Incremental Cost Consensus Algorithm for On/Off Loads to Enhance the Frequency Response of the Power System

Chen, Chen; Bao, Yu‐Qing; Wu, Xue-Hua; Wang, Beibei

doi:10.1109/access.2020.2985782

Cited by 5 publications

(2 citation statements)

References 28 publications

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“…Recently, metaheuristic optimization algorithms are utilized in many felds [22,23]. Te optimization problem, however, potentially adds extra complications to nonlinear control variables attributable to the penetration of renewable energy sources [9,[24][25][26][27]. On the other hand, metaheuristic approaches are supposed to be extremely competent to tackle high-dimensional ELD problems within less time.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Metaheuristic Model for Optimal Economic Load Dispatch in Renewable Hybrid Energy System

Miracle

Viral

Tiwari

et al. 2023

International Transactions on Electrical Energy Systems

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Hybrid generating systems in power networks have emerged as a result of the rapid growth of renewable infrastructure and widespread support for green energy. One of the most significant problems in designing and operating an electric power generation system is the efficient scheduling of all power generation facilities to meet the rising power demand. Economic load dispatch (ELD) is a generic procedure in the electrical power system, and the ELD in power system problems involves scheduling the power generating units to reduce cost and satisfy system constraints. Metaheuristic algorithms are gaining popularity for solving constrained ELD issues because of their larger global solution capacity, flexibility, and derivative-free construction. In this research, the ELD problem of integrated renewable resources is solved using a unique solution model based on hybrid optimization. Furthermore, this work considers multiobjectives such as total wind generation cost, total cost function of thermal units, and penalty cost function. The hybrid optimization model optimizes the power generation of thermal power plants within the maximum and minimum limitations. Additionally, the turbines are selected optimally by the hybrid optimization model to ensure the power generation of wind turbines based on the demands. The proposed hybrid optimization is a combination of particle swarm optimization (PSO) and cat swarm optimization (CSO), and the new algorithm is referred to as the particle oriented cat swarm optimization model (POCSO). Finally, the performance of the proposed work is compared to other conventional models. In particular, the cost function of POCSO is 6.25%, 6%, 11.7%, 36%, 27%, and 46.42% better than the cost function of whale optimization algorithm (WOA), elephant herd optimization (EHO), moth-flame optimization (MFO), dragonfly algorithm (DA), sealion optimization (SLnO), CSO, and PSO methods, respectively. Also, for IEEE-30 bus system, the best value of the proposed work is 7.46%, 5.41%, 16.30%, 14.88%, 17.60%, 13.86%, 15.21%, 17.49%, and 4.27% better than that of the PSO, CSO, SLnO, DA, MFO, EHO, WOA, multiagent glowworm swarm optimization (MAGSO), and Harris hawks optimization-based feed-forward neural network (HHO-FNN) methods, respectively.

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Section: Introductionmentioning

confidence: 99%

Hybrid Metaheuristic Model for Optimal Economic Load Dispatch in Renewable Hybrid Energy System

Miracle

Viral

Tiwari

et al. 2023

International Transactions on Electrical Energy Systems

View full text Add to dashboard Cite

show abstract

“…Wang and Liu (2020) represented two distributed IC consensus-based optimization algorithms to answer EDPs without and with generation constraints via small gain theorem. Chen et al (2020) presented a distributed consensus approach for cost-effective load dispatch under frequency regulation. The works in Wang et al (2019), Yu et al (2020), and Xing et al (2019) proposed EDP methods under random delay, for time-varying step-sizes, and transmission losses.…”

Section: Introductionmentioning

confidence: 99%

Optimality Condition and Distributed Optimization for Economic Dispatch Using a Novel Weighted Incremental Cost Consensus Approach

et al. 2022

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This research report investigates a novel optimization approach for the economic dispatch problem (EDP) based on the weighted sum of generators’ costs under supply-demand balance. Unlike conventional approaches, we present a distributed optimization approach that ensures optimality using weighted incremental cost (IC) consensus and sign-consensus error convergence. We can apply the optimization of a weighted sum of generators’ costs to address several constraints, such as capacity and environmental constraints, in addition to the supply-demand balance. The proposed distributed weighted incremental cost consensus approach has been applied to the IEEE-30 bus and IEEE-118 bus systems over a communication topology. The results indicate the efficacy of weights to address generation constraints and the convergence of weighted ICs under supply-demand balance.

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A novel consensus-oriented distributed optimization scheme with convergence analysis for economic dispatch over directed communication graphs

et al. 2023

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Incremental Cost Consensus Algorithm for On/Off Loads to Enhance the Frequency Response of the Power System

Cited by 5 publications

References 28 publications

Hybrid Metaheuristic Model for Optimal Economic Load Dispatch in Renewable Hybrid Energy System

Hybrid Metaheuristic Model for Optimal Economic Load Dispatch in Renewable Hybrid Energy System

Optimality Condition and Distributed Optimization for Economic Dispatch Using a Novel Weighted Incremental Cost Consensus Approach

A novel consensus-oriented distributed optimization scheme with convergence analysis for economic dispatch over directed communication graphs

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