Multi-objective optimal energy management of storage system and distributed generations via water cycle algorithm concerning renewable resources uncertainties and pollution reduction

Abstract

This paper intends to analyze micro-grid energy management optimally with consideration of renewable resources, and taking into account the objectives such as pollution decrease and uncertainty, as well as economic objective. Accordingly, the micro-grid has been optimized with assumption of resources like solar and wind energy, and the use of fuel cells, micro turbines and storage batteries with the purpose of minimization of energy consumption price accompanied by reduction of the amounts of emitted pollutants in a micro-grid. The optimization process aims to determine how to charge and discharge storage elements by considering the type of load, and to determine the amount of micro turbine production, and fuel cell usage, as well as energy exchange with the upstream network according to the daily energy price. In this study, water cycle algorithm (WCA) utilization for energy consumption smart optimization has been considered, and besides, the use of probabilistic scenarios intended for modeling the renewable resources and the load uncertainty has been taken into account. From the derived results, it can be concluded that, once economic objective is considered, the optimizer effort is to deliver the load at the minimum cost, leading to increments in the pollution emission in this case. On the other hand, in the case of considering the emission function merely, the reverse will occur. If the mentioned functions are considered concurrently, a balance can be detected between in-network production and the upstream grid received energy with the aim of maintaining the pollutants emission and the costs at a reasonable amount. As a final point, the results derived from the presented method are analyzed against the operation costs of the micro-grid, acquired via mixed-integer linear programming(MILP), genetic algorithm(GA), and particle swarm optimization(PSO), authenticating the scheme precision.