Based on the dynamic changes of inertia factor and acceleration factor, this paper introduces compression factor to improve the particle swarm optimization …
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A quantum-behaved particle swarm optimization (QPSO) algorithm that optimizes the HESS capacity is ... The optimal configuration of energy storage capacity can effectively improve the system ...
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In [16], [17] we introduced a constrained particle swarm optimization (PSO) algorithm [15], [18], [19] to minimize the energy losses of a HESS from an electric vehicle, for a simple driving cycle ...
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The capacity of an energy storage device configuration not only affects the economic operation of a microgrid, but also affects the power supply''s reliability. An isolated microgrid is considered with typical loads, renewable energy resources, and a hybrid energy storage system (HESS) composed of batteries and ultracapacitors in this paper. A quantum …
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As the traditional multi-objective particle swarm algorithm is prone to local convergence, this study introduces variable inertia weight and learning factors to …
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Particle swarm optimization (PSO) algorithm is used to solve the concrete example, and the optimal capacity configuration result of integrated economy and power supply reliability is obtained.
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A particle swarm optimization algorithm is developed and fitted in order to solve this non-linear multi-objective function. With the aim of analyzing the …
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Multi-objective particle swarm optimization algorithm based on multi-strategy improvement for hybrid energy storage optimization configuration. …
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To address the limitations of single-objective solution algorithms and the lack of diversity and premature convergence in multi-objective optimization processes, a multi-objective …
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Abstract: The proposed approach involves a method of joint optimization configuration for wind– solar–thermal-storage (WSTS) power energy bases utilizing a dynamic inertia weight chaotic particle swarm optimization (DIWCPSO) algorithm. The power generated
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Optimizing the energy structure to effectively enhance the integration level of renewable energy is an important pathway for achieving dual carbon goals. This study utilizes an improved multi-objective particle swarm optimization algorithm based on load fluctuation rates to optimize the architecture and unit capacity of hydrogen production …
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The proposed day-ahead optimized scheduling controller outperforms the backtracking search algorithm and particle swarm optimization techniques in terms of iteration (53.56) and time consumption (2915.2 min) which in …
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Synthesis gas has a very wide range of applications in the chemical industry. In this paper, the experiment is needed to achieve the highest CH4 conversion rate, the highest CO selectivity, and the lowest H2/CO ratio in synthesis gas production by controlling the feedstock O2/CH4 feed ratio, gas hourly space velocity, and temperature in the synthesis …
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In order to improve the operation reliability and new energy consumption rate of the combined wind–solar storage system, an optimal allocation method for the capacity of the energy storage system (ESS) based on the improved sand cat swarm optimization algorithm is proposed. First, based on the structural analysis of the …
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An adaptive online adjustment particle swarm optimization (AOA-PSO) algorithm for system optimization is proposed to solve the technical issues of large-scale wind and light abandonment. Firstly, a linear adjustment factor is introduced into the particle swarm optimization (PSO) algorithm to adaptively adjust the search range of the …
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DOI: 10.1016/j.renene.2024.120086 Corpus ID: 267437180 Multi-objective particle swarm optimization algorithm based on multi-strategy improvement for hybrid energy storage optimization configuration @article{Xu2024MultiobjectivePS, title={Multi-objective particle ...
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Renewable energy sources such as wind power and solar energy have strong volatility and intermittence, while hybrid energy storage plays an important role in power balance of microgrid and smooth power fluctuation of renewable energy. Aiming at the microgrid island operation mode including wind power, photovoltaic and typical load, a hybrid energy …
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It can be obtained that the differential particle swarm algorithm outperforms the standard particle swarm algorithm in the energy storage siting and capacity determination problem. Energy storage access nodes and capacities of 18 (0.7650) and 33 (0.6001), the charging and discharging power of energy storage for 24 h …
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From Figs. 3 and 4, it can be seen that the improved compression factor particle swarm optimization algorithm has a faster convergence speed than the standard particle swarm optimization algorithm om Table 4, it can be seen that compared with the standard particle swarm optimization algorithm, the improved compression factor …
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In order to make rational use of power grid resources, it is necessary to optimize the distributed energy storage scheduling. The particle swarm optimization algorithm proposed in this paper is ...
