1. Introduction
The recent surge in energy demand, coupled with growing environmental concerns, has made a notable change in the worldwide energy scene, placing more importance on sustainable energy solutions, and it has prompted the integration of distributed energy resources (DERs), particularly those based on renewable sources [
1]. During this transition, microgrids (MGs) have surfaced as promising options, enabling communities to have more influence over how they produce, use, and distribute energy [
1,
2,
3]. Especially in distant and underserved areas, MGs offer significant potential for tackling energy accessibility issues while promoting economic, social, and environmental advantages [
2]. The use of MGs has been recommended as a solution to harness various benefits, including enhanced reliability, sustainability, and cost-effectiveness, by aggregating DERs and loads within distribution networks [
2,
3].
A community MG is a compact and decentralized energy system that blends diverse energy sources, including renewables like solar energy, wind energy, and hydropower, alongside energy storage solutions like batteries, as well as traditional generators. Its purpose is to provide electricity to a particular community or a group of consumers. MGs can function independently or in coordination with the primary utility grid. This capability allows the community MG to sustain its power supply during grid failures and can also serve as a complementary source of electricity when the main grid is operational, with surplus renewable energy available [
3].
In recent years, the idea of community MGs has gained considerable momentum, driven by the demand for sustainable energy solutions. Scholars and industry experts alike have delved into the possible advantages and obstacles associated with deploying community-based MG systems across various global contexts [
1,
2,
3,
4].
Resilience in MGs pertains to the capacity of energy systems to endure and re-bound from disturbances, interruptions, or unexpected circumstances while ensuring crucial energy services. This concept encompasses the ability to adjust, recover, and persist in operation under adverse conditions, including natural disasters, power grid failures, cyber-attacks, or severe weather events. Community MGs strengthen the resilience of the community power system, particularly in rural or isolated regions, by offering a dependable electricity source during emergencies or when the primary grid experiences disruptions [
4].
To effectively make the most of the available resources and attain economic and environmental objectives, it is crucial to coordinate the operation of various resources efficiently [
1,
2,
3,
4,
5]. An operation management system (OMS) takes on the responsibility of overseeing energy scheduling while considering operational objectives and technical and system constraints. This task can be particularly demanding, especially during abnormal operating conditions when unforeseen disruptions like natural disasters occur, and there is not enough power generation to meet the electricity demands of all power-consuming units [
4,
5,
6,
7,
8,
9,
10].
In [
6], the authors present an OMS designed for MGs, which employs a genetic algorithm (GA) framework. The model they propose integrates forecasted data for solar photovoltaic (PV) and wind power generation and focuses on optimizing the utilization of both batteries and grid power over a 24 h period. The aim of OMS is the optimal utilization of renewable energy sources (RESs) and storage while taking into account factors such as the cost and health of the batteries.
The authors in [
7] propose an OMS designed for MGs to optimize energy generation while simultaneously reducing CO
2 emissions and minimizing economic expenses. This OMS incorporates model predictive control, a multi-objective optimization algorithm, and a decision-making tool that can adapt to changing operational circumstances. The multi-objective algorithm produces Pareto optimal solutions, balancing the reduction in CO
2 emissions and economic costs. Meanwhile, the decision tool automates the selection of the most appropriate solution from the set of Pareto optimal solutions.
In [
8], an OMS tailored for MGs is introduced. It employs GA to optimize the utilization of the battery and grid power over 24 h. This model is designed to accomplish operation management, making the most use of RESs and storage, considering cost and battery health. Furthermore, the study introduces two distinct OMSs for MGs and smart homes. These systems are designed to operate under both normal and emergency conditions, showcasing their ability to ensure grid resilience by supplying power to both internal and external loads during disruptions or outages.
A forward-looking operation management strategy is introduced in [
9], emphasizing the combination of flexible load control and MG resource planning. This approach is specifically designed to bolster the resilience of the power grid in the aftermath of natural disasters. The framework places a priority on managing loads by considering factors such as health and economic importance, thereby substantially mitigating the adverse impacts of power outages. The study underscores the vital importance of including MGs in resource planning to ensure a robust power grid. It emphasizes the need to invest in both PVs and battery energy storage systems (BESSs) to maintain uninterrupted load service during periods of grid downtime.
In [
10], the economic dispatch problem within MGs is addressed by employing different optimization algorithms on an IEEE 30-bus system. The selected ant colony-based algorithm optimizes the energy costs of the MG, considering seasonal variations. This study also offers insights and recommendations for potential enhancements in this context.
