Distributed wind-solar power generation cluster optimization regulation and control method

Abstract

The invention belongs to the field of power system optimization scheduling, and relates to a distributed wind and light power generation cluster optimization regulation and control method, which comprises the following steps of: regulating and controlling a distributed wind and light power generation cluster through a double-layer regulation and control model by adopting a bidirectional feedback mechanism based on an obtained photovoltaic output power curve, a wind power output power curve and historical selling electricity price, and until the income change rate of the distributed wind and light power generation cluster is smaller 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 charging and discharging cost, user load regulation cost and wind power operation and maintenance cost. The problems that a scheduling plan deviates from reality and global optimization is difficult to realize due to the fact that a single-layer optimization model is difficult to carry out multi-target and multi-subject benefit optimization and is difficult to fully integrate various types of distributed resources are solved.

Description

Technical Field

[0001] The present invention relates to the field of power system optimization and dispatching, and specifically discloses a distributed wind-solar power generation cluster optimization and control method. Background Art

[0002] Traditional distribution network distributed resource scheduling methods mostly adopt a single-layer optimization model, focusing on minimizing operating costs or optimizing a single goal of market benefits, which makes it difficult to coordinate the interests of multiple entities including distributed resource aggregators, users and power grids.

[0003] The existing distributed resource scheduling methods have the following disadvantages: 1. The market usually formulates power purchase and sales plans based on fixed electricity prices or historical data, lacking dynamic interaction with the underlying physical operations, which can easily lead to reduced profits or unfeasible scheduling due to deviations from actual output.

[0004] 2. Distributed resources are diverse in types, such as photovoltaics, 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 the interests of multiple entities and it is difficult to balance economy, safety, and user satisfaction.

[0005] 3. The feedback effect of actual operating costs on upper-level decisions is not taken into account. There is a lack of a two-way feedback mechanism, and operating costs cannot correct upper-level decisions in real time, resulting in scheduling plans deviating from reality.

[0006] 4. The lack of a dynamic interaction mechanism between revenue and operating costs makes it difficult to achieve global optimization. Summary of the invention

[0007] The purpose of the present invention is to provide a distributed wind and solar power generation cluster optimization and control method to solve the problem that a single-layer optimization model is difficult to optimize multiple objectives and multi-subject interests, and it is difficult to fully integrate various types of distributed resources, resulting in a scheduling plan that deviates from reality and difficulty in achieving global optimization.

[0008] The specific scheme of the present invention is as follows: A distributed wind and solar power generation cluster optimization control method, comprising: Based on the acquired photovoltaic output power curve, wind power output power curve and historical electricity sales price, a two-way feedback mechanism is adopted to regulate the distributed wind and solar power generation cluster through a two-layer control model until the profit change rate of the distributed wind and solar power generation cluster is less than or equal to the threshold, and the regulated electricity sales price, photovoltaic output, wind power output, photovoltaic operation and maintenance cost, energy storage charging and discharging cost, user load adjustment cost and wind power operation and maintenance cost are generated and output.

[0009] In some embodiments, the two-way feedback mechanism includes: The upper revenue module of the two-layer control model generates the current distributed wind and solar dispatching scale and revenue based on the obtained photovoltaic output power curve, wind power output power curve and historical electricity price, and transmits the current distributed wind and solar dispatching scale to the lower operating cost module; The lower-level operating cost module of the two-layer control model generates the current actual operating cost based on the current distributed wind and solar dispatch scale, and feeds the current actual operating cost back to the upper-level revenue module; The upper-level revenue module uses the dispatch cost correction formula to correct the distributed wind and solar dispatch cost under the current distributed wind and solar dispatch scale based on the current actual operating cost to generate a new distributed wind and solar dispatch cost; The upper-level revenue module iteratively updates the revenue objective function of the upper-level revenue module based on the new distributed wind-solar scheduling cost to generate a new distributed wind-solar scheduling scale and revenue, and passes the new distributed wind-solar scheduling scale to the lower-level operating cost module, and repeats this cycle until the revenue change rate generated based on the current revenue and the new revenue is less than or equal to the threshold.

[0010] In some embodiments, the revenue objective function of the upper layer revenue module is: , in, To sell electricity price, is the electricity sales at time t, It is the scale of distributed wind and solar dispatch; is the distributed wind and solar dispatching cost, Adjust costs for user loads; The amount of user load adjustment.

