Demand response‐based operation of a hybrid renewable energy system with energy storage by multi‐objective optimization and multi‐criteria decision making

Bakhshaei, Peyman; Askarzadeh, Alireza; Arababadi, Reza

doi:10.1002/ep.13992

Cited by 2 publications

(3 citation statements)

References 33 publications

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“…In Formula (1), TD represents the total duration, and j f means the end time of the j th task. The total project cost consists of direct and indirect costs, expressed as formula (2).…”

Section: A Sfla Combined With Multi-objectivesmentioning

confidence: 99%

“…The goal of oilfield development planning is to achieve maximum profit, but the problem of oilfield development planning is extremely complex, involving multiple conflicting goals and various constraints, such as maximizing recovery rate, minimizing costs, maximizing production efficiency, etc. [2][3]. Meanwhile, it is necessary to consider the constraints and impacts of geological conditions, environmental policies, etc.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Objective Optimization of Oilfield Development Planning Based on Shuffled Frog Leaping Algorithm

Wei

2024

ijacsa

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Oilfield development planning is a complex task that involves multiple optimization objectives and constraints. Therefore, a study proposes an improved shuffled frog leaping algorithm to achieve multi-objective optimization tasks. In multi-objective problems, the fitness value of the algorithm is not adaptive to the memetic evolution, resulting in local search failures. Research is conducted on improving the shuffled frog leaping algorithm through non-dominated sorting genetic algorithm-II, memetic evolution, and traversal methods, and then verifying the effectiveness of the algorithm. The outcomes denoted that when the population was 30 and the grouping was 5, the algorithm proposed in the study had the fastest search speed and better optimization effect. The improved shuffled frog leaping algorithm had advantages in both construction period and cost compared to the shuffled frog leaping algorithm, with a construction period difference of 19 days and a cost difference of $13871. In comparative experiments with other algorithms, the average optimal solution and running time of the proposed algorithm were 0.324 and 7.2 seconds, respectively, which can quickly find the optimal solution in a short time. The algorithm proposed in the study can effectively optimize the complex objectives and constraints in oilfield development planning problems.

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“…In Formula (1), TD represents the total duration, and j f means the end time of the j th task. The total project cost consists of direct and indirect costs, expressed as formula (2).…”

Section: A Sfla Combined With Multi-objectivesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of Oilfield Development Planning Based on Shuffled Frog Leaping Algorithm

Wei

2024

ijacsa

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“…Design an improved Multi Crow Search Algorithm (MCSA) to further enhance the impact and energy of trajectory optimization indicators: improve on the shortcomings of traditional CSA, and the obtained MCSA has better energy consumption and impact size in trajectory optimization. And three other computational intelligence algorithms were selected to analyze and compare the optimized results, further demonstrating the superiority of MCSA [29].…”

Section: B Csa Algorithm Process Design Integrating Multi-objective T...mentioning

confidence: 99%

Trajectory Planning Application of Rehabilitation Robots Based on Improved CSA Algorithm

Xia

2023

IEEE Access

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With the aging global population, there are more patients with hemiplegia caused by stroke. The demand for rehabilitation robots to assist rehabilitation therapists is also increasing. To solve the above problems, a reasonable trajectory planning of the rehabilitation robot joints is constructed to achieve rehabilitation assistance, improve safety and practicality.Firstly, a Kinematics model is constructed, and B-spline is applied to realize the trajectory planning of the rehabilitation robot. By introducing crow search algorithm and multiple crow search algorithms, the energy impact multi-objective optimization for the upper limb movement of the rehabilitation robot is carried out. Relevant experiments are designed to compare and verify the optimization effects of the algorithms. From the results, by optimizing the crow search algorithm, only 25.36% and 34.20% of the impact and energy are used. The average impact size per unit of time decreases by 74.68%. The energy consumption per unit time decreases by 65.39%.After optimizing the crow search algorithm, the impact and energy per unit time are only 14.41% and 17.25%, respectively. The energy per unit time decreases by 74.53%, and the impact decreases by 82.97%. Compared with other algorithms, the space complexity of the improved method is very small, and the standard deviation is 0.03. The optimization algorithm performs well in trajectory planning optimization. It is of great significance in the trajectory planning and rehabilitation role of rehabilitation robots.

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Demand response‐based operation of a hybrid renewable energy system with energy storage by multi‐objective optimization and multi‐criteria decision making

Cited by 2 publications

References 33 publications

Multi-Objective Optimization of Oilfield Development Planning Based on Shuffled Frog Leaping Algorithm

Multi-Objective Optimization of Oilfield Development Planning Based on Shuffled Frog Leaping Algorithm

Trajectory Planning Application of Rehabilitation Robots Based on Improved CSA Algorithm

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