Bilevel energy optimization for grid-connected AC multimicrogrids

Abstract

As a key component of an alternating current (AC) multimicrogrid (MMG), an energy management system must gather the necessary information and coordinate the corresponding equipment to optimize the economic benefits. To achieve this goal, this paper proposes a bilevel energy optimization model. The lower level model considers the real-time electricity price of distribution networks (DNs) to optimize the operation cost of each grid-connected submicrogrid (SMG). The upper level constructs an MMG optimization model based on graph theory, which optimizes the energy transmission path among the SMGs by considering the transmission losses. In this manner, the upper level model can further reduce the economic cost of an MMG by energy trading among the SMGs. To solve this model, a nested Jaya–Dijkstra algorithm is customized. Moreover, a Jaya algorithm with nonlinear variation capability (NVCJA) is developed to avoid the premature convergence problem and enhance the solution precision. The performance of the bilevel optimization strategy is evaluated considering a typical AC MMG system. For the proposed NVCJA, the convergence time is 24.07%, 39.47%, 37.76%, and 48.77% less, and the standard deviation is 12.97%, 28.71%, 23.89% and 35.92% lower than those for the Jaya, genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony (ABC) algorithms, respectively. The analysis results show that the proposed optimization algorithm exhibits a high operating speed and stability. The economic cost of the proposed bilevel optimization model is 19% and 29% lower than those of the hierarchical optimization and linear relaxation optimization models, respectively. The test results show that the proposed optimization model can effectively enhance the economic benefits of an MMG.