[0011] In some embodiments, the lower-level operating cost module includes an operating cost objective function, and the operating cost objective function is: , in, is the photovoltaic operation and maintenance cost, is the energy storage charging and discharging cost, Adjust costs for user load, For wind power operation and maintenance costs, For photovoltaic power output, To contribute to wind power, The charging power for energy storage, is the energy storage discharge power, Load adjustment power.

[0012] In some embodiments, the scheduling cost correction formula is: , , in, is the weight factor, , is the distributed wind and solar dispatching cost under the current distributed wind and solar dispatching scale, The actual current operating cost fed back by the lower-level operating cost module. It is the new distributed wind-solar dispatching cost generated after correction.

[0013] In some embodiments, the upper-layer revenue module further includes an electricity price fluctuation range, and the electricity price fluctuation range is: , in, The lowest price for selling electricity. The highest price for selling electricity.

[0014] In some embodiments, the upper layer revenue module further includes a scheduling scale limit range, and the scheduling scale limit range is: , in, is the lower limit of the distributed wind and solar dispatching scale, It is the upper limit of the distributed wind and solar dispatching scale.

[0015] In some embodiments, the upper layer revenue module further includes a user satisfaction constraint range, and the user satisfaction constraint range is: , in, The maximum value of the user load adjustment.

[0016] In some embodiments, the lower layer operation cost module further includes a power balance constraint range, and the power balance constraint range is: , in, For photovoltaic power generation, To contribute to wind power, The charging power for energy storage, is the energy storage discharge power, Load adjustment power, It is the scale of distributed wind and solar dispatching. In some embodiments, the lower layer operation cost module further includes an energy storage SOC constraint range, and the energy storage SOC constraint range is: , in, is the energy storage charge state during time period t.

[0017] Compared with the prior art, the present invention has the following advantages and beneficial effects: The present invention adopts a double-layer control model and a two-way feedback mechanism, dynamically adjusts the selling price of electricity and the scale of distributed wind and solar dispatching through the upper-layer revenue module, and combines the user load adjustment cost game to maximize the benefits of distributed wind and solar aggregators. The lower-layer operating cost module fully integrates the coordinated dispatching of photovoltaic, wind power, energy storage, and flexible loads to minimize the operating costs. The actual operating costs are used to dynamically correct the distributed wind and solar dispatching costs to avoid the deviation of the dispatching plan from the actual situation. The user load adjustment mechanism improves the participation in demand response, thereby significantly improving the operating efficiency of distributed wind and solar power generation cluster control, coordinating the interests of multiple subjects, balancing economy, safety and user satisfaction, taking into account the dynamic interaction of benefits and operating costs, and achieving global optimization, providing a flexible and scalable distributed wind and solar power generation cluster control method for the power market; and is suitable for being embedded in a virtual power plant VPP or an intelligent distribution network platform, supporting the access of a high proportion of renewable energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of the two-way feedback mechanism in Example 1 of the present invention.

[0019] Figure 2 This is a flow chart of a distributed wind-solar power generation cluster optimization and control method in Example 1 of the present invention.

[0020] Figure 3 This is a block diagram of the double-layer control model in Example 1 of the present invention. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0022] A distributed wind and solar power generation cluster optimization control method, comprising: Based on the acquired photovoltaic output power curve, wind power output power curve and historical electricity sales price, a two-way feedback mechanism is adopted to regulate the distributed wind and solar power generation cluster through a two-layer control model until the profit change rate of the distributed wind and solar power generation cluster is less than or equal to the threshold, and the regulated electricity sales price, photovoltaic output, wind power output, photovoltaic operation and maintenance cost, energy storage charging and discharging cost, user load adjustment cost and wind power operation and maintenance cost are generated and output.

[0023] Through the double-layer control model and two-way feedback mechanism, the operating efficiency of distributed wind and solar power generation cluster control is significantly improved, the interests of multiple subjects are coordinated, the economy, safety and user satisfaction are balanced, the dynamic interaction of revenue and operating costs is taken into account, and global optimization is achieved, providing the power market with a flexible and scalable distributed wind and solar power generation cluster control method; and it is suitable for embedding in virtual power plants VPP or smart distribution network platforms, supporting the access of a high proportion of renewable energy.

