A distributed wind-solar power generation cluster optimization and control method

Abstract

The present invention belongs to the field of power system optimal dispatching, and relates to an optimization control method for a distributed wind-solar power generation cluster, including: based on the obtained photovoltaic output power curve, wind power output power curve and historical selling electricity price, adopting a two-way feedback mechanism, and regulating the distributed wind-solar power generation cluster through a two-layer regulation model until the revenue change rate of the distributed wind-solar power generation cluster is less than or equal to a threshold value, generating and outputting the regulated selling electricity price, photovoltaic output, wind power output, photovoltaic operation and maintenance cost, energy storage charge and discharge cost, user load adjustment cost and wind power operation and maintenance cost. It solves the problems that it is difficult for a single-layer optimization model to optimize multi-objectives and multi-agent interests, difficult to fully integrate various types of distributed resources, resulting in the deviation of the dispatching plan from the actual situation and difficult to achieve global optimization.

Description

Technical Field

[0001] The present invention relates to the field of optimal dispatching of power systems, and specifically discloses an optimal regulation method for a distributed wind-solar power generation cluster. Background Art

[0002] Traditional distribution network distributed resource dispatching methods mostly adopt a single-layer optimization model, focusing on the optimization of a single objective such as minimizing operating costs or maximizing market revenue, and it is difficult to coordinate the multi-agent interests of distributed resource aggregators, users, and the power grid.

[0003] The existing distributed resource dispatching methods have the following disadvantages:

[0004] 1. In the market, power purchase and sale plans are usually formulated based on fixed electricity prices or historical data, lacking dynamic interaction with the underlying physical operation, and it is easy to cause revenue shrinkage or infeasible dispatching due to actual output deviation.

[0005] 2. There are various types of distributed resources, such as photovoltaic, wind power, energy storage, and flexible loads. The heterogeneous constraints and response characteristics of distributed resources have not been fully integrated. Single-objective optimization is difficult to coordinate multi-agent interests and difficult to balance economy, security, and user satisfaction.

[0006] 3. The feedback effect of actual operating costs on upper-layer decisions is not considered, and there is a lack of a two-way feedback mechanism. The operating costs cannot correct the upper-layer decisions in real time, resulting in the dispatching plan deviating from reality.

[0007] 4. There is a lack of a dynamic interaction mechanism between revenue and operating costs, and it is difficult to achieve global optimization. Summary of the Invention

[0008] The purpose of the present invention is to provide an optimal regulation method for a distributed wind-solar power generation cluster, which solves the problems that it is difficult to optimize multiple objectives and multi-agent interests with a single-layer optimization model, difficult to fully integrate various types of distributed resources, resulting in the dispatching plan deviating from reality, and difficult to achieve global optimization.

[0009] The specific solution of the present invention is as follows:

[0010] An optimal regulation method for a distributed wind-solar power generation cluster, comprising:

[0011] Based on the obtained photovoltaic output power curve, wind power output power curve, and historical selling electricity price, adopting a two-way feedback mechanism, and regulating the distributed wind-solar power generation cluster through a two-layer regulation model until the revenue change rate of the distributed wind-solar power generation cluster is less than or equal to the threshold, and generating and outputting the regulated selling electricity price, photovoltaic output, wind power output, photovoltaic operation and maintenance cost, energy storage charge and discharge cost, user load adjustment cost, and wind power operation and maintenance cost.

[0012] In some embodiments, the two-way feedback mechanism includes:

[0013] The upper-layer revenue module of the two-layer regulation model generates the current distributed wind-solar scheduling scale and revenue based on the obtained photovoltaic output power curve, wind power output power curve, and historical selling electricity price, and transfers the current distributed wind-solar scheduling scale to the lower-layer operating cost module;

[0014] The lower-layer operating cost module of the two-layer regulation model generates the current actual operating cost based on the current distributed wind-solar scheduling scale, and feeds back the current actual operating cost to the upper-layer revenue module;

[0015] The upper-layer revenue module corrects the distributed wind-solar scheduling cost under the current distributed wind-solar scheduling scale through the scheduling cost correction formula based on the current actual operating cost to generate a new distributed wind-solar scheduling cost;

[0016] The upper-layer revenue module iteratively updates the revenue objective function of the upper-layer revenue module based on the new distributed wind-solar scheduling cost to generate a new distributed wind-solar scheduling scale and revenue, and transfers the new distributed wind-solar scheduling scale to the lower-layer operating cost module, and so on in a loop until the revenue change rate generated based on the current revenue and the new revenue is less than or equal to the threshold.

