Application Example of Particle Swarm Optimization on Operation Scheduling of Microgrids

Takano, Hirotaka; Asano, Hiroshi; Gupta, Neeraj

doi:10.1007/978-981-15-2133-1_10

Cited by 11 publications

(15 citation statements)

References 28 publications

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“…The right path is decided either by the optimum solution found by all of the cooperating particles G Best or by the best local solution P Best . The equation governing the best location is shown in Equation (7), which is as follows [54,55]:…”

Section: Particle Swarm Optimization Methods (Pso)mentioning

confidence: 99%

Artificial Intelligence and Bio-Inspired Soft Computing-Based Maximum Power Plant Tracking for a Solar Photovoltaic System under Non-Uniform Solar Irradiance Shading Conditions—A Review

et al. 2021

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Substantial progress in solar photovoltaic (SPV) dissemination in grid-connected and standalone power generation systems has been witnessed during the last two decades. However, weather intermittency has a non-linear characteristic impact on solar photovoltaic output, which can cause considerable loss in the system’s overall output. To overcome these inevitable losses and optimize the SPV output, maximum power point tracking (MPPT) is mounted in the middle of the power electronics converters and SPV to achieve the maximum output with better precision from the SPV system under intermittent weather conditions. As MPPT is considered an essential part of the SPV system, up to now, many researchers have developed numerous MPPT techniques, each with unique features. A Google Scholar survey from 2015–2021 was performed to scrutinize the number of published review papers in this area. An online search established that on different MPPT techniques, overall, 100 review articles were published; out of these 100, seven reviews on conventional MPPT techniques under shading or partial shading and only four under non-uniform solar irradiance are published. Unfortunately, no dedicated review article has explicitly focused on soft computing MPPT (SC-MPPT) techniques. Therefore, a comprehensive review of articles on SC-MPPT techniques is desirable, in which almost all the familiar SC-MPPT techniques have to be summarized in one piece. This review article concentrates explicitly on soft computing-based MPPT techniques under non-uniform irradiance conditions along with their operating principles, block/flow diagram. It will not only be helpful for academics and researchers to provide a future direction in SC-MPPT optimization research, but also help the field engineers to select the appropriate SC-MPPT for SPV according to system design and environmental conditions.

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Section: Particle Swarm Optimization Methods (Pso)mentioning

confidence: 99%

Artificial Intelligence and Bio-Inspired Soft Computing-Based Maximum Power Plant Tracking for a Solar Photovoltaic System under Non-Uniform Solar Irradiance Shading Conditions—A Review

et al. 2021

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“…Kennedy and Eberhart suggested PSO, i.e., a famous global search easy implementation algorithm introduced at the last of the previous century and required short requirements of the memory [47]. Few recent applications of the PSO are fuel ignition model [48], balancing stochastic U-lines problems [49], nonlinear physical systems [50], feature classification [51] and operation scheduling of microgrids [52].…”

Section: Optimization Procedure: Pso-ip Algorithmmentioning

confidence: 99%

A neuro-swarming intelligent heuristic for second-order nonlinear Lane–Emden multi-pantograph delay differential system

Sabir

Raja

et al. 2021

Complex Intell. Syst.

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The current study is related to present a novel neuro-swarming intelligent heuristic for nonlinear second-order Lane–Emden multi-pantograph delay differential (NSO-LE-MPDD) model by applying the approximation proficiency of artificial neural networks (ANNs) and local/global search capabilities of particle swarm optimization (PSO) together with efficient/quick interior-point (IP) approach, i.e., ANN-PSOIP scheme. In the designed ANN-PSOIP scheme, a merit function is proposed by using the mean square error sense along with continuous mapping of ANNs for the NSO-LE-MPDD model. The training of these nets is capable of using the integrated competence of PSO and IP scheme. The inspiration of the ANN-PSOIP approach instigates to present a reliable, steadfast, and consistent arrangement relates the ANNs strength for the soft computing optimization to handle with such inspiring classifications. Furthermore, the statistical soundings using the different operators certify the convergence, accurateness, and precision of the ANN-PSOIP scheme.

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“…Thermal power generation has taken a large portion in the power supply, e.g., approximately 85% in Japan [28], and its fuel cost, as is well-known, has powerful influence on the operational costs of the power suppliers. The fuel costs of thermal power units are traditionally approximated as quadratic functions by means of generating power [29][30][31][32][33][34]. For these reasons, the authors added the following assumptions to make the operational cost function.…”

Section: Utility Function For Suppliersmentioning

confidence: 99%

Calculation Method for Electricity Price and Rebate Level in Demand Response Programs

et al. 2021

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Demand response programs (DRs) can be implemented with less investment costs than those in power plants or facilities and enable us to control power demand. Therefore, they are highly expected as an efficient option for power supply–demand-balancing operations. On the other hand, DRs bring new difficulties on how to evaluate the cooperation of consumers and to decide electricity prices or rebate levels with reflecting its results. This paper presents a theoretical approach that calculates electricity prices and rebate levels in DRs based on the framework of social welfare maximization. In the authors’ proposal, the DR-originated changes in the utility functions of power suppliers and consumers are used to set a guide for DR requests. Moreover, optimal electricity prices and rebate levels are defined from the standpoint of minimal burden in DRs. Through numerical simulations and discussion on their results, the validity of the authors’ proposal is verified.

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Application Example of Particle Swarm Optimization on Operation Scheduling of Microgrids

Cited by 11 publications

References 28 publications

Artificial Intelligence and Bio-Inspired Soft Computing-Based Maximum Power Plant Tracking for a Solar Photovoltaic System under Non-Uniform Solar Irradiance Shading Conditions—A Review

Artificial Intelligence and Bio-Inspired Soft Computing-Based Maximum Power Plant Tracking for a Solar Photovoltaic System under Non-Uniform Solar Irradiance Shading Conditions—A Review

A neuro-swarming intelligent heuristic for second-order nonlinear Lane–Emden multi-pantograph delay differential system

Calculation Method for Electricity Price and Rebate Level in Demand Response Programs

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