Optimal Planning of Renewable Distributed Generators and Battery Energy Storage Systems in Reconfigurable Distribution Systems with Demand Response Program to Enhance Renewable Energy Penetration

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104304 - 104304

Published: Feb. 1, 2025

Language: Английский

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Nature‐ınspired algorithms for optimizing fractional order PID controllers in time‐delayed systems

Optimal Control Applications and Methods, Journal Year: 2024, Volume and Issue: 45(3), P. 1251 - 1279

Published: Jan. 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.

Language: Английский

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Hybrid EVO-CFNN approach for improve voltage stability of distribution system performance using superconducting magnetic energy storages inter linked with wind turbine

Journal of Energy Storage, Journal Year: 2025, Volume and Issue: 112, P. 115465 - 115465

Published: Feb. 4, 2025

Language: Английский

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A novel hybrid algorithm based on optimal size and location of photovoltaic with battery energy storage systems for voltage stability enhancement

Electrical Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: July 3, 2024

Language: Английский

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Crayfish optimisation algorithm for strategic planning of distributed generation and capacitor bank with network reconfiguration on radial distribution network

International Journal of Ambient Energy, Journal Year: 2024, Volume and Issue: 45(1)

Published: Aug. 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.

Language: Английский

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Boosting prairie dog optimizer for optimal planning of multiple wind turbine and photovoltaic distributed generators in distribution networks considering different dynamic load models

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: June 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.

Language: Английский

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An improved moth flame optimization for optimal DG and battery energy storage allocation in distribution systems

Cluster Computing, Journal Year: 2024, Volume and Issue: 27(10), P. 14767 - 14810

Published: Aug. 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.

Language: Английский

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Optimal scheduling of photovoltaic and battery energy storage in distribution networks using an ameliorated sand cat swarm optimization algorithm: Economic assessment with different loading scenarios

Journal of Energy Storage, Journal Year: 2025, Volume and Issue: 116, P. 116026 - 116026

Published: March 6, 2025

Language: Английский

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Optimized Coordination of Distributed Energy Resources in Modern Distribution Networks Using a Hybrid Metaheuristic Approach

Processes, Journal Year: 2025, Volume and Issue: 13(5), P. 1350 - 1350

Published: April 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.

Language: Английский

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Ultra-Short-Term Prediction Limited Planning Load for Determination of Storage Capacity in Grid-Connected Pv Systems

Published: Jan. 1, 2024

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Language: Английский

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