[0017] In some embodiments, the revenue objective function of the upper-layer revenue module is:

[0018] ,

[0019] where, is the selling electricity price, is the electricity sales volume at time t, is the distributed wind-solar scheduling scale; is the distributed wind-solar scheduling cost, is the user load adjustment cost; is the user load adjustment volume.

[0020] In some embodiments, the lower-layer operating cost module includes an operating cost objective function, and the operating cost objective function is:

[0021] ,

[0022] where, is the photovoltaic operation and maintenance cost, is the energy storage charge and discharge cost, is the user load adjustment cost, is the wind power operation and maintenance cost, is the photovoltaic output, is the wind power output, is the energy storage charging power, is the energy storage discharging power, Load adjustment power.

[0023] In some embodiments, the scheduling cost correction formula is:

[0024] ,

[0025] ,

[0026] wherein, is the weight factor, , is the distributed wind and solar scheduling cost under the current distributed wind and solar scheduling scale, is the current actual operating cost fed back by the lower-layer operating cost module, is the new distributed wind and solar scheduling cost generated after correction processing.

[0027] In some embodiments, the upper-layer revenue module further includes a range of electricity price fluctuations, and the range of electricity price fluctuations is:

[0028] ,

[0029] wherein, is the lowest price of the selling electricity price, is the highest price of the selling electricity price.

[0030] In some embodiments, the upper-layer revenue module further includes a range of scheduling scale limits, and the range of scheduling scale limits is:

[0031] ,

[0032] wherein, is the lower limit value of the distributed wind and solar scheduling scale, is the upper limit value of the distributed wind and solar scheduling scale.

[0033] In some embodiments, the upper-layer revenue module further includes a range of user satisfaction constraints, and the range of user satisfaction constraints is:

[0034] ,

[0035] wherein, is the maximum value of the user load adjustment amount.

[0036] In some embodiments, the lower-layer operating cost module further includes a range of power balance constraints, and the range of power balance constraints is:

[0037] ,

[0038] wherein, is the photovoltaic output, is the wind power output, is the energy storage charging power, is the energy storage discharging power, load adjustment power, is the distributed wind and solar dispatch scale.

[0039] In some embodiments, the lower-layer operating cost module further includes an energy storage SOC constraint range, and the energy storage SOC constraint range is:

[0040] ,

[0041] wherein, is the state of charge of the energy storage at time period t.

[0042] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0043] Through the double-layer regulation model and the bidirectional feedback mechanism, the present invention dynamically adjusts the selling electricity price and the distributed wind and solar dispatch scale through the upper-layer revenue module, combines the game of user load adjustment cost, realizes the maximization of the revenue of the distributed wind and solar aggregator, fully integrates the coordinated dispatch of photovoltaic, wind power, energy storage, and flexible load through the lower-layer operating cost module, realizes the minimization of the operating cost, uses the actual operating cost to dynamically correct the distributed wind and solar dispatch cost, avoids the deviation of the dispatch plan from the actual situation, improves the participation degree of demand response through the user load adjustment mechanism, thus significantly improving the operating efficiency of the distributed wind and solar power generation cluster regulation, coordinating the interests of multiple parties, balancing the economy, security and user satisfaction, taking into account the dynamic interaction between revenue and operating cost, realizing global optimization, and providing a flexible and scalable distributed wind and solar power generation cluster regulation method for the power market; and is applicable to being embedded in a virtual power plant VPP or an intelligent distribution network platform, and supports the access of a high proportion of renewable energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is the flowchart of the bidirectional feedback mechanism in Embodiment 1 of the present invention.