In a world facing the pressing imperative of addressing climate change and transitioning to RESs, Indonesia, blessed with abundant natural resources, stands at a critical junction where it must reconsider and reshape its energy landscape [
11,
12,
13,
14]. Lombok Island, situated within the Indonesian archipelago, presents an ideal setting for investigating the integration of community MGs. Its distinctive geographical characteristics, encompassing remote villages and challenging terrain, contribute to energy access disparities that a conventional grid infrastructure struggles to rectify [
15,
16]. Consequently, implementing a community MG emerges as a promising solution to bridge this energy gap and enhance the overall quality of life for the island’s residents [
15,
16].
Indonesia faces various natural disasters, such as earthquakes, floods, and volcanic eruptions [
11]. An overview of the number of natural disasters that have occurred in Indonesia from 2012 to 2022 is shown in
Figure 1. In 2019, the National Disaster Mitigation Agency (BNPB) recorded a total of 3622 natural disasters, including tornadoes, floods, and landslides across the nation. An important incident occurred in August 2019 when a powerful 6.9-magnitude earthquake struck Java, causing a blackout lasting 9 h, and affecting around 21.3 million customers, including industries, mass transit, and the telecommunication system [
11,
12,
13].
As climate change continues, it is expected that natural disasters and extreme weather events will become more frequent and prolonged worldwide [
11]. This underscores the importance of urgently establishing strong and resilient electrical power networks in Indonesia and other susceptible regions to reduce the impacts of such events and guarantee a dependable power supply during challenging situations [
14,
18].
To ensure that the load demands are met while adhering to cost and emission constraints, the MG’s economic and environmental optimization must be approached as a multi-objective problem.
A review of the literature on the optimal operation of PV-based MGs is presented in [
19]. Different modeling strategies, formulation, optimization objectives, and solving methods are investigated. The importance of applying metaheuristics algorithms to facilitate the optimization process of the operation management of MGs and future challenges are discussed.
A multi-objective differential evolution algorithm is used in [
20] to solve the operation management of a renewable-based MG. The objective of this paper is to minimize the operational cost and environmental emissions. However, the effect of natural disasters and the resulting abnormal conditions are not investigated in this paper.
The techno-economic-environmental performance of grid-connected MGs under uncertainties is studied in [
21]. A bi-objective algorithm is suggested to minimize the operation cost and emissions of the system. The only considered condition in this paper is the normal operation condition.
The authors suggested the improved weighted mean of the vectors algorithm in [
22] to solve the day-ahead operation management of MGs to minimize the operation cost of the system. However, the operation of the system in abnormal conditions is not studied in this paper.
The intelligent Golden Jackal Optimization (GJO) algorithm is proposed in [
23] to solve the operation management of an MG with hybrid resources. The only objective in this paper is to minimize the operation cost. Moreover, the effect of abnormal conditions on the operation of the MG is not considered in the paper.
Different methods, including mathematical models, such as mixed-integer linear programming, or metaheuristic algorithms, such as GA and the harmony search algorithm, are studied and compared in [
24] to investigate the single-objective cost optimization in MGs under normal operation conditions.
To study the single-objective cost minimization of a renewable-based energy system, a nature-inspired algorithm, namely the Kestrel-based search algorithm (KSA), is suggested in [
25], and the results are compared with some other algorithms. The operation of the system during faulty condition is not evaluated in this paper.
A multi-objective GA is employed in [
26] to solve the operation management of an MG while considering emission and cost minimizations. Nevertheless, this paper does not explore the impact of natural disasters and the subsequent abnormal conditions.
In this paper, we explore operation management strategies for a multi-node community MG situated in the scenic surroundings of Lombok Island, Indonesia. The optimal MG operation management challenge is formulated as a multi-objective optimization problem to minimize both the operational costs of the community MG and its environmental impact. To address this optimization challenge, the multi-objective particle swarm optimization (MPSO) algorithm is employed. To gauge the effectiveness of the proposed strategy, various normal and abnormal operating scenarios are considered for evaluation. Accordingly, the main contributions of this paper are summarized as follows:
An economic–environmental operation management problem of a multi-node community MG is formulated using the real data of Lombok Island in Indonesia.
The MPSO algorithm is suggested to solve the multi-objective optimal operation management of the considered multi-node community MG.
Different normal and abnormal operating conditions are considered for both the single- and multi-objective operation management of the multi-node community MG to justify the efficiency of the suggested method.
A comprehensive analysis of the performance of the OMS designed for community MGs under both normal and abnormal conditions is performed while the technical constraints including the power flow and battery constraints are considered.
In the case of faults, the load curtailment strategy for each abnormal scenario is investigated.
The remainder of this paper is as follows: In
Section 2, we explore the system structure of the proposed multiple-node community MG and the problem formulation.
Section 3 is devoted to the applied methodology for solving the optimal operation of the proposed multi-node community MG. The results are studied in
Section 4. The conclusions and future works are highlighted in
Section 5.