A Nash–Stackelberg game approach to analyze strategic bidding for multiple DER aggregators in electricity markets

[1]  Mingbo Liu,et al.  Multiplayer Nash–Stackelberg Game Analysis of Electricity Markets With the Participation of a Distribution Company , 2023, IEEE Systems Journal.

[2]  Linquan Bai,et al.  Stochastic Strategic Participation of Active Distribution Networks With High-Penetration DERs in Wholesale Electricity Markets , 2023, IEEE Transactions on Smart Grid.

[3]  Zhi-nong Wei,et al.  Coordinating Urban Power-Traffic Networks: A Subsidy-Based Nash–Stackelberg–Nash Game Model , 2023, IEEE Transactions on Industrial Informatics.

[4]  Marthe Fogstad Dynge,et al.  Distributed energy resource participation in electricity markets: A review of approaches, modeling, and enabling information and communication technologies , 2022, Energy Strategy Reviews.

[5]  S. Shojaabadi,et al.  A game theory-based price bidding strategy for electric vehicle aggregators in the presence of wind power producers , 2022, Renewable Energy.

[6]  Yizhou Zhou,et al.  Bidding strategy for a prosumer aggregator with stochastic renewable energy production in energy and reserve markets , 2022, Renewable Energy.

[7]  Li-hui Zhang,et al.  Trading strategy and benefit optimization of load aggregators in integrated energy systems considering integrated demand response: A hierarchical Stackelberg game , 2022, Energy.

[8]  Mingbo Liu,et al.  Shared-constraint approach for multi-leader multi-follower game between generation companies and independent system operator with carbon emission trading mechanism , 2022, Journal of Cleaner Production.

[9]  Tao Jiang,et al.  Optimal participation of ADN in energy and reserve markets considering TSO-DSO interface and DERs uncertainties , 2022, Applied Energy.

[10]  Jianjian Shen,et al.  Impacts, challenges and suggestions of the electricity market for hydro-dominated power systems in China , 2022, Renewable Energy.

[11]  J. Iria,et al.  MV-LV network-secure bidding optimisation of an aggregator of prosumers in real-time energy and reserve markets , 2021, Energy.

[12]  Vahid Talavat,et al.  A Risk-based Competitive Bi-level Framework for Operation of Active Distribution Networks with Networked Microgrids , 2021, Journal of Modern Power Systems and Clean Energy.

[13]  Ali Ahmadian,et al.  Optimal bidding strategy of a virtual power plant in day-ahead energy and frequency regulation markets: A deep learning-based approach , 2021 .

[14]  Mohammad Shahidehpour,et al.  Optimal Operation of Energy Hubs With Large-Scale Distributed Energy Resources for Distribution Network Congestion Management , 2021, IEEE Transactions on Sustainable Energy.

[15]  Xin Ai,et al.  Coordinated Energy Management of Prosumers in a Distribution System Considering Network Congestion , 2021, IEEE Transactions on Smart Grid.

[16]  Joao P. S. Catalao,et al.  Bi-level optimization model for the coordination between transmission and distribution systems interacting with local energy markets , 2021, International Journal of Electrical Power & Energy Systems.

[17]  Pierluigi Siano,et al.  Day-ahead optimal bidding and scheduling strategies for DER aggregator considering responsive uncertainty under real-time pricing , 2020 .

[18]  Hanchen Xu,et al.  Fundamentals and business model for resource aggregator of demand response in electricity markets , 2020 .

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

[20]  Ioannis P E Pistikopoulos,et al.  DOMINO: Data-driven Optimization of bi-level Mixed-Integer NOnlinear Problems , 2020, Journal of global optimization : an international journal dealing with theoretical and computational aspects of seeking global optima and their applications in science, management and engineering.

[21]  Haiwang Zhong,et al.  Incentivizing distributed energy resource aggregation in energy and capacity markets: An energy sharing scheme and mechanism design , 2019, Applied Energy.

[22]  Yajun Leng,et al.  A Nash-Stackelberg game approach in regional energy market considering users’ integrated demand response , 2019, Energy.

[23]  Mohammad Shahidehpour,et al.  DER Aggregator’s Data-Driven Bidding Strategy Using the Information Gap Decision Theory in a Non-Cooperative Electricity Market , 2019, IEEE Transactions on Smart Grid.

[24]  Anthony Papavasiliou,et al.  A game-theoretic analysis of transmission-distribution system operator coordination , 2019, Eur. J. Oper. Res..

[25]  Pierluigi Siano,et al.  Networked Stackelberg Competition in a Demand Response Market , 2019, Applied Energy.

[26]  Farshid Nazari,et al.  Multi‐leader–follower game theory for modelling interaction between virtual power plants and distribution company , 2018, IET Generation, Transmission & Distribution.

[27]  Pierre Pinson,et al.  Trading strategies for distribution company with stochastic distributed energy resources , 2016 .

[28]  Boming Zhang,et al.  An Exact Linearization Method for OLTC of Transformer in Branch Flow Model , 2016, IEEE Transactions on Power Systems.

[29]  Sairaj V. Dhople,et al.  Scalable Optimization Methods for Distribution Networks With High PV Integration , 2016, IEEE Transactions on Smart Grid.

[30]  A. Conejo,et al.  Short-Term Trading for a Wind Power Producer , 2010, IEEE Transactions on Power Systems.

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

[32]  T. Dragičević,et al.  Smart grid evolution: Predictive control of distributed energy resources—A review , 2023, International Journal of Electrical Power & Energy Systems.

[33]  J. Zhang,et al.  Bi-level Optimal Operation Model of Mobile Energy Storage System in Coupled Transportation-power Networks , 2022, Journal of Modern Power Systems and Clean Energy.

[34]  Jiangfeng Zhang,et al.  Optimal scheduling of a hybrid AC/DC multi-energy microgrid considering uncertainties and Stackelberg game-based integrated demand response , 2022, International Journal of Electrical Power & Energy Systems.