CN-122026455-A - Method, system, equipment and storage medium for evaluating comprehensive response characteristics of energy storage clusters

CN122026455ACN 122026455 ACN122026455 ACN 122026455ACN-122026455-A

Abstract

The invention discloses a method, a system, equipment and a storage medium for evaluating comprehensive response characteristics of an energy storage cluster, wherein the method comprises the steps of obtaining a power grid regulation instruction sequence; the method comprises the steps of collecting real-time operation data of an energy storage cluster, constructing an energy storage cluster aggregation response model according to the real-time operation data of the energy storage cluster, calculating dynamic available adjustment margin of the energy storage cluster based on the energy storage cluster aggregation response model, defining a comprehensive response characteristic evaluation index set by combining a power grid regulation instruction sequence and the dynamic available adjustment margin of the energy storage cluster, determining evaluation indexes including response speed, adjustment precision, duration, reliability margin and aggregation available capacity by adopting a combined weighting method, normalizing and weighting and summing all evaluation indexes, and outputting a comprehensive response capacity index. The method can solve the problem that the prior art lacks a comprehensive response characteristic quantitative evaluation method which is suitable for heterogeneous energy storage clusters, faces to power grid regulation and control requirements, considers real-time operation constraint and can be applied online.

Inventors

TANG WENHU
Liang Xiuzhuang
ZHANG LULIANG
QIAN TONG
LIU QI
XU JING
ZENG SHUNQI
WU ZHAOYU

Assignees

华南理工大学
广东电网有限责任公司广州供电局

Dates

Publication Date: 20260512
Application Date: 20251217

Claims (10)

