_KPI_12: P2P platform participation ................................................... 44




1. INTRODUCTION

A first version of the raw demonstration results based on the KPI measurements review.

1.1. Scope of the document

The aim of this deliverable report is to present a raw demonstration of the KPIs within Work

Package 8. The different KPIs target separate areas of intreset that is applicable to at leact

one of the Use Cases. The report describes these KPIs and which Use Cases relates to each

KPI. Thereafter, are the results presented one by one for each specific Use Case to show an

overview of the progress so far.

1.2. Notations, abbreviations and acronyms

Table 1.1 provides an overview of the notations, abbreviations and acronyms used in this

report.

Table 1.1 List of notations, abbreviations and accronyms

API Application Programming Interface

BESS Battery Energy Storage Systems

BUG Back-up Generator

CR Customer Recruitment

DER Distributed Energy Recourses

DSR Demand Side Response

ECSI European Customer Satisfaction Index

EM Energy Manager

EMS Energy Management System

HP Heat Pump

HTW Hot Tap Water

IoT Internet of Things

KPI Key Performance Indicator

LES Local Energy System

LCOF Levelized Cost of Energy

LTE Long Term Evolution

NPS Net Promotor Score

P2P Pear to Pear

PV Photovoltaics

RES Renewable Energy Sources

TBD To Be Determined

THD Total Harmonic Distortion

UBV Unit Block Variable

UC Use Case

VIM Virtual Island Mode

WTG Wind Turbine Generator



2. SUMMARY OF KPI’S

This chapter summarizes the measured KPI’s and to give an overview of which Use Cases

falls under wich demo, demo 4A (Use Case 1 and 2) and demo 4B (Use Case 3, 4 and 5). This

is shown in table 2.1 along the which of the KPI´s each Use Case should handle based on the

two documents Deliverable D8.1 & 8.2 and the 2nd Amendment.

2.1. KPI Relationships and Summary List

Table 2.1 Lists the KPIs and shows the relevant use cases per KPI.

DEMO 4A DEMO 4B

ID KPI Definition UC 1 UC 2 UC 3 UC 4 UC 5

WP8_KPI_1 DSR economic and operational impact on distribution network (district Heating/cooling)

x x

WP8_KPI_2 System Peak load reduction (district cooling grid)

x x

WP8_KPI_3 DSR Dispatch Quality x x x

x

WP8_KPI_4 Observability of microgrid performance

x

WP8_KPI_5 Increase of renewable penetration x x x

x

WP8_KPI_6 DSR technical availability x

x

WP8_KPI_7 DSR flexibility response time x

x

WP8_KPI_8 DSR Potential x x x

WP8_KPI_9 Customer energy awareness

x

WP8_KPI_10 Customer Satisfaction Index x

x

WP8_KPI_11 Customer Recruitment x

x x

WP8_KPI_12 P2P platform participation

x



3. KPI DETAILS

3.1. WP8_KPI_1: DSR Economic and Operational Impact on Distribution

Network (District Heating/Cooling)

BASIC KPI INFORMATION

KPI Name DSR economic and operational impact on distribution network (district Heating/cooling)

KPI ID WP8_KPI_1

Strategic Objective To analyze the cost impact of deploying DSR in the thermal network

KPI Description

The overall (peak plants and central production) measured impact of DSR on the grid will reveal its value for money. The DSR cost should be less that the ‘Peak plant LCOE’. The time when the RES generation is in excess, the conventional peak plants will be curtailed providing the economic benefits to the operator and would result into increased penetration of renewables. This KPI intends to measure the business Case calculation of benefits achieved for specific grid areas with supply constraints, by introducing the DSR systems with flexible loads. Also with the help of modelling and simulation of the system, different scenarios can be validated for highest economic impact of the RES penetrations on distribution network.

Changes

If any changes have been made to any part of the BASIC KPI INFORMATION for this KPI (based on Deliverable D8.1-8.2 and 2nd Amendment), these shall be stated here.

KPI Formula

𝐶𝑓𝑙𝑒𝑥 =𝐶𝐷𝑆𝑅

(𝑃wo⁄_DSR − PwDSR)

𝑛𝑓𝑙𝑒𝑥 =𝐶flex

𝐿𝐶𝑂𝐸𝑝𝑒𝑎𝑘_𝑝𝑙𝑎𝑛𝑡

Variable Description

𝑛𝑓𝑙𝑒𝑥 Factor of economic benefit due to DSR flexibility

𝐶𝑓𝑙𝑒𝑥 Cost of flexibility (€/kW)

𝐶𝐷𝑆𝑅 Cost of DSR System

𝑃𝑤𝑜 ⁄_𝐷𝑆𝑅 Peak plant total production without DSR

𝑃𝑤_𝐷𝑆𝑅 Peak plant total production with DSR

𝐿𝐶𝑂𝐸𝑝𝑒𝑎𝑘_𝑝𝑙𝑎𝑛𝑡 LCOE of the peak production plant

The calculation will intend to find out the cost of flexibility offered by the loads and will be compared against the LCOE of the peak plant, which is generally the most expensive source of energy.

Unit of Measurement The unit of measurement of this KPI will be percentage base.

Expectations

The project has to determine how to measure the total production impact as this is something that has never been done. A baseline will have to be drawn to be able to measure the curtailment impact. Decrease peak energy production should be >10% Decrease at customers site >50% for >1h

Overlap of this KPI with other relevant KPIs and Use Cases

UC1, UC2



KPI DATA COLLECTION

Data Data ID Methodology

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 DSR System

𝐶𝐷𝑆𝑅 Direct Calculation Excel 1/year 1 year

Peak plant total production with DSR

𝑃𝑤𝐷𝑆𝑅 Simulation Simulation Data

Peak plant total production without DSR

𝑃𝑤𝑜⁄_𝐷𝑆𝑅

Simulation

Simulation

Data

KPI BASELINE

Source of Baseline Condition

Literature values

☐

Company Historical

Values

☐

Values Measured at Start of Project

☒

Details of Baseline The values calculated at the start of the project will form the baseline.

GENERAL COMMENTS



Major Results From Use Cases

Results from Use Case 1

The aim with this KPI is to investigate if the DSR can be cheaper than the cost of peak

production. The typical installation cost for peak generation can be estimated to 500

kEUR/MW, while the operational cost estimates to 0.1 kEUR/MWh. This can be compared to

the installation cost for the DSR system and the DSR flexibility, i.e. the avoided peak

production.

During the winter 2018-19 testing of the DSR system, the highest heat power reduction

estimated to 14,5 MW during 1 hour. This was achieved via the DSR system installed in, so

far, 24 buildings. The installation cost for one building is approximately 2 kEUR. This is the

marginal cost for each new installation and does not include R&D costs for the DSR system.

These numbers give a cost of flexibility according to the KPI of:

Cflex = (24 x 2) / 14.5 = 3.3 kEUR/MW.

While a boiler delivers fully dispatchable power, in terms of short term flexibility, the DSR

system offers a significantly lower cost, 3.3 kEUR/MWh compared to 500 kEUR/MWh.

The factor of economic benefit due to DSR flexibility then becomes:

nflex = 3.3/500 = 0.066.