[0024] Among them, Figure 1 As shown, the two-way feedback mechanism includes: The upper revenue module of the two-layer control model generates the current distributed wind and solar dispatching scale and revenue based on the obtained photovoltaic output power curve, wind power output power curve and historical electricity price, and transmits the current distributed wind and solar dispatching scale to the lower operating cost module; The lower-level operating cost module of the two-layer control model generates the current actual operating cost based on the current distributed wind and solar dispatch scale, and feeds the current actual operating cost back to the upper-level revenue module; The upper-level revenue module uses the dispatch cost correction formula to correct the distributed wind and solar dispatch cost under the current distributed wind and solar dispatch scale based on the current actual operating cost to generate a new distributed wind and solar dispatch cost; The upper-level revenue module iteratively updates the revenue objective function of the upper-level revenue module based on the new distributed wind-solar scheduling cost to generate a new distributed wind-solar scheduling scale and revenue, and passes the new distributed wind-solar scheduling scale to the lower-level operating cost module, and repeats this cycle until the revenue change rate generated based on the current revenue and the new revenue is less than or equal to the threshold.

[0025] The initialized distributed wind-solar dispatching scale is passed to the lower-level operation cost module through the upper-level revenue module. The lower-level operation cost module optimizes the actual operation cost based on the distributed wind-solar dispatching scale, and feeds back the actual operation cost to the upper-level revenue module. The upper-level revenue module adjusts the distributed wind-solar dispatching scale based on the fed-back actual operation cost, forming a closed-loop optimization and improving the feasibility of distributed wind-solar power generation cluster regulation.

[0026] like Figure 2 As shown, a distributed wind and solar power generation cluster optimization control method specifically includes the following steps: S1. Obtain photovoltaic output power curve, wind power output power curve and historical electricity prices; The electricity sales prices for the past three years are collected, and the average of the electricity sales prices for the past three years is calculated to obtain the historical electricity sales prices.

[0027] Combined with the weather forecast released by the meteorological department, the output of distributed wind and solar power generation clusters is predicted to obtain the photovoltaic output power curve and wind power output power curve.

[0028] S2. Based on the photovoltaic output power curve, the wind power output power curve and the historical electricity price, the upper layer revenue module of the double-layer control model is initialized; The photovoltaic output power curve, wind power output power curve and historical electricity price are input into the upper revenue module of the double-layer control model for initialization and iteration.

[0029] S3. After initialization processing, the upper-layer revenue module generates the current distributed wind-solar scheduling scale and revenue, and transmits the current distributed wind-solar scheduling scale to the lower-layer operation cost module; Based on the photovoltaic output power curve, wind power output power curve and historical electricity prices, the current revenue is maximized through the revenue objective function and Gurobi solver of the upper-level revenue module to generate the current distributed wind-solar scheduling scale, and the current distributed wind-solar scheduling scale is passed to the lower-level operating cost module; based on the current distributed wind-solar scheduling scale, the current revenue is generated through the upper-level revenue module.

[0030] The revenue objective function of the upper-level revenue module is: , in, To sell electricity price, is the electricity sales at time t, It is the scale of distributed wind and solar dispatch; is the distributed wind and solar dispatching cost, Adjust costs for user loads; The amount of user load adjustment.

[0031] income = electricity sales revenue - distributed wind and solar dispatching costs - user load adjustment costs, , in, To sell electricity price, is the electricity sales amount at time t, t=1,…,T.

[0032] S4. Based on the current distributed wind and solar dispatching scale, the current distributed wind and solar power generation cluster output combination and actual operating cost are generated through the lower-level operating cost module, and the current actual operating cost is fed back to the upper-level revenue module; Based on the current distributed wind and solar dispatching scale transmitted by the upper-level revenue module, the current operating cost is minimized through the operating cost objective function of the lower-level operating cost module and the Gurobi solver to generate the current distributed wind and solar power generation cluster output combination. The distributed wind and solar power generation cluster output combination includes photovoltaic output, wind power output, energy storage charging power, energy storage discharging power and user load adjustment power.

