CN115514014B - A novel power system flexibility resource supply and demand game optimization scheduling method with high proportion of wind power - Google Patents

Abstract

The present invention discloses a new power system flexibility resource supply and demand game optimization scheduling method containing a high proportion of wind power, including establishing a two-layer game architecture of flexibility regulation resource supply and demand based on master-slave game; establishing a quantitative model of flexibility regulation resource demand of wind power operators according to the quantitative index of wind power volatility; establishing an incentive price optimization decision model for upper-layer wind power operators to lower-layer energy storage operators, thermal power operators, and demand response aggregators; establishing a flexibility regulation resource supply decision model for lower-layer energy storage operators, thermal power operators, and demand response aggregators; combining the established incentive price optimization decision model with the established flexibility regulation resource supply decision model to jointly constitute a new power system flexibility regulation resource supply and demand game optimization model containing a high proportion of wind power, and solving it. The present invention can effectively improve the enthusiasm of source-load-storage multi-party flexibility regulation resources to participate in regulation, and promote the access to the grid of high-proportion wind power.

Description

A new power system with high proportion of wind power and flexibility resource supply and demand game optimization scheduling method

Technical Field

The present invention relates to a power system flexibility resource supply and demand game optimization scheduling method, in particular to a new power system flexibility resource supply and demand game optimization scheduling method containing a high proportion of wind power, and belongs to the technical field of power system operation and control.

Background Art

As the penetration rate of new energy, mainly wind power, continues to increase, its volatility and anti-peak characteristics continue to increase, the power balance of the system faces huge challenges, and improving the flexibility of power system operation has become a research focus of scholars at home and abroad. On the one hand, the flexibility of power system operation depends on the technical characteristics of various resources of "source-load-storage", and on the other hand, the system flexibility potential corresponding to various technical means needs to be activated by reasonable incentives.

At present, the research on the compensation mechanism of flexibility regulation mainly adopts the electricity price compensation of downward peak regulation, and the peak regulation subject generally considered is the power grid, not the wind power group, which cannot effectively mobilize the regulation potential of multiple flexibility regulation resources, and the value of flexibility regulation resources has not been fully reflected. There is a lack of research on how high-proportion wind power can participate in the transaction of flexibility regulation services and coordinate and optimize with flexibility regulation resources.

Summary of the invention

The present invention provides a novel power system flexibility resource supply and demand game optimization scheduling method containing a high proportion of wind power. By introducing an electric power balance management mechanism based on wind power volatility assessment, considering the individual profit-seeking attributes of wind power and various flexibility regulation resource entities, and applying the idea of master-slave game, a flexibility service supply and demand multi-agent game optimization scheduling model is established to achieve supply and demand optimization of flexibility regulation resources and reasonable compensation of flexibility value.

The technical solution of the present invention is: a new power system with a high proportion of wind power flexibility resource supply and demand game optimization scheduling method, including: step S1, based on the master-slave game to establish a flexible resource supply and demand two-layer game architecture, including the upper wind power operator and the lower energy storage operator, thermal power operator, demand response aggregator; step S2, put forward the wind power volatility quantitative index; according to the wind power volatility quantitative index, establish the wind power operator flexibility regulation resource demand quantitative model; step S3, according to the wind power operator flexibility regulation resource demand, with the goal of maximizing benefits to establish the upper wind The incentive price optimization decision model of the power operator for the lower-level energy storage operators, thermal power operators, and demand response aggregators is established; step S4, with the goal of maximizing benefits, a flexibility regulation resource supply decision model for the lower-level energy storage operators, thermal power operators, and demand response aggregators is established; step S5, the incentive price optimization decision model established in step S3 and the flexibility regulation resource supply decision model established in step S4 are combined to jointly form a flexibility regulation resource supply and demand game optimization model for a new power system containing a high proportion of wind power, and the flexibility regulation resource supply and demand game optimization model is solved.

