Dynamic Coalition Formation in Energy Micro-Grids

In recent years the notion of electrical energy micro-grids, in which communities share their locally-generated power, has gained increasing interest. Typically the energy generated comes from renewable resources, which means that its availability is variable-sometimes there may be energy surpluses and at other times energy deficits. This energy variability can be ameliorted by trading energy with a connected main electricity utility grid. But since main electricity grids are subject to faults or other outages, it can be advantageous for energy micro-grids to form coalitions and share their energy among themselves. In this work we present our model for the dynamic formation of such micro-grid coalitions. Our agent-based model, which is scalable and affords autonomy among the micro-grids participating in the coalition (agents can join and depart from coalitions at any time), features methods to reduce overall discomfort, so that even when all participating micro-grids in a coalition experience deficits; they can share energy so that overall discomfort is minimized. We demonstrate the efficacy of our model by showing empirical studies conducted with real energy production and consumption data.

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

[2]  Peter Martinsson,et al.  The Effect of Power Outages and Cheap Talk on Willingness to Pay to Reduce Outages , 2011, SSRN Electronic Journal.

[3]  Zita A. Vale,et al.  MASCEM - An Electricity Market Simulator providing Coalition Support for Virtual Power Players , 2009, 2009 15th International Conference on Intelligent System Applications to Power Systems.

[4]  Bastin Tony Roy Savarimuthu,et al.  Agent-based community coordination of local energy distribution , 2015, AI & SOCIETY.

[5]  Walid Saad,et al.  Game Theoretic Methods for the Smart Grid , 2012, ArXiv.

[6]  Onn Shehory,et al.  Coalition structure generation with worst case guarantees , 2022 .

[7]  S.D.J. McArthur,et al.  Multi-Agent Systems for Power Engineering Applications—Part I: Concepts, Approaches, and Technical Challenges , 2007, IEEE Transactions on Power Systems.

[8]  Walid Saad,et al.  Game-Theoretic Methods for the Smart Grid: An Overview of Microgrid Systems, Demand-Side Management, and Smart Grid Communications , 2012, IEEE Signal Processing Magazine.

[9]  Mark Z. Jacobson,et al.  Providing all global energy with wind, water, and solar power, Part I: Technologies, energy resources, quantities and areas of infrastructure, and materials , 2011 .

[10]  Ana L. C. Bazzan,et al.  Dynamic constrained coalition formation among electric vehicles , 2014, Journal of the Brazilian Computer Society.

[11]  Walid Saad,et al.  Coalitional Game Theory for Cooperative Micro-Grid Distribution Networks , 2011, 2011 IEEE International Conference on Communications Workshops (ICC).

[12]  Nei Kato,et al.  A survey of game theoretic approaches in smart grid , 2011, 2011 International Conference on Wireless Communications and Signal Processing (WCSP).

[13]  A. Shaalan Outages Cost Estimation for Residential Sector@@@تقدير تكاليف انقطاعات الخدمة الكهربائية للقطاع السكني , 2000 .

[14]  Shantanu Chakraborty,et al.  Scalable and optimal coalition formation of microgrids in a distribution system , 2014, IEEE PES Innovative Smart Grid Technologies, Europe.

[15]  D. S. Zinger,et al.  A variable speed wind turbine power control , 1997 .

[16]  Sudip Misra,et al.  Dynamic coalition formation in a smart grid: A game theoretic approach , 2013, 2013 IEEE International Conference on Communications Workshops (ICC).

[17]  Willett Kempton,et al.  Deploying power grid-integrated electric vehicles as a multi-agent system , 2011, AAMAS.

[18]  Peter Martinsson,et al.  Does it Matter When a Power Outage Occurs? - A Choice Experiment Study on the Willingness to Pay to Avoid Power Outages , 2008 .