[0045] Figure 2 is the flowchart of a distributed wind and solar power generation cluster optimization regulation method in Embodiment 1 of the present invention.

[0046] Figure 3 is the block diagram of the double-layer regulation model in Embodiment 1 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Usually, the components of the embodiments of the present invention described and illustrated in the accompanying drawings here can be arranged and designed in various different configurations.

[0048] A distributed wind-solar power generation cluster optimization control method includes:

[0049] Based on the obtained photovoltaic output power curve, wind power output power curve, and historical selling electricity price, a two-way feedback mechanism is adopted, and the distributed wind-solar power generation cluster is controlled through a two-layer control model until the revenue change rate of the distributed wind-solar power generation cluster is less than or equal to the threshold, and the regulated selling electricity price, photovoltaic output, wind power output, photovoltaic operation and maintenance cost, energy storage charge and discharge cost, user load adjustment cost, and wind power operation and maintenance cost are generated and output.

[0050] Through the two-layer control model and the two-way feedback mechanism, the operation efficiency of the distributed wind-solar power generation cluster control is significantly improved, the interests of multiple parties are coordinated, the economy, safety, and user satisfaction are balanced, the dynamic interaction between revenue and operation cost is taken into account, global optimization is achieved, and a flexible and scalable distributed wind-solar power generation cluster control method is provided for the power market; and it is applicable to being embedded in a virtual power plant (VPP) or an intelligent distribution network platform and supports the access of a high proportion of renewable energy.

[0051] Among them, as Figure 1 shown, the two-way feedback mechanism includes:

[0052] The upper-layer revenue module of the two-layer control model generates the current distributed wind-solar power dispatch scale and revenue based on the obtained photovoltaic output power curve, wind power output power curve, and historical selling electricity price, and transfers the current distributed wind-solar power dispatch scale to the lower-layer operation cost module;

[0053] The lower-layer operation cost module of the two-layer control model generates the current actual operation cost based on the current distributed wind-solar power dispatch scale and feeds back the current actual operation cost to the upper-layer revenue module;

[0054] The upper-layer revenue module corrects the distributed wind-solar power dispatch cost under the current distributed wind-solar power dispatch scale through a dispatch cost correction formula based on the current actual operation cost to generate a new distributed wind-solar power dispatch cost;

[0055] The upper-layer revenue module iteratively updates the revenue objective function of the upper-layer revenue module based on the new distributed wind-solar scheduling cost to generate a new distributed wind-solar scheduling scale and revenue, and transfers the new distributed wind-solar scheduling scale to the lower-layer operating cost module, and loops in this way until the revenue change rate generated based on the current revenue and the new revenue is less than or equal to the threshold value.

[0056] The upper-layer revenue module transfers the initialized generated distributed wind-solar scheduling scale to the lower-layer operating cost module. The lower-layer operating cost module optimizes the actual operating cost based on the distributed wind-solar scheduling scale and feeds back the actual operating cost to the upper-layer revenue module. The upper-layer revenue module adjusts the distributed wind-solar scheduling scale based on the fed-back actual operating cost to form a closed-loop optimization and improve the executability of the distributed wind-solar power generation cluster regulation.

[0057] As Figure 2 shown, a method for optimizing the regulation of a distributed wind-solar power generation cluster specifically includes the following steps:

[0058] S1. Obtain the photovoltaic output power curve, the wind power output power curve, and the historical selling electricity price;

[0059] Collect the selling electricity prices in the past 3 years, and calculate the average value of the selling electricity prices in the past 3 years to obtain the historical selling electricity price.

[0060] Combine the weather forecast released by the meteorological department to predict the output of the distributed wind-solar power generation cluster to obtain the photovoltaic output power curve and the wind power output power curve.

[0061] S2. Based on the photovoltaic output power curve, the wind power output power curve, and the historical selling electricity price, perform initialization processing on the upper-layer revenue module of the double-layer regulation model;

[0062] Input the photovoltaic output power curve, the wind power output power curve, and the historical selling electricity price into the upper-layer revenue module of the double-layer regulation model for initialization iteration.