The two-layer game architecture of supply and demand of flexibility regulation resources based on master-slave game is characterized by upper-layer wind power operators as leaders of the master-slave game, and lower-layer thermal power operators, energy storage operators, and demand response aggregators as followers of the master-slave game; wherein, the upper-layer leader formulates the optimal incentive price according to its own flexibility regulation resource demand, and transmits the incentive price information and flexibility regulation resource demand information to the followers; the lower-layer followers make optimal flexibility regulation resource supply decisions according to the information transmitted by the upper layer, and feed back the optimal flexibility regulation resource supply information to the upper layer; through continuous game iterations between the upper-layer leader and the lower-layer followers, the balance of supply and demand of flexibility regulation resources is finally achieved.

The step S2 comprises:

S2.1. Given wind power and load power sequence and Define the change rate sequence of wind power and load power and The Euclidean distance is used as a quantitative indicator of wind power volatility, and the calculation model of wind power volatility quantitative indicators is:

Where: is the rate of change of wind power in period t, and is the tth element in the wind power rate of change sequence ^Rw ; _Ptw is the wind ^power in period t, and is the tth element in the wind power sequence ^Pw ; is the rate of change of load power in period t, and is the tth element in the load power rate of change sequence R ^d ; P _t ^d is the load power in period t, and is the tth element in the load power sequence P ^d ; r(P ^w ,P ^d ) represents the volatility index of the wind power sequence P ^w when the load power sequence is P ^d ; T is the total scheduling period;

S2.2. Wind power prediction based on ideal model method Carry out power equivalence, aiming at minimizing the demand for flexible resource regulation, and construct an ideal wind power sequence that meets the preset wind power volatility index. The construction model of the ideal wind power sequence is expressed as:

Where: P _t ^flexible represents the flexible adjustment resource demand of the wind power operator in period t. When it is positive, the wind power operator needs to adjust the power flexibly upward, and when it is negative, the wind power needs to adjust the power flexibly downward. r(P ^w,equal ,P ^d ) is the volatility index of the ideal wind power sequence P ^w,equal when the load power sequence is P ^d . R is the threshold of the wind power volatility index. P _t ^w,pre represents the predicted power of wind power in period t, which is the tth element in the predicted wind power sequence P ^w,pre . P _t ^w,equal represents the ideal power of wind power in period t, which is the tth element in the ideal wind power sequence P ^w,equal .

S2.3. By calculating the difference between the ideal wind power and the planned wind power on-grid power formulated by the wind power operator, the quantification of the flexibility adjustment resource demand of the wind power operator is realized. The quantification model of the flexibility adjustment resource demand of the wind power operator is expressed as:

Where: ΔP _t ^w is the flexibility regulation resource demand of the wind power operator in period t.

The step S3 comprises:

S3.1. The objective function of the incentive price optimization decision model of the upper-level wind power operator is:

Where: _Fw is the profit function of the wind power operator; Gain income from wind power grid connection; The operation and maintenance costs of wind power; The cost of wind power abandonment; Adjust resource incentive costs for wind operators’ flexibility;

S3.2: The constraints of the incentive price optimization decision model of the upper-level wind power operator include: wind power output constraints, flexibility regulation resource supply and demand balance constraints, flexibility regulation resource regulation direction state variable constraints, and flexibility regulation resource incentive price constraints.

The income from the on-grid electricity of wind power is the operation and maintenance cost of wind power, the cost of wind power abandonment, and the cost of the flexible adjustment resource incentive of the wind power operator, which can be expressed as follows:

Where: π _t is the market electricity price in period t; is the wind power in period t; is the operation and maintenance cost coefficient of wind power; is the wind power abandonment cost coefficient; P _t ^w,pre represents the predicted wind power in period t; An incentive price for flexibility regulation resources is set for wind power operators in period t; ΔP _t ^store , ΔP _t ^thermal , and ΔP _t ^demand are the flexibility regulation resource supply of energy storage operators, thermal power operators, and demand response aggregators in period t, respectively.

