Self-Forecasting Energy Load Stakeholdersfor Smart GridsDejan Ilić, P&I Mobile M2M (SAP AG, Karlsruhe)Primary Adv. Prof. Dr. Michael Beigl; Secondary Adv. Prof. Dr. Orestis TerzidisJuly 2014

Where do we go?

Close to 100% of today’s customer demand is predicted by theirsuppliers. If a product is not available they just wait for its delivery. Otherwise high availability of a product requires a storage.

Both high availability and waiting bring costs, that are (of course)

paid by consumers. If an accurate consumption can be provided

by a consumer further supply optimizations can be done.

This way we can adopt aware consumption of consumers to (urgencyof) other stakeholders and therefore actively involve them.Today, I will show you how this can be done with electricity.

Overview

Introduction Research challenges

Improving Forecast Accuracy Variable energy storage

Introducing Self-Forecasting Stakeholders System architecture Real-world evaluation

Conclusion

Future Work

Smart GridsAnd its stakeholders

Definition“A modernized electrical grid that uses analogue or digital ICT to gather and act on information, such as the behaviour of suppliers and consumers, in an automated fashion to improve the efficiency, reliability, economics, and sustainability of the production and distribution of electricity.” [1]

Stakeholders of our focus Consumers and prosumers, both residential and commercial Energy producers Distribution system operators Ancillary service providers …

[1] http://en.wikipedia.org/wiki/Smart_grid

Motivation

Reliability is continually decreasing in electrical grids Resource distribution and renewable energies reduced the

reliability [2] Value of reliable resources grows

Active contribution of consumers Smart Grids envision their active involvement [3] Huge research effort targeted to small scales and individuals

Service prerequisites Many require predictability, which is hard to achieve [4] Systems to enable predictable behaviour of consumers are

needed[2] Building a smarter smart grid through better renewable energy information (IEEE PES)[3] Smart metering: what potential for householder engagement? (Building Research&Information)[4] The Impact of Smart Grid Prosumer Grouping on Forecasting Accuracy and Its Benefits for Local Electricity Market Trading (IEEE TSG)

Research Questions

The main research question"how to incorporate traditionally passive stakeholders

to benefit from Smart Grid services?"

Research challenges1. Efficient bi-directional communication in between

stakeholders2. Achieve sufficient forecasting accuracy at smaller scales, or

individuals3. Enable traditionally passive stakeholders to have

deterministic energy loads

Related work

Demand side management Controlling appliances [5] Only predictable consumers can join Demand Response

programs [6]

Software solutions Services of Smart Grids for active participation [7] Customer engagement via web and mobile applications [3]

Beyond state-of-the art Engage stakeholders autonomously (through the capability of

their assets) Deterministic load of a consumer makes them “predictable” Predictability opens an entire spectrum of opportunities

[5] DSM: DR, Intelligent Energy Systems, and Smart Loads (IEEE TII)[6] Quantifying Changes in Building Electricity Use, With Application to Demand Response (IEEE TSG)[7] Energy services for the smart grid city (IEEE DEST)[3] Smart metering: what potential for householder engagement? (Building Research&Information)

Why forecast accuracy is important?

It is critical in an energy system – demand must meet supply. Without it, (costly) balancing mechanisms need to be in place.

On a small scale this is extremely hard to achieve. Even retailers

today suffer 2-5% of error (for tens of thousands of consumers) [8].

[8] Value of Aggregation In Smart Grids (IEEE SmartGridComm)

Improving accuracy By aggregating (one or more) consumer loads in a group .

Resulting load is used to produce the forecast . By absorbing the forecast errors with assets e.g. in an energy

storage

Measuring the errorWith Mean Absolute Percentage Error (MAPE), calculated as sum of absolute error fitted over the measured value and divided by number of intervals .

Forecasting energy loadsOverview

– discrete-time signal; – sequence number

Absorbing Forecast ErrorsWith focus on storage

Use of energy varies Forecast errors (in kWh) vary together with the load Errors are absorbed quantitatively (in kWh)

Measuring the effect Real-world data of a commercial building On average, up to 6 times more error for working hours [9]

Usage of an energy storage Renewables use storage to improve accuracy [10] Absorb forecast errors of traditionally passive consumers Evaluate different capacity distributions

Data source: offices of SAP AG, 2.7 GWh in 2011

[9] Addressing Energy Forecast Errors: An Empirical Investigation of the Capacity Distribution Impact in a Variable Storage (Springer)[10] Improving wind power quality with energy storage (IEEE PES)

Distributing Storage Capacity

Storage shapesDifferent distributions are proposed, including the measured Absolute Energy ERRor (aeerr) of the forecast.

Efficiency of a storage shapeStorage efficiency vary together with its distribution. Storage shape “aeerr” resulted with the best efficiency.

290 kWh

580 kWh

retailer’s accuracy

Published in Springer Power Systems

Absorbing Errors with AssetsIntroducing Variable Energy Storage

EV fleet as a storage Historical opportunity Vehicles are idle 96% of their time Their power is not to be omitted [11] Real world pool of EVs peaked

around 33% of office presence

Defining Variable Energy Storage (VES) Combines static and dynamic storage units into one (virtual)

unit of storage For example, EVs as dynamic units

Unit management logic is introduced

Data source: Pool electric vehicles of SAP AG

[11] Using fleets of electric-drive vehicles for grid support (Journal of Power Sources)

Why deterministic energy load?

If a forecasting accuracy is achieved internally, the outside world cannot validate an active load behaviour.

By reporting an accurate forecast, the deterministicbehaviour is achieved and load changes can be verified.

