WSEAS Transactions on Systems
Print ISSN: 1109-2777, E-ISSN: 2224-2678
Volume 23, 2024
Multi-Objective Optimization for Load Balancing and Trading Scheduling in Networked Microgrids
Authors: , , ,
Abstract: Renewable energy resources are considered an integral part of networked microgrids. Networked microgrids provide an optimal solution for stable and reliable energy to the end consumers even in case of its islanded mode of operation. There are several explicit analytical mathematical formulations for optimization of operations of networked microgrids including load balancing and trade scheduling of energy. Mostly, the scheduling problem in networked microgrids is the job of distribution operators to use common scheduling tools. In this paper, we present the mathematical formulation of multi-objective cost functions and their optimization by using the multi-objective particle swarm optimization (MOPSO) algorithm. Multi-objective cost functions and duality gap are designed and then the problem is solved by introducing MOPSO which has more than one objective function. The total energy cost function is then optimized in order to get swarm swarm-optimal solution. This algorithm computed the local energy generation and demand of each networked microgrid and made the decision of what energy is needed to buy or sell supported by each iteration of the operation of networked microgrids. We used an optimization formulation and its dual conception in order to propose the problem as a multi-objective formulation. The proposed method can deal with convex and nonconvex cost functions. In the case of non-convex functions, we have used the uncertainty cost functions for renewable sources attached to the networked microgrids.
Search Articles
Keywords: Networked Microgrids, Load balance, Distributed Convex Optimization, Duality Gap, Multiobjective Function, Energy Trading
Pages: 339-353
DOI: 10.37394/23202.2024.23.38