Metaheuristic optimization algorithms for multi-area economic dispatch of power systems: Part I—a comprehensive survey

Artificial Intelligence Review, Journal Year: 2025, Volume and Issue: 58(4)

Published: Jan. 25, 2025

Language: Английский

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Multi‐objective multiperiod stable environmental economic power dispatch considering probabilistic wind and solar PV generation

IET Renewable Power Generation, Journal Year: 2024, Volume and Issue: 18(16), P. 3903 - 3922

Published: Aug. 27, 2024

Abstract The economic‐environmental power dispatch (EEPD) problem, a widely studied bi‐objective non‐linear optimization challenge in systems, traditionally focuses on the economic of thermal generators without considering network security constraints. However, environmental sustainability necessitates reducing emissions and increasing penetration renewable energy sources (RES) into electrical grid. integration high levels RES, such as wind solar PV, introduces stability issues due to their uncertain intermittent nature. This article addresses these concerns by formulating solving stable (EESPD) which includes fixed zonal reserve capacity from conventional reserves RES. Uncertainties RES load demand are modelled using random variable generation techniques, applying Gaussian, Weibull, log‐normal probability density functions (PDFs) for demand, velocity, irradiance, respectively. stochastic EESPD problem extends multiple periods replicating single‐period each interval planning horizon, linking through intertemporal ramping costs, physical ramp rate, constraints variables. Multi‐objective evolutionary algorithms (MOEAs) have gained prominence complex problems involving multi‐objective functions. applies latest MOEAs tackle proposed incorporating PV sources. Network constraints, transmission line capacities bus voltage limits, considered along with generator capabilities spinning reserves, ramp‐up ramp‐down generators. A bidirectional coevolutionary‐based algorithm is employed, integrating an advanced constraint‐handling technique ensure compliance system simulation results show that formulation achieves better trade‐off between various conflicting objective compared other state‐of‐the‐art MOEAs.

Language: Английский

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Solving multi-area economic dispatch with disjoint operating regions using special ordered sets

Electric Power Systems Research, Journal Year: 2025, Volume and Issue: 242, P. 111454 - 111454

Published: Jan. 31, 2025

Language: Английский

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Economic Load Dispatch of A Multi‐Area Power System Using Multi‐Agent Distributed Optimization Based on Genetic Algorithm

Energy Science & Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: March 5, 2025

ABSTRACT This study presents a new methodology for distributed multi‐agent optimization utilizing genetic algorithm to address Multi‐Area Economic Dispatch Problem (MAEDP) in power system. While numerous studies have been conducted on various methods systems, this paper proposes model solving the optimal economic dispatch equations different areas of system and coordinated manner. In model, each area is represented by an agent responsible coordinating data exchange with other generation within its own area. The coordination between agents described form algorithm, whereby exchanged values converge after several iterations, final solution problem obtained from perspective agent. objective minimize costs meet area's load demand while maintaining voltage profiles. Each sets resources using rules then solves flow proposed method. Upon achieving convergence, evaluates all operational constraints designated region, calculates associated cost, shares cost value agents, thereby facilitating computation total process continues until best possible found. results implementing test networks systems demonstrate capability effectiveness method decomposing into smaller sub‐problems finding through simultaneous consensus steps.

Language: Английский

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Multi-area Dynamic Economic Dispatch considering the Demand Response and Price Uncertainty

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135532 - 135532

Published: March 1, 2025

Language: Английский

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Hybrid Beluga whale and jellyfish search optimizer for optimizing proton exchange membrane fuel cell parameter estimation

Ionics, Journal Year: 2025, Volume and Issue: unknown

Published: April 14, 2025

Language: Английский

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Dynamic economic dispatch of multi-area wind-solar-thermal power systems with fractional order comprehensive learning differential evolution

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 136233 - 136233

Published: April 1, 2025

Language: Английский

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Optimal Solution of Multiobjective Stable Environmental Economic Power Dispatch Problem Considering Probabilistic Wind and Solar PV generation

Heliyon, Journal Year: 2024, Volume and Issue: 10(20), P. e39041 - e39041

Published: Oct. 1, 2024

Language: Английский

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Design of chaotic Young's double slit experiment optimization heuristics for identification of nonlinear muscle model with key term separation

Chaos Solitons & Fractals, Journal Year: 2024, Volume and Issue: 189, P. 115636 - 115636

Published: Oct. 12, 2024

Language: Английский

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A Novel Computational Intelligence-Based Parameter Extraction of UAV-Integrated Photovoltaic System

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 97664 - 97677

Published: Jan. 1, 2024

The precise modeling of unmanned aerial vehicle (UAV)-integrated photovoltaic (PV) systems is central for their implementation on UAVs, especially due to continuous movement. Accurate essential developing control methods, conducting performance studies, and designing the whole system considering PV parameters. This original study presents a novel procedure comprehensive UAV-integrated modules using multi-input multi-output (MIMO) single-output (MISO) machine learning models. These models are developed based historical environmental dataset randomly generated representing flight conditions. Both datasets get processed during preparation phase. In this phase, two sets empirically derived group modified equations (GME) module utilized. addition, Whale Optimization Algorithm (WOA) employed optimize one set GME containing five unknown Moreover, Dogleg Trust Region (DTRA) used as an unconstrained problem solver in each loop WOA solve consisting Finally, MIMO MISO will be trained with dataset. proposed approach predicts behavior under diverse conditions, interprets panel movements, validated through experiments.

Language: Английский

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