
Technology Analysis and Strategic Management, Год журнала: 2024, Номер unknown, С. 1 - 14
Опубликована: Сен. 23, 2024
Язык: Английский
Technology Analysis and Strategic Management, Год журнала: 2024, Номер unknown, С. 1 - 14
Опубликована: Сен. 23, 2024
Язык: Английский
Results in Engineering, Год журнала: 2025, Номер unknown, С. 104304 - 104304
Опубликована: Фев. 1, 2025
Язык: Английский
Процитировано
5Optimal Control Applications and Methods, Год журнала: 2024, Номер 45(3), С. 1251 - 1279
Опубликована: Янв. 25, 2024
Abstract Time‐delayed systems frequently appear, especially in sectors such as fluid flow processes, chemical procedures, and the food industry. This paper addresses optimization of parameters for a fractional order PID (FOPID) controller, which is used to control time‐delayed system, using five distinct algorithms inspired by nature. These are NewBAT, Cuckoo search (CS), Firefly (FF), Gray Wolf Optimizer (GWO), Whale algorithm (WOA). The FOPID controller parameters, namely K P , I D λ μ have been optimized these algorithms. During process, integral time absolute error (ITAE) was considered primary measurement criterion. In addition this value, maximum overshoot, settling time, reach values were examined. Simulations conducted with obtained tested system's resilience disturbances introduced at output, responses also evaluated during tests. reactions determined different reference inputs analyzed, results presented graphs tables. efficiency reliability substantiated comprehensive statistical analyses. analyses play critical role selection objective evaluation results. Simulation studies Matlab Simulink environments. FOMCON Toolbox fractional‐order processes.
Язык: Английский
Процитировано
12Journal of Energy Storage, Год журнала: 2025, Номер 112, С. 115465 - 115465
Опубликована: Фев. 4, 2025
Язык: Английский
Процитировано
1Electrical Engineering, Год журнала: 2024, Номер unknown
Опубликована: Июль 3, 2024
Язык: Английский
Процитировано
5International Journal of Ambient Energy, Год журнала: 2024, Номер 45(1)
Опубликована: Авг. 29, 2024
This study presents a cutting-edge strategy for the effective integration of distributed generation (DG) and capacitor banks (CB) with optimal network reconfiguration inside radial distribution network. The crayfish optimisation algorithm is employed to identify configuration allocation compensating devices. main aim this planning methodology elevate system performance by curtailing power losses improving voltage profiles. potency approach validated through simulation studies on 118-bus standard systems several load models, such as constant current, power, impedance ZIP models. Furthermore, comprehensive examination case underscores benefits suggested approach. results demonstrate that integrating DG units operating at factor, along coordinated operation CB reconfiguration, can substantially enhance performance. simultaneous approach, applied model, led an 86.90% reduction in active loss, 86.42% reactive loss increase minimum bus level from 0.8688 0.9815 p. u.
Язык: Английский
Процитировано
5Scientific Reports, Год журнала: 2024, Номер 14(1)
Опубликована: Июнь 19, 2024
Abstract Deploying distributed generators (DGs) supplied by renewable energy resources poses a significant challenge for efficient power grid operation. The proper sizing and placement of DGs, specifically photovoltaics (PVs) wind turbines (WTs), remain crucial due to the uncertain characteristics energy. To overcome these challenges, this study explores an enhanced version meta-heuristic technique called prairie dog optimizer (PDO). modified dogs (mPDO) incorporates novel exploration phase inspired slime mold algorithm (SMA) food approach. mPDO is proposed analyze substantial effects different dynamic load on performance distribution networks designing PV-based WT-based DGs. optimization problem various operational constraints mitigate loss in networks. Further, addresses uncertainties related random PV WT outputs employing appropriate probability distributions. evaluated using cec2020 benchmark suit test functions rigorous statistical analysis mathematically measure its success rate efficacy while considering type problems. developed applied incorporate both units, individually simultaneously, into IEEE 69-bus network. This achieved residential, commercial, industrial, mixed time-varying voltage-dependent demands. demonstrated standard functions, comparative conducted with original PDO other well-known algorithms, utilizing metrics. numerical findings emphasize influence generation DG planning. Moreover, beats alternatives improves generators' technical advantages across all examined scenarios.
Язык: Английский
Процитировано
4Cluster Computing, Год журнала: 2024, Номер 27(10), С. 14767 - 14810
Опубликована: Авг. 1, 2024
Abstract Deploying distributed generators (DGs) powered by renewable energy poses a significant challenge for effective power system operation. Optimally scheduling DGs, especially photovoltaic (PV) systems and wind turbines (WTs), is critical because of the unpredictable nature speed solar radiation. These intermittencies have posed considerable challenges to grids, including oscillation, increased losses, voltage instability. To overcome these challenges, battery storage (BES) supports PV unit, while biomass aids WT mitigating fluctuations boosting supply continuity. Therefore, main innovation this study presenting an improved moth flame optimization algorithm (IMFO) capture optimal multiple dispatchable non-dispatchable DGs loss in considering different dynamic load characteristics. The IMFO comprises new update position expression based on roulette wheel selection strategy as well Gaussian barebones (GB) quasi-opposite-based learning (QOBL) mechanisms enhance exploitation capability, global convergence rate, solution precision. algorithm's success rate effectiveness are evaluated using 23rd benchmark functions compared with basic MFO other seven competitors rigorous statistical analysis. developed optimizer then adopted performance 69-bus 118-bus distribution deterministic stochastic DG's planning. findings reflect superiority against its rivals, emphasizing influence types varying generations DG Numerically, deployment BES + significantly maximizes reduction percent 68.3471 98.0449 69-bus's commercial type 54.833 52.0623 118-bus's type, respectively, confirming efficacy maximizing diverse situations.
Язык: Английский
Процитировано
3Journal of Energy Storage, Год журнала: 2025, Номер 116, С. 116026 - 116026
Опубликована: Март 6, 2025
Язык: Английский
Процитировано
0Processes, Год журнала: 2025, Номер 13(5), С. 1350 - 1350
Опубликована: Апрель 28, 2025
This paper presents a comprehensive optimization framework for modern distribution systems, integrating system reconfiguration (DSR), soft open point (SOP) operation, photovoltaic (PV) allocation, and energy storage (ESS) management to minimize daily active power losses. The proposed approach employs novel hybrid metaheuristic algorithm, the Cheetah-Grey Wolf Optimizer (CGWO), which synergizes global exploration capabilities of Cheetah (CO) with local exploitation strengths Grey Optimization (GWO). model addresses time-varying loads, renewable generation profiles, dynamic network topology while rigorously enforcing operational constraints, including radiality, voltage limits, ESS state-of-charge dynamics, SOP capacity. Simulations on 33-bus demonstrate effectiveness across eight case studies, full DER integration (DSR + PV SOP) achieving 67.2% reduction in losses compared base configuration. By combining CO GWO, CGWO algorithm outperforms traditional techniques (such as PSO GWO) avoids premature convergence preserving computational efficiency—two major drawbacks standalone metaheuristics. Comparative analysis highlights CGWO’s superiority over algorithms, yielding lowest (997.41 kWh), balanced utilization, stable profiles. results underscore transformative potential coordinated enhancing grid efficiency reliability.
Язык: Английский
Процитировано
0Опубликована: Янв. 1, 2024
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Язык: Английский
Процитировано
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