1. The method for evaluating the comprehensive response characteristics of the energy storage clusters is characterized by comprising the following steps: acquiring a power grid regulation instruction sequence; collecting real-time operation data of an energy storage cluster, wherein the real-time operation data of the energy storage cluster comprise equipment types, rated power upper limit, rated power lower limit, charge state, health state, temperature, communication state and protection locking signals; according to the real-time operation data of the energy storage clusters, an energy storage cluster aggregation response model is constructed; calculating the dynamic available adjustment margin of the energy storage clusters based on the energy storage cluster aggregation response model; the method comprises the steps of combining a power grid regulation instruction sequence and an energy storage cluster dynamic available adjustment margin, and defining a comprehensive response characteristic evaluation index set, wherein evaluation indexes of the evaluation index set comprise response speed, adjustment precision, persistence capability, reliability margin and aggregate available capacity; determining the weight of each evaluation index by adopting a combined weighting method; and normalizing the evaluation indexes, weighting and summing, and outputting the comprehensive response capability index.
2. The method for evaluating comprehensive response characteristics of an energy storage cluster according to claim 1, wherein the energy storage cluster aggregate response model is to equivalent a plurality of types of energy storage clusters into a virtual energy storage unit, and the equivalent power upper and lower limits of the virtual energy storage unit are as follows: Wherein, the The equivalent power upper limit weight coefficient of the ith subunit of the energy storage cluster at the moment t, For the equivalent power lower limit weight coefficient of the ith subunit of the energy storage cluster at the moment t, P maxi is the rated power upper limit of the ith subunit of the energy storage cluster, P min,i is the rated power lower limit of the ith subunit of the energy storage cluster, The upper limit of the equivalent power of the virtual energy storage unit at the time t, The equivalent power lower limit of the virtual energy storage unit at the moment t; Is calculated as follows: Wherein, the Represents the reduction of the climbing capacity, R max,i is the maximum climbing rate of the ith subunit of the energy storage cluster, For communication delay reduction, λ is an attenuation coefficient, SOC i is a state of charge of an i-th subunit of the energy storage cluster, SOC max is a state of charge maximum value, and SOC min is a state of charge minimum value.
3. The method for evaluating the comprehensive response characteristics of an energy storage cluster according to claim 1, wherein the calculation of the dynamic available adjustment margin of the energy storage cluster is performed according to the following formula: Wherein Δp up (t) is the maximum power adjusted upward at time t, Δp down (t) is the maximum power adjusted downward at time t, k soc is the SOC safety margin coefficient, E rated,i represents the energy storage rated capacity, Δt represents the scheduling time step, P max,i is the rated power upper limit of the ith subunit of the energy storage cluster, P min,i is the rated power lower limit of the ith energy storage unit of the energy storage cluster, SOC max is the state of charge maximum value, and SOC min is the state of charge minimum value.
4. The method for evaluating comprehensive response characteristics of an energy storage cluster according to claim 1, wherein the response speed is calculated by the following formula: R speed ＝1/(τ resp +∈) Wherein τ resp is the actual response delay and e is the response coefficient; The calculation of the adjustment accuracy is as follows: R acc ＝1-RMSE(P actual ,u)/P rated,cluster wherein, the RMSE is root mean square error, Measuring the deviation degree between the actual output P actual of the energy storage cluster and the power grid dispatching instruction u; the persistence capability is calculated as follows: R dur ＝t full /T horizon Wherein T full represents a sustainable full power response time period, and T horizon represents a typical peak shaving period; the reliability margin is calculated as follows: Wherein, the (Response failure k) is based on historical failure rate estimation; The aggregate available capacity is calculated as follows: Wherein Δp up (t) is the maximum power adjusted upward at time t, Δp down (t) is the maximum power adjusted downward at time t, and P max,i is the rated power upper limit of the ith subunit of the energy storage cluster.
5. The method for evaluating the comprehensive response characteristics of an energy storage cluster according to claim 1, wherein the determining the weights of the evaluation indexes by using a combined weighting method comprises: calculating subjective weight of each evaluation index by adopting an optimal worst method; calculating objective weights of all evaluation indexes by adopting an entropy weight method; And according to the subjective weight and the objective weight of each evaluation index, adopting game theory combination weighting to solve the optimal linear combination, and obtaining the final weight of each evaluation index.
6. The method for evaluating the comprehensive response characteristics of an energy storage cluster according to claim 5, wherein the calculating the subjective weight of each evaluation index by using the optimal worst method comprises: Establishing the following optimization model to solve the subjective weight of each evaluation index Wherein a Bj is the importance ratio of the optimal evaluation index B to the evaluation index j, a jW is the importance ratio of the evaluation index j to the worst evaluation index W, For the subjective weight of the normalized rating index j, For the weight of the optimal evaluation index B, The weight of the worst evaluation index W; The method for calculating the objective weight of each evaluation index by adopting the entropy weight method comprises the following steps: assuming that m historical scenes are provided, evaluating each evaluation index to construct a historical evaluation matrix, wherein the following formula is as follows: X=[x ij ] m×5 and carrying out normalization processing to ensure that all data are on the same scale, wherein the following formula is adopted: For each evaluation index j, its normalized value in all m scenes is calculated as follows: Calculating entropy value after normalization Calculating objective weight of each evaluation index according to entropy value According to subjective weight and objective weight of each evaluation index, adopting game theory combination weighting to solve the optimal linear combination, wherein the following formula is adopted: min||λ 1 w sub +λ 2 w obj -w sub || 2 +||λ 1 w sub +λ 2 w obj -w obj || 2 s.t.λ 1 +λ 2 ＝1,λ 1 ,λ 2 ≥0 and obtaining a final weight vector of each evaluation index according to the coefficient lambda 1 、λ 2 obtained by solving, wherein the final weight vector is as follows:
7. the method for evaluating the comprehensive response characteristics of an energy storage cluster according to claim 1, wherein normalizing each evaluation index, and weighting and summing the same to output a comprehensive response capability index comprises: each evaluation index was linear normalized as follows: wherein R j is the value of the evaluation index j, In order to evaluate the maximum value of the index j, The minimum value of the evaluation index j; And (3) carrying out weighted summation on each linearly normalized evaluation index, and outputting a comprehensive response capability index, wherein the comprehensive response capability index is represented by the following formula: Wherein CRI is the comprehensive response capability index, and w j is the weight of the evaluation index j.
8. An energy storage cluster comprehensive response characteristic evaluation system, which is characterized by comprising: The acquisition module is used for acquiring a power grid regulation instruction sequence; The system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring real-time operation data of an energy storage cluster, and the real-time operation data of the energy storage cluster comprise equipment types, rated power upper limits, rated power lower limits, charge states, health states, temperatures, communication states and protection locking signals; the construction module is used for constructing an energy storage cluster aggregation response model according to the real-time operation data of the energy storage clusters; the calculation module is used for calculating the dynamic available adjustment margin of the energy storage cluster based on the energy storage cluster aggregation response model; The definition module is used for combining the power grid regulation instruction sequence and the dynamic available regulation margin of the energy storage cluster to define a comprehensive response characteristic evaluation index set, wherein the evaluation indexes of the evaluation index set comprise response speed, regulation precision, persistence capability, reliability margin and aggregation available capacity; The determining module is used for determining the weight of each evaluation index by adopting a combined weighting method; and the evaluation module is used for normalizing each evaluation index, weighting and summing the evaluation indexes and outputting the comprehensive response capability index.
9. A computer device comprising a processor and a memory for storing a program executable by the processor, wherein the processor, when executing the program stored in the memory, implements the method for evaluating the integrated response characteristics of an energy storage cluster according to any one of claims 1-7.
10. A storage medium storing a program, wherein the program, when executed by a processor, implements the method for evaluating the integrated response characteristics of an energy storage cluster according to any one of claims 1 to 7.