With the goal of maximizing benefits, a flexibility regulation resource supply decision model is established for lower-level energy storage operators, thermal power operators, and demand response aggregators, including: establishing a flexibility regulation resource supply decision model and constraints for energy storage operators; establishing a flexibility regulation resource supply decision model and constraints for thermal power operators; establishing a flexibility regulation resource supply decision model and constraints for demand response aggregators; adding system operation constraints; system operation constraints include: power balance constraints, flexibility regulation resource adjustment amount constraints, and flexibility regulation resource adjustment direction constraints.

The objective function of establishing a decision model for the flexible regulation of resource supply by energy storage operators is:

Where: F _store is the revenue function of the energy storage operator; Energy revenue obtained from peak-valley arbitrage of energy storage; The operation and maintenance costs of energy storage; The benefits gained from providing energy storage with flexibility regulation resources;

Where: π _t is the market electricity price in period t; P _t ^store,c and P _t ^store,d are the charging and discharging power of the energy storage in period t; is the operation and maintenance cost coefficient of energy storage; The incentive price for the flexible regulation resources is set for the wind power operator in period t; ΔP _t ^store is the flexible regulation resource supply of the energy storage operator in period t;

Add energy storage operator optimization model constraints, including energy storage charging and discharging power constraints, energy storage capacity constraints, and energy storage charging and discharging state variable constraints.

The objective function of establishing a decision-making model for the flexible regulation of resource supply by thermal power operators is:

Where: F _thermal represents the revenue function of thermal power operators; Energy gain obtained by thermal power units from providing electricity to loads; Profits from providing flexible regulation resources for thermal power; is the operating cost of thermal power units;

Where: N is the number of thermal power units; π _t is the market electricity price during period t; represents the actual output of the i-th thermal power unit during period t; represents the amount of flexible regulation resources provided by the i-th thermal power unit in period t; Set incentive prices for wind power operators to provide flexible regulation resources during period t;

The operating cost of a thermal power unit varies according to the operating status, which can be expressed as:

Where: They represent the coal consumption cost, life loss cost, oil input cost, and start-up and shutdown cost of the i-th thermal power unit in period t respectively; Li _,t , Mi _,t , and Ki _,t are 0-1 state variables indicating that the thermal power unit is in the basic peak-shaving stage, the deep peak-shaving stage without oil input, and the deep peak-shaving stage with oil input;

The amount of flexible resources provided by thermal power operators to wind power operators is:

Where: Adjust resource supply for the flexibility of thermal power operators during period t;

Add constraints to the thermal power operator optimization model: thermal power unit output constraints, thermal power unit ramp constraints, minimum start and stop time constraints, and peak load state variable constraints.

The establishment of a demand response aggregator flexibility regulation resource supply decision model includes:

S4.3.1. Differentiate and refine the transfer time of the transferable load, and divide the ideal transfer time period specified by the user within the time period in which the transferable load is allowed to be transferred; define the degree to which the actual transfer time of the transferable load deviates from the ideal transfer time as the transfer deviation degree, and there is a negative correlation between the user's energy consumption experience and the transfer deviation degree of the transferable load; model the time characteristics of the transferable load by constructing the following transferable load transfer deviation degree matrix:

Where: is the transfer deviation matrix when the transferable load j is transferred in; It represents the transfer deviation coefficient of the transferable load j in time period t, and its size can be calculated by the following formula:

Where: are the start time and end time of the ideal transfer-in time period of transferable load j; the transfer deviation matrix of transferable load j when transferring out Calculation method and similar;

S4.3.2. Taking into account the impact of the power characteristics and time characteristics of the transferable load on user comfort, the response cost of the transferable load user participating in demand response is expressed as:

Where: The response costs for users of shiftable loads to participate in demand response; are the transfer power of transferable load and the loss of user comfort caused by transfer deviation; _cj is the transfer cost coefficient of transferable load j; is the deviation cost coefficient of unit electricity of transferable load j; is the transfer power of transferable load j in each period; is the transfer power of transferable load j in each period; Respectively The transpose of

