Optimal Transactive Energy Management in Microgrids

Abstract

The combination of renewable energy generation systems and battery energy storage system (BESS) in a microgrid is a promising solution for the rapid increase in electricity demand and the decline of fossil fuel sources. To gain competency in the present market, microgrids are actively connected to the grid and optimally controlled in order to avoid unnecessary usage fees due to the variability of renewable energy generation. Here, the challenge rests with the imbalance of dynamic power demand and renewable power generation with consideration of variable energy pricing conditions. This research work focuses on the modeling and design of an optimal transactive energy management system (EMS) to minimize the electricity bill of a commercial building supplied with a microgrid. Following a comprehensive literature survey on relevant topics, the first phase of this work refines the models of a realworld building microgrid equipped with power electronic converters. Incorporating different kWh pricing and feed-in tariff values, the building energy cost is cast as a multiobjective optimization problem subject to variable constraints. In the second phase of this work, effective control and optimization schemes are developed for optimal transactive energy management of the microgrid and dealing with nonlinearities associated with energy conversion losses. Here, a particle swarm optimization (PSO) and a model predictive control (MPC) approach based on the mixed integer linear programming (MILP) are utilized in an optimal EMS for minimizing the electricity bill of the building’s on-grid system. As compared with other meta-heuristic algorithms, the PSO method, on one hand, provides an effective solution, particularly in handling multi-objective, dynamic and constraints. On the other hand, PSO suffers from high computational time and local optima. As MILP is mostly based on the branch-and-bound algorithm, which more likely reaches a global optimum solution, the combined MILP-MPC strategy is used in this work to achieve optimal EMS in the microgrid. In this regard, the proposed strategy is formulated as a MILP-MPC problem subject to time-varying constraints. The constraints are then linearized at each sampling time so that the receding horizon principle can be used to determine the control input applied to the plant and update the system model. In this work, the efficiency of power converters is considered time-varying and evaluated for each time interval persistently for the prediction time horizon. Performance of the proposed EMS using both PSO and MILP-MPC is verified through the extensive simulation results of the microgrid in consideration.