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CN-121984186-A - Storage battery charging and discharging strategy and parameter matching method for energy storage system

CN121984186ACN 121984186 ACN121984186 ACN 121984186ACN-121984186-A

Abstract

The invention discloses a storage battery charging and discharging strategy and a parameter matching method of an energy storage system, which relate to the technical field of energy storage systems and comprise the following steps of constructing a multi-objective optimization model comprising economy and reliability; the multi-target optimization model is solved through a non-dominant sorting genetic algorithm to generate a front edge solution set, a scene classification library and a fuzzy rule library are established, power grid state data are collected in real time, the current running state and scene library characteristics are matched through a clustering algorithm, the fuzzy rule library is dynamically selected according to scene types, an optimal charge-discharge strategy is selected from the front edge solution set based on a fuzzy decision method, and the charge-discharge power of the storage battery is dynamically controlled according to the optimal strategy and is matched with the parameter constraint of the storage battery. According to the storage battery charging and discharging strategy and parameter matching method of the energy storage system, the strategy is flexibly adjusted according to the actual condition of the power grid through multi-objective optimization, and the maximization of the power grid operation benefit is facilitated.

Inventors

  • ZHANG HUILI
  • YAN BO

Assignees

  • 郑州电力高等专科学校

Dates

Publication Date
20260505
Application Date
20250721

Claims (7)

  1. 1. The storage battery charging and discharging strategy and parameter matching method for the energy storage system is characterized by comprising the following steps of: (1) Constructing a multi-objective optimization model comprising economy and reliability: (2) Solving the multi-objective optimization model through a non-dominant ordering genetic algorithm to generate a leading edge solution set; (3) Establishing a scene classification library and a fuzzy rule library, collecting power grid state data in real time, and matching the current running state with scene library characteristics through a clustering algorithm; (4) Dynamically selecting a fuzzy rule base according to scene types; (5) Selecting an optimal charge-discharge strategy from the front solution set based on a fuzzy decision method; (6) And dynamically controlling the charge and discharge power of the storage battery according to the optimal strategy, and matching the parameter constraint of the storage battery.
  2. 2. The method for matching the charge and discharge strategies and parameters of the storage battery of the energy storage system according to claim 1, wherein the economic objective function is minimized to be in an optimized direction by using total operation cost, and the total operation cost comprises storage battery operation cost, transmission loss cost, electricity purchasing cost and electricity selling income sum.
  3. 3. The method for matching a battery charge and discharge strategy and parameters of an energy storage system according to claim 1, wherein the reliability objective function is the sum of absolute values of voltage deviations of nodes of the power distribution network.
  4. 4. The method for matching the charge and discharge strategies and parameters of the storage battery of the energy storage system according to claim 1, wherein the scene classification library comprises a renewable energy output scene, a high-load demand scene, an electricity price peak-valley salient scene, a voltage frequent fluctuation scene and an equipment fault scene, and the fuzzy rule library comprises decision rules corresponding to scene types.
  5. 5. The method for matching the charge and discharge strategy and the parameters of the storage battery of the energy storage system according to claim 1, wherein the fuzzy decision method comprises the following steps: The method comprises the steps of (a) carrying out normalization processing on an economic target value and a reliability target value, (b) determining fuzzy sets corresponding to the target values through membership functions, (c) carrying out logic reasoning according to a fuzzy rule base of a current scene, calculating the priority of each Pareto solution, and (d) selecting a solution with the highest priority as an optimal charge-discharge strategy.
  6. 6. The method for matching the charge and discharge strategy and the parameters of the storage battery of the energy storage system according to claim 1, wherein the parameter matching comprises real-time verification of the charge and discharge strategy parameters, and the strategy parameters at least comprise charge and discharge power and an SOC target value.
  7. 7. The method for matching the charge and discharge strategies and parameters of the storage battery of the energy storage system according to claim 1, wherein the real-time verification and adjustment mechanism comprises the steps of adjusting the power to be within a constraint range through a limiting algorithm when the charge and discharge power exceeds the maximum charge and discharge power constraint of the storage battery, triggering a strategy re-optimization process when the SOC target value exceeds a safe interval of the SOC of the storage battery, and adjusting the distribution or the power of the charge and discharge time interval to ensure that the SOC is within an allowable range.