To summarize, the DSR system indeed offers flexibility to a significantly lower cost than

conventional peak generation. Furthermore, when it comes to the operational cost, the DSR

system offers practically zero cost (excluding maintenance etc.) compared to the fuel cost

of a peak boiler of around 0.1 kEUR/MWh.


Cost flexibility has not been considered for UC2. Focus has been on energy carrier shifting.



3.2. WP8_KPI_2: System Peak Load Reduction (District Cooling Grid)


KPI Name System Peak load reduction (district cooling grid) KPI ID WP8_KPI_2

Strategic Objective To achieve cost savings by activating the flexibilities which will lead to System Peak load reduction (district cooling grid) via simulation

KPI Description

With the help of simulation the baselining Case of the project will be demonstrated. Later, with the integration of thermal model in the simulation (district cooling grid), the additional system peak load reduction can be realised. The project intends to validate the impact of DSR system in reducing the maximum peaks of the load. The times when the RES is available in excess the expensive conventional source of thermal energy will be turned down, to achieve cost savings. Decrease in system maximum peak production should be >30%.

Changes


KPI Formula

% ΔPpeakred=

Ppeak,without − Ppeak, with

Ppeak, without

This KPI, the percentage system maximum peak load reduction, will be measured by ratio of the difference between the system peaks with and without the DSR system, to the system maximum peak without the DSR system in the district cooling grid. For better overview of the reduction and to even-out the abnormal peaks during some

special days, the value considered in the 𝑃𝑝𝑒𝑎𝑘 will be the mean value of the daily Peak observed over a period of one month.

Unit of Measurement The unit of measurement of this KPI will be %

Expectation Decrease peak energy production should be >30%


UC1, UC2

KPI DATA COLLECTION


for data collection


Location of Data

collection

Frequency of data

collection

Minimum monitoring

period


System peak load with thermal model

𝑃𝑝𝑒𝑎𝑘,𝑎𝑓𝑡𝑒𝑟 Simulation Field Data Data file 1/year 2 year



System peak load without thermal model

𝑃𝑝𝑒𝑎𝑘,𝑏𝑒𝑓𝑜𝑟𝑒 Simulation Field Data Data file 1/year 2 year

KPI BASELINE


Literature values

☐

Company Historical Values

☐


☒

Details of Baseline The baselining will be done via simulation with the values from start of the project

GENERAL COMMENTS



The DSR Platform was used extensively to perform peak load reduction tests during the

summer of 2016. A significant amount of the cooling load in the grid was connected to the

DSR Platform through IoT Field Gateway devices. By analyzing historic consumption data,

the tests were conducted to identify the amount of steerable load during different conditions

as well as the on-site impact on cooling systems and indoor climate. The tests were

conducted in the Western Harbour district cooling grid of Malmö with 11 major consumers

on the grid were controlled with DSR.

The peak load without DSR control 𝑃𝑝𝑒𝑎𝑘,𝑤𝑖𝑡ℎ𝑜𝑢𝑡 was defined as the demand at 30 °C outdoor

temperature at the hour of maximum demand for weekdays (weekends have much lower

consumption). This was estimated using historical data and time-binned linear regression to

approximately 8.8 MW 𝑃𝑝𝑒𝑎𝑘,𝑤𝑖𝑡ℎ𝑜𝑢𝑡 for the Western Harbour grid.

During a major DSR test where maximum load reduction was performed, it was estimated

that approximately 20 %, or 1.5 MW, of the load could be curtailed. The total demand

without DSR was estimated to 7.7 MW at the time.

Figure 2:1 Demand forecast from DSR test period around August 25th, 2016. The red line shows the forecasted production power in kW and the blue line shows the outdoor

temperature for the Western Harbour district in Malmö.



Extrapolating this result to the 𝑃𝑝𝑒𝑎𝑘,𝑤𝑖𝑡ℎ𝑜𝑢𝑡 case yields a DSR-adjusted 𝑃𝑝𝑒𝑎𝑘,𝑤𝑖𝑡ℎ of about 7

MW. Thus, the KPI % ΔPpeakred can be estimated to 20 %. These results are summarized below

in table 2.2.

Table 2:2 Shows the peak with and without controling along with the peak reduktion in procentage.

Variable Result

𝑃𝑝𝑒𝑎𝑘,𝑤𝑖𝑡ℎ𝑜𝑢𝑡 8.8 MW

𝑃𝑝𝑒𝑎𝑘,𝑤𝑖𝑡ℎ 7.0 MW

KPI: % ΔPpeakred 20.5 %


Although UC2 is not connected to a district cooling grid KPI 2 was considered for the district

heating grid and its potential for peak load shaving.

Ectocloud is an optimization tool developed by e.on in collaboration with RWTH Aachen

University which continuously optimises the operation of an ectogrid. As the UC2 asset is a

small ectogrid pilot simulations in the Modelica software has been made. As a base load the

whole DH grid of Malmö City was scaled 1/1000 to simulate a small city district. The results

are shown in figures 2.3 and 2.4 but has not been fully analysed yet. More simulations need

to be done to get detailed results and costs needs to be added to the equation.

Figure 2.3. Shows the scaled load of the DH grid in Malmö without ectogrid balancing.



Figure 2.4. Shows the DH grid of a small city district when balanced with ectogrid (including PV).



3.3. WP8_KPI_3: DSR Dispatch Quality


KPI Name DSR Dispatch Quality KPI ID WP8_KPI_3

Strategic Objective Improving forecasting capabilities of residential DSR assets. Reduction in the Ferror by >10%

KPI Description Improvement in the reliability of forecasting capabilities by the advanced control and algorithms. The dispatch schedule will be forecasted based on the weather and load forecasts.

Changes


KPI Formula

The forecast error will be calculated based on the difference between the forecasted dispatch setpoints and the actual dispatch setpoints. The forecasted

setpoints will be for 2-4 hrs (exact number TBD) ahead of the actual time instants.

Ferror = Sactual − Sforecasted

%Ferror =𝐹𝑒𝑟𝑟𝑜𝑟,𝑠𝑡𝑎𝑟𝑡 − 𝑆𝑒𝑟𝑟𝑜𝑟,𝑒𝑛𝑑

𝐹𝑒𝑟𝑟𝑜𝑟,𝑒𝑛𝑑∗ 100

The minimisation of this forecasted error over the period of the project will be

aimed and recorded. Later the Ferror, at the end of the project will be compared with the Ferror, at the start of the project, for getting the percentage value of

improvement in DSR Dispatch Quality.

Unit of Measurement Unit for this KPI measurement will be % percentage base

Expectation >95% of the forecasted events. Overlap of this KPI with other relevant KPIs and Use Cases

UC1, UC2, UC3, UC5

KPI DATA COLLECTION


for data collection


Location of Data

collection

Frequency of data

collection

Minimum monitoring

period


Forecast Error

𝐹𝑒𝑟𝑟𝑜𝑟 DSR Algorithms DSR Server

1 year

Actual dispatch setpoints

𝑆𝑎𝑐𝑡𝑢𝑎𝑙 DSR Algorithms DSR Server

1 year Actual dispatch setpoints

Forecasted dispatch setpoints

𝑆𝑓𝑜𝑟𝑒𝑐𝑎𝑠𝑡𝑒𝑑 DSR Algorithms DSR Server

1 year



KPI BASELINE


Literature values

☐


☐


☒

Details of Baseline The values measured at the start of the project will be the baseline

GENERAL COMMENTS



The following observations were made (see also Figures 3:1 – 3:6):

The indoor temperature is difficult to see

The model we use to create forecasts is approx. 500 during actual energy use.

