CN-122000958-A - Intelligent electric energy adjusting device and method for distributed energy storage area

CN122000958ACN 122000958 ACN122000958 ACN 122000958ACN-122000958-A

Abstract

The application relates to the technical field of energy storage system scheduling, and discloses an intelligent electric energy adjusting device and method for a distributed energy storage area. The health index is calculated through weighting of the acquisition module, the health index is converted into power distribution weight through the calculation module, the protection coefficient is dynamically adjusted through predicting the health decline through the monitoring module, and the scheduling mode is adaptively switched through the switching module according to the global health balance. The application improves the health degree evaluation precision, the power distribution rationality, the health state balance, the whole service life of the system and the running economy of the distributed energy storage area.

Inventors

YUAN LIANG
LU XIN
GUO ZHIDUAN
LIU ZHENG
LI YUANHENG
ZHANG LEI
WANG KE
DONG HUIFENG
Ren fuli
CHEN BINGXIA

Assignees

国网河南综合能源服务有限公司

Dates

Publication Date: 20260508
Application Date: 20260126

Claims (10)

1. Electric energy intelligent regulation device of distributed energy storage district, its characterized in that, the device includes: the acquisition module is used for acquiring cycle times, internal resistance growth rate, capacity attenuation rate and charge and discharge depth history sequences of each energy storage unit, and obtaining health indexes of each energy storage unit through weighted fusion calculation; The calculation module is used for converting the health index into power distribution attention weight, calculating charge and discharge power instructions of each energy storage unit by combining the charge state and the platform load demand, and executing power distribution; The monitoring module comprises a monitoring unit, a prediction unit and a calculation unit, wherein the monitoring unit is used for monitoring the difference between the health degree change rate of the high-load energy storage group and the health degree change rate of the normal energy storage group, the prediction unit is used for predicting the future health degree reduction of each energy storage unit when the difference exceeds a threshold value, the calculation unit is used for dynamically amplifying health protection coefficients according to the ratio of the health degree reduction to a set threshold value, recalculating charge and discharge power instructions and dispersing the generated power gap to the middle health group energy storage unit according to the residual capacity weight; The switching module is used for calculating the global health balance degree as the ratio of the standard deviation to the average value of the health degrees of all the energy storage units, adaptively switching the scheduling mode according to the numerical value interval where the global health balance degree is located and adjusting the weight coefficient combination.
2. The intelligent power conditioning apparatus of claim 1, wherein the collection module is configured to: Collecting the current charge state, the accumulated times of cyclic charge and discharge, the actual measured value of the energy storage internal resistance and the ratio of the rated capacity to the actual available capacity of each energy storage unit to obtain the capacity attenuation rate; Respectively calculating the cycle life health, the internal resistance health, the capacity health and the charge and discharge depth health; the cycle life health, the internal resistance health, the capacity health and the charge and discharge depth health are weighted and summed to obtain health indexes of all energy storage units; Setting a first health degree threshold value and a second health degree threshold value, and dividing the energy storage unit into a high health group, a medium health group and a low health group according to the magnitude relation between the health degree index and the first health degree threshold value and the second health degree threshold value.
3. The intelligent power conditioning apparatus of claim 1, wherein the computing module is configured to: Constructing a health evaluation sub-network and a scheduling decision sub-network; Inputting the cycle times, the internal resistance increase rate, the capacity attenuation rate and the average value and standard deviation of the charge and discharge depth history sequences of each energy storage unit into the health evaluation sub-network, and outputting a corrected health index; substituting the corrected health index into an index function and carrying out normalization processing to obtain the power distribution attention weight of each energy storage unit; and inputting the power distribution attention weight, the corrected health index, the state of charge and the district load demand into the scheduling decision sub-network, and outputting the charge and discharge power instructions of each energy storage unit.
4. The intelligent power conditioning apparatus of claim 1, wherein the monitoring unit is configured to: Calculating the average charge and discharge power of each energy storage unit in the past period according to the set monitoring period, and counting the high-power operation time length when the absolute value of the charge and discharge power exceeds the set proportion of rated power; screening to obtain a high-load energy storage group according to the average charge and discharge power and the high-power operation time length; Calculating the average value of the health degree change rate of the high-load energy storage group and the average value of the health degree change rate of the normal energy storage group, and obtaining the difference of the health degree change rates by making a difference; when the health degree change rate difference exceeds a threshold value, a new imbalance phenomenon is identified.
