Integration of Load Shifting and Storage to Reduce Gray Energy Demand

Iván S. Razo-Zapata, Mihail Mihaylov, Ann Nowé

Abstract

The smart grid concept offers an opportunity to design new environmentally friendly energy markets for reducing CO2 emissions. To achieve this goal, we should increase the use and penetration of green energy while softening our dependency on gray (non-environmentally friendly) energy too. In this work we show how load shifting and storage can be incorporated into new energy markets to reduce gray energy consumption. We used multi-agent-based simulations that are fed with real data to analyze the influence of load shifting and storage to reduce gray energy demand as well as the behaviour of prices for gray and green energy. Results suggest that reduction in gray energy consumption is feasible during peak times, i.e. up to 15%. Nonetheless, if the amount of renewable resources is increased 50%, higher reductions can be achieved, i.e. up to 30%. Furthermore, one of the findings also suggests that storage helps to keep the price of green energy low.

References

  1. Aghaei, J. and Alizadeh, M.-I. (2013). Demand response in smart electricity grids equipped with renewable energy sources: A review. Renewable and Sustainable Energy Reviews, 18:64 - 72.
  2. Bosch (2016). Bpt-s 5 hybrid solar power storage. http://bosch-power-tec.com/en/bpte/ produkte/storage solutions/bpt s 5 hybrid/vs 5 hybrid. [Online; accessed 04-February-2016].
  3. Bush, S. F. (2014). Smart Grid: Communication-Enabled Intelligence for the Electric Power Grid. IEEE Press.
  4. Capodieci, N., Pagani, G. A., Cabri, G., and Aiello, M. (2011). Smart meter aware domestic energy trading agents. In Proceedings of the IEEMC 7811 Workshop on E-energy Market Challenge, pages 1-10, New York, NY, USA. ACM.
  5. Gottwalt, S., Ketter, W., Block, C., Collins, J., and Weinhardt, C. (2011). Demand side management-a simulation of household behavior under variable prices. Energy Policy, 39(12):8163 - 8174. Clean Cooking Fuels and Technologies in Developing Economies.
  6. Grid4eu (2016). Nice grid demonstrator. http://www.nice grid.fr/. Online; accessed 04-February-2016.
  7. Ilic, D., Da Silva, P., Karnouskos, S., and Griesemer, M. (2012). An energy market for trading electricity in smart grid Neighbourhoods. In 6th IEEE International Conference on Digital Ecosystems Technologies (DEST), pages 1-6.
  8. Kok, J. K., Warmer, C. J., and Kamphuis, I. G. (2005). Powermatcher: Multiagent control in the electricity infrastructure. In Proceedings of the Fourth International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 7805, pages 75-82, New York, NY, USA. ACM.
  9. Ma, H. and Leung, H.-F. (2007). An adaptive attitude bidding strategy for agents in continuous double auctions. Electronic Commerce Research and Applications, 6(4):383 - 398.
  10. Mert, W., Suschek-Berger, J., and Tritthart, W. (2008). Consumer acceptance of smart appliances. Technical report, EIE project Smart Domestic Appliances in Sustainable Energy Systems (Smart-A).
  11. Mihaylov, M., Jurado, S., Avellana, N., Razo-Zapata, I., Van Moffaert, K., Arco, L., Bezunartea, M., Grau, I., Can˜adas, A., and Nowé, A. (2015). Scanergy: a scalable and modular system for energy trading between prosumers. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems, AAMAS 7815, pages 1917-1918.
  12. Mihaylov, M., Jurado, S., Van Moffaert, K., Avellana, N., and Nowé, A. (2014). Nrg-x-change: : A novel mechanism for trading of renewable energy in smart grids. In 3rd International Conference on Smart Grids and Green IT Systems (SmartGreens).
  13. Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system.
