Cooperative electricity consumption shifting

Abstract In this paper, we propose the formation of agent cooperatives offering large-scale electricity demand shifting services, and put forward a complete framework for their operation. Individuals, represented by rational agents, form cooperatives to offer demand shifting from peak to non-peak intervals, incentivized by the provision of a better electricity price for the consumption of the shifted peak load, similar to economy of scale schemes. We equip the cooperatives with a novel, directly applicable, and effective consumption shifting scheme, that allows for the proactive balancing of electricity supply and demand. Our scheme employs several algorithms to promote the formation of the most effective shifting coalitions. It takes into account the shifting costs of the individuals, and rewards them according to their shifting efficiency. In addition, it employs internal pricing methods that guarantee individual rationality, and allow agents with initially forbidding costs to also contribute to the shifting effort. The truthfulness of agent statements regarding their shifting behaviour is ascertained via the incorporation of a strictly proper scoring rule. Moreover, by employing stochastic filtering techniques for effective individual performance monitoring, the scheme is able to better anticipate and tackle the uncertainty surrounding the actual agent shifting actions. We provide a thorough evaluation of our approach on a simulations setting constructed over a real-world dataset. Our results clearly demonstrate the benefits arising from the use of agent cooperatives in this domain.

[1]  Violeta Pukelienė,et al.  Economy Scale Impact on the Enterprise Competitive Advantages , 2015 .

[2]  Ramachandra Kota,et al.  Cooperatives of distributed energy resources for efficient virtual power plants , 2011, AAMAS.

[3]  A. Raftery,et al.  Strictly Proper Scoring Rules, Prediction, and Estimation , 2007 .

[4]  Martin A. Riedmiller,et al.  Electricity Demand Forecasting using Gaussian Processes , 2013, AAAI Workshop: Trading Agent Design and Analysis.

[5]  Alex Rogers,et al.  A scoring rule-based mechanism for aggregate demand prediction in the smart grid , 2012, AAMAS.

[6]  Sebastian Thrun,et al.  Probabilistic robotics , 2002, CACM.

[7]  Katia P. Sycara,et al.  Mechanisms for coalition formation and cost sharing in an electronic marketplace , 2003, ICEC '03.

[8]  Lennart Söder,et al.  Distributed generation : a definition , 2001 .

[9]  Alfred Menezes,et al.  Cryptocash, cryptocurrencies, and cryptocontracts , 2015, Designs, Codes and Cryptography.

[10]  Nursyarizal Mohd Nor,et al.  A review on optimized control systems for building energy and comfort management of smart sustainable buildings , 2014 .

[11]  P. Asmus Microgrids, Virtual Power Plants and Our Distributed Energy Future , 2010 .

[12]  Katia P. Sycara,et al.  Multiagent Coordination for Energy Consumption Scheduling in Consumer Cooperatives , 2013, AAAI.

[13]  Jean Kumagai Virtual power plants, real power , 2012 .

[14]  Georgios Chalkiadakis,et al.  Predicting the Power Output of Distributed Renewable Energy Resources within a Broad Geographical Region , 2012, ECAI.

[15]  D. Kirschen,et al.  Fundamentals of power system economics , 1991 .

[16]  Michael Wooldridge,et al.  Computational Aspects of Cooperative Game Theory , 2011, KES-AMSTA.

[17]  Alma Y. Alanis,et al.  Neural Model with Particle Swarm Optimization Kalman Learning for Forecasting in Smart Grids , 2013 .

[18]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[19]  Ramachandra Kota,et al.  Cooperatives for Demand Side Management , 2012, ECAI.

[20]  Vincent W. S. Wong,et al.  Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid , 2010, IEEE Transactions on Smart Grid.

[21]  Xi Fang,et al.  3. Full Four-channel 6.3-gb/s 60-ghz Cmos Transceiver with Low-power Analog and Digital Baseband Circuitry 7. Smart Grid — the New and Improved Power Grid: a Survey , 2022 .

[22]  Wolfgang Ketter,et al.  Demand side management—A simulation of household behavior under variable prices , 2011 .

[23]  Ramachandra Kota,et al.  Cooperative Virtual Power Plant Formation Using Scoring Rules , 2012, AAAI.

[24]  Craig Boutilier,et al.  Eliciting forecasts from self-interested experts: scoring rules for decision makers , 2011, AAMAS.

[25]  Carl E. Rasmussen,et al.  A Unifying View of Sparse Approximate Gaussian Process Regression , 2005, J. Mach. Learn. Res..

[26]  Georgios Chalkiadakis,et al.  Decentralized Large-Scale Electricity Consumption Shifting by Prosumer Cooperatives , 2016, ECAI.

[27]  Sarvapali D. Ramchurn,et al.  Putting the 'smarts' into the smart grid , 2012, Commun. ACM.

[28]  B. Hobbs,et al.  When It Comes to Demand Response, Is FERC Its Own Worst Enemy? , 2009 .

[29]  Paul Resnick,et al.  Eliciting Informative Feedback: The Peer-Prediction Method , 2005, Manag. Sci..

[30]  Christian Ibars,et al.  Distributed Demand Management in Smart Grid with a Congestion Game , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[31]  Katia P. Sycara,et al.  A stable and efficient buyer coalition formation scheme for e-marketplaces , 2001, AGENTS '01.

[32]  Gregor Verbic,et al.  Towards a realistic implementation of mechanism design in demand response aggregation , 2014, 2014 Power Systems Computation Conference.

[33]  Georgios Chalkiadakis,et al.  Agent Cooperatives for Effective Power Consumption Shifting , 2013, AAAI.

[34]  Hamed Mohsenian Rad,et al.  Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments , 2010, IEEE Transactions on Smart Grid.

[35]  R. Machete Contrasting probabilistic scoring rules , 2011, 1112.4530.

[36]  Georgios Chalkiadakis,et al.  Stochastic Filtering Methods for Predicting Agent Performance in the Smart Grid , 2014, ECAI.

[37]  Dieter Fox,et al.  GP-UKF: Unscented kalman filters with Gaussian process prediction and observation models , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.