5. The intelligent power conditioning apparatus of claim 4, wherein the prediction unit is configured to: collecting a health degree time sequence of past set hours of each energy storage unit; Inputting the health degree time sequence into a long-period and short-period memory network, and predicting the health degree sequence of a set number of hours in the future; Calculating the average value of the health degree sequences of the future set hours to obtain average predicted health degree; and the current health degree index is subjected to difference with the average predicted health degree, so that the future health degree reduction amount of each energy storage unit is obtained.
6. The intelligent power conditioning apparatus of claim 5, wherein the computing unit is configured to: Calculating the ratio of the future health degree reduction amount to a set threshold value, multiplying the basic health protection coefficient by the ratio and adding 1 when the ratio is larger than 1 to obtain a dynamically amplified health protection coefficient; substituting the dynamically amplified health protection coefficient into a multi-objective loss function, and recalculating the charge and discharge power instructions of each energy storage unit; calculating the difference between the charge-discharge power instruction of the strain risk energy storage source and the recalculated charge-discharge power instruction, and summing to obtain a power gap; calculating the difference value between the maximum charge state and the current charge state of each energy storage unit in the middle health group, and carrying out normalization processing to obtain the residual capacity weight; And multiplying the power gap by the residual capacity weight, and adding the power gap to a charge and discharge power instruction of the middle health group energy storage unit.
7. The intelligent power conditioning apparatus of claim 1, wherein the switching module is configured to: Calculating standard deviation and average value of health indexes of all energy storage units, and comparing the standard deviation with the average value to obtain global health balance; setting an equalization early warning threshold and a danger threshold, and dividing four scheduling modes of an economic priority mode, a health protection mode, an equalization intervention mode and a forced equalization mode according to the magnitude relation between the global health equalization and the equalization early warning threshold and the danger threshold; Selecting a combination of the corresponding economic cost weight coefficient, the healthy degradation rate weight coefficient and the energy storage utilization balance weight coefficient according to the scheduling mode; Substituting the weight coefficient combination into a multi-objective loss function, and adjusting the charge and discharge power instructions of each energy storage unit.
8. A method for intelligently adjusting electric energy of a distributed energy storage platform based on an intelligent electric energy adjusting device of the distributed energy storage platform as set forth in any one of claims 1-7, the method comprising: Step S1, acquiring cycle times, internal resistance increase rate, capacity attenuation rate and charge and discharge depth history sequences of each energy storage unit, and obtaining health indexes of each energy storage unit through weighted fusion calculation; s2, converting the health index into power distribution attention weight, calculating charge and discharge power instructions of each energy storage unit by combining the charge state and the district load demand, and executing power distribution; S3, monitoring the difference between the health degree change rate of the high-load energy storage group and the health degree change rate of the normal energy storage group, when the difference exceeds a threshold value, predicting the future health degree reduction amount of each energy storage unit, dynamically amplifying a health protection coefficient according to the ratio of the health degree reduction amount to a set threshold value, recalculating a charge and discharge power instruction, and dispersing the generated power gap to the energy storage unit of the middle health group according to the residual capacity weight; And S4, calculating global health balance degree as the ratio of standard deviation to mean value of the health degrees of all the energy storage units, adaptively switching a scheduling mode according to the numerical range where the global health balance degree is located, and adjusting weight coefficient combinations.
9. The method according to claim 8, wherein step S1 comprises: Collecting the current charge state, the accumulated times of cyclic charge and discharge, the actual measured value of the energy storage internal resistance and the ratio of the rated capacity to the actual available capacity of each energy storage unit to obtain the capacity attenuation rate; Respectively calculating the cycle life health, the internal resistance health, the capacity health and the charge and discharge depth health; the cycle life health, the internal resistance health, the capacity health and the charge and discharge depth health are weighted and summed to obtain health indexes of all energy storage units; Setting a first health degree threshold value and a second health degree threshold value, and dividing the energy storage unit into a high health group, a medium health group and a low health group according to the magnitude relation between the health degree index and the first health degree threshold value and the second health degree threshold value.
10. The method according to claim 8, wherein step S2 comprises: Constructing a health evaluation sub-network and a scheduling decision sub-network; Inputting the cycle times, the internal resistance increase rate, the capacity attenuation rate and the average value and standard deviation of the charge and discharge depth history sequences of each energy storage unit into the health evaluation sub-network, and outputting a corrected health index; substituting the corrected health index into an index function and carrying out normalization processing to obtain the power distribution attention weight of each energy storage unit; and inputting the power distribution attention weight, the corrected health index, the state of charge and the district load demand into the scheduling decision sub-network, and outputting the charge and discharge power instructions of each energy storage unit.

