Hierarchical Optimization Method for Energy Scheduling of Multiple Microgrids

Abstract

This paper proposes a hierarchical optimization method for the energy scheduling of multiple microgrids (MMGs) in the distribution network of power grids. An energy market operator (EMO) is constructed to regulate energy storage systems (ESSs) and load demands in MMGs. The optimization process is divided into two stages. In the first stage, each MG optimizes the scheduling of its own ESS within a rolling horizon control framework based on a long-term forecast of the local photovoltaic (PV) output, the local load demand and the price sent by the EMO. In the second stage, the EMO establishes an internal price incentive mechanism to maximize its own profits based on the load demand of each MG. The optimization problems in these two stages are solved using mixed integer programming (MIP) and Stackelberg game theory, respectively. Simulation results verified the effectiveness of the proposed method in terms of the promotion of energy trading and improvement of economic benefits of MMGs.

Special Issue on Microgrids

“…While [1][2][3] focus on a single microgrid, [4] proposes a hierarchical optimization method for the energy scheduling of multiple microgrids connected to the distribution grid with participation in the energy market. The optimization procedure is separated into two stages.…”

A review on optimization strategies integrating renewable energy sources focusing uncertainty factor – Paving path to eco-friendly smart cities

“…Author of study observed that current MG applications are not able to manage the uncertainty and variability of energy resources, diversity of RES and suggested advanced stochastic algorithm, predictive analytics and no linear schemes for future MGs Due to the penetration of RES and the increasing number of DER data compatibility and exchange are emerging as major challenges for implementation of dynamic MG capabilities. Rui et al [116] proposed the two stage optimization method to address the uncertainties of RES and load. Energy market operator (EMO) is the upper level manager of multi MGs and MG operator (MGO) is the lower level manager of MGs in the proposed optimization model.…”

Robust Energy Management and Economic Analysis of Microgrids Considering Different Battery Characteristics

“…The autoregressive integrated moving average model was applied to obtain the predicted value of electrical loads and PV units. Rui et al [32] developed a mixed integer programming model and game model for optimal energy scheduling of multiple MGs. Mazzola et al [33] proposed a framework to determine the optimal energy scheduling of isolated rural MGs considering forecast-based dispatch in the MGs operation using a normalized root-mean-square error approach.…”