Probably the fault of seasonal variations.

The model forecast coincides well with the actual outcome during the schedule

periods.

Additional analysis is needed.

Figure 3:1 Energy: Actual energy consumption



Figure 3:2 Steerable forecast: Forecast

Figure 3:3 Current steerable: Scaling up and down forecast according to schedules

Figure 3:4 - Indoor temperature



Figure 3:5 - Global Amplitude: Schedule signal

Figure 3:6 - All energy plots: Showing the last of the three schedule periods with hourly

resolution. Data from energy, steerable forecast and current steerable.


The EMS of the building supplied by heat from the UC2 heat pump is not connected to

weather forecasting service and therefore KPI 3 has not been considered for UC2.




The forecast models are based on several input and output factors. The underlying

mathematical expressions have been improved upon to achieve a better representation of

the power balance in Simris. From this were the result deducted for Use Case 3. The forecast

improved by 856,43%, %F_error = 856,43%. The big improvement is largely due the first

forecast model for the wind power production which were of by 30%. This can be seen in

table 3:7 and results in table 3:8 below.

Table 3:7 Shows the values that were measured along with the forecasted. This is shown

for WTG, PV and the power consumption in Simris.

Parameter WTG [KWh] PV [KWh] Consumption[KWh]

Actual 658939,50 289580,79 1056197,19

Start forecast 860007,55 286009,24 996219,61

End forecast 672968,01 267509,95 1021234,63

Table 3:8 Shows the parameters, values and explanations needed to answer the KPI

according to the accompanied equation.

Parameter Value Explanation

S_actual -107676,90 [KWh] Sum of actual production minus consumption

S_forcasted_start 149797,18 [KWh]

Sum of forecasted production minus consumption at project start

S_forcasted_end -80756,66 [KWh]

Sum of actual production minus consumption at project end

F_error_start -257474,08 [KWh] S_actual – S_forcasted_start

F_error_end -26920,24 [KWh] S_actual – S_forcasted_end

%F_error 856,43 %

See KPI formula above for %F_error


Results from Use Case 5 are based on the same data as the results in Use Case 3, therefore

the same results as well.



3.4. WP8_KPI_4: Observability of microgrid performance


KPI Name Observability of microgrid performance KPI ID WP8_KPI_4

Strategic Objective Observe the Microgrid system parameters for reliable operations and non-violation of the technical constraints

KPI Description

The important technical parameters will be continuously monitored by the specific instruments installed in the substations for the reliable operation of the Microgrid when in islanded mode. The active power, reactive power, frequency, voltage, and harmonic THD constraints will be monitored continuously. During the total project period, the time will be divided into units of 60mins. This KPI will be measured as the ratio of, number of units of time (60mins) in which the violations of these important parameters occur to the total number of units of the operational time for the Microgrid.

1. Frequency Constraints

Short term frequency drops and rises. It will be considered as a frequency violation if the frequency falls into Box C. If the frequency gets into box B, EMS will automatically reconnect The frequency drops and spikes should ideally be within the boundaries of A1 The Enercon WTG will trip in 200ms if the frequency reaches 47 Hz or 51 Hz. 2. Voltage Constraints:

a. Short term voltage spikes (valid for voltage levels up to 1000 V)

It would be considered a violation of voltage parameter if voltage spikes falls into Box C. If the voltage gets into box B, EMS will automatically reconnect

The voltage spikes should ideally never exceed the boundaries of A1 b. For voltage levels up to 45 kV, short-term voltage drops:

It would be considered a violation of voltage parameter if voltage spikes falls into Box C. If the voltage gets into box B, EMS will automatically reconnect

The voltage drops should ideally be kept within the boundaries of A1 3. Harmonic Constraints:



a. Voltage asymmetry During a period of a week the 10 minute value of the voltage asymmetry should be equal to or less than 2%. b. Voltage Harmonics: For voltage levels up to 36 kV: During a period of a week the 10-minute values for each individual harmonic should be equal to or less than the values in the table below. Furthermore, each 10 minute value of the total amount of harmonics should be equal to or less than 8 %.

Changes


KPI Formula

Pactual : Active Power measured at PCC Qactual: Reactive Power measured at PCC VMV, actual: Medium voltage in the Microgrid at generation side VLV, actual: Low voltage in the Microgrid close to customers factual: Frequency of the Microgrid HTHD: Total Harmonic Distortion 𝑈𝐵𝑉 : unit block variable (time period like e.g. 15 mins with either a violation or without violation)

αmicrogrid =𝑈𝐵𝑉𝑡𝑜𝑡𝑎𝑙 − 𝑈𝐵𝑉𝑐𝑜𝑛𝑠𝑡𝑟𝑎𝑖𝑛𝑡,𝑣𝑖𝑜𝑙𝑎𝑡𝑖𝑜𝑛

𝑈𝐵𝑉𝑡𝑜𝑡𝑎𝑙∗ 100

(per measured parameter)

The different monitoring devices in the substations along with the ENCORP Microgrid controller will be measuring these values as against the violations of the standard constraints. If within a unit block variable (specific time period) a violation occurs, this violation will be counted. In the end, all unit block variables without violations will be compared to the total amount of unit block variables.

Unit of Measurement The unit of the measurement will be pu for Power measurements, Hz for frequency measurements, and % for total harmonic distortion. The unit of the KPI will be in percentage.

Expectation >75% of the detected events.



Overlap of this KPI with other relevant KPIs and Use Cases UC3

KPI DATA COLLECTION


for data collection


Location of Data

collection

Frequency of data

collection

Minimum monitoring

period

Data collection

responsible

Active power

SRS bay 7

SRS.7.P Excel Metrum PQ120 SRS.7 0.1 Hz 12 months

Reactive power

SRS bay 7

SRS.7.Q Excel Metrum PQ120 SRS.7 0.1 Hz 12 months

Voltage N149403

bay 1

N149403.1.V Excel Janitza UMG605 N149403.1 0.1 Hz 12 months

Voltage SRS-131

SRS-131.V Excel Janitza UMG605 SRS-131 0.1 Hz 12 months

Voltage N106160

N106160.V Excel Janitza UMG604 N106160 0.1 Hz 12 months

Frequency N149403

bay 1

N149403.1.f Excel Janitza UMG605 N149403.1 0.1 Hz 12 months

KPI BASELINE


Literature values

☒


☐


☐

Details of Baseline

GENERAL COMMENTS





P_actual= 0.0 pu as Simris operates in island mode.

Q_actual = 0.0 pu as Simris operates in island mode.

KPI: 100%

V_MV,actual_mid =(395-1)/395*100 = 99,7 %

The measurements are performed at medium voltage level at point N149403-1.

V_MV,actual_low = (395-1)/395*100 = 99,7 %

The measurements are performed at low voltage level at point SRS-131 and N106160.

f_actual = 100 %

Frequency is measured at point N149403-1.

