CN-122023062-A - Optimizing strategy for maximizing energy storage benefit of combined new energy

CN122023062ACN 122023062 ACN122023062 ACN 122023062ACN-122023062-A

Abstract

The invention relates to the technical field of novel energy storage and discloses an optimization strategy for maximizing energy storage benefits of combined new energy, which comprises the steps of collecting new energy power generation capacity data and energy storage system operation parameter data, constructing a new energy storage combined benefit model based on the data, optimizing a new energy power generation and energy storage cooperative scheduling strategy according to benefit model data, generating new energy storage cooperative scheduling strategy optimization data, generating charge and discharge dynamic adjustment data based on dynamic adjustment of an energy storage system charge and discharge strategy, constructing market response integration data according to the charge and discharge adjustment data, generating risk assessment data based on market electricity price demand response integration data and historical benefit data, generating algorithm feature data according to the risk assessment data, and finally generating new energy storage optimization summary data. The invention reduces the optimization deviation caused by data asynchronization and errors, and improves the response efficiency and economic benefit of the system.

Inventors

TANG SHIJUN
Deng ang
XIAO DINGYAO
DONG YIFENG
ZHU XIAOFAN
LIU YANBO
ZHANG JINGPEI
PAN SHUYAN

Assignees

义乌万里扬能源服务有限公司

Dates

Publication Date: 20260512
Application Date: 20260129

Claims (10)

