The value of aggregators in local electricity markets: A game theory based comparative analysis

Abstract Demand aggregators are expected to have a key role in future electricity systems. More specifically, aggregators can facilitate the harnessing of consumers’ flexibility. This paper focuses on understanding the value of the aggregator in terms of aggregation of both flexibility and information. We consider the aggregation of flexibility as the ability to exercise a direct control over loads, while the aggregation of information refers to knowledge of the flexibility characteristics of the consumers. Several game theory formulations are used to model the interaction between the energy provider, consumers and the aggregator, each with a different information structure. We develop a potential game to obtain the Nash equilibrium of the non-cooperative game with complete information and we analyze the system dynamics of consumers using the adaptive expectations method in an incomplete information scenario. Several key insights about the value of aggregators are found. In particular, the value of the aggregator is mainly related to the aggregation of information rather than flexibility, and flexibility is valuable only when it can be coordinated. In this sense, prices are not enough to guarantee an effective coordination.

[1]  Phuong H. Nguyen,et al.  Local electricity market design for the coordination of distributed energy resources at district level , 2014, IEEE PES Innovative Smart Grid Technologies, Europe.

[2]  Christos V. Verikoukis,et al.  A Survey on Demand Response Programs in Smart Grids: Pricing Methods and Optimization Algorithms , 2015, IEEE Communications Surveys & Tutorials.

[3]  Anna Scaglione,et al.  How will demand response aggregators affect electricity markets? — A Cournot game analysis , 2012, 2012 5th International Symposium on Communications, Control and Signal Processing.

[4]  Pierre Pinson,et al.  A DSO-Level Contract Market for Conditional Demand Response , 2019, 2019 IEEE Milan PowerTech.

[5]  Behnam Mohammadi-Ivatloo,et al.  A Bayesian game theoretic based bidding strategy for demand response aggregators in electricity markets , 2020 .

[6]  Farhad Samadi Gazijahani,et al.  Game Theory Based Profit Maximization Model for Microgrid Aggregators With Presence of EDRP Using Information Gap Decision Theory , 2019, IEEE Systems Journal.

[7]  Stefanos Delikaraoglou,et al.  Offering Strategy of a Flexibility Aggregator in a Balancing Market Using Asymmetric Block Offers , 2018, 2018 Power Systems Computation Conference (PSCC).

[8]  Tansu Alpcan,et al.  Game-Theoretic Frameworks for Demand Response in Electricity Markets , 2015, IEEE Transactions on Smart Grid.

[9]  Chongqing Kang,et al.  Modeling and algorithm to find the economic equilibrium for pool-based electricity market with the changing generation mix , 2015, 2015 IEEE Power & Energy Society General Meeting.

[10]  Christos H. Papadimitriou,et al.  Worst-case equilibria , 1999 .

[11]  Heresh Seyedi,et al.  Optimal operation of smart distribution networks in the presence of demand response aggregators and microgrid owners: A multi follower Bi-Level approach , 2020 .

[12]  Goran Strbac,et al.  Demand side management: Benefits and challenges ☆ , 2008 .

[13]  Vincent W. S. Wong,et al.  Advanced Demand Side Management for the Future Smart Grid Using Mechanism Design , 2012, IEEE Transactions on Smart Grid.

[14]  Chen Chen,et al.  A Cournot game analysis on market effects of queuing energy request as demand response , 2012, 2012 IEEE Power and Energy Society General Meeting.

[15]  Joshua A. Taylor Convex Optimization of Power Systems , 2015 .

[16]  G. Strbac,et al.  Decentralized Participation of Flexible Demand in Electricity Markets—Part I: Market Mechanism , 2013, IEEE Transactions on Power Systems.

[17]  Mohammed H. Albadi,et al.  A summary of demand response in electricity markets , 2008 .

[18]  Ali Kakhbod,et al.  Competition in Electricity Markets with Renewable Energy Sources , 2017 .

[19]  Gian Italo Bischi,et al.  Equilibrium selection in a nonlinear duopoly game with adaptive expectations , 2001 .

[20]  Sairaj V. Dhople,et al.  Power Systems Without Fuel , 2015, ArXiv.

[21]  Rachid Cherkaoui,et al.  Coordinating strategic aggregators in an active distribution network for providing operational flexibility , 2020 .

[22]  Seth Blumsack,et al.  Electricity rates for the zero marginal cost grid , 2019, The Electricity Journal.

[23]  Chongqing Kang,et al.  Reformulation for Nash-Cournot equilibrium in pool-based electricity market supported by introducing the potential function , 2015, 2015 IEEE Eindhoven PowerTech.

[24]  José Pablo Chaves-Ávila,et al.  A review of the value of aggregators in electricity systems , 2017 .

[25]  Jiming Chen,et al.  A Survey on Demand Response in Smart Grids: Mathematical Models and Approaches , 2015, IEEE Transactions on Industrial Informatics.

[26]  J. Goodman Note on Existence and Uniqueness of Equilibrium Points for Concave N-Person Games , 1965 .

[27]  Ali Mohammad Ranjbar,et al.  Balancing management of strategic aggregators using non-cooperative game theory , 2020 .

[28]  Tapan Kumar Saha,et al.  Modelling demand response aggregator behavior in wind power offering strategies , 2014 .

[29]  Reza Ghorbani,et al.  Non-cooperative game-theoretic model of demand response aggregator competition for selling stored energy in storage devices , 2017 .

[30]  Majid Nili Ahmadabadi,et al.  Model-Based and Learning-Based Decision Making in Incomplete Information Cournot Games: A State Estimation Approach , 2015, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

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

[32]  Hamed Mohsenian Rad,et al.  Vehicle-to-Aggregator Interaction Game , 2012, IEEE Transactions on Smart Grid.

[33]  Mehdi Abapour,et al.  Short-term scheduling of electricity retailers in the presence of Demand Response Aggregators: A two-stage stochastic Bi-Level programming approach , 2020 .

[34]  Marco Pangallo,et al.  Best reply structure and equilibrium convergence in generic games , 2017, Science Advances.

[35]  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.

[36]  L. Shapley,et al.  REGULAR ARTICLEPotential Games , 1996 .

[37]  Lazaros Gkatzikis,et al.  The Role of Aggregators in Smart Grid Demand Response Markets , 2013, IEEE Journal on Selected Areas in Communications.

[38]  Duncan S. Callaway,et al.  Real-Time Charging Strategies for an Electric Vehicle Aggregator to Provide Ancillary Services , 2018, IEEE Transactions on Smart Grid.

[39]  Daniel E. Olivares,et al.  Participation of Demand Response Aggregators in Electricity Markets: Optimal Portfolio Management , 2018, IEEE Transactions on Smart Grid.

[40]  Manying Bai,et al.  Chaos Control on a Duopoly Game with Homogeneous Strategy , 2016 .

[41]  Esther Mengelkamp,et al.  A comprehensive modelling framework for demand side flexibility in smart grids , 2018, Computer Science - Research and Development.