Developing MSA Algorithm by New Fitness-Distance-Balance Selection Method to Optimize Cascade Hydropower Reservoirs Operation

Sharifi, Mohammad Reza; Akbarifard, Saeid; Qaderi, Kourosh; Madadi, Mohamad Reza

doi:10.1007/s11269-020-02745-8

Cited by 27 publications

(9 citation statements)

References 22 publications

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“…A group of the moth with the highest luminescence intensities is selected as the prospectors. The number of prospectors should be reduced in each iteration (transverse orientation phase) 34 , 35 .…”

Section: Methodsmentioning

confidence: 99%

Optimization of hydropower energy generation by 14 robust evolutionary algorithms

Sharifi

Akbarifard

Madadi

et al. 2022

Sci Rep

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The use of evolutionary algorithms (EAs) for solving complex engineering problems has been very promising, so the application of EAs for optimal operation of hydropower reservoirs can be of great help. Accordingly, this study investigates the capability of 14 recently-introduced robust EAs in optimization of energy generation from Karun-4 hydropower reservoir. The best algorithm is the one that produces the largest objective function (energy generation) and has the minimum standard deviation (SD), the minimum coefficient of variations (CV), and the shortest time of CPU usage. It was found that the best solution was achieved by the moth swarm algorithm (MSA), with the optimized energy generation of 19,311,535 MW which was 65.088% more than the actual energy generation (11,697,757). The values of objective function, SD and CV for MSA were 0.147, 0.0029 and 0.0192, respectively. The next ranks were devoted to search group algorithm (SGA), water cycle algorithm (WCA), symbiotic organism search algorithm (SOS), and coyote optimization algorithm (COA), respectively, which have increased the energy generation by more than 65%. Some of the utilized EAs, including grasshopper optimization algorithm (GOA), dragonfly algorithm (DA), antlion optimization algorithm (ALO), and whale optimization algorithm (WOA), failed to produce reasonable results. The overall results indicate the promising capability of some EAs for optimal operation of hydropower reservoirs.

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Section: Methodsmentioning

confidence: 99%

Optimization of hydropower energy generation by 14 robust evolutionary algorithms

Sharifi

Akbarifard

Madadi

et al. 2022

Sci Rep

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“…Fixed cost [22]: (7) where M is the controllable unit with different power sources; K k,OM and F k are the operation and maintenance factors and investment costs of the k th power unit; P k,t is the active power output from the k th power supply at time t…”

Section: Objective Functionsmentioning

confidence: 99%

Optimal Scheduling of the Wind-Photovoltaic-Energy Storage Multi-Energy Complementary System Considering Battery Service Life

Li,

Wang,

Zhang

et al. 2023

Energies

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Under the background of “peak carbon dioxide emissions by 2030 and carbon neutrality by 2060 strategies” and grid-connected large-scale renewables, the grid usually adopts a method of optimal scheduling to improve its ability to cope with the stochastic and volatile nature of renewable energy and to increase economic efficiency. This article proposes a short-term optimal scheduling model for wind–solar storage combined-power generation systems in high-penetration renewable energy areas. After the comprehensive consideration of battery life, energy storage units, and load characteristics, a hybrid energy storage operation strategy was developed. The model uses the remaining energy in the system after deducting wind PV and energy storage output as the “generalized load”. An improved particle swarm optimization (PSO) is used to solve the scheduling schemes of different running strategies under different objectives. The optimization strategy optimizes the battery life-loss coefficient from 0.073% to 0.055% under the target of minimizing the mean squared deviation of “generalized load”, which was optimized from 0.088% to 0.053% under the minimized fluctuation of combined system output and optimized from 0.092% to 0.081% under the minimized generation costs of the combined system. The results show that the model can ensure a stable operation of the combined system, and the operation strategy proposed in this article effectively reduces battery life loss while reducing the total power generation cost of the system. Finally, the superiority of the improved PSO algorithm was verified.

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“…One of the strongest single-objective algorithms within the family of evolutionary algorithms is the moth swarm algorithm (MSA). The MSA has been proven to be superior to over 80 other evolutionary algorithms [25][26][27][28][29][30][31][32][33] . Due to the novelty of the MSA, there is still no study in the literature to design a multi-objective version of this algorithm.…”

Section: A New Optimization Algorithm To Solve Multi-objective Problemsmentioning

confidence: 99%

A new optimization algorithm to solve multi-objective problems

Sharifi

Akbarifard

Qaderi

et al. 2021

Sci Rep

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Simultaneous optimization of several competing objectives requires increasing the capability of optimization algorithms. This paper proposes the multi-objective moth swarm algorithm, for the first time, to solve various multi-objective problems. In the proposed algorithm, a new definition for pathfinder moths and moonlight was proposed to enhance the synchronization capability as well as to maintain a good spread of non-dominated solutions. In addition, the crowding-distance mechanism was employed to select the most efficient solutions within the population. This mechanism indicates the distribution of non-dominated solutions around a particular non-dominated solution. Accordingly, a set of non-dominated solutions obtained by the proposed multi-objective algorithm is kept in an archive to be used later for improving its exploratory capability. The capability of the proposed MOMSA was investigated by a set of multi-objective benchmark problems having 7 to 30 dimensions. The results were compared with three well-known meta-heuristics of multi-objective evolutionary algorithm based on decomposition (MOEA/D), Pareto envelope-based selection algorithm II (PESA-II), and multi-objective ant lion optimizer (MOALO). Four metrics of generational distance (GD), spacing (S), spread (Δ), and maximum spread (MS) were employed for comparison purposes. The qualitative and quantitative results indicated the superior performance and the higher capability of the proposed MOMSA algorithm over the other algorithms. The MOMSA algorithm with the average values of CPU time = 2771 s, GD = 0.138, S = 0.063, Δ = 1.053, and MS = 0.878 proved to be a robust and reliable model for multi-objective optimization.

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Developing MSA Algorithm by New Fitness-Distance-Balance Selection Method to Optimize Cascade Hydropower Reservoirs Operation

Cited by 27 publications

References 22 publications

Optimization of hydropower energy generation by 14 robust evolutionary algorithms

Optimization of hydropower energy generation by 14 robust evolutionary algorithms

Optimal Scheduling of the Wind-Photovoltaic-Energy Storage Multi-Energy Complementary System Considering Battery Service Life

A new optimization algorithm to solve multi-objective problems

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