Abstract
With the proposed penetration of electric vehicles and advanced metering technology, the demand side is foreseen to play a major role in flexible energy consumption scheduling. On the other hand, the past several years have witnessed utility companies' growing interests to integrate more renewable energy resources. These renewable resources, for example, wind or solar, due to their intermittent nature, brought great uncertainty to the power grid system. In this article, we propose a mechanism that attempts to mitigate the grid operational uncertainty induced by renewable energies by properly exploiting demand flexibility with the help of advanced smart-metering technology. To address the challenge, we develop a novel locational marginal price (LMP)-based pricing scheme that involves active demand-side participation by casting the network objective as a two-stage Stackelberg game between the local grid operator and several aggregators. In contrast to the conventional notion that generation follows load, our game formulation provides more flexibility for the operators and tries to provide adequate incentives for the loads to follow the (stochastic renewable) generation. We use the solution concept of subgame perfect equilibrium to analyze the resulting game. Subsequently, we discuss the optimal real-time conventional capacity planning for the local grid operator to achieve the minimal mismatch between supply and demand with the wind power integration. Finally, we assess our proposed scheme with field data. The simulation results show that our proposed scheme works reasonably well in the long term, even with simple heuristics.
- M. L. Baughman, S. N. Siddiqi, and J. W. Zarnikau. 1997a. Advanced pricing in electrical systems. I. Theory. IEEE Trans. Power Syst. 12, 1, 489--495.Google Scholar
Cross Ref
- M. L. Baughman, S. N. Siddiqi, and J. W. Zarnikau. 1997b. Advanced pricing in electrical systems. II. Implications. IEEE Trans. Power Syst. 12, 1, 496--502.Google Scholar
Cross Ref
- A. W. Berger and F. C. Schweppe. 1989. Real time pricing to assist in load frequency control. IEEE Trans. Power Syst. 4, 3, 920--926.Google Scholar
Cross Ref
- S. Boyd and L. Vandenberghe. 2004. Convex Optimization. Cambridge University Press. Google Scholar
Digital Library
- M. C. Caramanis, R. E. Bohn, and F. C. Schweppe. 1987. System security control and optimal pricing of electricity. Int. J. Elect. Power Energy Syst. 9, 4 (1987), 217--224.Google Scholar
Cross Ref
- I. Cvetkovic, T. Thacker, Dong Dong, G. Francis, V. Podosinov, D. Boroyevich, F. Wang, R. Burgos, G. Skutt, and J. Lesko. 2009. Future home uninterruptible renewable energy system with vehicle-to-grid technology. In Proceedings of the IEEE European Conference on Cognitive Ergonomics (ECCE). 2675--2681.Google Scholar
- GE Energy. 2010. New England wind integration study. Tech. rep. (Dec. 2010).Google Scholar
- Miao He, S. Murugesan, and Junshan Zhang. 2011. Multiple timescale dispatch and scheduling for stochastic reliability in smart grids with wind generation integration. In Proceedings of the IEEE INFOCOM.Google Scholar
Cross Ref
- M. D. Ilic, Le Xie, U. A. Khan, and J. M. F. Moura. 2010. Modeling of future cyber-physical energy systems for distributed sensing and control. IEEE Trans. Syst. Man Cybernet. Part A: Systems and Humans 40, 4, 825--838. Google Scholar
Digital Library
- M. C. Kisacikoglu, B. Ozpineci, and L. M. Tolbert. 2010. Examination of a PHEV bidirectional charger system for V2G reactive power compensation. In Proceedings of the IEEE Asia-Pacific Economic Coperation (APEC). 458--465.Google Scholar
- A. Mas-colell, M. D. Whiston, and J. R. Green. 1995. Microeconomic Theory. Oxford University Press.Google Scholar
- David McLaughlin, Peter Clive, and Joanna McKenzie. 2010. Staying ahead of the wind power curve. Renew. Energy World Mag.Google Scholar