H_THD = 100 % The Total Harmonic are not exciding once.

Results from Use Case 5 Results from use case 3 also applies to use case 5 as they are based on the same

powersystem, therefore no addition to be made.



3.5. WP8_KPI_5: Increase of Renewable Penetration


KPI Name Increase of renewable penetration KPI ID WP8_KPI_5

Strategic Objective Increase the utilization of the renewable energy.

KPI Description

Identify how each of the Use Cases contribute to increased penetration of renewable energy. Evaluate how penetration of renewable energy were influenced as flexibility and controlling strategies were included. Comparisons are made within each Use Case, without the flexibility induced works as baseline.

Changes

This KPI was not included in Deliverable D8.1 & 8.2 and breafly mention in 2nd Amendment thus very little information. Existing KPI information were added by EONs Interflex representatives

KPI Formula

%E𝑐ℎ𝑎𝑛𝑔𝑒 = Ewithout

curtailed − Ewithcurtailed

Ewithoutcurtailed ⋅ 100%

Unit of Measurement

E_curtailed_without: The energy that would be curtailed without the implementation of BESS, DSR-assets along with EMS. P_curtailed_with: The energy that were curtailed despite the implemented BESS, DSR-assets along with EMS.


KPI DATA COLLECTION


for data collection


Location of Data

collection

Frequency of data

collection

Minimum monitoring

period


KPI BASELINE


Literature values

☐


☐


☐

Details of Baseline

GENERAL COMMENTS



Major results from the Use Case


See results from Use Case 2.


In Sweden, were UC2 is situated, variations in electricity demand is by 98 % regulated by

hydropower which contributes to approximately 45 % of the total electricity production. Only

approximately 1 % of the electricity production in Sweden is related to the burning of fossil

fuels. Due to the nature of the energy mix it was considered that the UC2-project does not

have the potential to increase the penetration of renewable energy in Sweden thus it was

not tested.

In most countries in the EU the energy mix looks somewhat different from Sweden. As a total

43 % (2018) of the electricity production in the EU origins from fossil fuel usage with a small

contribution of hydro power. Due to the limited quantity of flexible and renewable energy

sources UC2 could have an impact on the penetration of renewable energy sources like wind,

solar etc. in the EU.


The flexibility originates from two sources, the batteries and the DSR assets. Two quantities

were needed to evaluate the influence the project have had so far regarding renewable

penetration in Simris. First of, calculate the amount of energy that would have been

curtailment without the project’s implementation of flexibility within Simris. This were

compared to the actual curtailment using the formula of this KPI shows the curtailed energy

(%Echange) was decreased by 18 %. This is based on data from periods when the project

influenced the electrical situation in Simris. In other words, during test weeks (islanding

weeks) during the period April – November in 2018. Data could be collected more frequently

from that point on due to the implementation of Virtual Island Mode (grid connected with

full DSR and battery utilization) in Simris from mid-November 2018. Due to battery failure

from end of January in 2019, the possibility to run Simris in island mode and VIM stopped.

From week 20 in 2019 the battery has been repaired and is once again functional.

Table 5.1 Shows the curtailment with and without the induced flexibility to Simris

energy system along with the curtailment decrease in percentage.

E_curtailed_with 77247,959 KWh

E_curtailed_without 94357,130 KWh

%E_change 18,132 %


No additional information that relates to the Use Case 5 compared to the Use Case 3, thus

no further information to be added which gives the same results.



3.6. WP8_KPI_6: DSR technical availability


KPI Name DSR technical availability KPI ID WP8_KPI_6

Strategic Objective Maximize DSR System availability

KPI Description

An unavailable asset is not only providing zero flexibility but could create a risk where a system has a certain expectancy of flexibility. Identify key drivers for an asset being unavailable and increase overall asset availability Availability of the DSR System should be >99.5%

Changes


KPI Formula

The DSR Platform will send a ‘Check Status’ signal, to every connected asset in every 5 mins. The asset will send an ‘Acknowledgement Signal’ back to DSR Platform. During the total project period, the time will be divided into units of 15 mins. The instants when the ‘Acknowledgement Signal’ is not received from a particular asset, it will be considered as that asset is unavailable for the DSR Support. The Availability of a particular asset will be measured as the ratio of the units where the Acknowledgement signal is not received for consecutive three times to the total number of units of operations time for a particular asset. These individual availabilities will be aggregated and averaged to get the final outcome of this KPI.

𝐴𝑎𝑠𝑠𝑒𝑡.𝑖 =𝑈𝐵𝑉𝑡𝑜𝑡𝑎𝑙 − 𝑈𝐵𝑉𝑢𝑛𝑎𝑣𝑎𝑙𝑖𝑏𝑙𝑒

𝑈𝐵𝑉𝑡𝑜𝑡𝑎𝑙

𝐷𝑆𝑅𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦 =∑ 𝐴𝑎𝑠𝑠𝑒𝑡,𝑖

𝑖=𝑛𝑖=0

𝑛∗ 100

𝑈𝐵𝑉 : Unit block variable (15 mins of e.g. 3 response checks)

𝑛 : Total number of the assets connected to the DSR system

𝐴asset_i : Availability of a particular asset in the DSR system as

percentage of the unit blocks with availability to the total amount of unit blocks

𝐷𝑆𝑅𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦 : DSR technical availability

Unit of Measurement The KPI unit will be in percentage basis. Overlap of this KPI with other relevant KPIs and Use Cases

UC1, UC3



KPI DATA COLLECTION

Data Data

ID

Methodology for data

collection


Location of Data

collection

Frequency of data

collection

Minimum monitoring

period

Data collection

responsible

Check Signal CHK Internet DSR Platform Server 5 mins 2 years

Acknowledgement Signal

ACK Internet DSR Platform Server 5 mins 2 years

KPI BASELINE


Literature values

☐


☐


☒

Details of Baseline The values measured at the start of the project will be the baseline

GENERAL COMMENTS





Calculations made from EM offline alarms on the 30 buildings connected to CESO during a period of 30 days.

Table 6:1 Shows the availability of the DSR assets and an average availability.

Devices = 30

Days = 30

UBVtotal = 2880

Aasset UVBavailable UVBunavailable Percent

1 2875 5 99,83%

2 2865 15 99,48%

3 2875 5 99,83%

4 2867 13 99,55%

5 2868 12 99,58%

6 2862 18 99,38%

7 2879 1 99,97%

8 2869 11 99,62%

9 2865 15 99,48%

10 2870 10 99,65%

11 2865 15 99,48%

12 2877 3 99,90%

13 2878 2 99,93%

14 2877 3 99,90%

15 2864 16 99,44%

16 2873 7 99,76%

17 2864 16 99,44%

18 2868 12 99,58%

19 2863 17 99,41%

20 2864 16 99,44%

21 2878 2 99,93%

22 2878 2 99,93%

23 2877 3 99,90%

24 2875 5 99,83%

25 2875 5 99,83%

26 2871 9 99,69%

27 2860 20 99,31%

28 2872 8 99,72%

29 2876 4 99,86%

30 2871 9 99,69%

DSRavailability 2870,7 9,3 99,70%




Installations which were not performed has been excluded from the dataset for this KPI.

