Self-Forecasting Energy Load Stakeholders
for Smart Grids
Dejan Ilić, P&I Mobile M2M (SAP AG, Karlsruhe)
Primary Adv. Prof...
Where do we go?
Close to 100% of today’s customer demand is predicted by their
suppliers. If a product is not available th...
Overview
Introduction
 Research challenges
Improving Forecast Accuracy
 Variable energy storage
Introducing Self-Forecas...
Smart Grids
And its stakeholders
Definition
“A modernized electrical grid that uses analogue or digital ICT to
gather and ...
Motivation
Reliability is continually decreasing in electrical grids
Resource distribution and renewable energies reduced ...
Research Questions
The main research question
"how to incorporate traditionally passive stakeholders
to benefit from Smart...
Related work
Demand side management
Controlling appliances [5]
Only predictable consumers can join Demand Response program...
Why forecast accuracy is important?
It is critical in an energy system – demand must meet supply.
Without it, (costly) bal...
Improving accuracy
By aggregating (one or more) consumer loads 𝑦[𝑛] in a group G.
Resulting load 𝑦 𝐺
[𝑛] is used to produc...
Absorbing Forecast Errors
With focus on storage
Use of energy varies
Forecast errors (in kWh) vary together with the load
...
Distributing Storage Capacity
Storage shapes
Different distributions are
proposed, including the measured
Absolute Energy ...
Absorbing Errors with Assets
Introducing Variable Energy Storage
EV fleet as a storage
Historical opportunity
Vehicles are...
Why deterministic energy load?
If a forecasting accuracy is achieved internally, the outside world
cannot validate an acti...
Enabling Passive Stakeholders
Active loads on Smart Grids
Many researchers try to involve the traditionally passive consum...
Achieving Deterministic Load Behaviour
The SFERS system [12]
Continuous real-time operation
The general architecture is pr...
Evaluating the SFERS system
On a real world case
Commercial building (with offices)
Location in Karlsruhe with 100 employe...
Key Performance Indicators
For the SFERS system
Forecasting on an offset
Horizon averages MAPE for all the
intervals in be...
Using an EV Fleet for SFERS
As a Variable Energy Storage
Vehicles composing VES
Dynamic units are hard(er) to manage
Repla...
Conclusion
Active consumers are needed
Smart Grid services have entry barriers [4]
Methods to surpass those entry barriers...
Future Work
Assess capabilities of other assets
Involve other assets available e.g. data servers
Advancing in VES controll...
Thank you.
Contact information:
M.Sc. Dejan Ilić
Vincenz-Priessnitz-Str. 1
76131 Karlsruhe
dejan.ilic@sap.com
List of publ...
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PhD defence: Self-Forecasting EneRgy load Stakeholders (SFERS) for Smart Grids

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PhD defence: Self-Forecasting EneRgy load Stakeholders (SFERS) for Smart Grids

  1. 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. 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. 3. Overview Introduction  Research challenges Improving Forecast Accuracy  Variable energy storage Introducing Self-Forecasting Stakeholders  System architecture  Real-world evaluation Conclusion Future Work
  4. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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)

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