Description

Method, system, equipment and storage medium for evaluating comprehensive response characteristics of energy storage clusters Technical Field The invention relates to an energy storage cluster comprehensive response characteristic evaluation method, an energy storage cluster comprehensive response characteristic evaluation system, energy storage cluster comprehensive response characteristic evaluation equipment and a storage medium, and belongs to the technical field of energy storage. Background Currently, the evaluation methods for energy storage systems are mainly focused on the following three categories: The evaluation methods for single-station operation effect are Zeng Xing et al (reviewed in comprehensive evaluation methods for safety of lithium battery energy storage power stations) and Chen Xijiang et al (reviewed in evaluation models for operation risks and application studies of electrochemical energy storage power stations), respectively construct evaluation systems with battery body safety, thermal runaway risks, operation and maintenance management and the like as cores, and carry out safety grade division on the energy storage power stations by adopting a fuzzy analytic hierarchy process, a weight determination method based on inter-index correlation, a two-dimensional cloud model and the like. The power grid side energy storage power station operation effect evaluation model based on the combined weighted TOPSIS, which is proposed by Wang et al, constructs an index system (such as relative charge-discharge capacity, comprehensive efficiency, availability coefficient and the like) with three dimensions of charge-discharge effect, energy efficiency and reliability, and is used for evaluating the actual operation performance of the energy storage power station in Jiangsu certain city. The method focuses on equipment-level operation performance, rather than the dynamic response capability of clusters to external scheduling instructions. The cluster flexibility-oriented quantification method is a multi-node distributed energy storage system aggregation flexibility evaluation method proposed by Zhang et al, defines power type flexibility and energy type flexibility indexes, introduces flexibility utilization rate lambda and margin eta, and is used for measuring the adjustable capacity of a cluster under given SOC and network constraint. The method considers the aggregation effect, but the flexibility definition is not directly coupled with specific power grid regulation instructions (such as AGC signals and peak regulation curves), and a comprehensive evaluation index capable of being updated in a rolling way is not formed. Fan Wenxuan et al (comprehensive evaluation System of Green Hydrogen energy storage System and double-layer optimization method) propose an economic-reliability-environmental comprehensive evaluation System based on analytic hierarchy process and embed the economic-reliability-environmental comprehensive evaluation System into a double-layer optimization model for capacity configuration and operation scheduling. The method introduces indirect manifestation of response capability (such as load loss probability LPSP and power reduction rate PCR), but the evaluation object is a green hydrogen system with a specific architecture, and the evaluation index and the power grid regulation instruction are not directly coupled, so that the method is not suitable for real-time comprehensive response characteristic quantification of the multi-type heterogeneous energy storage clusters under the source network load cooperative scene. The energy storage cluster power grid supporting capability evaluation and aggregation model which is proposed by Li et al and takes peak shaving requirements into consideration is characterized in that an energy storage-power grid scene supporting capability matrix is established through AHP-entropy weight combination weighting, units with strong peak shaving capability are screened, and an improved Chino polyhedron is adopted for aggregation. Although the method is oriented to regulation and control requirements, the evaluation result is mainly used for cluster construction and aggregation modeling, lacks uniform quantitative output of the overall comprehensive response characteristic of the clusters, and is difficult to directly use for real-time capability labels in a dispatching system. The above prior art has the following disadvantages: 1) The characteristic evaluation dimension of response towards a cluster level is lacking, namely the existing method is mostly aimed at a single station or a single technical route (such as a lithium battery station and a green hydrogen system), and the coupling characteristic and the aggregation effect of a heterogeneous energy storage cluster consisting of a plurality of energy storage technologies (lithium battery, liquid flow, flywheel, hydrogen storage and the like) in cooperative response are not considered. 2) The