- Pedro S. Moura and A. T. de Almeida. 2010. The role of demand-side management in the grid integration of wind power. Appl. Energy 87, 8, 2581--2588.Google Scholar
Cross Ref
- M. J. Neely, A. S. Tehrani, and A. G. Dimakis. 2010. Efficient algorithms for renewable energy allocation to delay tolerant consumers. In Proceedings of IEEE SmartGridComm.Google Scholar
- A. B. Philpott and E. Pettersen. 2006. Optimizing demand-side bids in day-ahead electricity markets. IEEE Trans. Power Sys. 21, 2, 488--498.Google Scholar
Cross Ref
- E. Rasmusen. 2007. Games and Information: An Introduction to Game Theory. Wiley Blackwell.Google Scholar
- S. J. Rassenti, V. L. Smith, and B. J. Wilson. 2003. Controlling market power and price spikes in electricity networks: Demand-side bidding. Proc. Nat. Acad. Sci. 100, 5, 2998--3003.Google Scholar
Cross Ref
- J. B. Rosen. 1965. Existence and uniqueness of equilibrium points for concave N-person games. Econometrica 33, 3, 520--534.Google Scholar
Cross Ref
- Fred C. Schweppe, Michael C. Caramanis, Richard D. Tabors, and Roger E. Bohn. 1988. Spot Pricing of Electricity. Springer.Google Scholar
- S. N. Siddiqi and M. L. Baughman. 1995. Reliability differentiated pricing of spinning reserve. IEEE Trans. Power Syst. 10, 3, 1211--1218.Google Scholar
Cross Ref
- G. Strbac, E. D. Farmer, and B. J. Cory. 1996. Framework for the incorporation of demand-side in a competitive electricity market. IEE Proc. Gen. Transmis. Distrib. 143, 3, 232--237.Google Scholar
Cross Ref
- J. D. Weber and T. J. Overbye. 1999. A two-level optimization problem for analysis of market bidding strategies. In Proceedings of the IEEE PES Summer Meeting. Vol. 2. 682--687.Google Scholar
- Chenye Wu and Soummya Kar. 2012. LMP-based real time pricing for optimal capacity planning with maximal wind power integration. In Proceedings of IEEE SmartGridComm. 67--72.Google Scholar
Cross Ref
- Chenye Wu, Hamed Mohsenian-Rad, and Jianwei Huang. 2012. Wind power integration via aggregator-consumer coordination: A game theoretic approach. In Proceedings of the IEEE Innovative Smart Grid Technologies Conference (ISGT). Google Scholar
Digital Library
- Chenye Wu, Hamed Mohsenian-Rad, Jianwei Huang, and Amy Yuexuan Wang. 2011. Demand side management for wind power integration in microgrid using dynamic potential game theory. In Proceedings of the IEEE GLOBECOM Workshops. 1199--1204.Google Scholar
Cross Ref
Index Terms
Exploring demand flexibility in heterogeneous aggregators: An LMP-based pricing scheme
Recommendations
Designing the Optimal Pricing Policy for Aggregators in the Smart Grid
GREENTECH '14: Proceedings of the 2014 Sixth Annual IEEE Green Technologies ConferenceThe real-time pricing policy can incentivize the electricity users to dynamically change or shift their electricity consumption, thereby improving reliability of the grid. In the smart grid infrastructure, aggregators between the electricity suppliers ...
A Stackelberg game-theoretic approach to optimal real-time pricing for the smart grid
This paper proposes a Stackelberg game approach to maximize the profit of the electricity retailer (utility company) and minimize the payment bills of its customers. The electricity retailer determines the retail price through the proposed smart energy ...
Game-Theoretic Distributed Virtual Energy Cloud Topology Control for Mobile Smart Grid
CLOUDCOM '14: Proceedings of the 2014 IEEE 6th International Conference on Cloud Computing Technology and ScienceIn this paper, the problem of energy distribution using virtual energy-cloud to the plug-in hybrid electric vehicles (PHEVs) is studied as a single leader multiple follower non-cooperative Stackelberg game. In this game, the energy-cloud service ...






Comments