Placement and capacity selection of battery energy storage system in the distributed generation integrated distribution network based on improved NSGA-II optimization

Gu, Tianming; Wang, Puyu; Liang, Fangyu; Xie, Guangen; Guo, Lixin; Zhang, Xiaoping; Shi, Fang

doi:10.1016/j.est.2022.104716

Cited by 36 publications

(11 citation statements)

References 18 publications

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“…Many other studies look into battery sizing optimisation in other applications, such as for prosumers in renewable energy communities [11], as neighbourhood-level storage at a low-voltage distribution level [12,13], and as storage in a microgrid setting [14][15][16].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Popular optimisation methods are the particle swarm optimisation algorithm, used in [5,7], and genetic algorithms (or variants thereof) such as in [11][12][13]17]. The objective function often seeks to minimise the annual investment cost, although other objective functions may include maximising self-consumption from PV production or minimising grid interaction.…”

Section: Literature Reviewmentioning

confidence: 99%

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Optimal Sizing of a Battery-Supported Electric Vehicle Charging Hub with a Limited-Capacity Grid Connection

Heath,

Wolbertus,

Heller

2024

WEVJ

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The ever-increasing electrification of society has been a cause of utility grid issues in many regions around the world. With the increased adoption of electric vehicles (EVs) in the Netherlands, many new charge points (CPs) are required. A common installation practice of CPs is to group multiple CPs together on a single grid connection, the so-called charging hub. To further ensure EVs are adequately charged, various control strategies can be employed, or a stationary battery can be connected to this network. A pilot project in Amsterdam was used as a case study to validate the Python model developed in this study using the measured data. This paper presents an optimisation of the battery energy storage capacity and the grid connection capacity for such a P&R-based charging hub with various load profiles and various battery system costs. A variety of battery control strategies were simulated using both the optimal system sizing and the case study sizing. A recommendation for a control strategy is proposed.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Optimal Sizing of a Battery-Supported Electric Vehicle Charging Hub with a Limited-Capacity Grid Connection

Heath,

Wolbertus,

Heller

2024

WEVJ

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“…In [92], the voltage timing sensitivity was proposed to plan the installation location and capacity of the energy storage system from the perspective of improving the voltage distribution. In [93], by calculating the power loss sensitivity, the energy storage was selected in the position with the highest power loss sensitivity.…”

Section: Ess Application Scenarios On the New Energy Sidementioning

confidence: 99%

A Comprehensive Review on Energy Storage System Optimal Planning and Benefit Evaluation Methods in Smart Grids

Li,

Wang,

Liu

et al. 2023

Sustainability

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Smart grids are the ultimate goal of power system development. With access to a high proportion of renewable energy, energy storage systems, with their energy transfer capacity, have become a key part of the smart grid construction process. This paper first summarizes the challenges brought by the high proportion of new energy generation to smart grids and reviews the classification of existing energy storage technologies in the smart grid environment and the practical application functions of energy storage in smart grids. Secondly, optimization planning and the benefit evaluation methods of energy storage technologies in the three different main application scenarios, including the grid side, user side, and new energy side, are analyzed. The advantages and shortcomings of the current research are also pointed out. Furthermore, the paper sheds light on the pressing issues that demand further consideration in energy storage planning. Finally, the aspects that warrant attention in the future application and promotion processes are elucidated in detail, culminating in a comprehensive understanding of the energy storage technologies in smart grids.

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“…In BES sizing studies, BES (dis)charge losses tend to be simplified as a fixed value or neglected entirely [52,53]. Similarly, network losses and grid constraints tend to be simplified (e.g., so they can be included in a linear optimization algorithm) or omitted [42,54,55]. When network losses/constraints are included, they tend to be implemented in a two-step process: by first selecting a site which minimises network losses for given power flows, and then minimising the battery capacity needed to provide a given service [15,30,37,42,44,49,52,[56][57][58][59].…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, network losses and grid constraints tend to be simplified (e.g., so they can be included in a linear optimization algorithm) or omitted [42,54,55]. When network losses/constraints are included, they tend to be implemented in a two-step process: by first selecting a site which minimises network losses for given power flows, and then minimising the battery capacity needed to provide a given service [15,30,37,42,44,49,52,[56][57][58][59]. However, optimal battery siting is not always feasible or practical due to access and/or space constraints, and it is not always clear what the trade-off is between different siting configurations in terms of battery capacity requirements.…”

Section: Introductionmentioning

confidence: 99%

Sizing Batteries for Power Flow Management in Distribution Grids: A Method to Compare Battery Capacities for Different Siting Configurations and Variable Power Flow Simultaneity

van Someren,

Visser,

Slootweg

2023

Energies

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Battery energy storage (BES) can provide many grid services, such as power flow management to reduce distribution grid overloading. It is desirable to minimise BES storage capacities to reduce investment costs. However, it is not always clear how battery sizing is affected by battery siting and power flow simultaneity (PFS). This paper describes a method to compare the battery capacity required to provide grid services for different battery siting configurations and variable PFSs. The method was implemented by modelling a standard test grid with artificial power flow patterns and different battery siting configurations. The storage capacity of each configuration was minimised to determine how these variables affect the minimum storage capacity required to maintain power flows below a given threshold. In this case, a battery located at the transformer required 10–20% more capacity than a battery located centrally on the grid, or several batteries distributed throughout the grid, depending on PFS. The differences in capacity requirements were largely attributed to the ability of a BES configuration to mitigate network losses. The method presented in this paper can be used to compare BES capacity requirements for different battery siting configurations, power flow patterns, grid services, and grid characteristics.

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Placement and capacity selection of battery energy storage system in the distributed generation integrated distribution network based on improved NSGA-II optimization

Cited by 36 publications

References 18 publications

Optimal Sizing of a Battery-Supported Electric Vehicle Charging Hub with a Limited-Capacity Grid Connection

Optimal Sizing of a Battery-Supported Electric Vehicle Charging Hub with a Limited-Capacity Grid Connection

A Comprehensive Review on Energy Storage System Optimal Planning and Benefit Evaluation Methods in Smart Grids

Sizing Batteries for Power Flow Management in Distribution Grids: A Method to Compare Battery Capacities for Different Siting Configurations and Variable Power Flow Simultaneity

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