Reasons varied from customer withdraw to technical impossibilities to install the assets. So

all assets that were attempted to connected to the DSR-platform/system has been included.

The availability is shown in table 6:2.

Table 6:2 Shows the accessibility of the different asset types are shown in the table below, in descending order by DSR_availability.

Asset type Number of units Availability [%]

Bobbie (water heater) 7 54,840

Nibe (Heat pump) 2 92,884

Ngenic (Heat pump) 11 95,659

Fronius (battery) 9 98,294

Availability varies between 92 percent and 99 percent for both type of heat pumps and the

battery systems alike. The asset type that stand out is the water heater with an availability

during the project of approximately 55%.






3.7. WP8_KPI_7: DSR Flexibility Response Time


KPI Name DSR flexibility response time KPI ID WP8_KPI_7

Strategic Objective DSR flexibility response time shall be shorter or equal compared to the targeted response time

KPI Description

As the power fluctuations in a LES sourced by renewables can be significant, it is important to work on improving asset response times. Identify key drivers for response time in the system and reduce the asset’s response times. Achieve a response time per asset appropriate for the cost of the balancing technology and the availability of the flexibility, depending on each type of asset. To be defined by the project based on external benchmarking.

Changes


KPI Formula

𝑡𝑟𝑒𝑠𝑝𝑜𝑛𝑠𝑒,𝐴𝑠𝑠𝑒𝑡 = 𝑡𝑜𝑛 − 𝑡𝑎𝑐𝑡𝑖𝑣𝑒

𝜃 = 𝑡𝑟𝑒𝑠𝑝𝑜𝑛𝑠𝑒,𝑡𝑎𝑟𝑔𝑒𝑡

(𝑡𝑜𝑛 − 𝑡𝑎𝑐𝑡𝑖𝑣𝑒)

𝑡𝑎𝑐𝑡𝑖𝑣𝑎𝑡𝑒 : The instant when the DSR send the activation signal

𝑡𝑜𝑛 : The instant when the device is switched ON 𝑡𝑟_𝐴𝑠𝑠𝑒𝑡 : The response time of the particular asset

The DSR platform will be responsible to send the activation signals and based on the control algorithms the specific devices will be activated.

The figure above describes the generic architecture for the connections from DSR Platform to the assets. The response time for each asset, will be calculated as the time difference between the instant of asset’s actual turning on and the instant of the activation signal being sent by the DSR platform. This KPI will track the improvements in reducing this response time for asset activation and thereby trying to improve the communication speed of the activation signal.



Unit of Measurement The unit of the time measurement will be sec or millisec. The unit of the KPI will be in percentage basis.


UC1, UC3

KPI DATA COLLECTION


for data collection


Location of Data

collection

Frequency of data

collection

Minimum monitoring

period

Data collection

responsible

The instant of receiving of DSR

activation signal

𝑡𝑎𝑐𝑡𝑖𝑣𝑎𝑡𝑒 DSR API Server 2 years

The instant

for actual asset’s turning

on

𝑡𝑜𝑛 Smart-meter/

Customer API

Server 2 years

KPI BASELINE

Source of Baseline

Condition

Literature values

☐


☐


☒

Details of Baseline The baseline will be defined by the values measured at the start of the project

GENERAL COMMENTS





To get a grasp of the possible delays affecting the DSR flexibility response time, it is

important to highlight the data route from the DSR platform to actual actuation of physical

processes. In principal, such a route is shown in the figure 7:1. The may be transmission

delays in between each entity as well as internal data processing delay.

Figure 7:1 Shows the information flow logic responsible for how the response time is

defined.

In the image, each step is highlighted with a number for which response time is discussed in

the list below.

1. Internal processing delay within the DSR Platform is negligible, even if DSR signals

are to be sent to many customer sites located in different grids.

2. In normal operation, the transmission delay over e.g. LTE network is very small

(less than a few seconds).

3. The internal data processing of the IoT Field Gateway device depends on its

calculation and control sample time. This has been set to 1 minute for initial use

cases but can be reconfigured as needed.

4. Transmission over the on-site network depends on the used technology (Modbus is

used extensively) and set up of the network entities. Depending on which entity is

driving communication, different sample times may be applied. This typically

introduces delays ranging from 5 seconds up to 1 minute.

5. The data processing of the Customer Control System (BMS, PLC or similar) depends

on the performance and operating logic used by the customer. In most cases, it is

assumed this processing does not introduce more than a minute's delay. Depending

on the type of controller and its configuration parameters, additional delay is

introduced - some controllers are set to control slowly whereas others may act

faster.

6. Some response time should be assumed from actuators such as valves, pumps, fans

etc.

7. DSR signal response of the physical process' is determined by their construction

properties, which in turn influences thermodynamic and fluid dynamic responses

on-site and propagated to grid.

In summary, response time may be broken down in to three categories:

Communication and data processing response time

Control and actuation response time

Response time of physical process




There were no reliable measurements of the assets response times that could be obtained.

Consequently, not the way to answer the KPI. This KPI were instead answered with

information given by the asset suppliers and their knowledge of each asset respectively.

Each of the suppliers provided the frequency of which their specific asset retrieves new input

values. These time periods were combined with the frequency of which the DSR-platform in

Simris updates the steering signals. Furthermore, the supplier of water heaters has been

unable to provide any data regarding response time. No results that relates to the water

heaters could therefore be deducted nor presented. The existing results are shown in table

7.2 below were the main result were θ = 0,714 and θ = 0,998 for heat pumps and batteries

respectively.

Table 7:2 Shows data related to calculating the response time for each of the asset types.

Parameter Heat pumps Batteries Water

heaters

Number of units 13 9 7

t_activate [s] (starting value) 0 0 0

t_on [s] 210 150,267 N/A

t_response, Asset [s] 210 150,267 N/A

t_response, target [s] 150 150 150

θ 0,714 0,998 N/A



3.8. WP8_KPI_8: DSR Potential


KPI Name DSR Potential KPI ID WP8_KPI_8

Strategic Objective DSR Potential of different flexible technology and impact on the system

KPI Description

This KPI will intend to measure how much flexibility each household is able to deliver which has a direct impact on the system design. This KPI will be an evaluation of maximum theoretical exploitable potential of each house and will be compared against the actual DSR flexibility offered by that particular house. This comparison of ideal scenario versus the real scenario will assess the additional potential of the DSR System. Evaluating each asset technology to realize the highest potential assets. External benchmarking of each technology will be done.

Changes


KPI Formula

%𝑃𝐷𝑆𝑅,𝑎𝑠𝑠𝑒𝑡 =𝑃𝑎𝑣𝑎𝑖 − 𝑃𝑢𝑠𝑒𝑑

𝑃𝑎𝑣𝑎𝑖∗ 100

% 𝑃𝐷𝑆𝑅_𝑎𝑠𝑠𝑒𝑡 : Percentage DSR potential of the asset 𝑃_𝑎𝑣𝑎𝑖 : DSR Potential of an asset, available in total for flexibility contribution 𝑃_𝑢𝑠𝑒𝑑 : DSR Potential of an asset, actually contributed The DSR potential of an asset is the asset’s flexibility contribution for one year (kW/year). This is compared to the actual flexibility contributed by that asset, which quanties it’s potential for the DSR flexibility contribution capability. The DSR assets will be either the Buildings in UC1, commercial heat pump in UC2, and HTW boiler, heat pumps, PV + Batteries in UC3. This KPI will evaluate and rank the assets based on its DSR potential, which will be Useful for business Case calculations. During the start of the project, some assumptions were made about each of these assets, about their DSR Potential.