[0033] The operating cost objective function of the lower-level operating cost module is: , in, is the photovoltaic operation and maintenance cost, is the energy storage charging and discharging cost, Adjust costs for user load, For wind power operation and maintenance costs, For photovoltaic power generation, To contribute to wind power, The charging power for energy storage, is the energy storage discharge power, Load adjustment power.

[0034] Based on the current photovoltaic output, wind power output, energy storage charging power, energy storage discharging power and user load adjustment power, the actual operating cost is generated through the lower-level operating cost module; and the actual operating cost is fed back to the upper-level revenue module.

[0035] The actual operating cost refers to the sum of the photovoltaic operation and maintenance costs, wind power operation and maintenance costs, energy storage charging and discharging costs, and user load adjustment costs corresponding to the current photovoltaic output, wind power output, energy storage charging power, energy storage discharging power, and user load adjustment power.

[0036] S5. Based on the current actual operating cost, the upper-layer revenue module corrects the distributed wind and solar scheduling cost to generate a new distributed wind and solar scheduling cost; Based on the current actual operating cost fed back by the lower operating cost module, the upper revenue module corrects the distributed wind and solar scheduling cost under the current distributed wind and solar scheduling scale generated by the upper revenue module in step S3 through the scheduling cost correction formula to generate a new distributed wind and solar scheduling cost.

[0037] The scheduling cost correction formula is: , , in, is the weight factor, , is the distributed wind-solar dispatching cost under the current distributed wind-solar dispatching scale in step S3, The actual current operating cost fed back by the lower-level operating cost module. It is the new distributed wind-solar dispatching cost generated after correction.

[0038] The weight factor is adjusted dynamically according to the deviation between the distributed wind and solar dispatching cost under the current distributed wind and solar dispatching scale generated by the upper-level revenue module and the current actual operating cost fed back by the lower-level operating cost module. .

[0039] S6. Iteratively update the revenue objective function of the upper revenue module based on the new distributed wind and solar dispatching cost to generate a new distributed wind and solar dispatching scale and electricity selling price; Based on the new distributed wind and solar scheduling cost, the revenue objective function of the upper-level revenue module is iteratively updated. The current revenue is maximized through the Gurobi solver to generate a new distributed wind and solar scheduling scale and electricity price.

[0040] S7. Generate new revenue through the upper-layer revenue module based on the new distributed wind and solar dispatching scale and electricity price; Based on the corresponding distributed wind and solar dispatching costs, user load adjustment costs, and new electricity selling prices under the new distributed wind and solar dispatching scale, the revenue calculation generates new revenue.

[0041] S8. Generate a profit change rate based on the current profit in step S3 and the new profit in step S7, and determine whether the profit change rate is less than or equal to a threshold value. When the profit change rate is determined to be greater than the threshold value, repeat S3 to S8. When the profit change rate is determined to be less than or equal to the threshold value, terminate the iterative update. The two-layer control model then outputs the regulated electricity price strategy, distributed wind and solar power generation cluster control plan, and operating cost.

[0042] The formula for calculating the rate of change of return is: , in, For current income, for new revenue.

[0043] The electricity price strategy refers to the selling price of electricity; the distributed wind and solar power generation cluster control plan refers to the photovoltaic output and wind power output; the operating cost refers to the sum of photovoltaic operation and maintenance costs, energy storage charging and discharging costs, user load adjustment costs and wind power operation and maintenance costs.

[0044] When the rate of change of revenue is less than or equal to the threshold, the new distributed wind-solar dispatching scale and selling electricity price generated in step S6 are used as the distributed wind-solar dispatching scale and selling electricity price at this time respectively; based on the distributed wind-solar dispatching scale at this time, the photovoltaic output and wind power output at this time are generated through the lower-level operation 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 charging and discharging cost, user load adjustment cost and wind power operation and maintenance cost at this time are obtained; then the two-layer control model outputs the selling electricity price, photovoltaic output, wind power output, photovoltaic operation and maintenance cost, energy storage charging and discharging cost, user load adjustment cost and wind power operation and maintenance cost at this time, that is, the two-layer control model outputs the optimal selling electricity price, photovoltaic output, wind power output, photovoltaic operation and maintenance cost, energy storage charging and discharging cost, user load adjustment cost and wind power operation and maintenance cost, so as to maximize the revenue of distributed wind-solar aggregators and minimize the operating costs.

