CN-121984187-A - Charging and discharging method, device, equipment and storage medium of energy storage equipment

CN121984187ACN 121984187 ACN121984187 ACN 121984187ACN-121984187-A

Abstract

The application provides a charge and discharge method, a charge and discharge device, charge and discharge equipment and a storage medium of energy storage equipment, and relates to the technical field of power storage. The method comprises the steps of obtaining data information related to the charging and discharging processes of the energy storage equipment, constructing a multi-objective optimization function according to the data information, wherein the multi-objective optimization function comprises electricity selling price, electricity purchasing price and battery loss cost of the energy storage equipment, inputting the multi-objective optimization function into a self-adaptive moth algorithm to carry out iterative solution so as to obtain an optimal charging and discharging strategy of the energy storage equipment, wherein the self-adaptive moth algorithm dynamically adjusts flame quantity in the iterative solution process, and generating a periodic charging and discharging plan of the energy storage equipment within a set duration according to the optimal charging and discharging strategy. By the method and the device, the flexibility and the overall operation efficiency of the charging and discharging strategy of the energy storage equipment are improved.

Inventors

WEN ZHENXING
LIU SHUMIN
LIN GUANQIANG
ZHAO MINGXI
WANG LEI
LI GUANQIAO
WANG CAO
CHEN JIEHONG
LI CHONG
LUO ZHENHUA

Assignees

广东电网有限责任公司惠州供电局

Dates

Publication Date: 20260505
Application Date: 20251211

Claims (10)

1. A method of charging and discharging an energy storage device, comprising: Acquiring data information related to the charging and discharging processes of the energy storage equipment; constructing a multi-objective optimization function according to the data information, wherein the multi-objective optimization function comprises electricity selling price, electricity purchasing price and battery loss cost of the energy storage equipment; inputting the multi-objective optimization function into a self-adaptive moth algorithm for iterative solution to obtain an optimal charge-discharge strategy of the energy storage equipment, wherein the self-adaptive moth algorithm dynamically adjusts the flame quantity in the iterative solution process; And generating a periodic charge-discharge plan within a set duration of the energy storage equipment according to the optimal charge-discharge strategy.
2. The method of claim 1, wherein the inputting the multi-objective optimization function into an adaptive moth algorithm for iterative solution to obtain an optimal charge-discharge strategy of the energy storage device comprises: initializing a moth population in the self-adaptive moth algorithm, wherein the position vector of each moth in the moth population corresponds to an initial energy storage device charging and discharging strategy; constructing an adaptability function of the self-adaptive moth algorithm according to the multi-objective optimization function and a preset constraint violation penalty term; performing an iterative optimization process on the moth population until the moth population meets a preset termination condition, and performing the following operations in each iterative optimization process: calculating the fitness value of the corresponding position of each moth in the moth population based on the fitness function; sorting each moth in the moth population based on the fitness value; determining flames of the current iteration times according to the sorted moths, and recording positions of the flames and corresponding fitness values, wherein the number of the flames is determined according to the current iteration times and the maximum iteration times through a preset convergence function; updating the position of each moth according to the relative position relation between the moth and each flame; and when the moth population meets a preset termination condition, taking the position corresponding to the flame with the maximum fitness value as an optimal charging and discharging strategy of the energy storage equipment.
3. The method of claim 2, wherein updating the position of the moths based on the relative positional relationship between the moths and each flame comprises: and updating the position of the moth based on the relative position relation between the moth and each flame by adopting a logarithmic spiral motion model.
4. A method according to any one of claims 1 to 3, wherein said constructing a multi-objective optimization function from said data information comprises: According to the data information, predicting electricity selling prices and electricity purchasing prices within the set time length through an electricity price prediction model; Determining a battery loss cost of the energy storage device based on a battery loss function; and determining the multi-objective optimization function according to the electricity selling price, the electricity purchasing price and the battery loss cost based on multi-constraint conditions.
5. The method of claim 4, wherein the determining the multi-objective optimization function based on the electricity selling price, the electricity purchasing price, and the battery loss cost based on multi-constraint conditions comprises: The method comprises the steps of obtaining charging power and discharging power of energy storage equipment, determining a net benefit component of the energy storage equipment according to the charging power, the discharging power, the electricity selling price and the electricity purchasing price, and carrying out weighted summation on the charging power and the discharging power to obtain a battery loss cost component; summing the net benefit components of each time period to obtain a total net benefit component; summing the battery loss cost components of each time period to obtain a total battery loss cost quantity; Subtracting the total battery loss cost amount from the total net benefit component to determine a total net benefit function; And based on the multi-constraint condition, maximizing the total net benefit function and constructing the multi-objective optimization function.
6. The method of claim 5, wherein the multiple constraints comprise state of charge boundary constraints of the energy storage device, power limitation constraints of the energy storage device, charge-discharge mutual exclusion constraints of the energy storage device.
7. A method according to any one of claims 1 to 3, further comprising: And after the periodic charge-discharge plan is generated, the periodic charge-discharge plan is adjusted according to the dispatching instruction of the power grid and/or the load demand of the user.
8. A charging and discharging apparatus for an energy storage device, comprising: The acquisition module is used for acquiring data information related to the charging and discharging processes of the energy storage equipment; the construction module is used for constructing a multi-objective optimization function according to the data information, wherein the multi-objective optimization function comprises electricity selling price, electricity purchasing price and battery loss cost of the energy storage equipment; the solution module is used for inputting the multi-objective optimization function into a self-adaptive moth algorithm for iterative solution so as to obtain an optimal charge-discharge strategy of the energy storage equipment, wherein the self-adaptive moth algorithm dynamically adjusts the flame quantity in the iterative solution process; and the generation module is used for generating a periodic charge-discharge plan within a set duration of the energy storage equipment according to the optimal charge-discharge strategy.
9. An electronic device is characterized by comprising a memory and a processor; the memory stores computer-executable instructions; the processor executing computer-executable instructions stored in the memory, causing the processor to perform the method of any one of claims 1-7.
10. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor are adapted to carry out the method of any one of claims 1-7.

