Distributed Stochastic Model Predictive Control for Scheduling Deterministic Peer-to-Peer Energy Transactions Among Networked Microgrids With Hybrid Energy Storage Systems

The current tendency toward increases in energy prices makes it necessary to discover new ways in which to provide electricity to end consumers. Cooperation among the various self-consumption facilities that form energy communities based on networked microgrids could be a more efficient means of managing the renewable resources that are available. However, the complexity of the associated control problem is leading to unresolved challenges from the point of view of its formulation. The optimization of energy exchanges among microgrids in the day-ahead electricity market requires the generation of an optimal profile for the purchase of energy from and sale of energy to the main grid, in addition to enabling the community to be charged for any deviation from the schedule proposed in the regulation service market. Microgrids based on renewable generation are systems that are subject to inherited uncertainties in their energy forecast whose interconnection generates a distributed control problem of stochastic systems. Microgrids are systems of subsystems that can integrate various components, such as hybrid energy storage systems (ESS), generating multiple terms to be included in the associated cost function for their optimization. In this work, the problem of solving complex distributed stochastic systems in the Mixed Logic Dynamic (MLD) framework is addressed, as is the generate of a tractable formulation with which to generate deterministic values for both exchange and output variables in interconnected systems subject to uncertainties using hybrid, stochastic and distributed Model Predictive Control (MPC) techniques.