S4.3.3. Establish a demand response aggregator decision model based on profit maximization, and its objective function is:

Where: F _demand is the revenue function of the demand response aggregator; Revenue from providing flexibility to demand response aggregators; Revenues for demand response aggregators to provide power for shiftable loads; The response costs for users of shiftable loads to participate in demand response;

Where: ΔP _t ^demand is the flexible resource supply of the demand response aggregator in period t; Respectively The tth element in ;

S4.3.4. Add demand response aggregator optimization model constraints: transfer power constraints for transferable loads, transfer time constraints for transferable loads, and transfer-in and transfer-out state variable constraints.

The step S5 comprises:

S5.1. The resource supply and demand game optimization model of the new power system flexibility regulation with a high proportion of wind power is expressed as follows:

Where: They represent the optimal flexibility regulation resource supply of energy storage operators, thermal power operators, and demand response aggregators during period t respectively; It means that when the incentive price set by the wind power operator is The current strategic combination of the lower level; represents the optimal incentive price set for wind power operation during period t;

S5.2. The Gurobi solver nested particle swarm algorithm is used to solve the flexibility regulation resource supply and demand game optimization model.

The beneficial effects of the present invention are:

1. The market-based balancing management mechanism based on volatility assessment in the present invention improves the enthusiasm of flexible regulation resources to participate in regulation, fully taps the flexibility regulation potential of source-load-storage, and can effectively improve the enthusiasm of flexible regulation resources from multiple sources, loads, and storage to participate in regulation, and promote the grid-connected consumption of a high proportion of wind power.

2. After the master-slave game strategy proposed in the present invention optimizes the supply and demand of flexibility regulation resources, it effectively reflects the supply and demand relationship of flexibility regulation resources, and the flexibility value of thermal power operators, energy storage operators, and demand response aggregators is reasonably compensated.

3. The volatility index can manage the volatility of wind power access to the grid, effectively reducing the difficulties caused by high-proportion wind power volatility on power balance.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic diagram of a multi-agent two-layer game interactive architecture for flexible regulation of resource supply and demand according to the present invention;

FIG2 is a flow chart of the model solution of the present invention;

FIG3 is a schematic diagram of a method for quantifying flexibility requirements of wind power operators according to the present invention;

FIG4 is a system load forecast data curve, a wind power forecast data curve, and a real-time electricity price data curve of a specific example of the present invention;

FIG5 is a simulation result of power and electricity balance of a specific example scenario 1 of the present invention;

FIG6 is a simulation result of power and electricity balance of a specific example scenario 2 of the present invention;

FIG. 7 is a simulation result of the flexibility adjustment resource supply and demand game in the specific example scenario 2 of the present invention.

DETAILED DESCRIPTION

The invention is further described below in conjunction with the accompanying drawings and embodiments, but the content of the invention is not limited to the scope of the embodiments.

Embodiment 1: As shown in Figures 1-7, a new power system with a high proportion of wind power flexibility resource supply and demand game optimization scheduling method, including: step S1, establish a two-layer game architecture of flexibility regulation resource supply and demand based on master-slave game, including upper-layer wind power operators and lower-layer energy storage operators, thermal power operators, and demand response aggregators; step S2, propose wind power volatility quantitative indicators; based on wind power volatility quantitative indicators, establish a wind power operator flexibility regulation resource demand quantitative model; step S3, according to the wind power operator's flexibility regulation resource demand, establish an upper-layer flexibility regulation resource demand model with the goal of maximizing benefits The incentive price optimization decision model of wind power operators for lower-level energy storage operators, thermal power operators, and demand response aggregators; step S4, with the goal of maximizing benefits, establish a flexibility regulation resource supply decision model for lower-level energy storage operators, thermal power operators, and demand response aggregators; step S5, combine the incentive price optimization decision model established in step S3 with the flexibility regulation resource supply decision model established in step S4 to jointly form a flexibility regulation resource supply and demand game optimization model for a new power system containing a high proportion of wind power, and solve the flexibility regulation resource supply and demand game optimization model.