[0063] After the initialization processing, the upper-layer revenue module generates the current distributed wind-solar scheduling scale and revenue, and transfers the current distributed wind-solar scheduling scale to the lower-layer operating cost module;

[0064] Based on the photovoltaic output power curve, the wind power output power curve, and the historical selling electricity price, through the revenue objective function of the upper-layer revenue module and the Gurobi solver, maximize the solution of the current revenue to generate the current distributed wind-solar scheduling scale, and transfer the current distributed wind-solar scheduling scale to the lower-layer operating cost module; based on the current distributed wind-solar scheduling scale, generate the current revenue through the upper-layer revenue module.

[0065] The revenue objective function of the upper-layer revenue module is:

[0066] ,

[0067] Among them, is the selling electricity price, is the electricity sales volume at time t, is the distributed wind and solar scheduling scale; is the distributed wind and solar scheduling cost, is the user load adjustment cost; is the user load adjustment volume.

[0068] Revenue = Electricity sales revenue - Distributed wind and solar scheduling cost - User load adjustment cost,

[0069] ,

[0070] Among them, is the selling electricity price, is the electricity sales volume at time t, t = 1, ……, T.

[0071] S4. Based on the current distributed wind and solar scheduling scale, generate the output combination and actual operating cost of the current distributed wind and solar power generation cluster through the lower-layer operating cost module, and feedback the current actual operating cost to the upper-layer revenue module;

[0072] Based on the current distributed wind and solar scheduling scale transmitted by the upper-layer revenue module, through the operating cost objective function and Gurobi solver of the lower-layer operating cost module, minimize the current operating cost to generate the output combination of the current distributed wind and solar power generation cluster. The output combination of the distributed wind and solar power generation cluster includes photovoltaic output, wind power output, energy storage charging power, energy storage discharging power, and user load adjustment power.

[0073] The operating cost objective function of the lower-layer operating cost module is:

[0074] ,

[0075] Among them, is the photovoltaic operation and maintenance cost, is the energy storage charge and discharge cost, is the user load adjustment cost, is the wind power operation and maintenance cost, is the photovoltaic output, is the wind power output, is the energy storage charging power, is the energy storage discharging power, Load adjustment power.

[0076] Generate the actual operation cost through the lower - layer operation cost module based on the current photovoltaic output, wind power output, energy storage charging power, energy storage discharging power, and user load adjustment power; and feedback the actual operation cost to the upper - layer revenue module.

[0077] The actual operation cost refers to the sum of the photovoltaic operation and maintenance cost, wind power operation and maintenance cost, energy storage charge - discharge cost, and user load adjustment cost corresponding to the current photovoltaic output, wind power output, energy storage charging power, energy storage discharging power, and user load adjustment power.

[0078] S5. Based on the current actual operation cost, the upper - layer revenue module corrects the distributed wind - solar dispatch cost to generate a new distributed wind - solar dispatch cost;

[0079] Based on the current actual operation cost fed back by the lower - layer operation cost module, the upper - layer revenue module corrects the distributed wind - solar dispatch cost at the current distributed wind - solar dispatch scale generated in step S3 through the dispatch cost correction formula to generate a new distributed wind - solar dispatch cost.

[0080] The dispatch cost correction formula is:

[0081] ,

[0082] ,

[0083] Among them, is the weight factor, , is the distributed wind - solar dispatch cost at the current distributed wind - solar dispatch scale in step S3, is the current actual operation cost fed back by the lower - layer operation cost module, is the new distributed wind - solar dispatch cost generated after correction processing.

[0084] Dynamically adjust the weight factor according to the deviation between the distributed wind - solar dispatch cost at the current distributed wind - solar dispatch scale generated by the upper - layer revenue module and the current actual operation cost fed back by the lower - layer operation cost module .