Description

Storage battery charging and discharging strategy and parameter matching method for energy storage system Technical Field The invention relates to the technical field of energy storage systems, in particular to a storage battery charging and discharging strategy and a parameter matching method of an energy storage system. Background In the existing distributed energy storage power supply scheduling technology, most schemes are difficult to consider the economical efficiency and reliability of power grid operation, and lack dynamic adaptability to complex and changeable power grid scenes. The traditional method generally adopts a fixed charge-discharge strategy, can only optimize aiming at a single target (such as cost or voltage stabilization), and cannot realize multi-target coordination under the scenes of renewable energy fluctuation, load abrupt change and the like. The multi-objective unbalance is that part of schemes are used for seeking to maximize economy, the service life loss of a battery is aggravated due to frequent deep charge and discharge, or the power supply reliability is emphasized excessively, the voltage stability is maintained regardless of the cost, and the operation cost of an energy storage system is high. The scene adaptability is insufficient, when the renewable energy source high-permeability scene is faced, the traditional scheme cannot timely adjust the energy storage and charging strategy to fully consume redundant electric quantity, so that the electricity discarding rate is increased, and in the high-load demand or electricity price peak-valley period, the fixed strategy is difficult to dynamically switch the economical and reliability priority, and the voltage out-of-limit or missing of the profit opportunity can be caused. Therefore, it is necessary to provide a battery charging and discharging strategy and a parameter matching method for an energy storage system to solve the above problems. Disclosure of Invention (One) solving the technical problems The invention aims to provide a charging and discharging strategy and a parameter matching method for a storage battery of an energy storage system, so as to solve the problems in the background technology. (II) technical scheme In order to achieve the aim, the invention is realized by the following technical scheme that the method for matching the charge and discharge strategy and the parameters of the storage battery of the energy storage system comprises the following steps of (1) constructing a multi-objective optimization model comprising economy and reliability: (2) Establishing a scene classification library and a fuzzy rule library, collecting power grid state data in real time, and matching the current running state with scene library features through a clustering algorithm; (4) Dynamically selecting a fuzzy rule base according to scene types; (5) And (6) dynamically controlling the charge and discharge power of the storage battery according to the optimal strategy and matching the parameter constraint of the storage battery. Preferably, the economic objective function is minimized to an optimized direction with a total operation cost including a battery operation cost, a transmission loss cost, a purchase cost, and a sum of sales returns. Preferably, the reliability objective function is the sum of absolute values of voltage deviations of all nodes of the power distribution network to be minimized. Preferably, the scene classification library comprises a renewable energy output scene, a high-load demand scene, an electricity price peak-valley salient scene, a voltage frequent fluctuation scene and an equipment fault scene, and the fuzzy rule library comprises decision rules corresponding to scene types. Preferably, the step of executing the fuzzy decision method includes: The method comprises the steps of (a) carrying out normalization processing on an economic target value and a reliability target value, (b) determining fuzzy sets corresponding to the target values through membership functions, (c) carrying out logic reasoning according to a fuzzy rule base of a current scene, calculating the priority of each Pareto solution, and (d) selecting a solution with the highest priority as an optimal charge-discharge strategy. Preferably, the parameter matching comprises real-time verification of charge-discharge strategy parameters, and the strategy parameters at least comprise charge-discharge power and SOC target values. Preferably, the real-time checking and adjusting mechanism comprises the steps of adjusting the power to be within a constraint range through a limiting algorithm when the charging and discharging power exceeds the maximum charging and discharging power constraint of the storage battery, triggering a strategy re-optimization process when the SOC target value exceeds the SOC safety interval of the storage battery, and adjusting the charging and discharging time interval distribution or the power size to