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Citation: Aldosari O, Ali ZM, Abdel Aleem SHE, Mostafa MH (2024) Optimizing microgrid performance: Strategic integration of electric vehicle charging with renewable energy and storage systems for total operation cost and emissions minimization. PLoS ONE 19(10): e0307810. https://doi /10.1371/journal.pone.0307810
Editor: Yu Zhou, Inner Mongolia University, CHINA
Received: February 28, 2024; Accepted: July 11, 2024; Published: October 3, 2024
Data Availability: All relevant data are within the manuscript.
Funding: This project was supported by the Deanship of Scientific Research at Prince Sattam bin Abdulaziz University under the research project (PSAU/2023/01/25194). The contributions of the funder to this publication include supervision, approving the methodology, and reviewing the paper.
Competing interests: The authors have no relevant financial or non-financial interests to disclose. The authors have no competing interests to declare that are relevant to the content of this article.
Within the context of integrating traffic networks and power grids, Lixun et al. [37] developed a comprehensive evaluation system and methodology for EV charging networks. Initially, an EV travel model is constructed, utilizing a trip probability matrix to analyze the geographical and temporal characteristics of EV usage. Subsequently, the interconnectedness among users, the road network, the power grid, and the charging infrastructure is examined. The study proposes four critical criteria: user feedback, the operational impact on the road network, the functionality of the charging network, and the influence on the power grid''s operation. For each criterion, specific evaluation indices are established, culminating in a holistic evaluation index system.
Subramaniam and Singh [38] detailed a strategic optimization approach for the charging of EVs and the selection of optimal installation sites. The main objective is to develop a charging network that is cost-effective while maintaining the operational integrity of the distribution network. The methodology addresses these challenges by employing renewable energy sources and meta-heuristic algorithms for optimal planning, taking into consideration the impacts of various factors. Consequently, this study introduces a novel perspective on managing the distribution of RES and charging station challenges, advocating for a multi-objective approach that incorporates the characteristics of charging stations.
Wenchao et al. [39] proposed a methodology to ascertain the optimal number of charging stations alongside a pricing strategy for EVs, considering various configurations of component commonality. The study explored four distinct scenarios characterized by differing common components and levels of quality. For each scenario, the optimal quantity of charging stations and the corresponding EV pricing were determined. Subsequently, through numerical simulations, the researchers evaluated the most favorable solutions and manufacturer profits across these scenarios, yielding insightful managerial implications.
In this study, the optimal EM of an μG, encompassing PVs, FCs, WTs, ESSs, and EVCSs, is optimized using the innovative krill herd algorithm (KHA), an algorithm that has garnered significant attention. This paper delivers an optimization strategy for managing the energy of an μG to fulfill multi-objective functions, including the minimization of total operational costs, maximization of BSS profits, and reduction of total emissions. The novelty of this work can be summarized as follows:
The main contributions of this research are outlined as follows:
Fig 2 illustrates the framework of the proposed energy management strategy. In this structure, the μG central control (μGCC) gathers energy pricing proposals from various generation units [42], taking into account the main grid''s energy market price and operational constraints of both the generation units and the μG itself. Furthermore, it incorporates the concept of vehicle-to-grid (V2G) energy transfer, leveraging the storage capacity of EVs [43]. The diagram also showcases the dynamic interactions within the modern grid model, highlighting bidirectional power flow between the EV and the power grid, as well as between the BSS and the power grid.
Wind serves as the fundamental energy source for these turbines, as they convert wind energy into electrical power. The power generation capacity of these turbines is directly proportional to the speed of the wind [46].(2)where, Pwt,t and denote the output power and the rated power of the WTs respectively. The terms and correspond to the wind speed at the current time step and the WT''s rated wind speed, respectively. Moreover, and refer to the minimum and maximum wind speeds at which the WTs start and stop operating, respectively [47].
A fuel cell (FC) is an electrochemical apparatus that generates electrical power through the reaction of specific substances. One prevalent model, known as the polymer electrolyte membrane (PEM) fuel cell, PEMFC, harnesses a chemical process involving hydrogen and oxygen to produce electricity, with water as a byproduct [48]. This PEMFC is characterized by its semipermeable membrane, which facilitates the movement of protons but prevents electrons from passing through, thus distinguishing it as a proton-exchange membrane fuel cell due to its unique operation [49].
In the operation of the FC, hydrogen atoms are separated into protons and electrons upon arrival at the anode side. The electrons then take a path through an external circuit, generating electrical current, whereas the protons move directly through the membrane to the other side [50]. At the cathode end, the reunion of protons and electrons with incoming oxygen results in the production of heat and water, completing the chemical process. FCs, by bypassing the need for the combustion of traditional fuels, avoid the pollution associated with conventional fuel burning, thereby representing a sustainable and clean energy source. Fig 3 illustrates the fundamental principles of a PEMFC''s operation [51].
The Nernst equation relates the cell voltage (E) of the FC to the standard cell potential (E0), the gas constant (R), the temperature (T) in Kelvin, the Faraday constant (F), number of electrons transferred in the reaction (n) and the concentrations of reactants and products [52], where Q represents the reaction quotient (ratio of product concentrations to reactant concentrations).
The relationship between power (Pfc,t) in watts, cell voltage in the FC (voltage across the FC) and I represents the electric current flowing through the FC can be described by the following equation [53] and:(4)
Rechargeable batteries, also known as energy storage devices, play a vital role in storing energy derived from both renewable and non-renewable sources for subsequent use [54]. These devices, functioning on direct current (DC), effectively bridge the gap between energy supply and demand by releasing stored electrochemical energy as required to meet electrical demands. BSS emerge as a pivotal technology for μGs in light of the increasing reliance on RESs and the global ambition to achieve net-zero carbon emissions. The deployment of BSS is instrumental in advancing toward net-zero energy goals, offering a critical pathway towards the adoption of green energy solutions [55].
This section outlines the formulation of the optimization problem aimed at identifying the optimal EM strategy for an μG to accomplish either a multi-objective function, which includes minimizing both the total operational cost and emissions, or a single objective function, focusing solely on reducing the total operational cost or emissions. In all three scenarios, the formulation considers various constraints. In a multi-objective optimization model, there are typically several objectives to consider, such as minimizing total operation cost, maximizing the profits of BSS, and minimizing total emissions.
Achieving the optimal balance between these objectives involves finding solutions that offer the best compromise considering assigning relative weights to each objective based on their importance and priorities.
This work is dedicated to minimizing the operating costs of an μG, which incorporates PVs, WTs, FCs, BSSs, and EVCSs. The first objective function under consideration (OF1) is formulated as follows:(5)
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