Unit of Measurement This KPI will be measured in percentage base

Expectation One of the outcomes of the project is a cost impact analysis of deploying a local energy market.


UC1, UC2, UC3

KPI DATA COLLECTION


for data collection


Location of Data

collection

Frequency of data

collection

Minimum monitoring

period


KPI BASELINE


Literature values

☐


☐


☒

Details of Baseline

GENERAL COMMENTS





Modeling for using hot tap water boilers to reduce excess RES generation for a case study in

Simris.

The modeling used 10-minute and 1-minute values for simulations, note that the 1-minute

data were interpolated based on the 10-minute measurements. This probably flattens out

some spike that otherwise would exist.

In total, three variables were varied:

- Installed tap boilers [ranging from 9, which are currently installed to 30, 50, 100 and 140, which is the number of households in Simris]

- Limiting excess generation (75%, 80%, 85%, 90%, 95% of the original profile of RES generation minus Simris load)

- Lag between sending a DSR signal and implementing it (0, 1 time step in the 10-minute simulations and 5 minutes in the minute-by-minute simulation)

When neglecting any activation lag, major improvements occurred when using tap boilers

(note that there is hardly any difference between the 10-minute and 1-minute simulations

when neglecting activation lags).

When having only 9 boilers and limiting excess generation to 75% of the original profile, we

can already avoid 20% of the curtailment. This number quickly increases with more

households (50 households almost 80% curtailment avoided) and eventually all curtailment

can be avoided when providing all buildings with tap boilers and setting an excess limit of

75% of the original profile. [When going for lower excess limits, e.g. 50%, would more

flexibility likely be needed, this is one finding that have not been further studied]. This also

translates into the increased hosting capacity. The increase is almost linear (9 buildings

1,6% increase; 30 buildings 5,3% increase; 50 buildings 8,9% increase).

When considering a lag between sending the DSR signal and actually implementing there is

less benefits. Here, the avoided curtailment is slightly reduced but we cannot avoid all

curtailment due to the time delay. Up to 95% reduced curtailment when 100 of 140 buildings

are equipped with tap boilers.

The positive effects in hosting capacity is reduced when only look at power peaks with

activation lag. Due to the delayed reaction, the hosting capacity only increases by up to 8%

in comparison to up to 20% without activation lag.

Excess limit hereby describes when the DSR should start. In the original data, one peak was

730592 W of RES excess generation. With excess limit being 90%, we would start activating

the boilers if we notice a RES surplus of at least 90%*730592 W. Excess limit hereby describes

when the DSR should start. In our original data, we had a peak of 730592 W of RES excess

generation.



Table 8:1 - Inst. Devices is the number of households with a tap boiler installed (assumed to have 1300 W power). Lag describes the time delay between sending an activation signal and actually turning on the tap boiler. P_avaliable and P_used is used to calculate the KPI

using above-mentioned definition.

Minutely simulations

Inst. Devices Excess limit Lag Peak DSR

Peak_original P_available P_used KPI

9 75 0 718892 730592 11700 11700 100,00%

9 80 0 718892 730592 11700 11700 100,00%

9 85 0 718892 730592 11700 11700 100,00%

9 90 0 718892 730592 11700 11700 100,00%

9 95 0 718892 730592 11700 11700 100,00%

30 75 0 691592 730592 39000 39000 100,00%

30 80 0 691592 730592 39000 39000 100,00%

30 85 0 691592 730592 39000 39000 100,00%

30 90 0 691592 730592 39000 39000 100,00%

30 95 0 691592 730592 39000 39000 100,00%

50 75 0 665592 730592 65000 65000 100,00%

50 80 0 665592 730592 65000 65000 100,00%

50 85 0 665592 730592 65000 65000 100,00%

50 90 0 665592 730592 65000 65000 100,00%

50 95 0 684970 730592 65000 45622 70,20%

100 75 0 601884 730592 130000 128708 99,00%

100 80 0 600592 730592 130000 130000 100,00%

100 85 0 619973 730592 130000 110619 85,10%

100 90 0 656351 730592 130000 74241 57,10%

100 95 0 683851 730592 130000 46741 36,00%

140 75 0 601884 730592 182000 128708 70,70%

140 80 0 584320 730592 182000 146272 80,40%

140 85 0 619973 730592 182000 110619 60,80%

140 90 0 656351 730592 182000 74241 40,80%

140 95 0 683308 730592 182000 47284 26,00%

9 75 5 718892 730592 11700 11700 100,00%

9 80 5 718892 730592 11700 11700 100,00%

9 85 5 718892 730592 11700 11700 100,00%

9 90 5 718892 730592 11700 11700 100,00%

9 95 5 727051 730592 11700 3541 30,30%

30 75 5 691592 730592 39000 39000 100,00%

30 80 5 691592 730592 39000 39000 100,00%

30 85 5 691592 730592 39000 39000 100,00%



Minutely simulations

Inst. Devices Excess limit Lag Peak DSR

Peak_original P_available P_used KPI

30 90 5 707164 730592 39000 23428 60,10%

30 95 5 719172 730592 39000 11420 29,30%

50 75 5 671019 730592 65000 59573 91,70%

50 80 5 672452 730592 65000 58140 89,40%

50 85 5 682241 730592 65000 48351 74,40%

50 90 5 700249 730592 65000 30343 46,70%

50 95 5 711669 730592 65000 18923 29,10%

100 75 5 671019 730592 130000 59573 45,80%

100 80 5 671019 730592 130000 59573 45,80%

100 85 5 682241 730592 130000 48351 37,20%

100 90 5 695269 730592 130000 35323 27,20%

100 95 5 701919 730592 130000 28673 22,10%

140 75 5 671019 730592 182000 59573 32,70%

140 80 5 671019 730592 182000 59573 32,70%

140 85 5 682241 730592 182000 48351 26,60%

140 90 5 695269 730592 182000 35323 19,40%

140 95 5 695135 730592 182000 35457 19,50%




To find out the DSR potential for UC2 simulations were made using a script turning the heat

pump on and off according to a predetermined schedule. The results were used to find out

the potential of load reduction for the electricity grid ( and DH grid) during times of high

peak loads, RES availability etc.

Figure 8.1. UC2 electricy and heat load and during test run.

As shown in figure 8.1 the heat pump was turned on and off several times during a period of

10 hours (several similar test were made). For the particular asset the power demand during

full heat pump load is approximately 18-20 kW producing 60 kW of heat for the connected

building. As a back-up the energy central is connected to the local DH grid and when turning

the heat pump off the DH load is automatically increased to meet the heat demand. As

shown in figure 8.2, when turned off, the power quickly goes down to about 0,5 kW (stand-

by mode) and the DH load increases accordingly.