Optionally, the two-layer game architecture of supply and demand of flexibility-adjusting resources based on master-slave game is that the upper-layer wind power operator is the leader of the master-slave game, and the lower-layer thermal power operator, energy storage operator, and demand response aggregator are the followers of the master-slave game; wherein the upper-layer leader formulates the optimal incentive price according to its own flexibility-adjusting resource demand, and transmits the incentive price information and flexibility-adjusting resource demand information to the followers; the lower-layer followers make optimal flexibility-adjusting resource supply decisions according to the information transmitted by the upper layer, and feed back the optimal flexibility-adjusting resource supply information to the upper layer; through continuous game iterations between the upper-layer leader and the lower-layer followers, the supply and demand balance of flexibility-adjusting resources is finally achieved.

Optionally, the step S2 includes:

S2.1. Given wind power and load power sequence and Define the change rate sequence of wind power and load power and The Euclidean distance is used as a quantitative indicator of wind power volatility, and the calculation model of wind power volatility quantitative indicators is:

Where: is the rate of change of wind power in period t, and is the tth element in the wind power rate of change sequence ^Rw ; _Ptw is the wind ^power in period t, and is the tth element in the wind power sequence ^Pw ; is the rate of change of load power in period t, and is the tth element in the load power rate of change sequence ^Rd ; is the load power in period t, which is the tth element in the load power sequence ^Pd ; r( ^Pw , ^Pd ) represents the volatility index of the wind power sequence ^Pw when the load power sequence is ^Pd ; T is the total dispatch period;

S2.2. Wind power prediction based on ideal model method Carry out power equivalence, aiming at minimizing the demand for flexible resource regulation, and construct an ideal wind power sequence that meets the preset wind power volatility index. The construction model of the ideal wind power sequence is expressed as:

Where: P _t ^flexible represents the flexible adjustment resource demand of the wind power operator in period t. When it is positive, the wind power operator needs to adjust the power flexibly upward, and when it is negative, the wind power needs to adjust the power flexibly downward. r(P ^w,equal ,P ^d ) is the volatility index of the ideal wind power sequence P ^w,equal when the load power sequence is P ^d . R is the threshold of the wind power volatility index. P _t ^w,pre represents the predicted power of wind power in period t, which is the tth element in the predicted wind power sequence P ^w,pre . P _t ^w,equal represents the ideal power of wind power in period t, which is the tth element in the ideal wind power sequence P ^w,equal .

S2.3. By calculating the difference between the ideal wind power and the planned wind power on-grid power formulated by the wind power operator, the quantification of the flexibility adjustment resource demand of the wind power operator is realized. The quantification model of the flexibility adjustment resource demand of the wind power operator is expressed as:

Where: The flexibility of wind power operators in period t regulates resource demand.

Optionally, the step S3 includes:

S3.1. The objective function of the incentive price optimization decision model of the upper-level wind power operator is:

Where: _Fw is the profit function of the wind power operator; Gain income from wind power grid connection; The operation and maintenance costs of wind power; The cost of wind power abandonment; Adjust resource incentive costs for wind operators’ flexibility;

S3.2: The constraints of the incentive price optimization decision model of the upper-level wind power operator include: wind power output constraints, flexibility regulation resource supply and demand balance constraints, flexibility regulation resource regulation direction state variable constraints, and flexibility regulation resource incentive price constraints.

