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Generation and Evaluation of Flex-Offers from Flexible Electrical Devices

Published:16 May 2017Publication History

ABSTRACT

There exists an immense potential in utilizing the demand reduction and shifting potential (flexibility) of household devices to confront the challenges of intermittent Renewable Energy Sources. However, a widely accepted general flexibility extraction and evaluation process is missing. This paper proposes a generalized Flex-offer Generation and Evaluation Process (FOGEP) that extract flexibility from wet-devices (e.g. dishwashers), electric vehicles, and heat pumps and capture it in a unified model, a so-called flex-offer. The proposed process analyses the past consumption behavior of a device to automatically capture flexibility in its usage. It utilizes two device-level forecasting techniques and algorithms to capture various attributes and temporal patterns required for flexibility extraction. Further, the paper evaluates the performance of FOGEP regarding the accuracy of the extracted flexibility and performs an economic assessment to identify the device-specific best market to trade flexibility. The experimental results, based on real-world measurement data, show that household devices have up to 32% of reduction and 15 hours of shifting flexibility in their energy demands. Further, FOGEP can extract flexibility with up to 98% accuracy. The flexibilities can provide up to 51% and 11% savings in the spot and regulating market for Balance Responsible Party (BRP) and/or consumer, respectively.

References

  1. 2013. The MIRABEL Project, 2013. http://www.mirabel-project.eu.. (2013).Google ScholarGoogle Scholar
  2. 2016. Source of Dataset: EVnetNL EV charging dataset. https://www.elaad.nl/. (2016).Google ScholarGoogle Scholar
  3. 2016. Source of Dataset: INTrEPID, INTelligent systems for Energy Prosumer buildIngs at District level, funded by the European Commission under FP7, Grant Agreement N. 317983. (2016).Google ScholarGoogle Scholar
  4. 2017. Dark Sky API. https://darksky.net/poweredby/. (2017).Google ScholarGoogle Scholar
  5. M. Alizadeh, A. Scaglione, A. Applebaum, G. Kesidis, and K. Levitt. 2015. Reduced-Order Load Models for Large Populations of Flexible Appliances. IEEE Transactions on Power Systems 30 (2015), 1758--1774. Google ScholarGoogle ScholarCross RefCross Ref
  6. B. Biegel, P. Andersen, T. S. Pedersen, K. M. Nielsen, J. Stoustrup, and L. H. Hansen. 2013. Electricity market optimization of heat pump portfolio. In Control Applications (CCA), 2013 IEEE International Conference on. 294--301. Google ScholarGoogle ScholarCross RefCross Ref
  7. C. Develder, N. Sadeghianpourhamami, M. Strobbe, and N. Refa. 2016. Quantifying flexibility in EV charging as DR potential: Analysis of two real-world data sets. In 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm). 600--605. Google ScholarGoogle ScholarCross RefCross Ref
  8. R. DâĂŹhulst, W. Labeeuw, B. Beusen, S. Claessens, G. Deconinck, and K. Vanthournout. 2015. Demand response flexibility and flexibility potential of residential smart appliances: Experiences from large pilot test in Belgium. Applied Energy 155 (2015), 79--90.Google ScholarGoogle ScholarCross RefCross Ref
  9. Stefan Feuerriegel and Dirk Neumann. 2014. Measuring the financial impact of demand response for electricity retailers. Energy Policy (2014), 359--368. Google ScholarGoogle ScholarCross RefCross Ref
  10. Isabelle Guyon and André Elisseeff. 2006. An Introduction to Feature Extraction. Springer Berlin Heidelberg, Berlin, Heidelberg, 1--25.Google ScholarGoogle Scholar
