This document discusses enabling traditionally passive electricity consumers to actively participate in smart grids through self-forecasting of their energy loads. It proposes a system called SFERS that uses smart metering and variable energy storage to allow consumers to accurately forecast and control their energy usage, making it predictable. This would meet prerequisites for smart grid services. The system is evaluated using real usage data from an office building, showing it can improve forecasting accuracy beyond what energy retailers currently achieve.

1. Self-Forecasting Energy Load Stakeholders
for Smart Grids
Dejan Ilić, P&I Mobile M2M (SAP AG, Karlsruhe)
Primary Adv. Prof. Dr. Michael Beigl; Secondary Adv. Prof. Dr. Orestis Terzidis
July 2014

2. Where do we go?
Close to 100% of today’s customer demand is predicted by their
suppliers. 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
(urgency
of) other stakeholders and therefore actively involve them.
Today, I will show you how this can be done with electricity.

3. Overview
Introduction
 Research challenges
Improving Forecast Accuracy
 Variable energy storage
Introducing Self-Forecasting Stakeholders
 System architecture
 Real-world evaluation
Conclusion
Future Work

4. Smart Grids
And 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

5. 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 Trad

6. Research Questions
The main research question
"how to incorporate traditionally passive stakeholders
to benefit from Smart Grid services?"
Research challenges
1. Efficient bi-directional communication in between stakeholders
2. Achieve sufficient forecasting accuracy at smaller scales, or individuals
3. Enable traditionally passive stakeholders to have deterministic energy
loads

7. 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 (IEE
[7] Energy services for the smart grid city (IEEE DEST)
[3] Smart metering: what potential for householder engagement? (Building Research&Informat

8. 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 SmartGridCom

9. Improving accuracy
By aggregating (one or more) consumer loads 𝑦[𝑛] in a group G.
Resulting load 𝑦 𝐺
[𝑛] is used to produce the forecast 𝑦 𝐺
[𝑛].
By absorbing the forecast errors with assets e.g. in an energy storage
Measuring the error
With Mean Absolute Percentage Error (MAPE), calculated as sum of
absolute error fitted over the measured value and divided by number of
intervals 𝑛.
Forecasting energy loads
Overview
𝑦[𝑛] – discrete-time signal; 𝑛 – sequence number

10. Absorbing Forecast Errors
With 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 Stora
[10] Improving wind power quality with energy storage (IEEE PES)

11. Distributing Storage Capacity
Storage shapes
Different distributions are
proposed, including the measured
Absolute Energy ERRor (aeerr) of
the forecast.
Efficiency of a storage shape
Storage 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

12. Absorbing Errors with Assets
Introducing 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 So

13. 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 deterministic
behaviour is achieved and load changes can be verified.
[8] Value of Aggregation In Smart Grids

14. 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 Δ
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 Trad

15. 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 (S

16. Evaluating the SFERS system
On 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

17. Key Performance Indicators
For the SFERS system
Forecasting on an offset
Horizon averages MAPE for all the
intervals in between, while the
offset forecast averages over the
offset (at 𝑛0 + Δ) error. Two robust
time series forecast algorithms are
used.
Adjusting State-of-Charge
State 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

18. 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% of
average daily consumption),accuracy
went beyond retailer’s
Data: offices of SAP AG, 234 MWh in 2011
retailer’s accuracy

19. 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
[13] Smart grid communications: Overview of research challenges, solutions, and standardization activities (IEEE Comm. Surv

20. 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

21. Thank you.
Contact information:
M.Sc. Dejan Ilić
Vincenz-Priessnitz-Str. 1
76131 Karlsruhe
dejan.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)