Optimal sizing of renewables and battery systems for hybrid AC/DC microgrids based on variability management

Xie, Peng; Jia, Youwei; Lyu, Cheng; Wang, Han; Shi, Mengge; Chen, Hongkun

doi:10.1016/j.apenergy.2022.119250

Cited by 9 publications

(5 citation statements)

References 26 publications

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“…Compared to single DC or AC microgrids, hybrid AC/DC MGs combine both advantages (Yue et al, 2022). Xie et al learned through simulation analysis that an AC/DC MG ensures power quality during the grid-connected process and significantly improves the system stability of the MG compared to a single MG (Xie et al, 2022).…”

Section: Related Workmentioning

confidence: 99%

A bi-layer optimization method of the grid-connected microgrid based on the multi-strategy of the beluga whale algorithm

Zhong,

Sun,

2024

Front. Energy Res.

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Endeavoring to enhance the penetration rate of renewable energy sources, concurrently ensuring economic and operational stability, this study proposes a novel bi-layer optimization method of the wind–solar-storage AC/DC microgrid (MG). First, by incorporating a superordinate electric and seasonal hydrogen hybrid energy storage system (E&SHESS), the topology structure of the microgrid is established. Subsequently, to rectify the intrinsic limitations of the conventional beluga whale optimization (BWO) algorithm, this paper proposes a multi-strategy hybrid improvement to BWO (MHIBWO). This innovative improvement integrates an MTent strategy, a step size adjustment mechanism, and a crisscross strategy. Then, constructing a bi-layer iterative model based on the topology, annual net income and grid-connected friendliness are introduced as optimization objectives for the outer and inner layers, respectively, utilizing MHIBWO and CPLEX for resolution. Through a nested iteration of the two layers, the model outputs the capacity scheme with the best performance of economy and stability. Finally, the simulation unequivocally demonstrated the superiority of MHIBWO and the model proposed. In addition, based on the real data of the Elia power station, the validity of the method in operation is tested using the fuzzy C-means algorithm (FCMA) to extract and aggregate typical days, thereby presenting a sophisticated solution for the field of microgrids optimization configuration.

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Section: Related Workmentioning

confidence: 99%

A bi-layer optimization method of the grid-connected microgrid based on the multi-strategy of the beluga whale algorithm

Zhong,

Sun,

2024

Front. Energy Res.

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“…Mechanical energy storage devices may be designed using these kinetic and potential energies along with the energy conservation equation. Flywheel energy storage is one such mechanical storage device [43].…”

Section: Flywheel Energy Storagementioning

confidence: 99%

Multi-Objective Optimization Algorithms for a Hybrid AC/DC Microgrid Using RES: A Comprehensive Review

et al. 2023

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Optimization methods for a hybrid microgrid system that integrated renewable energy sources (RES) and supplies reliable power to remote areas, were considered in order to overcome the intermittent nature of RESs. The hybrid AC/DC microgrid system was constructed with a solar photovoltaic system, wind turbine, battery storage, converter, and diesel generator. There is a steady increase in the utilization of hybrid renewable energy sources with hybrid AC/DC microgrids; consequently, it is necessary to solve optimization techniques. Therefore, the present study proposed utilizing multi-objective optimization methods using evolutionary algorithms. In this context, a few papers were reviewed regarding multi-objective optimization to determine the capacity and optimal design of a hybrid AC/DC microgrid with RESs. Here, the optimal system consisted of the minimum cost of energy, minimum net present cost, low operating cost, low carbon emissions and a high renewable fraction. These were determined by using multi-objective optimization (MOO) algorithms. The sizing optimization of the hybrid AC/DC microgrid was based on the multi-objective grey wolf optimizer (MOGWO) and multi-objective particle swarm optimization (MOPSO). Similarly, multi-objective optimization with different evolutionary algorithms (MOGA, MOGOA etc.) reduces energy cost and net present cost, and increases the reliability of islanded hybrid microgrid systems.

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“…It should be noted that the status of these two goals is not equal. To maximize the interests of users, we need to take (28) as the primary goal of optimization. After achieving the maximum economic benefits, we will consider improving social benefits, which will help to improve the enthusiasm of investors.…”

Section: B Dispatching Model Of Load and Essmentioning

confidence: 99%

“…Through the analysis of three different test scenarios, the power purchase cost of the grid was reduced. Xie et al [28] established a bi-level mixed-integer nonlinear programming model and solved the mathematical model efficiently with the original decomposition coordination algorithm based on dual decomposition and alternative iteration strategy. Provide support for obtaining the optimal capacity of ESS under different scenarios.…”

Section: Introductionmentioning

confidence: 99%

“…There is still no unified conclusion on the selection of weights for each target [32]. Moreover, the above research only considers the load change in a short period [13], [16], [21][22], or only analyzes a few scenarios [17], [26][27] represented by typical data in recent two years, and does not consider the dynamic characteristics of the load in the life cycle of distributed power generation equipment [28][29][30], [34]. This does not provide comprehensive data support for the configuration of ESS capacity.…”

Section: Introductionmentioning

confidence: 99%

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Study of Long-Term Energy Storage System Capacity Configuration Based on Improved Grey Forecasting Model

Feng

Zhang³

et al. 2023

IEEE Access

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Distributed generation equipment improves renewable energy utilization and economic benefits through an energy storage system (ESS). However, dominated by short-term data, the configuration of long-period ESS capacity is absent based on the dynamic change of load, which leads to a large deviation from the expected return. Considering the system characteristics of lack of data and less information, after introducing the grey theory, we propose a new long-term capacity configuration method for ESS and establish the long-term grey forecasting model (GFM) of user load, improving the basic forecasting model to improve the accuracy of the long-term forecasting model. Then, the scheduling model is established with the maximum economic and social benefits as the optimization objective. Based on the forecast data of the improved grey forecasting model (IGFM), the hierarchical solution method is used to solve the scheduling model. Finally, the parameters are configured based on the service life of the equipment and the expected rate of return. The simulation results show that higher accuracy is realized in the improved prediction model, and the improved algorithm gets higher convergence speed and precision. Apart from that, the nonlinear correlation trend of the EES return rate between the capacity and life cycle is revealed. Compared with the ESS configuration in a short period, this study provides more comprehensive and accurate data support for the capacity configuration of the ESS, reducing the error between the actual return and the expected return significantly.

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Optimal sizing of renewables and battery systems for hybrid AC/DC microgrids based on variability management

Cited by 9 publications

References 26 publications

A bi-layer optimization method of the grid-connected microgrid based on the multi-strategy of the beluga whale algorithm

A bi-layer optimization method of the grid-connected microgrid based on the multi-strategy of the beluga whale algorithm

Multi-Objective Optimization Algorithms for a Hybrid AC/DC Microgrid Using RES: A Comprehensive Review

Study of Long-Term Energy Storage System Capacity Configuration Based on Improved Grey Forecasting Model

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