  14. Niesten, E. and Alkemade, F. (2016). How is value created and captured in smart grids? a review of the literature and an analysis of pilot projects. Renewable and Sustainable Energy Reviews, 53:629-638.
  15. North, M. J., Collier, N. T., Ozik, J., Tatara, E. R., Macal, C. M., Bragen, M., and Sydelko, P. (2013). Complex adaptive systems modeling with repast simphony. Complex adaptive systems modeling, 1(1):1-26.
  16. Paatero, J. V. and Lund, P. D. (2006). A model for generating household electricity load profiles. International Journal of Energy Research, 30(5):273-290.
  17. Prüggler, N. (2013). Economic potential of demand response at household level-are central-european market conditions sufficient? Energy Policy, 60:487 - 498.
  18. Prüggler, N., Prüggler, W., and Wirl, F. (2011). Storage and demand side management as power generator's strategic instruments to influence demand and prices. Energy, 36(11):6308 - 6317.
  19. Ramchurn, S. D., Vytelingum, P., Rogers, A., and Jennings, N. R. (2012). Putting the 'smarts' into the smart grid: a grand challenge for artificial intelligence. Commun. ACM, 55(4):86-97.
  20. Rickerson, W., Couture, T., Barbose, G. L., Jacobs, D., Parkinson, G., Chessin, E., Belden, A., Wilson, H., and Barrett, H. (2014). Residential prosumers: Drivers and policy options (re-prosumers). Technical report, International Energy Agency (IEA).
  21. Room, B. and Institute, W. R. (2010). Global ecolabel monitor 2010: Towards transparency. http://www.eco labelindex.com/downloads/Global Ecolabel Monitor 2010.pdf. [Online; accessed September,2015].
  22. Schuler, R. (2010). The smart grid: a bridge between emerging technologies society and the environment. The Bridge, 40(1):42-49.
  23. Shoham, Y. and Leyton-Brown, K. (2008). Multiagent systems: Algorithmic, game-theoretic, and Logical Foundations. Cambridge University Press.
  24. Siano, P. (2014). Demand response and smart grids-a survey. Renewable and Sustainable Energy Reviews, 30:461-478.
  25. Tesla (2016). Powerwall. http://www.teslamotors.com/ powerwall. [Online; accessed February,2016].
  26. van Werven, M. J. and Scheepers, M. J. (2005). The changing role of energy suppliers and distribution system operators in the deployment of distributed generation in liberalised electricity markets. Technical report, ECN-C-05-048, ECN.
  27. VEA (2014). Vlaams energieagentschap - rapport 2013/2. http://www2.vlaanderen.be/economie/energiesparen/ milieuvriendelijke/monitoring evaluatie/2013/20130 628Rapport2013 2-Deel2Actualisatie-OT Bf.pdf. [Online; accessed 23-September-2015].
  28. Vytelingum, P., Cliff, D., and Jennings, N. (2008). Strategic bidding in continuous double auctions. Artificial Intelligence, 172(14):1700 - 1729.
  29. Wang, Z. and Wang, L. (2013). Adaptive negotiation agent for facilitating bi-directional energy trading between smart building and utility grid. IEEE Transactions on Smart Grid, 4(2):702-710.
Download


Paper Citation


in Harvard Style

S. Razo-Zapata I., Mihaylov M. and Nowé A. (2016). Integration of Load Shifting and Storage to Reduce Gray Energy Demand . In Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS, ISBN 978-989-758-184-7, pages 154-165. DOI: 10.5220/0005784201540165


in Bibtex Style

@conference{smartgreens16,
author={Iván S. Razo-Zapata and Mihail Mihaylov and Ann Nowé},
title={Integration of Load Shifting and Storage to Reduce Gray Energy Demand},
booktitle={Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,},
year={2016},
pages={154-165},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005784201540165},
isbn={978-989-758-184-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,
TI - Integration of Load Shifting and Storage to Reduce Gray Energy Demand
SN - 978-989-758-184-7
AU - S. Razo-Zapata I.
AU - Mihaylov M.
AU - Nowé A.
PY - 2016
SP - 154
EP - 165
DO - 10.5220/0005784201540165