[0045] Through a two-layer control model and a two-way feedback mechanism, the upper-level revenue module dynamically adjusts the electricity price and the scale of distributed wind and solar dispatching, combined with the user load adjustment cost game, to maximize the benefits of distributed wind and solar aggregators. Through the lower-level operating cost module, the coordinated dispatch of photovoltaics, wind power, energy storage, and flexible loads is fully integrated to minimize operating costs. The actual operating cost is used to dynamically correct the distributed wind and solar dispatching cost to avoid the dispatch plan deviating from reality, and the demand response participation is improved through the user load adjustment mechanism.

[0046] Among them, Figure 3 As shown in the figure, the two-layer control model includes an upper-layer revenue module and a lower-layer operating cost module; the upper-layer revenue module is used to optimize the market revenue through the selling electricity price and the distributed wind-solar scheduling scale, with the goal of maximizing the revenue of distributed wind-solar aggregators; the lower-layer operating cost module is used to optimize the output combination and operating cost of distributed wind-solar power generation clusters based on the distributed wind-solar scheduling scale, with the goal of minimizing the operating cost; the two-layer control model outputs the optimal selling electricity price, photovoltaic output, wind power output, photovoltaic operation and maintenance cost, energy storage charging and discharging cost, user load adjustment cost and wind power operation and maintenance cost through a two-way feedback mechanism.

[0047] Through the two-layer control model, dynamic coordination of distributed wind and solar aggregator revenue and operating costs is achieved, thereby improving economic benefits.

[0048] The upper-level revenue module also includes revenue constraints, which include the electricity price fluctuation range, the dispatch scale restriction range and the user satisfaction constraint range.

[0049] The electricity price fluctuation range is: , in, The lowest price for selling electricity. The highest price for selling electricity; the lowest price and the highest price for selling electricity are customized based on market conditions.

[0050] The scheduling scale limit range is: , in, is the lower limit of the distributed wind and solar dispatching scale, is the upper limit value of the distributed wind-solar dispatching scale; the lower limit value and the upper limit value of the distributed wind-solar dispatching scale are obtained from the data information of the distributed wind-solar dispatching scale in the past three years.

[0051] The user satisfaction constraints are: , in, It is the maximum value of user load adjustment. The maximum value of user load adjustment is customized.

[0052] The lower-level operation cost module also includes operation cost constraints, which include power balance constraint range, energy storage SOC constraint range and network flow safety constraint range.

[0053] The power balance constraint range is: , in, For photovoltaic power generation, To contribute to wind power, The charging power for energy storage, is the energy storage discharge 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 photovoltaic output, wind power output, energy storage charging power, energy storage discharging power and load adjustment power is equal to the distributed dispatching scale.

[0054] The energy storage SOC constraint range is: , in, is the energy storage state of charge in time period t; the energy storage SOC constraint is used to prevent the energy storage from being overcharged or over-discharged.

[0055] The network power flow safety constraint range includes the voltage constraint range and the current constraint range.

[0056] The voltage constraint range is: controlling the voltage within the allowable range, the allowable range is ±5% of the nominal value; the voltage constraint range is used to prevent the voltage from exceeding the allowable range and causing damage to the equipment.

[0057] The current constraint range is: the current does not exceed the thermal stability limit; the current constraint range is used to avoid line overload.

[0058] By fully integrating the heterogeneous constraints of photovoltaics, wind power, energy storage, and flexible loads, and jointly optimizing power balance constraints and safety constraints, the contradiction between output volatility and user satisfaction can be resolved.

[0059] The present invention fully integrates photovoltaic, wind power, energy storage, and flexible loads through the revenue objective function, operating cost objective function, revenue constraint conditions, and operating cost constraint conditions of the double-layer control model to obtain the optimal selling price, photovoltaic output, wind power output, photovoltaic operation and maintenance cost, energy storage charging and discharging cost, user load adjustment cost, and wind power operation and maintenance cost, so that distributed resource aggregators, users, and power grids are fully coordinated, and while maximizing revenue and minimizing operating costs, a full balance is achieved between economy, safety, and user satisfaction. The problem of the inconsistency between the wind power output and photovoltaic output predicted by the weather forecast and the actual demand power, the phenomenon of excess or insufficient wind power output and photovoltaic output, and the resulting poor user satisfaction and low economy is solved.