[0085] S6. Iteratively update the revenue objective function of the upper - layer revenue module based on the new distributed wind - solar dispatch cost to generate a new distributed wind - solar dispatch scale and selling price;

[0086] Iteratively update the revenue objective function of the upper - layer revenue module based on the new distributed wind - solar dispatch cost, and through the Gurobi solver, maximize the current revenue to generate a new distributed wind - solar dispatch scale and selling price.

[0087] S7. Generate new revenue through the upper-layer revenue module based on the new distributed wind and solar power dispatching scale and selling electricity price.

[0088] Calculate the revenue based on the distributed wind and solar power dispatching costs, user load adjustment costs corresponding to the new distributed wind and solar power dispatching scale, and the new selling electricity price to generate new revenue.

[0089] S8. Generate a revenue change rate based on the current revenue in step S3 and the new revenue in step S7, and determine whether the revenue change rate is less than or equal to the threshold. When it is determined that the revenue change rate is greater than the threshold, repeat steps S3 to S8. When it is determined that the revenue change rate is less than or equal to the threshold, end the iterative update. Then, the two-layer regulation model outputs the regulated electricity price strategy, distributed wind and solar power generation cluster regulation plan, and operating cost.

[0090] The calculation formula for the revenue change rate is:

[0091] ,

[0092] where, is the current revenue, is the new revenue.

[0093] The electricity price strategy refers to the selling electricity price; the distributed wind and solar power generation cluster regulation plan refers to the photovoltaic output and wind power output; the operating cost refers to the sum of the photovoltaic operation and maintenance cost, energy storage charge and discharge cost, user load adjustment cost, and wind power operation and maintenance cost.

[0094] When the revenue change rate is less than or equal to the threshold, the new distributed wind and solar power dispatching scale and selling electricity price generated in step S6 are used as the distributed wind and solar power dispatching scale and selling electricity price at this time. Based on the distributed wind and solar power dispatching scale at this time, the photovoltaic output and wind power output at this time are generated through the lower-layer operating cost module and the Gurobi solver. Based on the photovoltaic output and wind power output at this time, the corresponding photovoltaic operation and maintenance cost, energy storage charge and discharge cost, user load adjustment cost, and wind power operation and maintenance cost at this time are obtained. Then, the two-layer regulation model outputs the selling electricity price, photovoltaic output, wind power output, photovoltaic operation and maintenance cost, energy storage charge and discharge cost, user load adjustment cost, and wind power operation and maintenance cost at this time, that is, the two-layer regulation model outputs the optimal selling electricity price, photovoltaic output, wind power output, photovoltaic operation and maintenance cost, energy storage charge and discharge cost, user load adjustment cost, and wind power operation and maintenance cost, realizing the maximization of the distributed wind and solar aggregator's revenue and the minimization of the operating cost.

[0095] Through the double - layer regulation model and the two - way feedback mechanism, the upper - layer revenue module dynamically adjusts the selling electricity price and the distributed wind - solar scheduling scale. Combining with the game of user load adjustment cost, it realizes the maximization of the revenue of the distributed wind - solar aggregator. The lower - layer operation cost module fully integrates the coordinated scheduling of photovoltaic, wind power, energy storage, and flexible load to minimize the operation cost. It uses the actual operation cost to dynamically correct the distributed wind - solar scheduling cost to avoid the deviation of the scheduling plan from the actual situation, and improves the participation degree of demand response through the user load adjustment mechanism.

[0096] Among them, as Figure 3 shown, the double - layer regulation model includes an upper - layer revenue module and a lower - layer operation cost module; the upper - layer revenue module is used to optimize the market revenue by adjusting the selling electricity price and the distributed wind - solar scheduling scale with the goal of maximizing the revenue of the distributed wind - solar aggregator; the lower - layer operation cost module is used to optimize the output combination and operation cost of the distributed wind - solar power generation cluster based on the distributed wind - solar scheduling scale with the goal of minimizing the operation cost; the double - layer regulation model outputs the optimal selling electricity price, photovoltaic output, wind power output, photovoltaic operation and maintenance cost, energy storage charge - discharge cost, user load adjustment cost, and wind power operation and maintenance cost through the two - way feedback mechanism.