1. An optimization strategy for maximizing energy storage benefits of a combined new energy source, which is characterized by comprising the following steps: S1, collecting new energy power generation output data and energy storage system operation parameter data; s2, constructing a benefit model of a new energy power generation and energy storage combined system based on the new energy power generation output data and the energy storage system operation parameter data, and generating new energy storage combined benefit model data; S3, optimizing a cooperative scheduling strategy of new energy power generation and energy storage according to the new energy storage combined profit model data, and generating new energy storage cooperative scheduling strategy optimization data; S4, based on the new energy storage collaborative scheduling policy optimization data, dynamically adjusting the charging and discharging policies of the energy storage system, and generating charging and discharging dynamic adjustment data of the energy storage system; S5, integrating market electricity price and demand response parameters according to the charging and discharging dynamic adjustment data of the energy storage system, and constructing market electricity price demand response integrated data; S6, based on the market electricity price demand response integrated data and the historical revenue data, performing revenue risk assessment and early warning to generate new energy storage revenue risk assessment data; s7, matching the multi-objective optimization algorithm type according to the new energy storage income risk assessment data to generate objective multi-objective optimization algorithm type characteristic data; And S8, constructing new energy storage benefit optimization summary data, and performing benefit maximization calculation to generate energy storage benefit maximization optimization result data of the combined new energy.
2. The optimization strategy for maximizing energy storage benefits in combination with new energy according to claim 1, wherein the collecting new energy power generation data and energy storage system operation parameter data in step S1 comprises the following operations: S11, collecting solar photovoltaic power generation output data, wind power generation output data and operation parameter data of an energy storage battery system through a new energy power generation station monitoring system, wherein the operation parameter data comprise capacity, charge and discharge efficiency and life cycle parameters of the energy storage battery; S12, the collected new energy power generation output data and the collected energy storage system operation parameter data are imported into a new energy storage supervision platform, abnormal values and missing values are removed by adopting a data cleaning algorithm, and standardized new energy power generation output data are generated And standardized energy storage system operating parameter data 。
3. An optimization strategy for maximizing energy storage returns in combination with new energy as claimed in claim 2 wherein power generation output data is based on said standardized new energy And the standardized energy storage system operating parameter data In the step S2, a revenue model of the new energy power generation and energy storage combined system is constructed based on the new energy power generation output data and the energy storage system operation parameter data, and the generation of new energy storage combined revenue model data includes the following operations: s21, acquiring the power generation output data of the standardized new energy And the standardized energy storage system operating parameter data ; S22, based on the And said Constructing a profit model of the new energy power generation and energy storage combined system by adopting a linear regression model and combining a profit function, wherein the profit function comprises power generation profits, energy storage charge and discharge profits, market subsidy profits and carbon emission trading profits; s23, performing model parameter calibration through a new energy storage supervision platform to generate new energy storage combined profit model data Wherein Representing an expected benefit value per unit time; the core formula of the profit model is normalized: ; Wherein, the For the total benefit in the time interval t, Setting a threshold for the historical maximum benefit value When (when) And when the model parameter recalibration is triggered, the accuracy and the adaptability of the profit model are ensured.
4. An optimization strategy for energy storage revenue maximization in combination with new energy according to claim 3, wherein based on the new energy storage revenue model data In the step S3, optimizing the new energy power generation and energy storage cooperative scheduling policy according to the new energy storage combined profit model data, and generating new energy storage cooperative scheduling policy optimization data includes the following operations: s31, storing energy according to the new energy storage combined profit model data Performing preliminary optimization of a new energy power generation and energy storage cooperative scheduling strategy by adopting a greedy algorithm, and generating preliminary cooperative scheduling strategy data; S32, performing strategy refinement processing based on the preliminary cooperative scheduling strategy data and combining space-time constraint conditions, wherein the space-time constraint conditions comprise power generation prediction errors, power grid scheduling limits and energy storage physical limits; s33, adjusting scheduling strategy parameters through an iterative optimization algorithm to generate new energy storage collaborative scheduling strategy optimization data ; The optimization objective function adds normalization constraints and thresholds: ; Wherein, the To calculate from the first time interval By the last time interval The sum of the benefits at each time point, To optimize the total number of time intervals of the cycle, Is a time interval The total benefit of the interior is that, For a specific point in time Is added to the total profit of (a), For the point in time The equipment costs incurred by use, For the value of the maximum benefit to be a historical value, And setting the profit threshold value to be an expected minimum profit value, ensuring that the profit is greater than the threshold value in the optimization process, and improving the stability of the strategy.
5. The optimization strategy for energy storage revenue maximization of a joint new energy source according to claim 4, wherein data is optimized based on the new energy storage collaborative scheduling strategy In the step S4, based on the new energy storage cooperative scheduling policy optimization data, dynamically adjusting the charging and discharging policy of the energy storage system, and generating the charging and discharging dynamic adjustment data of the energy storage system includes the following operations: s41, optimizing data based on the new energy storage cooperative scheduling strategy The method comprises the steps of dynamically adjusting a charging and discharging strategy of an energy storage system by adopting a model predictive control algorithm; S42, monitoring power grid electricity price fluctuation and new energy power generation fluctuation in real time, and dynamically updating a charging and discharging strategy; S43, generating charge-discharge dynamic adjustment data of the energy storage system , wherein, Comprises an optimal charge and discharge time point, a charge and discharge rate and depth parameters.
6. The optimization strategy for maximizing energy storage returns in combination with new energy as claimed in claim 5, wherein data is dynamically adjusted based on the charge and discharge of said energy storage system In the step S5, according to the charging and discharging dynamic adjustment data of the energy storage system, integrating market electricity price and demand response parameters, and constructing market electricity price demand response integrated data includes the following operations: S51, acquiring charge and discharge dynamic adjustment data of the energy storage system ; S52, integrating real-time market electricity price data, user demand response data and policy subsidy data, and constructing market electricity price demand response integrated data by adopting a data fusion technology ; S53, evaluating the benefit influence under different market scenes through sensitivity analysis, and optimizing integrated data Reliability of (3).
7. An optimization strategy for energy storage revenue maximization in conjunction with new energy according to claim 6, characterized by responding to integrated data based on the market price demand And historical revenue data, wherein in the step S6, based on the market electricity price demand response integrated data and the historical revenue data, revenue risk assessment and early warning are performed, and the generation of new energy storage revenue risk assessment data comprises the following operations: S61, responding to integrated data based on the market electricity price demand The historical profit database is used for calculating profit risk probability by adopting Monte Carlo simulation; s62, setting a risk threshold, and generating an early warning signal when the income risk exceeds the threshold; S63, outputting new energy storage income risk assessment data Including risk level, early warning advice and mitigation measures.
8. The optimization strategy for energy storage revenue maximization of a combined new energy source of claim 7, wherein based on the new energy storage revenue risk assessment data In the step S7, matching the multi-objective optimization algorithm type according to the new energy storage benefit risk assessment data, and generating the objective multi-objective optimization algorithm type feature data includes the following operations: s71, establishing a multi-target optimization algorithm type library, wherein the multi-target optimization algorithm type library comprises a standard gain optimization scene data matrix corresponding to a genetic algorithm, a particle swarm algorithm and a simulated annealing algorithm ; S72, storing energy storage income risk assessment data of the new energy And said at least one of Matching, and searching an optimal algorithm type by adopting an artificial intelligent bionic algorithm; s73, generating target multi-target optimization algorithm type characteristic data And identifies the algorithm parameter configuration.
9. The optimization strategy for maximizing energy storage returns in combination with new energy according to claim 8, wherein the optimization algorithm type characteristic data is based on the target multi-target optimization algorithm In the step S8, new energy storage benefit optimization summary data is constructed, benefit maximization calculation is performed, and generating energy storage benefit maximization optimization result data of the combined new energy comprises the following operations: s81, optimizing data of the new energy storage collaborative scheduling strategy Charging and discharging dynamic adjustment data of the energy storage system Said market price demand response integrated data The new energy storage income risk assessment data The type characteristic data of the target multi-target optimization algorithm Combining to construct new energy storage income optimization summary data ; S82, the new energy storage supervision platform is based on the following Calling corresponding multi-objective optimization program, for Performing profit maximization calculation to generate energy storage profit maximization optimization result data of combined new energy 。
10. The optimization strategy for maximizing energy storage gain of a combined new energy source according to claim 9, wherein the optimization strategy further comprises a gain optimization result verification and feedback step, and the energy storage gain maximization optimization result data based on the combined new energy source Comprising the following operations: S91, comparing the optimized result data with actual operation data Calculating a gain deviation rate; S92, automatically adjusting the gain model and algorithm parameters when the deviation rate exceeds the allowable range; And S93, storing the feedback data into a historical database, realizing subsequent optimization iteration and realizing self-learning optimization of the method.

