Location and Capacity Optimization of Distributed Energy Storage System in Peak-Shaving

Jin, Ruiyang; Song, Jie; Liu, Jie; Li, Wei; Lü, Chao

doi:10.3390/en13030513

Cited by 20 publications

(3 citation statements)

References 21 publications

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“…Considering the influence of battery energy storage system (BESS) providing reserve capacity on wind output uncertainty, a BESS optimal capacity model is proposed by Su et al (2017) for wind farm integration with adapting the scheduling plan. Focusing on the DESSs participating in peaking shaving of grid load, Jin et al (2020) are committed to searching for the optimal installation location and capacity allocation scheme of DESSs. Based on the spectral analysis method, Zhao et al (2015) propose a scheme for capacity allocation of hybrid ESS for power system peak shaving.…”

Section: Introductionmentioning

confidence: 99%

An energy storage system configuration strategy of public buildings for PV local consumption and three-phase unbalance management

Li,

Chen,

et al. 2024

Front. Energy Res.

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As the high carbon emission entities in the city, laying photovoltaic (PV) panels for public buildings is an effective way to reduce building carbon emissions. Meanwhile, public buildings play an important role as charging station access for the explosive growth of electric vehicles. However, the disorderly charging behavior of single-phase charging piles exacerbates the existing three-phase unbalance inside the buildings, which in turn affects operating costs and PV consumption. Energy storage system (ESS) configuration is considered an effective solution. Thus, An ESS configuration strategy is proposed for public buildings aiming at PV local consumption and three-phase unbalance management. To quantify the correlation between multiple loads and PV output, an improved affinity-propagation clustering algorithm based on the spatial weighted matrix distance is developed to obtain operational typical power supply-demand modes. Based on the construction of the three-phase power supply system with both single-phase and three-phase ESSs, a bi-level planning model is formulated for the configuration and operation optimization of ESSs inside the public building. The upper-level problem aims to minimize the life cycle cost of ESS allocation. The lower-level model deals with the coordinated economic scheduling of single-phase ESS and three-phase ESS under the obtained typical operational modes. Numerical results show the effectiveness and rationality of the proposed clustering algorithm and ESS configuration strategy.

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Section: Introductionmentioning

confidence: 99%

An energy storage system configuration strategy of public buildings for PV local consumption and three-phase unbalance management

Li,

Chen,

et al. 2024

Front. Energy Res.

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“…In [13], the objective was to find the optimal ESS size and location to reduce the power peaks caused by the uncertainty of renewable energy sources. It was concluded using the greedy algorithm method that the optimal location was at nodes with the highest load demand.…”

Section: Introductionmentioning

confidence: 99%

Modelling a DC Electric Railway System and Determining the Optimal Location of Wayside Energy Storage Systems for Enhancing Energy Efficiency and Energy Management

Alnuman

2024

Energies

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Global demand for fossil fuels is highly increasing, necessitating energy efficiency to be enhanced in transitioning to low-carbon energy systems. Electric railways are highly efficient in reducing the transportation demand for fossil fuels as they are lightweight and their energy demand can be fed by renewable energy resources. Further, the regenerative braking energy of decelerating trains can be fed to accelerating trains and stored in onboard energy storage systems (ESSs) and stationary ESSs. It is fundamental to model electric railways accurately before investigating approaches to enhancing their energy efficiency. However, electric railways are challenging to model as they are nonlinear, resulting from the rectifier substations, overvoltage protection circuits, and the unpredictability and uncertainty of the load according to the train position. There have been few studies that have examined the ESS location’s impact on improving the energy efficiency of electric railways while using specialised simulation tools in electric railways. However, no single study exists that has studied the location impact of stationary ESSs on the energy efficiency of electric railways while the trains are supported by onboard ESSs. Given these goals and challenges, the main objective of this work is to develop a model using commercial software used by industry practitioners. Further, the energy saving is aimed to be maximised using stationary ESSs installed in optimal locations while trains are supported by onboard ESSs. The model includes trains, onboard ESSs, rail tracks, passenger stations, stationary ESSs, and traction power systems involving power lines, connectors, switches, sectioning, and isolators. In this article, a test scenario is presented comprising two trains running on a 20 km with three passenger stations and two substations. The trains and track are modelled in OpenTrack simulation software (Version 1.9) while the power system is modelled in OpenPowerNet simulation software (Version 1.11). The two simulation tools are used in the railway industry and can produce realistic results by taking into account the entire electrical network structure. A stationary ESS is added on the wayside and moved in steps of 1 km to obtain the optimal location before investigating the impact of stationary ESSs on the performance and energy management of onboard ESSs. It is found that the energy saving when installing a stationary ESS at the optimal location is 56.05%, the peak-power reduction of Substation 1 is 4.37%, and the peak-power reduction of Substation 2 is 18.67%.

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“…Jin et al [23] discuss the issue of optimizing the location and capacity of peak-Shaving's distributed energy storage system. The general problem is this: Distributed Energy Storage System (DESS) technology does a very good job of the challenge.…”

Section: Introductionmentioning

confidence: 99%

Distributed Energy Resources: Operational Benefits

2022

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Location and Capacity Optimization of Distributed Energy Storage System in Peak-Shaving

Cited by 20 publications

References 21 publications

An energy storage system configuration strategy of public buildings for PV local consumption and three-phase unbalance management

An energy storage system configuration strategy of public buildings for PV local consumption and three-phase unbalance management

Modelling a DC Electric Railway System and Determining the Optimal Location of Wayside Energy Storage Systems for Enhancing Energy Efficiency and Energy Management

Distributed Energy Resources: Operational Benefits

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