Optionally, the income from the access to the grid of wind power is the operation and maintenance cost of wind power, the cost of wind power abandonment, and the cost of the wind power operator's flexibility adjustment resource incentive, and the expression is as follows:

Where: π _t is the market electricity price in period t; is the wind power in period t; is the operation and maintenance cost coefficient of wind power; is the wind power abandonment cost coefficient; P _t ^w,pre represents the predicted wind power in period t; An incentive price for flexibility regulation resources is set for wind power operators in period t; ΔP _t ^store , ΔP _t ^thermal , and ΔP _t ^demand are the flexibility regulation resource supply of energy storage operators, thermal power operators, and demand response aggregators in period t, respectively.

Optionally, with the goal of maximizing benefits, a flexibility regulation resource supply decision model is established for lower-level energy storage operators, thermal power operators, and demand response aggregators, including: establishing a flexibility regulation resource supply decision model and constraints for energy storage operators; establishing a flexibility regulation resource supply decision model and constraints for thermal power operators; establishing a flexibility regulation resource supply decision model and constraints for demand response aggregators; adding system operation constraints; system operation constraints include: power balance constraints, flexibility regulation resource adjustment amount constraints, and flexibility regulation resource adjustment direction constraints.

Optionally, the energy storage operator flexibility adjustment resource supply decision model is established, and its objective function is:

Where: F _store is the revenue function of the energy storage operator; Energy revenue obtained from peak-valley arbitrage of energy storage; The operation and maintenance costs of energy storage; The benefits gained from providing energy storage with flexibility regulation resources;

Where: π _t is the market electricity price in period t; P _t ^store,c and P _t ^store,d are the charging and discharging power of the energy storage in period t; is the operation and maintenance cost coefficient of energy storage; The incentive price for the flexible regulation resources is set for the wind power operator in period t; ΔP _t ^store is the flexible regulation resource supply of the energy storage operator in period t;

Add energy storage operator optimization model constraints, including energy storage charging and discharging power constraints, energy storage capacity constraints, and energy storage charging and discharging state variable constraints.

Optionally, the objective function of establishing a decision model for the flexibility adjustment of resource supply by thermal power operators is:

Where: F _thermal represents the revenue function of thermal power operators; Energy gain obtained by thermal power units from providing electricity to loads; Profits from providing flexible regulation resources for thermal power; is the operating cost of thermal power units;

Where: N is the number of thermal power units; π _t is the market electricity price during period t; represents the actual output of the i-th thermal power unit during period t; represents the amount of flexible regulation resources provided by the i-th thermal power unit in period t; Set incentive prices for wind power operators to provide flexible regulation resources during period t;

The operating cost of a thermal power unit varies according to the operating status, which can be expressed as:

Where: They represent the coal consumption cost, life loss cost, oil input cost, and start-up and shutdown cost of the i-th thermal power unit in period t respectively; Li _,t , Mi _,t , and Ki _,t are 0-1 state variables indicating that the thermal power unit is in the basic peak-shaving stage, the deep peak-shaving stage without oil input, and the deep peak-shaving stage with oil input;

The amount of flexible resources provided by thermal power operators to wind power operators is:

Where: Adjust resource supply for the flexibility of thermal power operators during period t;

Add constraints to the thermal power operator optimization model: thermal power unit output constraints, thermal power unit ramp constraints, minimum start and stop time constraints, and peak load state variable constraints.

Optionally, the step of establishing a demand response aggregator flexibility adjustment resource supply decision model includes:

S4.3.1. Differentiate and refine the transfer time of the transferable load, and divide the ideal transfer time period specified by the user within the time period in which the transferable load is allowed to be transferred; define the degree to which the actual transfer time of the transferable load deviates from the ideal transfer time as the transfer deviation degree, and there is a negative correlation between the user's energy consumption experience and the transfer deviation degree of the transferable load; model the time characteristics of the transferable load by constructing the following transferable load transfer deviation degree matrix:

Where: is the transfer deviation matrix when the transferable load j is transferred in; It represents the transfer deviation coefficient of the transferable load j in time period t, and its size can be calculated by the following formula:

Where: are the start time and end time of the ideal transfer-in time period of transferable load j; the transfer deviation matrix of transferable load j when transferring out Calculation method and similar;

