Sizing of Microgrid System Including Multi-Functional Battery Storage and Considering Uncertainties

Abstract

Battery storage units (BSUs) are usually used to perform a single function in most planning studies related to microgrids (MGs). This paper presents an effective methodology to use the BSUs to perform multi-function including supply/demand matching and energy arbitrage. This is done according to a system policy containing all possible scenarios to fully utilize the BSUs to maximize the benefit. In the proposed work, the optimal sizing of the MG system under study containing wind turbines (WTs), photovoltaic system (PV), BSUs, and diesel units (DUs) is obtained. The main objectives of the proposed methodology are; 1) minimizing the total costs of the MG, 2) minimizing the harmful gas emissions, and 3) minimizing the accumulated power difference between the generation from renewable energy systems (RESs) and the demand. Due to the stochastic behavior of the output from the RESs, the uncertainties of wind speed, solar irradiance, and temperature are considered in the study. Two modes of operation of the MG (grid-connected and islanded) and the demand side management (DSM) are also considered. The problem is formulated as a constrained nonlinear optimization problem and is solved using two metaheuristic optimization algorithms, Moth-Flame Optimization (MFO) and Hybrid Firefly and Particle Swarm Optimization (HFPSO). Moreover, the uncertainties in the different parameters are considered by using the Latin Hypercube Sampling (LHS) method to generate samples of wind speed, solar irradiance, and temperature. To examine the proposed methodology, different case studies are presented and discussed. Moreover, the results of the used two algorithms, MFO and HFPSO, are compared to show their effectiveness in solving the proposed problem and assure the optimal solution. The optimization problem is implemented and solved using MATLAB software.

A Comprehensive Review of Microgrid Energy Management Strategies Considering Electric Vehicles, Energy Storage Systems, and AI Techniques

“…The optimal adjusting factor of 1.761 produces the lowest total net present value of 200,653 USD, thereby reducing overall operation expenses for the duration of the project. A new methodology is tested in [83] for using battery storage units (BSUs) in microgrids (MGs) to perform energy arbitrage and supply/demand matching. The goal to reduce power discrepancies between demand and renewable energy systems (RESs) and gas emissions is addressed by the authors.…”

An Iterative Heuristic Optimization Method for the optimum Sizing of Battery Energy Storage System to augment the dispatchability of Wind Generators

“…However, the actual sequence of charging and discharging cycles may not support this hypothesis. There are several articles where authors have presented their work on optimizing the size of the BESS and the capacity of the RESs in respective microgrids, implementing optimization tools like GA [9], Bat algorithm [10], MILP [11], and PSO algorithm [12], GAMS [13], Moth-Flame Optimization (MFO) [14]. These research works formulate the objective function which refers to the cost comprised of investment cost, generation cost, and line-loss cost in the presence of various sources like DG set, SPV, WT along with the BESS cost.…”

A systematic decision-making approach to optimizing microgrid energy sources in rural areas through diesel generator operation and techno-economic analysis: A case study of Baron Technopark in Indonesia

“…However, these systems were not as effective as the Proposed System 4 with the lowest excess energy due to the possibility of increased losses which could indirectly reduce energy efficiency and escalate energy costs. It is widely acknowledged that configuring and sizing the microgrid system in accordance with load requirements is important to ensure efficient resource utilization, cost minimization, and emission reduction (Ibrahim et al, 2023;Meng et al, 2022).…”