[8] Value of Aggregation In Smart Grids

Enabling Passive Stakeholders

Active loads on Smart Grids Many researchers try to involve the traditionally passive

consumers Prerequisite for a service eligibility is hard [4] Methods for enablement are needed

Introducing Self-Forecasting EneRgy load Stakeholders (SFERS) Achieve “predictability” by Smart Metering with an offset Execute and report self-forecast (for new revenue

opportunities) Absorb (locally) the errors between the reported and

measured load

[4] The Impact of Smart Grid Prosumer Grouping on Forecasting Accuracy and Its Benefits for Local Electricity Market Trading (IEEE TSG)

Achieving Deterministic Load Behaviour

The SFERS system [12] Continuous real-time operation The general architecture is proposed

Strategy with VES is evaluated

Main architectural components: Energy Manager (EM) Energy Load Forecast (ELF) Variable Energy Storage (VES) Energy Trading (ET)

[12] A System for Enabling Facility Management to Achieve Deterministic Energy Behaviour in the Smart Grid Era (SmartGreens)

Evaluating the SFERS systemOn a real world case

Commercial building (with offices) Location in Karlsruhe with 100 employees Average daily consumption 642 kWh 46 employee vehicles in the fleet (non EVs)

Running system evaluation Simulation of each system component individually (over an

entire year) Use robust off-the-shelf forecasting algorithms Achieve accuracy via VES

Replace traditional vehicles with EVs Enhance with a static storage

Data: offices of SAP AG, 234 MWh in 2011

Key Performance IndicatorsFor the SFERS system

Forecasting on an offsetHorizon averages MAPE for all the intervals in between, while the offset forecast averages over the offset (at ) error. Two robust time series forecast algorithms are used.

Adjusting State-of-ChargeState has to be adjusted on the report offset, and is identified as a critical KPI for storage capacity sizing. Evaluation is done with a static storage.

accuracy of a retailer

Data: offices of SAP AG, 234 MWh in 2011Autoregressive integrated moving average (ARIMA)

~12 kWh

109 kWh

retailer’s accuracy

SARIMA Weekly

Using an EV Fleet for SFERS As a Variable Energy Storage

Vehicles composing VES Dynamic units are hard(er) to manage Replace (some of 46) vehicles with EVs (20%, 50%, 100%)

Accuracy achieved 20% of replacement (or 9 EVs)

already resulted in MAPE ≤ 3.5% Enhance with a static storage

Low presence out of working hours Enhancing with a static storage of

capacity equaling one EV (or 5.6% ofaverage daily consumption),accuracywent beyond retailer’s

Data: offices of SAP AG, 234 MWh in 2011

retailer’s accuracy

Conclusion

Active consumers are needed Smart Grid services have entry barriers [4] Methods to surpass those entry barriers are needed [13]

Addressing the research challenges Timely collection, processing and service providing can be

done (and on scale) Accuracy can be achieved on small scales

In particularly due to assets (that will be) available Enabling the traditionally passive consumers

Deterministic behavior is achieved by the SFERS system System architecture is proposed and evaluated

[4] The Impact of Smart Grid Prosumer Grouping on Forecasting Accuracy and Its Benefits for Local Electricity Market Trading (IEEE TSG)[13] Smart grid communications: Overview of research challenges, solutions, and standardization activities (IEEE Comm. Surveys & Tutorials)

Future Work

Assess capabilities of other assets Involve other assets available e.g. data servers

Advancing in VES controller Improve algorithms for unit management Improve the State-of-Charge adjustment algorithm

Investigate technology opportunities Different performance barriers of storage technologies Avoid barriers by rescheduling algorithms

Thank you.

Contact information:

M.Sc. Dejan IlićVincenz-Priessnitz-Str. 176131 Karlsruhedejan.ilic@sap.com

List of publications:A system for enabling facility management to achieve deterministic energy behaviour in the smart grid era (2014 SmartGreens)

Addressing energy forecast errors: An empirical investigation of the capacity distribution impact in a variable storage (2014 Energy Systems, Springer)

The impact of smart grid prosumer grouping on forecasting accuracy and its benefits for local electricity market trading (2014 IEEE Tran. on Smart Grids)

Assessment of an enterprise energy service platform in a smart grid city pilot (2013 IEEE INDIN)

Developing a web application for monitoring and management of smart grid neighborhoods (2013 IEEE INDIN)

Improving load forecast in prosumer clusters by varying energy storage size (2013 IEEE PowerTech)

A comparative analysis of smart metering data aggregation performance (2013 IEEE INDIN)

Impact assessment of smart meter grouping on the accuracy of forecasting Algorithms (2013 ACM SAC)

Evaluation of the scalability of an energy market for smart grid neighbourhoods (2013 IEEE INDIN)

Using flexible energy infrastructures for demand response in a smart grid city (2012 IEEE PES ISGT)

Energy services for the smart grid city (2012 IEEE DEST)

An energy market for trading electricity in smart grid neighbourhoods (2012 IEEE DEST)

Sensing in power distribution networks via large numbers of smart meters (2012 IEEE PES ISGT)

Using a 6lowpan smart meter mesh network for event-driven monitoring of power quality (2012 IEEE SmartGridComm)

A survey to wards understanding residential prosumers in smart grid neighbourhoods (2012 IEEE PES ISGT)

Assessment of high-performance smart metering for the web service enabled smart grid era (2011 ACM ICPE)

Performance evaluation of web service enabled smart metering platform (2010 ICST)

List of projects:SmartHouse/SmartGrid (EU FP7)

NOBEL: Neighbourhood Oriented Brokerage ELectricity monitoring system (EU FP7)

SmartKYE: Smart grid KeY nEighbourhood indicator cockpit (EU FP7)