[0060] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A distributed wind and solar power generation cluster optimization control method, characterized in that: include: Based on the acquired photovoltaic output power curve, wind power output power curve and historical electricity sales price, a two-way feedback mechanism is adopted to regulate the distributed wind and solar power generation cluster through a two-layer control model until the profit change rate of the distributed wind and solar power generation cluster is less than or equal to the threshold, and the regulated electricity sales price, photovoltaic output, wind power output, photovoltaic operation and maintenance cost, energy storage charging and discharging cost, user load adjustment cost and wind power operation and maintenance cost are generated and output.

2. A distributed wind and solar power generation cluster optimization control method according to claim 1, characterized in that: The two-way feedback mechanism includes: The upper revenue module of the two-layer control model generates the current distributed wind and solar dispatching scale and revenue based on the obtained photovoltaic output power curve, wind power output power curve and historical electricity price, and transmits the current distributed wind and solar dispatching scale to the lower operating cost module; The lower-level operating cost module of the two-layer control model generates the current actual operating cost based on the current distributed wind and solar dispatch scale, and feeds the current actual operating cost back to the upper-level revenue module; The upper-level revenue module uses the dispatch cost correction formula to correct the distributed wind and solar dispatch cost under the current distributed wind and solar dispatch scale based on the current actual operating cost to generate a new distributed wind and solar dispatch cost; The upper-level revenue module iteratively updates the revenue objective function of the upper-level revenue module based on the new distributed wind-solar scheduling cost to generate a new distributed wind-solar scheduling scale and revenue, and passes the new distributed wind-solar scheduling scale to the lower-level operating cost module, and repeats this cycle until the revenue change rate generated based on the current revenue and the new revenue is less than or equal to the threshold.

3. A distributed wind and solar power generation cluster optimization control method according to claim 2, characterized in that: The profit objective function of the upper-level profit module is: ， in, To sell electricity price, is the electricity sales at time t, It is the scale of distributed wind and solar dispatch; is the distributed wind and solar dispatching cost, Adjust costs for user loads; The amount of user load adjustment.

4. A distributed wind and solar power generation cluster optimization control method according to claim 2, characterized in that: The lower layer operation cost module includes an operation cost objective function, which is: ， in, is the photovoltaic operation and maintenance cost, is the energy storage charging and discharging cost, Adjust costs for user load, For wind power operation and maintenance costs, For photovoltaic power generation, To contribute to wind power, The charging power for energy storage, is the energy storage discharge power, Load adjustment power.

5. A distributed wind and solar power generation cluster optimization control method according to claim 2, characterized in that: The scheduling cost correction formula is: ， ， in, is the weight factor, , is the distributed wind and solar dispatching cost under the current distributed wind and solar dispatching scale, The current actual operating cost fed back by the lower-level operating cost module. It is the new distributed wind-solar dispatching cost generated after correction.

6. A distributed wind and solar power generation cluster optimization control method according to claim 2, characterized in that: The upper-layer revenue module also includes a power price fluctuation range, which is: ， in, The lowest price for selling electricity. The highest price for selling electricity.

7. A distributed wind and solar power generation cluster optimization control method according to claim 2, characterized in that: The upper layer revenue module also includes a scheduling scale limit range, and the scheduling scale limit range is: ， in, is the lower limit of the distributed wind and solar dispatching scale, It is the upper limit of the distributed wind and solar dispatching scale.

8. The method for optimizing and controlling a distributed wind and solar power generation cluster according to claim 2, characterized in that: The upper layer revenue module also includes a user satisfaction constraint range, which is: ， in, The maximum value of the user load adjustment.

9. A distributed wind and solar power generation cluster optimization control method according to claim 2, characterized in that: The lower layer operation cost module also includes a power balance constraint range, and the power balance constraint range is: ， in, For photovoltaic power generation, To contribute to wind power, The charging power for energy storage, is the energy storage discharge power, Load adjustment power, It is the scale of distributed wind and solar dispatching.

10. A distributed wind and solar power generation cluster optimization control method according to claim 2, 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: ， in, is the energy storage charge state during time period t.

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