[0097] Through the double - layer regulation model, the dynamic coordination of the revenue and operation cost of the distributed wind - solar aggregator is realized, improving the economic efficiency.

[0098] The upper - layer revenue module also includes revenue constraint conditions, which include the electricity price fluctuation range, the scheduling scale limit range, and the user satisfaction constraint range.

[0099] The electricity price fluctuation range is:

[0100] ,

[0101] Among them, is the lowest price of the selling electricity price, is the highest price of the selling electricity price; the lowest price and the highest price of the selling electricity price are customized based on the market conditions.

[0102] The scheduling scale limit range is:

[0103] ,

[0104] Among them, is the lower limit value of the distributed wind - solar scheduling scale, is the upper limit value of the distributed wind - solar scheduling scale; the lower limit value and the upper limit value of the distributed wind - solar scheduling scale are obtained from the data information of the distributed wind - solar scheduling scale in the past 3 years.

[0105] The user satisfaction constraint range is:

[0106] ,

[0107] Among them, is the maximum value of the user load adjustment amount; the maximum value of the user load adjustment amount is user-defined.

[0108] The lower-layer operating cost module also includes operating cost constraint conditions, which include power balance constraint range, energy storage SOC constraint range, and network power flow security constraint range.

[0109] The power balance constraint range is:

[0110] ,

[0111] Among them, is the photovoltaic output, is the wind power output, is the energy storage charging power, is the energy storage discharging power, load adjustment power, is the distributed wind and solar dispatching scale; the power balance constraint range is used to ensure that the sum of the photovoltaic output, wind power output, energy storage charging power, energy storage discharging power, and load adjustment power is equal to the distributed dispatching scale.

[0112] The energy storage SOC constraint range is:

[0113] ,

[0114] Among them, is the energy storage state of charge at time t; the energy storage SOC constraint is used to prevent overcharging or over-discharging of the energy storage.

[0115] The network power flow security constraint range includes voltage constraint range and current constraint range.

[0116] The voltage constraint range is: control the voltage within the allowable range, and the allowable range is ±5% of the nominal value; the voltage constraint range is used to prevent equipment damage caused by the voltage exceeding the allowable range.

[0117] The current constraint range is: the current does not exceed the thermal stability limit; the current constraint range is used to avoid line overload.

[0118] By fully integrating the heterogeneous constraints of photovoltaic, wind power, energy storage, and flexible load, as well as the joint optimization of power balance constraints and security constraints, the contradiction between output volatility and user satisfaction is solved.

[0119] Through the revenue objective function, operating cost objective function, revenue constraint conditions and operating cost constraint conditions of the double-layer regulation model, the present invention fully integrates photovoltaic power, wind power, energy storage, and flexible loads to obtain the optimal selling electricity price, photovoltaic power output, wind power output, photovoltaic operation and maintenance cost, energy storage charge and discharge cost, user load adjustment cost, and wind power operation and maintenance cost, so that the distributed resource aggregator, users, and the power grid are fully coordinated. While maximizing revenue and minimizing operating costs, a full balance is achieved among economy, security, and user satisfaction. It solves the problem that the wind power output and photovoltaic power output predicted according to weather forecasts are inconsistent with the actual demand power, resulting in excess or insufficient wind power output and photovoltaic power output, leading to poor user satisfaction and low economy.

