Optimal Capacity Configuration of a Hybrid Energy Storage System for an Isolated Microgrid Using Quantum-Behaved Particle Swarm Optimization

Wang, Hui; Wang, Tengxin; Xie, Xiaohan; Ling, Zhixiang; Gao, Guoliang; Dong, Xu

doi:10.3390/en11020454

Cited by 29 publications

(15 citation statements)

References 15 publications

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“…Artificial neural network effectiveness was also shown by Seo et al [31]. Instead, Wang et al [32] and Wu et al [33] exploited particle swarm algorithms to optimize the capacity of hybrid energy storage systems.…”

Section: Literature Reviewmentioning

confidence: 97%

Optimal Management of the Energy Flows of Interconnected Residential Users

Manservigi¹,

Cattozzo²,

Spina³

et al. 2020

Energies

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In recent years, residential users have begun to be equipped with micro-CHP (combined heat and power) generation technologies with the aim of decreasing primary energy consumption and reducing environmental impact. In these systems, the prime mover supplies both thermal and electrical energy, and an auxiliary boiler and the national electrical grid are employed as supplementary systems. In this paper, a simulation model, which accounts for component efficiency and energy balance, was developed to replicate the interaction between the users and the energy systems in order to minimize primary energy consumption. The simulation model identified the optimal operation strategy of two residential users by investigating different energy system configurations by means of a dynamic programming algorithm. The reference scenario was compared to three different scenarios by considering independent energy systems, shared thermal and electrical energy storage and also the shared prime mover. Such a comparison allowed the identification of the most suitable energy system configuration and optimized operation strategy. The results demonstrate that the optimized operation strategy smoothes the influence of the size of thermal and electrical energy storage. Moreover, the saving of primary energy consumption can be as high as 5.1%. The analysis of the economic feasibility reveals that the investment cost of the prime mover can be as high as 4000 €/kW.

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Section: Literature Reviewmentioning

confidence: 97%

Optimal Management of the Energy Flows of Interconnected Residential Users

Manservigi¹,

Cattozzo²,

Spina³

et al. 2020

Energies

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show abstract

“…For example, in India, TATA power has established an ESU at Rohini substation with a capacity of 10MW to manage the major challenges such as peak management, effective management of renewables, and power quality [16]. The concept of hybrid ESU (HESU) with the combination of super-capacitors or ultra-capacitors and battery storage is proposed for effective grid management [17]- [19].…”

Section: Hesumentioning

confidence: 99%

Environmental Constrained Optimal Hybrid Energy Storage System Planning for an Indian Distribution Network

2020

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In recent days, aiming for power generation with less carbon emission, led to the high penetration of renewable energy into the distribution grid. To improve the intermittency caused by renewables and to increase the grid flexibility, grid integrated Energy Storage Units (ESUs) are proposed as the promising solution in the literature. However, considering the investment cost, ESUs are optimally placed by satisfying the network electrical constraints. On the other hand, consideration of environmental impacts and other practical constraints are also equally important. Therefore, in this article, on top of grid performance parameters, realistic parameters that may affect the location and its size such as (1) environmental impact, (2) land requirement & its associated cost for ESU installation and (3) renewable purchase obligation are formulated in the objective function. Decision making on ESU installation is a planning activity, which follows uncertainty. Consequently, it is essential to model the uncertainty parameters into the objective function, for better planning. In this article, optimal planning of hybrid ESUs based on realistic parameters along with uncertainty is addressed. For this study, a practical 156-bus distribution system of Dehradun district, India is considered. From the results obtained, it is evident that, formulating hybrid ESU constrained with the environmental impact has significantly decreased the emission of CO2 with maximum grid stability. INDEX TERMS Batteries, distributed power generation, environmental economics, power system planning, sustainable development.

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“…Zhao et al [39] Presented a novel evolutionary extreme learning machine based on improved quantum-behaved particle swarm optimization for radar target classification. Wang et al [40] applied the QPSO algorithm in the hybrid energy storage system capacity optimization. Li et al [41] Applied the discrete particle swarm optimization strategy in network clustering.…”

Section: Literature Reviewmentioning

confidence: 99%

An Improved Multi-Objective Quantum-Behaved Particle Swarm Optimization for Railway Freight Transportation Routing Design

et al. 2019

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With the development of railway transportation, the railway transportation enterprises expand their freight transportation from station-to-station transportation to door-to-door transportation, which makes the routing design more complicated. The existing classical optimization algorithms are difficult to meet the needs of practical applications. Therefore, the paper introduces an Improved Multi-objective Quantumbehaved Particle Swarm Optimization algorithm (IMOQPSO). Then based on the continuous coding for the Railway Freight Transportation Routing Design, the proposed improved algorithm was applied to solve the problem to verify the performance of algorithm. Finally, the paper compared the performance of Improved Multi-objective Quantum-behaved Particle Swarm Optimization algorithm with other four continuous multiobjective swarm intelligence algorithms. The results shown that the proposed algorithm obtained the best Pareto front which is closer to the real Pareto front of Railway Freight Transportation Routing Design. Hence, the proposed Improved Multi-objective Quantum-behaved Particle Swarm Optimization algorithm can provide support for the railway transport enterprises routing design decisions to some extent.

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Optimal Capacity Configuration of a Hybrid Energy Storage System for an Isolated Microgrid Using Quantum-Behaved Particle Swarm Optimization

Cited by 29 publications

References 15 publications

Optimal Management of the Energy Flows of Interconnected Residential Users

Optimal Management of the Energy Flows of Interconnected Residential Users

Environmental Constrained Optimal Hybrid Energy Storage System Planning for an Indian Distribution Network

An Improved Multi-Objective Quantum-Behaved Particle Swarm Optimization for Railway Freight Transportation Routing Design

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