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Power grid side energy storage power station locating and sizing optimization method based on particle swarm optimization

A technology of particle swarm algorithm and energy storage power station, which is applied in the field of site selection and capacity optimization of power grid side energy storage power station based on particle swarm algorithm, can solve the problems of power grid stability, volatility, weak regulation, etc., and achieve improved calculation efficiency effect

Pending Publication Date: 2022-03-11
国网西藏电力有限公司
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  • Application Information

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Problems solved by technology

However, new energy power generation has the characteristics of randomness, volatility, indirectness and weak regulation, and large-scale grid connection will affect the stability of the power grid.

Method used

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  • Power grid side energy storage power station locating and sizing optimization method based on particle swarm optimization
  • Power grid side energy storage power station locating and sizing optimization method based on particle swarm optimization
  • Power grid side energy storage power station locating and sizing optimization method based on particle swarm optimization

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specific Embodiment approach

[0017] It should be noted that the structures, proportions, sizes, etc. shown in this specification are only used to cooperate with the content disclosed in the specification for the understanding and reading of those familiar with this technology, and are not used to limit the conditions for the implementation of the present invention , any modification of structure, change of proportional relationship or adjustment of size shall still fall within the scope covered by the technical content disclosed in the present invention without affecting the effect and purpose of the present invention. .

[0018] At the same time, terms such as "upper", "lower", "left", "right", "middle" and "one" quoted in this specification are only for the convenience of description and are not used to limit this specification. The practicable scope of the invention and the change or adjustment of its relative relationship shall also be regarded as the practicable scope of the present invention without...

Embodiment 1

[0020] Such as figure 1 As shown, a particle swarm optimization algorithm-based site selection and capacity optimization method for energy storage power stations on the grid side is characterized in that it includes the following steps:

[0021] Step 1: Pre-select the site according to the variance of the network loss sensitivity of each node. The number of pre-selected sites is 10-20% of the total number of nodes. In principle, the node with a larger network loss sensitivity variance is preferentially selected to install the energy storage power station;

[0022] Step 2: Establish a mathematical model of the three objective functions of new energy consumption level, node voltage fluctuation, and annual cost, and use the entropy weight method to objectively assign weights to convert the multi-objective problem into a single objective;

[0023] Step 3: The installed capacity of the energy storage power station and the proportion of the installed capacity of the energy storage p...

Embodiment 2

[0028] The present invention proposes an optimization method for site selection and capacity determination of grid-side energy storage power stations based on particle swarm algorithm. figure 1 The implementation steps of the optimization method are shown, as follows:

[0029] Step 1: Network loss sensitivity indicates the amount of change in network loss caused by a node increasing unit load power under a certain system operation mode. The larger the value, the worse the benefit of the node. However, new energy power generation is highly volatile and intermittent, and location selection based only on network loss sensitivity at certain moments may have limitations. Therefore, the present invention pre-selects sites according to the variance of network loss sensitivity of each node, and in principle, preferentially selects nodes with larger variance of network loss sensitivity to install energy storage power stations. The number of pre-selected sites is 10-20% of the total nu...

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Abstract

The invention provides a power grid side energy storage power station locating and sizing optimization method based on a particle swarm algorithm, and the method comprises the steps: firstly building a topological graph with a power grid transformer substation as a node, and carrying out the pre-locating according to the network loss sensitivity variance of each node; and then, by taking the new energy consumption percentage, the node voltage fluctuation and the annual cost as objective functions, distributing weights by adopting an entropy weight method, simplifying a multi-objective solving problem into a single objective, and completing addressing and sizing optimization of the energy storage power station through a particle swarm algorithm. Stability and economical efficiency of a power grid are improved, and scientific basis and technical guidance are provided for guiding construction of an energy storage power station.

Description

technical field [0001] The invention relates to the field of energy storage technology, in particular to an optimization method for site selection and capacity determination of power grid side energy storage power stations based on particle swarm algorithm. Background technique [0002] In recent years, the international and domestic requirements for energy conservation, emission reduction and vigorous development of clean energy have become more and more urgent. New energy power generation represented by wind power and photovoltaics has become an important part of my country's energy structure adjustment and "double carbon" goals. However, new energy power generation has the characteristics of randomness, volatility, indirectness and weak regulation, and large-scale grid connection will affect the stability of the power grid. The use of energy storage systems in new energy power generation or power grids can not only improve stability and reliability, but also alleviate th...

Claims

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Application Information

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IPC IPC(8): G06F30/25G06Q10/06H02J3/28H02J3/38G06F113/04
CPCG06F30/25G06Q10/06313H02J3/28H02J3/381G06F2113/04H02J2300/24H02J2300/28Y02E10/56Y02E40/70Y04S10/50
Inventor 厉瑜龙迪周杨兵徐潜孙帅旦增巴桑夏强
Owner 国网西藏电力有限公司
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