[0120] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A distributed wind-solar power generation cluster optimization and control method, characterized in that Including: Based on the obtained photovoltaic output power curve, wind power output power curve, and historical selling electricity price, a two-way feedback mechanism is adopted. Through a two-layer regulation model, the distributed wind-solar power generation cluster is regulated until the revenue change rate of the distributed wind-solar power generation cluster is less than or equal to the threshold, and the regulated selling electricity price, photovoltaic output, wind power output, photovoltaic operation and maintenance cost, energy storage charge and discharge cost, user load adjustment cost, and wind power operation and maintenance cost are generated and output; The two-way feedback mechanism includes: The upper-layer revenue module of the two-layer regulation model generates the current distributed wind-solar scheduling scale and revenue based on the obtained photovoltaic output power curve, wind power output power curve, and historical selling electricity price, and transmits the current distributed wind-solar scheduling scale to the lower-layer operation cost module; The lower-layer operation cost module of the two-layer regulation model generates the current actual operation cost based on the current distributed wind-solar scheduling scale and feeds back the current actual operation cost to the upper-layer revenue module; Through the operation cost objective function of the lower-layer operation cost module and the Gurobi solver, the current operation cost is minimized to generate the output combination of the distributed wind-solar power generation cluster. The output combination of the distributed wind-solar power generation cluster includes photovoltaic output, wind power output, energy storage charging power, energy storage discharging power, and user load adjustment power; Based on the current actual operation cost, the upper-layer revenue module corrects the distributed wind-solar scheduling cost under the current distributed wind-solar scheduling scale through the scheduling cost correction formula to generate a new distributed wind-solar scheduling cost; Based on the new distributed wind-solar scheduling cost, the upper-layer revenue module iteratively updates the revenue objective function of the upper-layer revenue module to generate a new distributed wind-solar scheduling scale and revenue, and transmits the new distributed wind-solar scheduling scale to the lower-layer operation cost module, and so on in a loop until the revenue change rate generated based on the current revenue and the new revenue is less than or equal to the threshold.

2. The optimized control method for a distributed wind-solar power generation cluster according to claim 1, characterized in that, The revenue objective function of the upper-layer revenue module is: ， Among them, is the selling electricity price, is the electricity sales volume at time t, is the distributed wind and solar scheduling scale; is the distributed wind and solar scheduling cost, is the user load adjustment cost; is the user load adjustment volume.

3. A distributed wind-solar power generation cluster optimization control method according to claim 1, characterized in that, The lower-layer operation cost module includes an operation cost objective function, and the operation cost objective function is: ， Among them, is the photovoltaic operation and maintenance cost, is the energy storage charge and discharge cost, is the user load adjustment cost, is the wind power operation and maintenance cost, is the photovoltaic output, is the wind power output, is the energy storage charging power, is the energy storage discharging power, is the load adjustment power.

4. A distributed wind-solar power generation cluster optimization control method according to claim 1, characterized in that The scheduling cost correction formula is: ， ， Among them, is the weight factor, , is the distributed wind and solar power scheduling cost under the current distributed wind and solar power scheduling scale, is the current actual operation cost fed back by the lower-level operation cost module, is the new distributed wind and solar power scheduling cost generated after correction processing.

5. A distributed wind-solar power generation cluster optimization control method according to claim 1, characterized in that, The upper-layer revenue module also includes a electricity price fluctuation range, and the electricity price fluctuation range is: ， Among them, is the lowest price of the selling electricity price, is the highest price of the selling electricity price, is the selling electricity price.

6. The optimized control method for a distributed wind-solar power generation cluster according to claim 1, characterized in that The upper-layer revenue module also includes a scheduling scale limit range, and the scheduling scale limit range is: ， Among them, is the lower limit value of the distributed wind and solar power dispatching scale, is the upper limit value of the distributed wind and solar power dispatching scale, is the distributed wind and solar power dispatching scale.

7. A method for optimizing and regulating a distributed wind-solar power generation cluster according to claim 1, characterized in that, The upper-layer revenue module also includes a user satisfaction constraint range, and the user satisfaction constraint range is: ， Among them, is the maximum value of the user load adjustment amount, is the user load adjustment amount.

8. A distributed wind-solar power generation cluster optimization control method according to claim 1, characterized in that The lower-layer operation cost module also includes a power balance constraint range, and the power balance constraint range is: ， Among them, is the photovoltaic output, is the wind power output, is the energy storage charging power, is the energy storage discharging power, is the load adjustment power, is the distributed wind and light dispatching scale.

9. A distributed wind-solar power generation cluster optimization control method according to claim 1, characterized in that, The lower-layer operation cost module also includes an energy storage SOC constraint range, and the energy storage SOC constraint range is: ， Among them, is the state of charge of the energy storage at time t.

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