Multi-Objective Stochastic Optimal Operation of a Grid-Connected Microgrid Considering an Energy Storage System

Abstract

This paper investigates the stochastic optimal operation of microgrid considering the influence of energy storage system (ESS). The uncertain factors related to renewable energies are also fully considered. Monte Carlo simulation (MCS) and scenario reduction technique are used to capture the randomness of renewable energy output. The objective is to minimize the operation cost and pollutant treatment cost of the microgrid system. Then, the multi-objective optimization problem is transformed to a single-objective problem by a line weighted sum method. A nonlinear programming tool—General Algebraic Modeling System (GAMS)—is adopted to solve the single-objective problem. Simulation results indicate that the total cost of the microgrid system can be reduced effectively when considering the effect of ESS and verify the correctness of the proposed strategy.

Multi-Objective Based Optimal Energy Management of Grid-Connected Microgrid Considering Advanced Demand Response

“…Then, the fuzzy-clustering technique was adopted to obtain the best result among the Pareto-optimal set. Conventional PSO, velocity-controlled PSO (VCPSO) [24], and the proposed CVCPSO algorithm were compared to show the superiority of our energy management approach. The simulations were performed in MATLAB R2017b on a computer with the following specifications: CPU, 3.4 GHz; RAM, 8 GB; operating system, Windows 10 Pro 64-bit.…”

“…To link the renewable and non-renewable energy, several power conversion systems could be requested by the MG [23]. Although converters can be synchronized with the grid to control the grid current and maximum power tracking point, the efficiency of the converter was not considered in our work because the DC/AC or DC/DC conversion efficiency is negligible in the MG system [24]. Moreover, in the MG system, various types of customers are assumed to be linked with an advanced metering infrastructure that provides the information required from the MG operator.…”

“…Equation 22represents the DE generation limit, which ensures that the generator is operated between the minimum and maximum power limits. Equation 23denotes the increased or decreased DE power output per unit time t. Equations (24) and (25) represent the constraints of the minimum and maximum WT and PV generation limits, respectively.…”

Dynamic Coordinated Active–Reactive Power Optimization for Active Distribution Network with Energy Storage Systems

“…It is worth noting that, although the accuracy can be improved by increasing the number of scenarios, the computational burden will also increase greatly. Therefore, the backward scenario reduction technique [32] is utilized to obtain a representative set.…”

“…Reference [31] investigated the coordinated optimal dispatch of ESS with renewable energies in microgrids to minimize the electricity costs. Fan et al [32] analyzed the stochastic optimal operation of microgrid with ESS; the influences of ESS on the optimization results were fully investigated.…”