Distributed Rolling Horizon Optimization for Energy Transactions among Multiple Microgrids

Abstract

This paper proposes a distributed rolling horizon optimization framework for energy trading problems among multiple microgrids (MMGs). The rolling horizon optimization based on model predictive control (MPC) method alleviates impacts of uncertainties in renewable energy resources and loads while the independent decision making for MMGs can be guaranteed by distributed algorithms in real time electricity markets. A mixed linear programming model with rolling timing windows is developed to describe energy transactions among MMGs. In order to enable different entities to meet their energy requirement closer to real-time operation, online alternative direction method of multipliers (ADMM) is implemented to solve the given model, in which coupled variables from energy transactions are separated by consistency constraints. In rolling timing windows, operating statuses could be updated based on real-time information to improve the precision of energy transactions. Case simulations based on different methods are presented to demonstrate the effectiveness of the proposed method in coordinating energy transactions among MMGs under uncertainties.