Using this flexible system in combination with DH as a peak and back-up heat source it is

possible to shift energy carrier from electricity to DH making it possible to relieve the DH

grid (or electricity grid) during peak hours, increase the RES share when available etc. When

implemented in larger numbers the potential to relive the grid, increase the RES share should

be considered as significant.

0,0

10,0

20,0

30,0

40,0

50,0

60,0

70,0

Tim

e0

8:5

9:4

51

0:0

0:0

01

1:0

0:1

51

2:0

0:3

01

3:0

0:4

51

4:0

1:0

01

5:0

1:1

51

6:0

1:3

01

7:0

1:4

51

8:0

2:0

01

9:0

2:1

52

0:0

2:3

02

1:0

2:4

52

2:0

3:0

02

3:0

3:1

50

0:0

3:3

00

1:0

3:4

50

2:0

4:0

00

3:0

4:1

50

4:0

4:3

00

5:0

4:4

50

6:0

5:0

00

7:0

5:1

50

8:0

5:3

00

9:0

5:4

51

0:0

6:0

01

1:0

6:1

51

2:0

6:3

01

3:0

6:4

51

4:0

7:0

01

5:0

7:1

51

6:0

7:3

01

7:0

7:4

51

8:0

8:0

0

kW

Nobelvägen, Malmö

Active power Heat production




Out of the total amount of energy that the DSR assets could provide were not all use. The

utilization for each asset is presented in table 8.2. The heat pumps and residential batteries

provides flexibility both in the positive and negative direction. In other words, they can be

turned off to decrease consumption and turned on to increase consumption. in contrast to

the other asset types can the water heaters only provide flexibility in one direction; used to

increase consumption. The reason for this is to not risk the health of Simris residences by

turning off the water heater. Decreasing water temperature within the water boiler system

could otherwise result in an increased bacterial growth.

Table 8:2 Shows the utilization of the the potential DSR for each of these assts in the folowing order, battary system, heat pump and water boiler.

Parameter Utilization [%]

%P_DSR_asset_BESS 25,569

%P_DSR_asset_HP 32,966

%P_DSR_asset_Boiler 37,250



3.9. WP8_KPI_9: Customer energy awareness


KPI Name Customer Energy Awareness KPI ID WP8_KPI_9

Strategic Objective To increase the Customer Energy Awareness due to the project

KPI Description

% of increase in the active participation in energy related activities (measured at the start and close to the end of the trial). A measurement index will be created to establish the level of awareness of customers at the beginning of the project. This will help to track its evolution and its influence on their decision making.

Changes


KPI Formula

The values of 𝑄𝑖 will be from {1, 4, 7, 10}, which will decide the weight of the question. The values for 𝐴𝑖 will be from {0, 2, 4} which will indicate customers’ unawareness, partial awareness or complete awareness about the respective question.

Customer Awareness Index at the start

𝐶𝐴𝐼𝑠 =∑ ∑ (𝑄𝑖 ∗ 𝐴𝑖)𝑛

𝑖=1𝑚𝑗=1

𝑚𝑡𝑜𝑡𝑎𝑙,𝑐𝑢𝑠𝑡𝑜𝑚𝑒𝑠

Customer Awareness Index at the end

𝐶𝐴𝐼𝑒 =∑ ∑ (𝑄𝑖 ∗ 𝐴𝑖)𝑛

𝑖=1𝑚𝑗=1

𝑚𝑡𝑜𝑡𝑎𝑙,𝑐𝑢𝑠𝑡𝑜𝑚𝑒𝑠

Rise in Customer Awareness Index

%𝐶𝐴𝐼 =𝐶𝐴𝐼𝑒 − 𝐶𝐴𝐼𝑠

𝐶𝐴𝐼𝑠∗ 100

𝑛 : Total number of questions

𝑚 : Total number of the customers The increase in the customer awareness index will be the percentage increase of the weighted average measured by the customer survey which will be carried out at the start of the project and then at the end of the project.

Unit of Measurement The unit will be measured in % percentage base

Expectation >50%


UC4

KPI DATA COLLECTION

Data Data ID

Methodology for data

collection


Location of Data

collection

Frequency of data collection

Minimum monitoring

period


The customer survey will be done and the

answers will be recorded

Ai Customer Survey

Customer Interviews Excel 2 times during the project

duration

KPI BASELINE


Literature values

☐


☐


☒

Details of Baseline The baseline depends on the values acquired at the start of the project

GENERAL COMMENTS




Results from Use Case1

No energy awareness index was set in the beginning of the project. Interviews have not been

performed according to the KPI - WP8_KPI_9.

Customer Energy Awareness of Simris vilagers in early 2018 (CAI_s) and 2019 (CAI_e)

respectivly


Four questions were used to monitor the customer’s energy awareness over an approximately

1-year period. The questions were rated on how well they could represent the customers

energy awareness were good capability gave a greater influence on the result. From the

replies of the customers could a slight increase of energy awareness can be observed in the

results. Worth mentioning is the decrease in replies that the survey received, out of 140

households 51 and 45 replies in 2018 and 2019 respectively. For the residents in Simris had

energy awareness increased (%CAI) by 1,96 %.

Table 9.1 Shows Customer Energy Awareness of Simris villagers in early 2018 (CAI_s), 2019 (CAI_e) respectively and the change in procentage.

Parameter Value

CAI_s 84,039

CAI_e 85,689

%CAI 1,963



3.10. WP8_KPI_10: Customer Satisfaction Index


KPI Name Customer Satisfaction Index KPI ID WP8_KPI_10

Strategic Objective NPS improvement: 10 points better after one year

KPI Description ECSI (European Customer Satisfaction Index) value measured at each year of the trial, or NPS-system (Net Promoter Score)

Changes


KPI Formula

𝑁𝑃𝑆𝑝𝑜𝑖𝑛𝑡𝑠 = %𝑃𝑝𝑟𝑜𝑚𝑜𝑡𝑒𝑟𝑠 − %𝐷𝑑𝑒𝑡𝑟𝑎𝑐𝑡𝑜𝑟𝑠

Net Promoter Score, measures customer experience and is difference between the percentage of People who are Promoters and percentage of people who are Detractors.

Unit of Measurement This KPI will be measured in terms of the points on the NPS system Overlap of this KPI with other relevant KPIs and Use Cases UC1, UC4

KPI DATA COLLECTION


for data collection


Location of Data

collection

Frequency of data

collection

Minimum monitoring

period


Promoters (people who rate

their satisfaction from 8-10)

𝑃𝑝𝑟𝑜𝑚𝑜𝑡𝑒𝑟𝑠 Customer surveys & interviews

Customer interviews Excel 1/year 12 Months

Passives (people who rate


𝑃𝑝𝑎𝑠𝑠𝑖𝑣𝑒𝑠 Customer surveys & interviews


Detractors (people who rate


𝐷𝑑𝑒𝑡𝑟𝑎𝑐𝑡𝑜𝑟𝑠 Customer surveys & interviews


KPI BASELINE


Literature values

☐

Company Historical

Values

☐


☒

Details of Baseline The values will be measured in the start of the project and that will form the baseline for this KPI

GENERAL COMMENTS





The results from the first trial period have been analyzed on a grid and building level. The

first analysis focused on the indoor temperature. The purpose of the analysis was to ensure

that the CESO steering did not negatively affect the buildings citizens. Some fluctuations

were detective, but the temperature difference was lower than 0,5℃.

