Energy Strategy Reviews, Год журнала: 2025, Номер 58, С. 101654 - 101654
Опубликована: Фев. 5, 2025
Язык: Английский
Applied Sciences, Год журнала: 2024, Номер 14(14), С. 6214 - 6214
Опубликована: Июль 17, 2024
This review comprehensively examines the burgeoning field of intelligent techniques to enhance power systems’ stability, control, and protection. As global energy demands increase renewable sources become more integrated, maintaining stability reliability both conventional systems smart grids is crucial. Traditional methods are increasingly insufficient for handling today’s grids’ complex, dynamic nature. paper discusses adoption advanced intelligence methods, including artificial (AI), deep learning (DL), machine (ML), metaheuristic optimization algorithms, other AI such as fuzzy logic, reinforcement learning, model predictive control address these challenges. It underscores critical importance system new challenges integrating diverse sources. The reviews various used in analysis, emphasizing their roles maintenance, fault detection, real-time monitoring. details extensive research on capabilities ML algorithms precision efficiency protection systems, showing effectiveness accurately identifying resolving faults. Additionally, it explores potential logic decision-making under uncertainty, integration IoT big data analytics monitoring optimization. Case studies from literature presented, offering valuable insights into practical applications. concludes by current limitations suggesting areas future research, highlighting need robust, flexible, scalable sector. a resource researchers, engineers, policymakers, providing detailed understanding
Язык: Английский
Процитировано
28
Results in Engineering, Год журнала: 2024, Номер 22, С. 102288 - 102288
Опубликована: Май 21, 2024
An optimal sizing of an off-grid microgrid system composed photovoltaic (PV)/building integrated (BIPV)/battery energy storage installation is undergone for Net Zero Energy Residential Building blocks across six different climates Morocco in order to reach the objective providing all load requirements at minimum. The Particle Swarm Optimization algorithm used find system, by considering hourly spatiotemporal variations both solar availability and demand variation, with lowest Total Annualized Cost as function capacities BIPV battery decision variables. methodology adopted focuses on main fulfillment through direct PV power supply, backed technology, continually guarantee self-sufficiency. A key metric, cover factor, introduced quantify ratio which satisfied systems. findings show that can help improve factor 0.68-2.58%. Moreover, integrating Battery leads a reduction Levelized approximately 8.7-20.72 %, opposed utilizing only battery. Depending local climate, levelized cost ranges from 0.366 $/kWh Ouarzazate city up 0.664 $/kWh.in Ifrane city. Lastly, this holistic approach aims transform building its traditional role consumer carbon-free electricity generator.
Язык: Английский
Процитировано
22
Heliyon, Год журнала: 2024, Номер 10(19), С. e37482 - e37482
Опубликована: Сен. 10, 2024
As global energy demand and warming increase, there is a need to transition sustainable renewable sources. Integrating different systems create hybrid system enhances the overall adoption deployment of resources. Given intermittent nature solar wind, storage are combined with these sources, optimize quantity clean used. Thus, various optimization strategies have been developed for integration operation systems. Existing studies either reviewed or systems, however, ignored integrated This study offers comprehensive analysis methods used in (HRES) (ESS). We examined models HRES ESS, their objectives, common constraints. Based on our review, capacity CO
Язык: Английский
Процитировано
20
Results in Engineering, Год журнала: 2024, Номер 24, С. 102816 - 102816
Опубликована: Авг. 30, 2024
Considering the rapidly evolving microgrid technology and increasing complexity associated with integrating renewable energy sources, innovative approaches to management are crucial for ensuring sustainability efficiency. This paper presents a novel Fuzzy Logic-Based Particle Swarm Optimization (FLB-PSO) technique enhance performance of hybrid systems. The proposed FLB-PSO algorithm effectively addresses challenge balancing exploration exploitation in optimization problems, thereby enhancing convergence speed solution accuracy robustness across diverse complex scenarios. By leveraging adaptability fuzzy logic adjust PSO parameters dynamically, method optimizes allocation utilization resources within grid-connected microgrid. Under fixed grid tariffs, investigation demonstrates that achieves power purchase battery degradation costs $1935.07 $49.93, respectively, compared $2159.67 $61.43 traditional PSO. results an optimal cost $1985.00 FLB-PSO, leading saving $236.09 $2221.10 Furthermore, under dynamic incurs $2359.20 $64.66, contrast $2606.47 $54.61 is $2423.86, representing reduction $237.23 $2661.08 proficiently manages sources while addressing complexities storage degradation. Overall, outperforms terms system dynamics, rate, operational reduction, improved
