Optimization of a PV-wind-diesel system using a hybrid genetic algorithm

Atia, Raji; Yamada, Noboru

doi:10.1109/epec.2012.6474984

Cited by 14 publications

(4 citation statements)

References 6 publications

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“…Due to the modification of the hypermutation operator used in the CLONALG and the wish to verify the obtained optimization results, a comparative analysis of these results was carried out with the results obtained using the evolutionary algorithm, which is com-monly used to solve optimization tasks in the field of power engineering [33][34][35][36][37]. In the evolutionary algorithm used for comparison, a stochastic sampling with replacement was used as a selection method.…”

Section: Comparison Of Calculation Results Obtained Using the Clonalg Algorithm And The Evolutionary Algorithmmentioning

confidence: 99%

Optimization of the Configuration and Operating States of Hybrid AC/DC Low Voltage Microgrid Using a Clonal Selection Algorithm with a Modified Hypermutation Operator

Rokicki

2021

Energies

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The issue of optimization of the configuration and operating states in low voltage microgrids is important both from the point of view of the proper operation of the microgrid and its impact on the medium voltage distribution network to which such microgrid is connected. Suboptimal microgrid configuration may cause problems in networks managed by distribution system operators, as well as for electricity consumers and owners of microsources and energy storage systems connected to the microgrid. Structures particularly sensitive to incorrect determination of the operating states of individual devices are hybrid microgrids that combine an alternating current and direct current networks with the use of a bidirectional power electronic converter. An analysis of available literature shows that evolutionary and swarm optimization algorithms are the most frequently chosen for the optimization of power systems. The research presented in this article concerns the assessment of the possibilities of using artificial immune systems, operating on the basis of the CLONALG algorithm, as tools enabling the effective optimization of low voltage hybrid microgrids. In his research, the author developed a model of a hybrid low voltage microgrid, formulated three optimization tasks, and implemented an algorithm for solving the formulated tasks based on an artificial immune system using the CLONALG algorithm. The conducted research consisted of performing a 24 h simulation of microgrid operation for each of the formulated optimization tasks (divided into 10 min independent optimization periods). A novelty in the conducted research was the modification of the hypermutation operator, which is the key mechanism for the functioning of the CLONALG algorithm. In order to verify the changes introduced in the CLONALG algorithm and to assess the effectiveness of the artificial immune system in solving optimization tasks, optimization was also carried out with the use of an evolutionary algorithm, commonly used in solving such tasks. Based on the analysis of the obtained results of optimization calculations, it can be concluded that the artificial immune system proposed in this article, operating on the basis of the CLONALG algorithm with a modified hypermutation operator, in most of the analyzed cases obtained better results than the evolutionary algorithm. In several cases, both algorithms obtained identical results, which also proves that the CLONALG algorithm can be considered as an effective tool for optimizing modern power structures, such as low voltage microgrids, including hybrid AC/DC microgrids.

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Section: Comparison Of Calculation Results Obtained Using the Clonalg Algorithm And The Evolutionary Algorithmmentioning

confidence: 99%

Optimization of the Configuration and Operating States of Hybrid AC/DC Low Voltage Microgrid Using a Clonal Selection Algorithm with a Modified Hypermutation Operator

Rokicki

2021

Energies

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“…GA [78] is based on the natural selection process similar to biological evolution. It uses tools such as mutations, crossover and selection to generate candidate solutions.…”

Section: Ga-based Sizing Resultsmentioning

confidence: 99%

“…In this paper, we use the co-optimization method with an EA [78] to solve the sizing problem, and then MILP to solve the operation problem. The simulation process is shown in Fig.…”

Section: Sizing Methodologymentioning

confidence: 99%

Sizing of a stand-alone microgrid considering electric power, cooling/heating, hydrogen loads and hydrogen storage degradation

et al. 2017

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Microgrids are small-scale power systems with local generation, storage systems and load demands, that can operate connected to the main grid or islanded. In such systems, optimal components sizing is necessary to make the system secure and reliable, while minimizing costs. In this paper, a stand-alone microgrid considering electric power, cooling/heating and hydrogen consumption is built. A unit commitment algorithm, formulated as a mixed integer linear programming problem, is used to determine the best operation strategy for the system. A genetic algorithm is used to search for the best size of each component. The influence of three factors (operation strategy, accuracy of load and renewable generation forecasts, and degradation of fuel cell, electrolyzer and battery) on sizing results is discussed. A 1-hour rolling horizon simulation is used to check the validity of the sizing results. A robust optimization method is also used to handle the uncertainties and evaluate their impact on results.

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“…Previously, a significant amount of research had been conducted to develop successful solutions focused on cost optimization and the efficiency of Hybrid Renewable Energy Systems (HRES). The objective was to find optimal configurations that minimize costs while maximizing the utilization and performance of renewable energy sources within the system [14], Mixed-integer Quadratic programming technique [15], iterative technique [16], meta-PSO [17], genetic algorithm(GA) [18], Greedy Particle Swarm Optimization (GPSO) [19], probabilistic approach [20], and graphical construction technique [21]. Previous studies have reduced system efficiency while increasing costs.…”

Section: Introductionmentioning

confidence: 99%

Techno-economic Analysis of Hybrid Renewable Energy Systems Considering Demand Side Management

Mohamed,

Youssef,

A.Ismeil

2024

SVU-International Journal of Engineering Sciences and Applicati

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This paper presents a study on the design of rural energy systems and provides an analysis of the technical and economic feasibility of proposed Hybrid Energy Systems (HES) for rural electrification in a village in Egypt. The Hybrid Renewable Energy Systems are designed using the standard software tool HOMER Pro, addressing the optimization challenge of designing a hybrid renewable energy system based on demand-side management during peak and off-peak hours. In this study, realistic electricity consumption data for a single-family residence is evaluated in Qena City and Red Sea City in the Arab Republic of Egypt. To evaluate the effectiveness of the recommended design plan, this study considers three different cases. Simulation results reveal that the suggested design scheme is more suitable for remote locations than the previously proposed systems, as represented in these cases. Within each city, three scenarios are examined, and the optimal combination with the lowest cost is selected. Furthermore, in unregulated energy systems in remote areas, demand-side management (DSM) proves effective in addressing uncertainties in renewable energy generation and loading.

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Optimization of a PV-wind-diesel system using a hybrid genetic algorithm

Cited by 14 publications

References 6 publications

Optimization of the Configuration and Operating States of Hybrid AC/DC Low Voltage Microgrid Using a Clonal Selection Algorithm with a Modified Hypermutation Operator

Optimization of the Configuration and Operating States of Hybrid AC/DC Low Voltage Microgrid Using a Clonal Selection Algorithm with a Modified Hypermutation Operator

Sizing of a stand-alone microgrid considering electric power, cooling/heating, hydrogen loads and hydrogen storage degradation

Techno-economic Analysis of Hybrid Renewable Energy Systems Considering Demand Side Management

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