S4.3.2. Taking into account the impact of the power characteristics and time characteristics of the transferable load on user comfort, the response cost of the transferable load user participating in demand response is expressed as:

Where: The response costs for users of shiftable loads to participate in demand response; are the transfer power of transferable load and the loss of user comfort caused by transfer deviation; _cj is the transfer cost coefficient of transferable load j; is the deviation cost coefficient of unit electricity of transferable load j; is the transfer power of transferable load j in each period; is the transfer power of transferable load j in each period; Respectively The transpose of

S4.3.3. Establish a demand response aggregator decision model based on profit maximization, and its objective function is:

Where: F _demand is the revenue function of the demand response aggregator; Revenue from providing flexibility to demand response aggregators; Revenues for demand response aggregators to provide power for shiftable loads; The response costs for users of shiftable loads to participate in demand response;

Where: ΔP _t ^demand is the flexible resource supply of the demand response aggregator in period t; Respectively The tth element in ;

S4.3.4. Add demand response aggregator optimization model constraints: transfer power constraints for transferable loads, transfer time constraints for transferable loads, and transfer-in and transfer-out state variable constraints.

Optionally, the step S5 includes:

S5.1. The resource supply and demand game optimization model of the new power system flexibility regulation with a high proportion of wind power is expressed as follows:

Where: They represent the optimal flexibility regulation resource supply of energy storage operators, thermal power operators, and demand response aggregators during period t respectively; It means that when the incentive price set by the wind power operator is The current strategic combination of the lower level; represents the optimal incentive price set for wind power operation during period t;

S5.2. The Gurobi solver nested particle swarm algorithm is used to solve the flexibility regulation resource supply and demand game optimization model.

Furthermore, the present invention provides optional specific implementation modes as follows:

Step S1, establishing a two-layer game architecture of flexibility regulation resource supply and demand based on master-slave game, as shown in Figure 1, including upper-layer wind power operators (flexibility regulation resource demanders) and lower-layer energy storage operators, thermal power operators, and demand response aggregators (flexibility regulation resource suppliers);

Step S2, propose a quantitative index of wind power volatility, and establish a quantitative model of wind power operator's flexibility regulation resource demand based on the index, as shown in FIG3 ;

Step S3: adjusting resource demand according to the flexibility of wind power operators, and establishing an incentive price optimization decision model for upper-level wind power operators to lower-level energy storage operators, thermal power operators, and demand response aggregators with the goal of maximizing benefits;

Step S4: Considering the differences and profit-seeking of decisions of energy storage operators, thermal power operators, and demand response aggregators, and taking profit maximization as the goal, a flexible resource supply decision-making model for lower-level energy storage operators, thermal power operators, and demand response aggregators is established;

Step S5, combining the incentive price optimization decision model established in step S3 and the flexibility regulation resource supply decision model established in step S4, together forming a new power system flexibility regulation resource supply and demand game optimization model containing a high proportion of wind power, and using the Gurobi solver nested particle swarm algorithm to solve the flexibility regulation resource supply and demand double-layer game optimization model. As shown in Figure 2, the incentive price optimization decision model is solved by the Gurobi solver, and the flexibility regulation resource supply decision model of the lower-level energy storage operator, thermal power operator, and demand response aggregator is solved by the particle swarm algorithm.

The step S1 is specifically as follows: As shown in FIG1, the two-layer game architecture of the flexibility regulation resource supply and demand based on the master-slave game is that the upper-layer wind power operator is the leader (subject) of the master-slave game, and the lower-layer thermal power operator, energy storage operator, and demand response aggregator are the followers (slaves) of the master-slave game. Among them, the upper-layer leader wind power operator formulates the optimal incentive price according to its own flexibility regulation resource demand, and transmits the incentive price information and flexibility regulation resource demand information to the followers. The lower-layer follower thermal power operators, energy storage operators, and demand response aggregators make optimal flexibility regulation resource supply decisions based on the information transmitted from the upper layer, and feed back the optimal flexibility regulation resource supply information to the upper layer. Through continuous game iterations between the upper-layer leader and the lower-layer followers, the balance of supply and demand of flexibility regulation resources is finally achieved. The two-layer game architecture G of the flexibility regulation resource supply and demand based on the master-slave game can be described as:

