Distributed multi-step power scheduling and cost allocation for cooperative microgrids

Microgrids are self-sufficient small-scale power grid systems that can employ renewable generation sources and energy storage devices and can connect to the main grid or operate in a stand-alone mode. Most research on energy-storage management in microgrids does not take into account the dynamic nature of the problem and the need for fully-distributed, multi-step scheduling. First, we address these requirements by extending our previously proposed multi-step cooperative distributed energy scheduling (CoDES) algorithm to include both purchasing power from and selling the generated power to the main grid. Second, we model the microgrid as a multi-agent system where the agents (e.g. households) act as players in a cooperative game and employ a distributed algorithm based on the Nash Bargaining Solution (NBS) to fairly allocate the costs of cooperative power management (computed using CoDES) among themselves. The dependency of the day-ahead power schedule and the costs on system parameters, e.g., the price schedule and the user activity level (measured by whether it owns storage and renewable generation devices), is analyzed for a three-agent microgrid example.

[1]  Konstantin Avrachenkov,et al.  Cooperative network design: A Nash bargaining solution approach , 2015, Comput. Networks.

[2]  Xiaorong Xie,et al.  Distributed Optimal Energy Management in Microgrids , 2015, IEEE Transactions on Smart Grid.

[3]  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.

[4]  Reza Olfati-Saber,et al.  Consensus and Cooperation in Networked Multi-Agent Systems , 2007, Proceedings of the IEEE.

[5]  Tomohiko Kawamori,et al.  Nash bargaining solution under externalities , 2016, Math. Soc. Sci..

[6]  David Simchi-Levi,et al.  Network Cost Allocation Games on Growing Electricity Grids , 2014 .

[7]  Aranya Chakrabortty,et al.  Game-Theoretic Multi-Agent Control and Network Cost Allocation Under Communication Constraints , 2016, IEEE Journal on Selected Areas in Communications.

[8]  R. Myerson Conference structures and fair allocation rules , 1978 .

[9]  M. Gloria Fiestras-Janeiro,et al.  Cooperative games and cost allocation problems , 2011 .

[10]  Walid Saad,et al.  Author manuscript, published in "IEEE Transactions on Wireless Communications (2009) Saad-ITransW-2009" A Distributed Coalition Formation Framework for Fair User Cooperation in Wireless Networks , 2022 .

[11]  Aranya Chakrabortty,et al.  Ensuring economic fairness in wide-area control for power systems via game theory , 2016, 2016 American Control Conference (ACC).

[12]  Daniel Pérez Palomar,et al.  Noncooperative and Cooperative Optimization of Distributed Energy Generation and Storage in the Demand-Side of the Smart Grid , 2013, IEEE Transactions on Signal Processing.

[13]  Jian-Xin Xu,et al.  Consensus based approach for economic dispatch problem in a smart grid , 2013, IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society.

[14]  Mohamed E. El-Hawary,et al.  The Smart Grid—State-of-the-art and future trends , 2014, 2016 Eighteenth International Middle East Power Systems Conference (MEPCON).

[15]  H. Farhangi,et al.  The path of the smart grid , 2010, IEEE Power and Energy Magazine.

[16]  Jakob Stoustrup,et al.  Distributed flexibility characterization and resource allocation for multi-zone commercial buildings in the smart grid , 2015, 2015 54th IEEE Conference on Decision and Control (CDC).

[17]  Yuan Zhang,et al.  Cooperative distributed scheduling for storage devices in microgrids using dynamic KKT multipliers and consensus networks , 2015, 2015 IEEE Power & Energy Society General Meeting.

[18]  Mo-Yuen Chow,et al.  Consensus-based distributed scheduling for cooperative operation of distributed energy resources and storage devices in smart grids , 2016 .

[19]  Walid Saad,et al.  Integrating energy storage into the smart grid: A prospect theoretic approach , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[20]  Aranya Chakrabortty,et al.  Sparsity-Constrained Games and Distributed Optimization with Applications to Wide-Area Control of Power Systems , 2016, ArXiv.