The data from the first trial period was controlled by the Ectocloud team. It was detected

that the CESO system gave a temperature flow higher than in normal operation. This could

be explained by the fact that the CESO system plans to be used during periods with high

heating consumption.

No effect on the indoor climate has been detected during the first trial so no changes in NPS

can be identified. The contact person for the property company can confirm that no increase

or decrease of complains has been identified after installing CESO.


The customer satisfaction indexes for KPI 10 were deducted from two customer surveys, one

preformed in the beginning of the 2018 and one in the beginning of 2019. The number of

completed interviews 2018 and 2019 were 45 and 51 respectively. From these datasets can

it be seen that NPS 2018 = 35.294 compared to NPS 2019 = 44.444. In the period of one

year had the NPS increased by 9.15 points. Unfortunately, just short of the stated goal of

increasing the satisfaction with 10 points.

Table 10:1 Shows the summary from two surveys and how satisfied the villagers in Simris are with the project.

Score 2018 Score 2019

Detractors 3,921 % 8,889 %

Neutral 56,863 % 37,778 %

Promoters 39,216 % 53,333 %

NPS 35,294 44,444



3.11. WP8_KPI_11: Customer Recruitment


KPI Name Customer recruitment rate KPI ID WP8_KPI_11 (WP2.2_KPI_4)

Strategic Objective Measure whether demos are managing to recruit enough customer base in order to attain demo objectives

KPI Description

Customer engagement is a heuristic for the new energy system. This KPI measures if customers are prone to be more active in the new system and this will have an impact on how new solutions will be designed in a commercialization phase. A prerequisite for this is that they are willing to take part, in the first place.

Changes

If any changes have been made to any part of the BASIC KPI INFORMATION for this KPI (based on D8.1-8.2), these shall be stated here.

KPI Formula

𝐶𝑅% =𝐶𝑅𝑠𝑢𝑐𝑐𝑒𝑠𝑠𝑓𝑢𝑙

𝐶𝑅𝑟𝑒𝑞𝑢𝑖𝑟𝑒𝑑∗ 100

CR% : percentage of required customer base that Use Case was able to recruit CRsuccessful : number of customers (installed capacity, energy volume) needed to obtain enough flexibility in demo in order to verify the use cases CRrequired : number of customers (installed capacity, energy volume) recruited

Unit of Measurement Unit of the CR depends on Use Case description, but should be either customer numbers (#), installed capacity (MW) or Energy (MWh). The unit of the KPI is in percentage basis.

Expectation Steering calls executed per month


UC1, UC3, UC4. This KPI also forms a part of Common KPIs defined for all the Demos in INTERFLEX

KPI DATA COLLECTION


for data collection


Location of Data

collection

Frequency of data

collection

Minimum monitoring

period


Numbers of customer/installed capacity/energy

volume needed to obtain enough

flexibility in demo in order to verify

Use Cases

CRrequired Analysis during Use Case design

phase

N/A N/A N/A N/A E.ON

Numbers of customer/installed capacity/energy volume recruited

CRsuccessful Records from

recruitment activities (customer

agreements if

applicable)

N/A N/A N/A N/A E.ON



KPI BASELINE


Literature values

☐

Company Historical

Values

☐


☐

Details of Baseline

GENERAL COMMENTS



UC1’s Trial was done at 30 buildings own by MKB. The steering of the heating also called

power control is done on building level. E.ON needs to connect 1000 of your largest energy

customers to be able to reduce 20% of customers' heating needs without the indoor

temperature falling below 17 degrees.

Of these 1000 buildings, approx. 100 EIA properties and currently 30 are connected to the

CESO system.

CR required: 1000 buildings

CR successful: 30 buildings

30 / 1000 * 100 = 3%

The goal is to have the 1000 buildings connected by 2020.


In the 140 households that make up Simris were assets installed which later been used for

DSR, the number of assets is 29. Compared to the required number of assets, which were

20, were the recruitment successful.

CR_successful = 29

CR_required = 20

Therefor are the customer recruitment 45 % above required or CR% = 145 %. Note, the

project did have an internal goal of 40 assets that have not been reached.






3.12. WP8_KPI_12: P2P platform participation


KPI Name P2P platform participation KPI ID WP8_KPI_12

Strategic Objective To interact with customers through P2P platform and promote DSR activities

KPI Description

This KPI aims to increase the P2P platform participation and involve the customer in the DSR activities. The customers’ log-in into their personal profiles provided by the P2P API will be recorded and will contribute towards measuring the increase in the platform engagement of the customers.

Changes

If any changes have been made to any part of the BASIC KPI INFORMATION for this KPI (based on D8.1-8.2), these shall be stated here.

KPI Formula

𝑁𝑓𝑟𝑒𝑞,𝑣𝑖𝑠𝑖𝑡𝑠 =∑ 𝐴𝑤𝑒𝑒𝑘,𝑖

𝑛𝑖=0

𝑛

%𝑁 =(𝑁𝑓𝑟𝑒𝑞,𝑣𝑖𝑠𝑖𝑡𝑠 − 𝑁𝑠𝑡𝑎𝑟𝑡,𝑣𝑖𝑠𝑖𝑡𝑠)

𝑁𝑠𝑡𝑎𝑟𝑡,𝑣𝑖𝑠𝑖𝑡𝑠∗ 100

𝑛 : Total number of customers 𝐴𝑤𝑒𝑒𝑘 : Total number of individual customers that logged in at least once during a week The KPI intends to measure the increase in customer engagement through P2P platform by calculating percentage increase in the number of individual customers that logged in at least once during a week measured at the start of the project and at the end of the project. The number of logins (= 𝑁𝑓𝑟𝑒𝑣𝑖𝑠𝑖𝑡𝑠) is calculated as average of total number of actions per visit by all the customers during the whole day. It is not possible to estimate in beforehand.

Unit of Measurement Unit will be percentage Overlap of this KPI with other relevant KPIs and Use Cases UC4

KPI DATA COLLECTION


for data collection


Location of Data

collection

Frequency of data

collection

Minimum monitoring

period


Number of actions per visit by the customer

𝐴𝑑𝑎𝑦 Internet P2P API / Customer API

Server 1 Hz 2 years

KPI BASELINE


Literature values

☐


☐


☒

Details of Baseline

GENERAL COMMENTS





The frequent which the customers have visited the P2P visualization platform have increased

over the measured period. Using the earlier stated equation for this KPI along with the

gathered data shows that the number of visits has increased by 5%.

Two things worth to keep in mind when seeing at the result.

First of, out of the total number of active participants (20) have several users not used the

service at all. Therefore, activity of the customers that use the service are far greater that

what is shown in these results.

Secondly, the data set these calculations are based on contains rather few datapoints and

stretch over a period of roughly two months, beginning of May 2019 – late June 2019. A fuller

dataset would represent the reality in a better way, but this is the data that have been

obtained.

30/06/2019 · accessibility ☒public ☐ consortium + ec ☐ restricted to a specific group + ec...

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