  11. Haider Tarish Haider, Ong Hang See, and Wilfried Elmenreich. 2016. Dynamic residential load scheduling based on adaptive consumption level pricing scheme. Electric Power Systems Research 133 (2016), 27--35. Google ScholarGoogle ScholarCross RefCross Ref
  12. David W Hosmer Jr and Stanley Lemeshow. 2004. Applied logistic regression. John Wiley & Sons.Google ScholarGoogle Scholar
  13. Rikke Hagensby Jensen, Jesper Kjeldskov, and Mikael B. Skov. 2016. HeatDial: Beyond User Scheduling in Eco-Interaction. In Proceedings of the 9th Nordic Conference on Human-Computer Interaction (NordiCHI '16). 1--10. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. M. U. Kajgaard, J. Mogensen, A. Wittendorff, A. T. Veress, and B. Biegel. 2013. Model predictive control of domestic heat pump. In 2013 American Control Conference. 2013--2018. Google ScholarGoogle ScholarCross RefCross Ref
  15. Jin-Ho Kim and Anastasia Shcherbakova. 2011. Common failures of demand response. Energy 36 (2011), 873--880. Google ScholarGoogle ScholarCross RefCross Ref
  16. Jesper Kjeldskov, Mikael B. Skov, Jeni Paay, Dennis Lund, Tue Madsen, and Michael Nielsen. 2015. Facilitating Flexible Electricity Use in the Home with Eco-Feedback and Eco-Forecasting. In Proceedings of the Annual Meeting of the Australian Special Interest Group for Computer Human Interaction (OzCHI '15). 388--396. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. K. Kouzelis, Z. H. Tan, B. Bak-Jensen, J. R. Pillai, and E. Ritchie. 2015. Estimation of Residential Heat Pump Consumption for Flexibility Market Applications. IEEE Transactions on Smart Grid 6 (2015), 1852--1864. Google ScholarGoogle ScholarCross RefCross Ref
  18. W. Labeeuw, J. Stragier, and G. Deconinck. 2015. Potential of Active Demand Reduction With Residential Wet Appliances: A Case Study for Belgium. IEEE Transactions on Smart Grid 6 (2015), 315--323. Google ScholarGoogle ScholarCross RefCross Ref
  19. Ontje Lünsdorf and Michael Sonnenschein. 2010. A pooling based load shift strategy for household appliances. In EnviroInfo 2010. 734--743.Google ScholarGoogle Scholar
  20. J. Ma, H. H. Chen, L. Song, and Y. Li. 2016. Residential Load Scheduling in Smart Grid: A Cost Efficiency Perspective. IEEE Transactions on Smart Grid 7 (2016), 771--784.Google ScholarGoogle Scholar
  21. Z. Ma, D. S. Callaway, and I. A. Hiskens. 2013. Decentralized Charging Control of Large Populations of Plug-in Electric Vehicles. IEEE Transactions on Control Systems Technology 21 (2013), 67--78. Google ScholarGoogle ScholarCross RefCross Ref
  22. Bijay Neupane, Torben Bach Pedersen, and Bo Thiesson. Towards Flexibility Detection in Device-Level Energy Consumption. 1--16.Google ScholarGoogle Scholar
  23. Bijay Neupane, Torben Bach Pedersen, and Bo Thiesson. 2015. Evaluating the Value of Flexibility in Energy Regulation Markets. In Proceedings of the 2015 ACM Sixth International Conference on Future Energy Systems (e-Energy '15). 131--140. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Silviu Nistor, Jianzhong Wu, Mahesh Sooriyabandara, and Janaka Ekanayake. 2015. Capability of smart appliances to provide reserve services. Applied Energy 138 (2015), 590--597. Google ScholarGoogle ScholarCross RefCross Ref
  25. N. G. Paterakis, O. ErdinÃğ, A. G. Bakirtzis, and J. P. S. CatalÃčo. 2015. Optimal Household Appliances Scheduling Under Day-Ahead Pricing and Load-Shaping Demand Response Strategies. IEEE Transactions on Industrial Informatics 11 (2015), 1509--1519. Google ScholarGoogle ScholarCross RefCross Ref
