Robust and effective parallel process to coordinate multi‐area economic dispatch (MAED) problems in the presence of uncertainty

Mokarram, Mohammad Jafar; Gitizadeh, Mohsen; Niknam, Taher; Niknam, Solmaz

doi:10.1049/iet-gtd.2019.0319

Cited by 20 publications

(7 citation statements)

References 42 publications

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“…Such an efficient system is likely to represent some biological neural properties, such as the smallworld phenomenon and scale-free distribution. In order to applications in other areas of research and applied developments, we suggest applications of the proposed method in environmental and energy studies which uses soft computing techniques [50][51][52]. This study also tried to make the HCESNs architecture more robust against noise on specific (hub) neurons by searching for the best centers of the backbone neurons.…”

Section: Discussionmentioning

confidence: 99%

Design of a reservoir for cloud-enabled echo state network with high clustering coefficient

Akrami

Rostami

Khosravi

2020

J Wireless Com Network

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Reservoir computing (RC) is considered as a suitable alternative for descending gradient methods in recursive neural networks (RNNs) training. The echo state network (ESN) is a platform for RC and nonlinear system simulation in the cloud environment with many external users. In the past researches, the highest eigenvalue of reservoir connection weight (spectral radius) was used to predict reservoir dynamics. Some researchers have illustrated; the characteristics of scale-free and small-world can improve the approximation capability in echo state networks; however, recent studies have shown importance of the infrastructures such as clusters and the stability criteria of these reservoirs as altered. In this research, we suggest a high clustered ESN called HCESN that its internal neurons are interconnected in form of clusters. Each of the clusters contains one backbone and a number of local nodes. We implemented a classical clustering algorithm, called K-means, and three optimization algorithms including genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) to improve the clustering efficiency of the new reservoir and compared them with each other. For investigating the spectral radius and predictive power of the resulting reservoirs, we also applied them to the laser time series and the Mackey-Glass dynamical system. It is demonstrated that new clustered reservoirs have some specifications of biologic neural systems and complex networks like average short path length, high clustering coefficient, and power-law distribution. The empirical results illustrated that the ESN based on PSO could strikingly enhance echo state property (ESP) and obtains less chaotic time series prediction error compared with other works and the original version of ESN. Therefore, it can approximate nonlinear dynamical systems and predict the chaotic time series.

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Section: Discussionmentioning

confidence: 99%

Design of a reservoir for cloud-enabled echo state network with high clustering coefficient

Akrami

Rostami

Khosravi

2020

J Wireless Com Network

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“…The general loss formula that contains a linear term and a constant term known as Kron's Loss formula is given as [17];…”

Section: Economic Dispatch Problemmentioning

confidence: 99%

Hybrid Optimization Technique for Solving Economic Dispatch Problem: A Case Study of Nigerian Thermal Power System

Adepoju

Okelola

Tijani³

2022

Afr. Sci. Rep.

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Economic Dispatch Problem (EDP) is a power system optimization problem that is required to be solved accurately using an efficient optimization technique. Hybrid optimization solutions have provided better optimum results than either deterministic or non-deterministic optimization methods. The hybridization of both Particle Swarm Optimization (PSO) and Bat Algorithm (BA) to for Hybrid Particle Swarm Bat Algorithm (H-PS-BA) optimization technique for solving EDP of Nigerian 21 thermal generating station power system was carried out in this work. The result of the work revealed that H-PS-BA performed better and gave the best optimal generation costs when compared to other methods such as PSO, Interior Point Method and BA.

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“…This method of calculating line flows outperforms the existing DC load flow method (DC) method with a relative error of 0.43%, which is smaller compared to 55.94% of DC. A robust parallel‐processing method is proposed in 52 to coordinate multi‐areas with the presence of wind turbines. The uncertainties in both generation as well as load is incorporated into the MAED problem by utilizing an analytical method called Unscented Transformation.…”

Section: Multi‐area Edmentioning

confidence: 99%

A comprehensive review of soft computing algorithms for optimal generation scheduling

et al. 2020

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Summary Strategic planning for optimal operation of modern power systems has always been a significant task to satisfy the techno‐social aspects in the sustainable energy scenario. In the light of restructured power systems and decentralized generation, the economic dispatch (ED) problem has remained the utmost important economic assignment for the network operators. The conventional techniques have failed to address the nonlinear characteristics of generators for solving the practical ED problem. The complications identified in the literature are valve point loading, prohibited operating zones, multiple fuels, ramp rate limits, and spinning reserve. Therefore, a diverse range of traditional and modern optimization techniques have been adopted to tackle those complexities and nonlinearities. With the recent advancements in centralized optimization techniques, several research works were reported in the past decade to accomplish better results. This review paper endeavors to cover the pioneering research on centralized approaches as well as present‐day research trends on decentralized and distributed approaches by focusing on the economic aspects. First, a detailed survey of several deterministic, stochastic, nature‐inspired, and meta‐heuristic based centralized approaches for solving the ED problem is reviewed. A brief analysis based on the performance evaluation of centralized algorithms on six standard test systems is presented. Then, the recent literature on decentralized and distributed optimization approach and the consensus protocols with respect to various network topologies is briefly reviewed. The prime objective of this review is to summarize the centralized, decentralized, and distributed approaches to solve the classic dispatch, dynamic dispatch, economic emission dispatch, and multi‐area ED problems.

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Robust and effective parallel process to coordinate multi‐area economic dispatch (MAED) problems in the presence of uncertainty

Cited by 20 publications

References 42 publications

Design of a reservoir for cloud-enabled echo state network with high clustering coefficient

Design of a reservoir for cloud-enabled echo state network with high clustering coefficient

Hybrid Optimization Technique for Solving Economic Dispatch Problem: A Case Study of Nigerian Thermal Power System

A comprehensive review of soft computing algorithms for optimal generation scheduling

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