PSO-Based Initial SOC and Capacity Optimization for Stationary Energy Storage Systems in DC Electric Railway System

Kim, Ki‐Suk; Kim, Jae-Won; Lee, Changmu; Kim, Joorak; Lee, Hansang

doi:10.1007/s42835-021-00746-0

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…The exploration happens if either or both of the differences between the particle's best (p t id ) and the previous particle's position (x t id ) and between the population's all-time best (p t id ) and the previous particle's position (x t id ) are large, and exploitation occurs when these values are both small. PSO has attracted wide attention in timetable scheduling and railway optimization problems due to its algorithmic simplicity and powerful search performance (see [19][20][21][22]).…”

Section: Searching Optimal Headwaysmentioning

confidence: 99%

Simulation-Based Headway Optimization for the Bangkok Airport Railway System under Uncertainty

Sasithong,

Parnianifard,

Sinpan

et al. 2023

Electronics

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The ever-increasing demand for intercity travel, as well as competition among all modes of transportation, is an unavoidable reality that today’s urban rail transit system must deal with. To meet this problem, urban railway companies must try to make better use of their existing plans and resources. Analytical approaches or simulation modeling can be used to develop or change a rail schedule to reflect the appropriate passenger demand. However, in the case of complex railway networks with several interlocking zones, analytical methods frequently have drawbacks. The goal of this article is to create a new simulation-based optimization model for the Bangkok railway system that takes into account the real assumptions and requirements in the railway system, such as uncertainty. The common particle swarm optimization (PSO) technique is combined with the developed simulation model to optimize the headways for each period in each day. Two different objective functions are incorporated into the models to consider both customer satisfaction by reducing the average waiting time and railway management satisfaction by reducing needed energy usage (e.g., reducing operating trains). The results obtained using a real dataset from the Bangkok railway system demonstrate that the simulation-based optimization approach for robust train service timetable scheduling, which incorporates both passenger waiting times and the number of operating trains as equally important objectives, successfully achieved an average waiting time of 11.02 min (with a standard deviation of 1.65 min) across all time intervals.

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Section: Searching Optimal Headwaysmentioning

confidence: 99%

Simulation-Based Headway Optimization for the Bangkok Airport Railway System under Uncertainty

Sasithong,

Parnianifard,

Sinpan

et al. 2023

Electronics

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“…The state of charge (SOC) of a battery represents the ratio of the battery's current remaining charge to its charge when fully charged [32,33]. The formula is:…”

Section: Constraintmentioning

confidence: 99%

Optimization of Energy Storage Allocation in Wind Energy Storage Combined System Based on Improved Sand Cat Swarm Optimization Algorithm

Zhang,

Xue,

et al. 2023

Processes

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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 combined system, an optimization model of energy storage configuration is established with the objectives of the lowest total investment cost of the ESS, the lowest load loss rate and the lowest new energy abandonment rate, which not only takes into account the economy of energy storage construction for investors and builders, but also reduces the probability of blackout for users to protect their interests and improves the utilization rate of the natural resources of wind and light, which can achieve a multi-win–win situation. The model can realize the win–win situation in many aspects. Secondly, an improved k-means clustering algorithm is used to cluster the renewable energy power and load data to realize the typical day data extraction. Then, for the proposed multi-objective optimization model, an SCSO is proposed based on the triangular wandering strategy, Lévy flight strategy and lens imaging reverse learning improvement, which can help the algorithm to jump out of the local optimum while improving its global optimization ability, and these improvements can significantly improve the optimization effect of the SCSO. Finally, simulation analysis is carried out in combination with typical daily extraction data, and the results verify the advantages and effectiveness of the proposed model and algorithm.

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PSO-Based Initial SOC and Capacity Optimization for Stationary Energy Storage Systems in DC Electric Railway System

Cited by 2 publications

References 14 publications

Simulation-Based Headway Optimization for the Bangkok Airport Railway System under Uncertainty

Simulation-Based Headway Optimization for the Bangkok Airport Railway System under Uncertainty

Optimization of Energy Storage Allocation in Wind Energy Storage Combined System Based on Improved Sand Cat Swarm Optimization Algorithm

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