Description

Optimizing strategy for maximizing energy storage benefit of combined new energy Technical Field The invention relates to the technical field of novel energy storage, in particular to an optimization strategy for maximizing energy storage benefits of combined new energy. Background The novel energy storage is an energy storage technology taking output power as a main form except pumped storage, and is an important supporting technology for constructing a novel power system taking new energy as a main body. At present, because the new energy power generation output has intermittence and uncertainty, when the energy storage income maximization optimization is carried out, high-precision power generation output data and energy storage system operation parameter data cannot be synchronously acquired in real time, the construction foundation of a income model is not firm, and the reliability of the optimization process cannot be ensured. Therefore, an optimization strategy for maximizing the energy storage benefit of the combined new energy is proposed to solve the above problems. Disclosure of Invention Aiming at the defects of the prior art, the invention provides an optimization strategy for maximizing the energy storage benefit of the combined new energy, and solves the problems that the high-precision power generation output data and the energy storage system operation parameter data cannot be synchronously acquired in real time and the reliability of the optimization process cannot be ensured in the background art. In order to achieve the above purpose, the invention provides the following technical scheme for realizing the optimization strategy for maximizing the energy storage benefit of the combined new energy, wherein the optimization strategy comprises the following steps: S1, collecting new energy power generation output data and energy storage system operation parameter data; s2, constructing a benefit model of a new energy power generation and energy storage combined system based on the new energy power generation output data and the energy storage system operation parameter data, and generating new energy storage combined benefit model data; S3, optimizing a cooperative scheduling strategy of new energy power generation and energy storage according to the new energy storage combined profit model data, and generating new energy storage cooperative scheduling strategy optimization data; S4, based on the new energy storage collaborative scheduling policy optimization data, dynamically adjusting the charging and discharging policies of the energy storage system, and generating charging and discharging dynamic adjustment data of the energy storage system; S5, integrating market electricity price and demand response parameters according to the charging and discharging dynamic adjustment data of the energy storage system, and constructing market electricity price demand response integrated data; S6, based on the market electricity price demand response integrated data and the historical revenue data, performing revenue risk assessment and early warning to generate new energy storage revenue risk assessment data; s7, matching the multi-objective optimization algorithm type according to the new energy storage income risk assessment data to generate objective multi-objective optimization algorithm type characteristic data; And S8, constructing new energy storage benefit optimization summary data, and performing benefit maximization calculation to generate energy storage benefit maximization optimization result data of the combined new energy. Preferably, the collecting new energy power generation output data and energy storage system operation parameter data in step S1 includes the following operations: S11, collecting solar photovoltaic power generation output data, wind power generation output data and operation parameter data of an energy storage battery system through a new energy power generation station monitoring system, wherein the operation parameter data comprise capacity, charge and discharge efficiency and life cycle parameters of the energy storage battery; S12, the collected new energy power generation output data and the collected energy storage system operation parameter data are imported into a new energy storage supervision platform, abnormal values and missing values are removed by adopting a data cleaning algorithm, and standardized new energy power generation output data are generated And standardized energy storage system operating parameter data。 Preferably, the power generation output data is based on the standardized new energyAnd the standardized energy storage system operating parameter dataIn the step S2, a revenue model of the new energy power generation and energy storage combined system is constructed based on the new energy power generation output data and the energy storage system operation parameter data, and the generation of new energy storage combined revenue model data