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For example, (Mesbahi et al., 2017) embedded the Nelder-Mead simplex method in Particle Swarm Optimization (PSO) algorithm to solve the capacity optimization problem. ( Guo, et al., 2020 ) proposed the multi-objective PSO to solve the capacity optimization in a wind-photovoltaic-thermal energy storage hybrid power …
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Simultaneously, the quantum particle swarm optimization algorithm is applied to get fast optimization speed and global results. References Hossain MS, Pandey AK, Rahim NA (2020) Self-cleaning assisted photovoltaic system with thermal energy storage: design and performance evaluation.
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Taking a natural village in China as an example, the improved particle swarm optimization algorithm is used to solve the optimal energy storage ca-pacity, power and typical daily energy storage output.
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Due to the large increase in the proportion of renewable energy such as wind energy and solar, it is necessary to configure energy storage in the distribution grid to make it economically and stably run. In this paper, the system economy and stability are taken as the optimization indexes, and the voltage deviation index and the life cycle …
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Optimal configuration of hybrid energy storage capacity in power system considering large-scale wind ... (DE) algorithm is adopted to optimize the particle swarm optimization (PSO) algorithm, and ...
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Research on Optimal Configuration of Hybrid Energy Storage Capacity Based on Improved Particle Swarm Optimization Algorithm Abstract: Renewable energy sources such …
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To address the limitations of single-objective solution algorithms and the lack of diversity and premature convergence in multi-objective optimization processes, a multi-objective particle swarm optimization by multi-strategy improvements (CMOPSO …
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The optimal configuration of battery energy storage system is key to the designing of a microgrid. In this paper, a optimal configuration method of energy storage in grid-connected microgrid is proposed. Firstly, the two-layer decision model to allocate the capacity of storage is established. The decision variables in outer programming model …
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Also, several methods have been proposed for optimizing energy systems, including particle swarm optimization (PSO), simulated annealing (SA), 13 genetic algorithm (GA), 14 monarch butterfly optimization, 15 improved harmony search, …
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Table 2 shows the proposed optimization algorithms used in this study to solve the micro-grid energy management problem. Fig. 4 shows the plot of the load power profile. In the morning, the micro-grid uses the energy …
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On this basis, the improved particle swarm optimization (IPSO) is used to solve the model, so that the optimal allocation scheme is obtained. Finally, in MATLAB …
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The capacity of an energy storage device configuration not only affects the economic operation of a microgrid, but also affects the power supply''s reliability. An isolated microgrid is considered with typical loads, renewable energy resources, and a hybrid energy storage system (HESS) composed of batteries and ultracapacitors in this …
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In Ref. [16], a particle swarm optimization (PSO) algorithm is used to optimize the capacity configuration of the hybrid energy storage system, considering the power fluctuation of the DC bus of the microgrid and the storage capacity ratio in each storage module
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1 INTRODUCTION Many countries around the world are currently experiencing a power shortage, especially in remote areas. 1 In these areas, fossil fuels, especially diesel generators, are commonly used to supply electricity. 2 Due to fossil fuels reduction and severe environmental concerns, the choice of alternative methods 3 and fuels is …
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Thus, an intelligent optimization algorithm based on the particle swarm optimization (PSO) was proposed to achieve the optimal system performance. The thermodynamic model was developed based on MATLAB, and the round-trip efficiency (RTE) and exergy efficiency were selected as performance evaluation indexes.
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The power difference in different time scales is allocated to the three types of storage and the PSO is used for the capacity optimization. [14] and [16] use a single-objective optimization ...
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