Maximizing renewable energy use with decentralized residential demand response

2015 IEEE 1st International Smart Cities Conference, ISC2 2015

Volumn , Issue , 2015, Pages

  Dusparic, Ivana ^a   Taylor, Adam ^a   Marinescu, Andrei ^a   Cahill, Vinny ^a   Clarke, Siobhan ^a  

^a TRINITY COLLEGE DUBLIN   (Ireland)

Author keywords

decentralized learning; demand response; multi agent systems; renewable energy

Indexed keywords

EID: 84969930826     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISC2.2015.7366212     Document Type: Conference Paper

References (30)

1. The Population Division of the Department of Economic and Social Affairs, "World urbanization prospects, " United Nations, Tech. Rep., 2014.
2. European Commission, "A roadmap for moving to a competitive low carbon economy in 2050, " 2011.
3. SEAI, "Renewable energy in ireland, " 2012.
4. -, "Electric vehicles roadmap, " 2011.
5. Eirgrid, "System performance data, wind generation, " http: //www. eirgrid. com/operations/systemperformancedata/windgeneration/, last accessed July 2014.
6. M. Klobasa, "Analysis of demand response and wind integration in germany's electricity market, " Renewable Power Generation, IET, vol. 4, no. 1, pp. 55-63, January 2010.
7. M. Klobasa and M. Ragwitz, "Demand response- A new option for wind integration, " in European Wind Energy Conference and Exhibition, 2006.
8. Sustainable Energy Authority of Ireland, "Energy in ireland: Key statistics, " 2013.
9. Eirgrid, "Curtailment report, " 2012.
10. S. Madaeni and R. Sioshansi, "Measuring the benefits of delayed priceresponsive demand in reducing wind-uncertainty costs, " Power Systems, IEEE Transactions on, vol. 28, no. 4, pp. 4118-4126, Nov 2013.
11. I. Dusparic, C. Harris, A. Marinescu, V. Cahill, and S. Clarke, "Multiagent residential demand response based on load forecasting, " in SusTech 2013. IEEE, 2013, pp. 90-96.
12. H. Jiang and Z. Tan, "Load forecasting in demand response, " in APPEEC, 2012.
13. Q. Dam, S. Mohagheghi, and J. Stoupis, "Intelligent demand response scheme for customer side load management, " in Energy 2030 Conference, 2008. ENERGY 2008. IEEE, nov. 2008, pp. 1-7.
14. S.-J. Kim and G. Giannakis, "Efficient and scalable demand response for the smart power grid, " in CAMSAP, 2011.
15. A. Dimeas and N. Hatziargyriou, "Multi-agent reinforcement learning for microgrids, " in PES General Meeting, 2010 IEEE, July 2010.
16. S. McArthur, E. Davidson, V. Catterson, A. Dimeas, N. Hatziargyriou, F. Ponci, and T. Funabashi, "Multi-agent systems for power engineering applications 2014; part i: Concepts, approaches, and technical challenges, " Power Systems, IEEE Transactions on, vol. 22, no. 4, Nov 2007.
17. S. Vandael, N. Boucke, T. Holvoet, and G. Deconinck, "Decentralized demand side management of plug-in hybrid vehicles in a smart grid, " in ATES, 2010.
18. A. Pina, C. Silva, and P. Ferrao, "The impact of demand side management strategies in the penetration of renewable electricity, " Energy, vol. 41, 2011.
19. N. Cicek and H. Delic, "Demand response for smart grids with solar power, " in Innovative Smart Grid Technologies-Asia (ISGT Asia), 2014 IEEE, May 2014, pp. 566-571.
20. A. Papavasiliou and S. Oren, "Large-scale integration of deferrable demand and renewable energy sources, " Power Systems, IEEE Transactions on, vol. 29, no. 1, pp. 489-499, Jan 2014.
21. I. Dusparic and V. Cahill, "Autonomic multi-policy optimization in pervasive systems: Overview and evaluation, " TAAS, vol. 7, no. 1, 2012.
22. R. S. Suton and A. G. Barto, Reinforcement Learning: An Introduction. Cambridge, Massachusetts: A Bradford Book. The MIT Press, 1998.
23. C. J. C. H. Watkins and P. Dayan, "Technical note: Q-learning, " Machine Learning, vol. 8, no. 3, May 1992.
24. M. Humphrys, "Action selection methods using reinforcement learning, " in Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior. MIT Press, 1996.
25. Z. Wen, H. R. Maei, and D. ONeill, "Optimal demand response using device based reinforcement learning, " Stanford University, Tech. Rep., 2012.
26. R. Fazal, J. Solanki, and S. Solanki, "Demand response using multiagent system, " in NAPS, 2012.
27. H. Li, X. Lin, H. Yang, X. Liang, R. Lu, and X. Shen, "Eppdr: An efficient privacy-preserving demand response scheme with adaptive key evolution in smart grid, " Parallel and Distributed Systems, IEEE Transactions on, vol. 25, no. 8, Aug 2014.
28. A. Cardenas, S. Amin, and G. A. Schwartz., "Privacy-aware sampling for residential demand response programs, " in HiCoNS, 2012.
29. Irish Social Science Data Archieve. Comission for Energy Regulation, "CER Smart Metering Project, Smart Metering Electricity Customer Behaviour Trials 2009-2011, " http: //www. ucd. ie/issda/data/commissionforenergyregulationcer/, last accessed July 2014.
30. GridLAB-D. U. S. Department of Energy at Pacific Northwest National Laboratory. http: //www. gridlabd. org/.