Язык: Английский
Процитировано
8
Scientific Reports, Год журнала: 2024, Номер 14(1)
Опубликована: Июнь 17, 2024
Abstract This study looks into how to make proton exchange membrane (PEM) fuel cells work more efficiently in environments that change over time using new Maximum Power Point Tracking (MPPT) methods. We evaluate the efficacy of Flying Squirrel Search Optimization (FSSO) and Cuckoo (CS) algorithms adapting varying conditions, including fluctuations pressure temperature. Through meticulous simulations analyses, explores collaborative integration these techniques with boost converters enhance reliability productivity. It was found FSSO consistently works better than CS, achieving an average increase 12.5% power extraction from PEM a variety operational situations. Additionally, exhibits superior adaptability convergence speed, maximum point (MPP) 25% faster CS. These findings underscore substantial potential as robust efficient MPPT method for optimizing cell systems. The contributes quantitative insights advancing green energy solutions suggests avenues future exploration hybrid optimization
Язык: Английский
Процитировано
7
Applied Energy, Год журнала: 2024, Номер 373, С. 123922 - 123922
Опубликована: Июль 17, 2024
Язык: Английский
Процитировано
7
Journal of Control Automation and Electrical Systems, Год журнала: 2025, Номер unknown
Опубликована: Янв. 28, 2025
Язык: Английский
Процитировано
1
Journal of Energy Storage, Год журнала: 2025, Номер 118, С. 116226 - 116226
Опубликована: Март 17, 2025
Язык: Английский
Процитировано
1
Energies, Год журнала: 2025, Номер 18(6), С. 1487 - 1487
Опубликована: Март 18, 2025
This study proposes a simplified mathematical formulation for optimizing isolated microgrids, enhancing computational efficiency while preserving solution quality. The research focuses on the influence of Operation and Maintenance (O&M) costs Non-Dispatchable Generators (NDGs) relationship between pollutant emissions. proposed simplification reduces requirements, improves result interpretability, increases scalability optimization techniques. O&M photovoltaic wind systems were excluded from initial calculated afterward. A Student’s t-test yielded p-value 87.3%, confirming no significant difference tested scenarios, ensuring that does not impact quality reducing complexity. For emission-related costs, scenarios with single multiple generators analyzed. When only one generator type is present, modifications are needed to enable effective multi-objective optimization. To address this, two alternative formulations tested, offering more suitable approaches problem. However, when sources exist, cost emission differences naturally define problem as without requiring adjustments. Future work will explore grid-connected microgrids additional objectives, such loss minimization, voltage control, device lifespan extension.
Язык: Английский
Процитировано
1
Scientific Reports, Год журнала: 2025, Номер 15(1)
Опубликована: Март 23, 2025
This paper investigates the economic energy management of a wireless electric vehicle charging stations (EVCS) connected to hybrid renewable system comprising photovoltaic (PV), wind, battery storage, and main grid. The study adopts an Improved Harris Hawk Optimization (IHHO) algorithm optimize minimize operational costs under varying scenarios. Three distinct EV load profiles are considered evaluate performance proposed optimization technique. Simulation results demonstrate that IHHO achieves significant cost reductions improves utilization efficiency compared other state-of-the-art algorithms such as Quantum Particle Swarm (IQPSO), Honeybee Mating (HBMO), Enhanced Exploratory Whale Algorithm (EEWOA). For scenarios with energies, reduced electricity by up 36.41%, achieving per-unit low 3.17 INR for most demanding profile. Under generation disconnection, maintained its superiority, reducing 37.89% unoptimized dispatch strategies. integration storage further enhanced system's resilience cost-effectiveness, particularly during periods unavailability. algorithm's robust performance, reflected in ability handle dynamic challenging conditions, demonstrates potential practical deployment real-world EVCS powered systems. findings highlight reliable efficient tool optimizing dispatch, promoting energy, supporting sustainable infrastructure development. outperforms all benchmark algorithms, 35.82% Profile 3, minimum 3.11 INR/kWh across Specifically, achieved lowest 6479.72 INR/day 1, 10,893.23 2, 20,821.63 consistently outperforming IQPSO, HBMO, EEWOA.
Язык: Английский
Процитировано
1