Multi objective optimization of a fuzzy logic controller for energy management in microgrids

Abstract

This paper presents a novel power flow optimization strategy in Micro Grids (MGs) connected to the main grid. When the MG includes stochastic energy sources, such as photovoltaic and micro eolic-generators, it is very useful to rely on Energy Storage Systems (ESSs) to buffer energy. In fact, an ESS can be employed to perform several functionalities, related to different user requirements, such as power stability, peak shaving, optimal energy trading, etc. The Energy Management System is based on a Fuzzy Logic Controller (FLC) optimized by a Multi-Objective Genetic Algorithm in order to maximize both the total profit in energy trading with the main grid and the State of Health (SOH) of the ESS. The FLC manages the neat aggregate energy deficit and surplus inside the MG, analyzing in real time the state of the MG (aggregated energy demand and production, State of Charge of the ESS, energy sale and purchase prices). The FLC is tested on a MG composed by a photovoltaic solar generator, a domestic user and a Li-ion battery. A multi-objective genetic algorithm is in charge to find the set of solutions on the Pareto front. The results are compared with the same FLC optimized by a mono-objective Genetic Algorithm (GA) minimizing in a first case only the total profit and in the second case a convex linear combination of the total profit and a measure of the battery stress

Energy management in smart grids for the integration of hybrid wind–PV–FC–battery renewable energy resources using multi‐objective particle swarm optimisation (MOPSO)

“…As there is no need of the FC, on the grounds that, all the power request is fulfilled with wind/PV, so its output power is zero all through this interim amid hours 7–20. Table 12 demonstrates comparison results of various optimisation algorithms: evolutionary programming (EP) [53], SOGA [54], SOPSO [41], enhanced bee colony optimisation (EBCO) [43], MOGA [55], and the proposed MOPSO algorithm during different operating modes. It can be observed that the proposed MOPSO optimisation strategy gives the optimum performance in terms of obtained optimum solutions for the problem, the minimum number of iterations required to reach to the optimum solutions.…”

Towards Smart Railways: A Charging Strategy for Railway Energy Storage Systems

“…A multi-objective system using an FLC for energy management, as proposed in [37] and extended in [38] and in [39], is a real-time charging strategy, with the rule weights and Membership Function (MF) parameters being the search space of the optimization algorithm.…”

“…Based on [38], there are two possible objectives for the charging strategy: (i) the financial objective function, purely based on the cost of buying/selling energy in different times; and (ii) the battery stress level, to represent the physical degradation of the battery.…”

Energy Management Optimization Techniques for Hybrid Renewable Energy Systems

“…This proposed technique improves the microgrid profits without affecting the BESS. The Energy Management System of this paper [19] includes a Fuzzy Logic Controller (FLC) which uses Multi-Objective Genetic Algorithm for optimization. It improves the net profit of the generating units and state of health of batteries.…”