Description

Charging and discharging method, device, equipment and storage medium of energy storage equipment Technical Field The present application relates to the field of power storage technologies, and in particular, to a method, an apparatus, a device, and a storage medium for charging and discharging an energy storage device. Background Along with the rapid development of energy structure transformation and renewable energy power generation, the importance of the energy storage technology as a key link for connecting a power supply with strong fluctuation and a stable power grid is increasingly highlighted. The energy storage device, in particular the battery energy storage device, is used for peak load regulation, frequency regulation and accident standby at the power grid side, and can be used for peak load clipping, power supply reliability improvement, participation in demand side response and the like at the user side. Therefore, how to efficiently and intelligently manage the charge and discharge processes of the energy storage device so as to maximize the economic benefit and the technical value of the energy storage device has become a hot spot for research and innovation in the current energy field. At present, the charging and discharging method of the energy storage device mainly comprises various strategies, and one of the strategies is a timing charging and discharging strategy which is widely applied. The core of the strategy is to decide the charging and discharging time of the energy storage device based on a preset fixed time table. For example, in a traditional electricity market environment, it is often set to discharge during the day (peak electricity usage) to meet high load demands or to use higher electricity prices, and to charge during the night (valley electricity usage) to use lower electricity prices or to supplement energy when grid loads are lower. The implementation process of the strategy is relatively simple, and the control system is mainly used for automatically starting or closing the charging and discharging functions of the energy storage equipment according to a preset time point, and possibly carrying out charging and discharging according to a set power value. However, the above-mentioned timing charging and discharging strategy, although simple in structure and easy to implement, has obvious limitations that it results in lower flexibility of the charging and discharging strategy of the energy storage device and lower overall operation efficiency. Disclosure of Invention The application provides a charging and discharging method, a device, equipment and a storage medium of energy storage equipment, which are used for improving the flexibility and the overall operation efficiency of a charging and discharging strategy of the energy storage equipment. In a first aspect, the present application provides a charging and discharging method of an energy storage device, including: Acquiring data information related to the charging and discharging processes of the energy storage equipment; Constructing a multi-objective optimization function according to the data information, wherein the multi-objective optimization function comprises electricity selling price, electricity purchasing price and battery loss cost of the energy storage equipment; Inputting the multi-objective optimization function into a self-adaptive moth algorithm for iterative solution to obtain an optimal charge-discharge strategy of the energy storage equipment, wherein the self-adaptive moth algorithm dynamically adjusts the flame quantity in the iterative solution process; and generating a periodic charge-discharge plan within a set time length of the energy storage equipment according to the optimal charge-discharge strategy. In one possible implementation, inputting the multi-objective optimization function into the adaptive moth algorithm for iterative solution to obtain an optimal charge-discharge strategy of the energy storage device, including: Initializing a moth population in a self-adaptive moth algorithm, wherein the position vector of each moth in the moth population corresponds to an initial energy storage device charging and discharging strategy; Constructing an adaptability function of the self-adaptive moth algorithm according to the multi-objective optimization function and a preset constraint violation penalty term; performing an iterative optimization process on the moth population until the moth population meets a preset termination condition, and performing the following operations in each iterative optimization process: calculating the fitness value of the corresponding position of each moth in the moth population based on the fitness function; Sorting each moth in the moth population based on the fitness value; Determining flames of the current iteration times according to the sorted moths, and recording positions of the flames and corresponding fitness values, wherein the number of the f