Multi-objective energy management strategy of unbalanced multi-microgrids considering technical and economic situations

This paper presents an energy management strategy (EMS) for unbalanced multi-micmgrids (UMGs) in the presence of active and reactive sources and active loads (ALs) to improve operation, reliability, emissions, and economic indices simultaneously. The proposed scheme is formulated as a four-objective optimization problem, in which the first to fourth functions represent the expected operating cost of UMGs and sources, expected emission level, expected energy not-supplied (EENS), and voltage deviation function, respectively. The problem is subject to optimal power flow equations and constraints of UMGs unbalance mode, reliability limits of networks, formulation of sources, and various ALs. Stochastic planning is utilized to model uncertainties of load, energy price, power generation of renewable energy sources (RESs), the energy of some ALs as electric vehicles (EVs) parking lot, and availability of network equipment. Then, the proposed multi-objective problem is converted into a single-objective formula using the Pareto optimization method based on the weighted sum. Furthermore, the hybrid algorithm based on teaching-learning-based optimization (TLBO) and grey wolf optimizer (GWO) algorithms is employed. Eventually, by implementing the suggested scheme on a sample test system, the obtained numerical results prove the capability of the scheme in improving the technical and economic situations of UMGs.

Automated summary

This paper presents an energy management strategy for unbalanced multi-microgrids, aiming to improve operation, reliability, emissions, and economic indices simultaneously through a four-objective optimization problem. By using stochastic planning and a hybrid algorithm, the proposed scheme demonstrates its capability in enhancing the technical and economic situations of UMGs.