Optimization of a microgrid energy management system based on a Fuzzy Logic Controller

Abstract

This paper presents a novel power flow optimization strategy for a Grid Connected microgrid (MG) equipped with a Battery Energy Storage System (BESS), namely a Li-Ion battery pack. A BESS can be employed to perform several functionalities, related to different user requirements, such as power stability, peak shaving, optimal energy trading, etc. In the proposed system the MG is composed by an aggregation of distributed power generators and loads and a BESS is adopted to manage the power over-production/over-demand in real time, in order to maximize the prosumer profit looking at the current energy prices and the BESS State of Charge (SOC). The Energy Management System (EMS) is based on a Fuzzy Logic Controller (FLC) with a suitable rule inference system designed by an Expert Operator (EO). The control strategy is tested with different power profiles and BESS capacities in order to verify its effectiveness and limits. Furthermore, the FLC has been optimized by a Genetic Algorithm to increase the total profit exploiting the BESS as energy buffer. The optimization results have been compared to the initial FLC designed by the EO, taking into account both the profit and the deterioration of the BESS measured through a suitable battery stress index

Greenhouse gas emission reduction system in photovoltaic nanogrid with battery and thermal storage reservoirs

“…Several studies have developed on-line (real-time) optimization solutions. Among others, Leonori et al (2016) presented an approach to enhance energy trading tasks and maximize the prosumer gain from an electricity price viewpoint (by deciding when to charge/discharge the battery). Arun and Selvan (2017) propose an online algorithm to minimize the electricity bill by taking advantage of low electricity pricing intervals.…”

Optimization strategies for Microgrid energy management systems by Genetic Algorithms

“…As stated in [50], such loss of accuracy can be reduced by nature inspired methods such as Evolutionary Algorithms (EAs) which would preserve the FIS interpretability. For example, in [62] and [63] the effectiveness in tuning the EMS FISs Membership Function (MFs) and Rule Consequences through EAs was analysed. In [30,43,64], a FIS EMSs has been modelled by means of a k-means based clustering algorithm.…”

“…Following previous works [62,65], in this paper it is proposed a FIS based EMS able to manage in real time the energy flows of a grid connected MG equipped with an ESS. In particular, it is in charge to define how to split the prosumer energy balance between a local ESS and the connected grid in order to maximize the profit generated by the energy trading with the main grid, assuming a Time Of Use (TOU) energy price policy.…”

Energy Management Optimization Techniques for Hybrid Renewable Energy Systems

“…Similarly a 9.10% of total price and 21.13% of peak power was achieved in terms of minimum peak power criteria. (as in Table-II)In this paper [18], the author proposed a system for a microgrid which consists of combination of distributed generators of power and the loads. To meet the surplus or deficient power, battery energy storage system is being used.…”