Description

Intelligent electric energy adjusting device and method for distributed energy storage area Technical Field The application relates to the technical field of energy storage system dispatching, in particular to an intelligent electric energy adjusting device and method for a distributed energy storage platform area. Background With the rapid development of distributed energy systems, energy storage technology plays an increasingly important role in aspects of peak clipping and valley filling of a power grid, new energy consumption, electric energy quality adjustment and the like. In a distributed energy storage platform, a plurality of energy storage units are generally deployed to meet the load demand of the platform, and the existing energy storage scheduling method is mainly based on a state-of-charge equalization strategy, and realizes the charge and discharge control of an energy storage system by monitoring the states of charge of the energy storage units and distributing according to the power demand. Part of the prior art also considers single health indexes such as cycle times, capacity fading and the like, and gives more use opportunities to the energy storage unit with higher health degree during power distribution so as to prolong the whole service life of the system. These approaches improve the economics and reliability of the energy storage system to some extent. However, the prior art has the following defects that firstly, the health degree evaluation dimension is single, the real health state of the energy storage unit cannot be comprehensively reflected only by depending on individual parameters such as the circulation times or capacity attenuation, and the like, so that deviation exists between a health degree evaluation result and the actual degradation degree, secondly, a dynamic response mechanism for the health state is lacking in a power distribution strategy, when the power distribution is carried out by adopting a fixed weight coefficient, the distribution strategy cannot be flexibly adjusted according to the real-time health state and load change of the energy storage unit, and thirdly, the balance between health protection and economic operation is difficult to balance, the degradation of high-health energy storage can be accelerated due to excessive pursuit of economy, and the overall utilization efficiency of the system is reduced due to excessive protection. Disclosure of Invention The application provides an intelligent electric energy adjusting device and method for a distributed energy storage platform, which solve a series of technical problems that in the existing energy storage scheduling technology, the health assessment is incomplete, a dynamic response mechanism is lacked in a power allocation strategy, the Martai effect inversion phenomenon caused by health perception scheduling is lacked, prospective health protection based on future prediction is lacked, the scheduling mode cannot be adaptively switched according to global health balance degree, and the like, and improve the health assessment precision, the power allocation rationality, the health state balance degree, the overall service life of a system and the running economy of the distributed energy storage platform. The application provides an intelligent electric energy adjusting device of a distributed energy storage platform area, which comprises an acquisition module, a first control module, a second control module and a third control module, wherein the acquisition module is used for acquiring cycle times, internal resistance growth rate, capacity attenuation rate and charge and discharge depth history sequences of all energy storage units, and obtaining health indexes of all the energy storage units through weighted fusion calculation; The calculation module is used for converting the health index into power distribution attention weight, calculating charge and discharge power instructions of each energy storage unit by combining the charge state and the platform load demand, and executing power distribution; The monitoring module comprises a monitoring unit, a prediction unit and a calculation unit, wherein the monitoring unit is used for monitoring the difference between the health degree change rate of the high-load energy storage group and the health degree change rate of the normal energy storage group, the prediction unit is used for predicting the future health degree reduction of each energy storage unit when the difference exceeds a threshold value, the calculation unit is used for dynamically amplifying health protection coefficients according to the ratio of the health degree reduction to a set threshold value, recalculating charge and discharge power instructions and dispersing the generated power gap to the middle health group energy storage unit according to the residual capacity weight; The switching module is used for calculating the global health balance degree as the ratio of the stand