  26. M. Pipattanasomporn, M. Kuzlu, S. Rahman, and Y. Teklu. 2014. Load Profiles of Selected Major Household Appliances and Their Demand Response Opportunities. IEEE Transactions on Smart Grid 5 (2014), 742--750. Google ScholarGoogle ScholarCross RefCross Ref
  27. N. Rotering and M. Ilic. 2011. Optimal Charge Control of Plug-In Hybrid Electric Vehicles in Deregulated Electricity Markets. IEEE Transactions on Power Systems 26 (2011), 1021--1029. Google ScholarGoogle ScholarCross RefCross Ref
  28. Nasrin Sadeghianpourhamami, Matthias Strobbe, and Chris Develder. 2016. Real-world User Flexibility of Energy Consumption: Two-stage Generative Model Construction. In Proceedings of the 31st Annual ACM Symposium on Applied Computing (SAC 16). 2148--2153. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. J. J. Shah, M. C. Nielsen, T. S. Shaffer, and R. L. Fittro. 2016. Cost-Optimal Consumption-Aware Electric Water Heating Via Thermal Storage Under Time-of-Use Pricing. IEEE Transactions on Smart Grid 7 (2016), 592--599. Google ScholarGoogle ScholarCross RefCross Ref
  30. C. Shao, X. Wang, X. Wang, C. Du, and B. Wang. 2016. Hierarchical Charge Control of Large Populations of EVs. IEEE Transactions on Smart Grid 7 (2016), 1147--1155. Google ScholarGoogle ScholarCross RefCross Ref
  31. Klaus Skytte. 1999. The regulating power market on the Nordic power exchange Nord Pool: an econometric analysis. Energy Economics (1999), 295--308. Google ScholarGoogle ScholarCross RefCross Ref
  32. O. Sundstrom and C. Binding. 2012. Flexible Charging Optimization for Electric Vehicles Considering Distribution Grid Constraints. Smart Grid, IEEE Transactions on (2012), 26--37.Google ScholarGoogle Scholar
  33. Emmanouil Valsomatzis, Katja Hose, and Torben Bach Pedersen. Balancing Energy Flexibilities Through Aggregation. 17--37.Google ScholarGoogle Scholar
  34. E. Valsomatzis, T. B. Pedersen, A. AbellÃş, and K. Hose. 2016. Aggregating energy flexibilities under constraints. In 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm). 484--490. Google ScholarGoogle ScholarCross RefCross Ref
  35. Emmanouil Valsomatzis, Torben Bach Pedersen, Alberto Abelló, Katja Hose, and Laurynas Šikšnys. 2016. Towards Constraint-based Aggregation of Energy Flexibilities. In Proceedings of the Seventh International Conference on Future Energy Systems Poster Sessions (e-Energy '16). Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Laurynas Šikšnys and Torben Bach Pedersen. 2016. Dependency-based FlexOffers: Scalable Management of Flexible Loads with Dependencies. In Proceedings of the Seventh International Conference on Future Energy Systems (e-Energy '16). 1--13.Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. L. Šikšnys, E. Valsomatzis, K. Hose, and T. B. Pedersen. 2015. Aggregating and Disaggregating Flexibility Objects. IEEE Transactions on Knowledge and Data Engineering 27 (2015), 2893--2906. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Zhuang Zhao, Won Cheol Lee, Yoan Shin, and Kyung-Bin Song. 2013. An Optimal Power Scheduling Method for Demand Response in Home Energy Management System. Smart Grid, IEEE Transactions on (2013), 1391--1400.Google ScholarGoogle Scholar

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  • Published in

    cover image ACM Conferences
    e-Energy '17: Proceedings of the Eighth International Conference on Future Energy Systems
    May 2017
    388 pages
    ISBN:9781450350365
    DOI:10.1145/3077839

    Copyright © 2017 ACM

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    Publication History

    • Published: 16 May 2017

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