Where: {WPO∪TPO∪ESO∪DRA} represents the participants in the master-slave game, where WPO is the wind power operator, TPO is the thermal power operator, ESO is the energy storage operator, and DRA is the demand response aggregator; represents the incentive price strategy set formulated by the wind power operator; ΔP ^thermal represents the flexibility regulation resource supply strategy set formulated by the thermal power operator; ΔP ^store represents the flexibility regulation resource supply strategy set formulated by the energy storage operator; ΔP ^demand represents the flexibility regulation resource supply strategy set formulated by the demand response aggregator; F _w is the revenue function of the wind power operator; F _thermal is the revenue function of the thermal power operator; F _store is the revenue function of the energy storage operator; F _demand is the revenue function of the demand response aggregator.

The step S2 comprises:

S2.1. Given wind power and load power sequence and Define the change rate sequence of wind power and load power and The Euclidean distance is used as a quantitative indicator of wind power volatility, and the calculation model of wind power volatility quantitative indicators is:

Where: is the rate of change of wind power in period t, and is the tth element in the wind power change rate sequence ^Rw ; is the wind power in period t, which is the tth element in the wind power sequence ^Pw ; is the rate of change of load power in period t, and is the tth element in the load power rate of change sequence ^Rd ; is the load power in period t, and is the tth element in the load power sequence ^Pd ; r( ^Pw , ^Pd ) represents the volatility index of the wind power sequence ^Pw when the load power sequence is ^Pd ; T is the total scheduling period, which is 24 in the embodiment of the present invention.

S2.2. The quantification process of the resource demand for wind power operators’ flexibility adjustment is as follows: Carry out power equivalence, aiming at minimizing the demand for flexible resource regulation, and construct an ideal wind power sequence that meets the preset wind power volatility index. The construction model of the ideal wind power sequence can be expressed as:

Where: represents the demand for flexible adjustment resources of wind power operators in period t. When it is positive, wind power operators need to adjust power flexibly upwards. When it is negative, wind power needs to adjust power flexibly downwards. r( ^Pw,equal , ^Pd ) is the volatility index of the ideal wind power sequence ^Pw,equal when the load power sequence is ^Pd . R is the threshold of the wind power volatility index. _Ptw ^,pre represents the predicted power of wind power in period t, which is the tth element in the predicted wind power sequence ^Pw,pre . _Ptw ^,equal represents the ideal power of wind power in period t, which is the tth element in the ideal wind power sequence ^Pw,equal .

S2.3, as shown in Figure 3, by calculating the difference between the ideal wind power and the planned wind power grid-connected power formulated by the wind power operator, the quantification of the flexibility adjustment resource demand of the wind power operator is realized. The quantification model of the flexibility adjustment resource demand of the wind power operator can be expressed as: Where: is the flexible adjustment resource demand of the wind power operator in period t, which is an unsigned value; P _t ^w is the wind power in period t, that is, the planned wind power grid-connected power formulated by the wind power operator in period t.

The step S3 comprises:

S3.1. The objective function of the incentive price optimization decision model of the upper-level wind power operator is:

Where: _Fw is the profit function of the wind power operator; Gain income from wind power grid connection; The operation and maintenance costs of wind power; The cost of wind power abandonment; Adjust resource incentive costs for wind operators’ flexibility;

Where: π _t is the market electricity price in period t; is the operation and maintenance cost coefficient of wind power; is the wind power abandonment cost coefficient; An incentive price for flexibility regulation resources is set for wind power operators in period t; ΔP _t ^store , ΔP _t ^thermal , and ΔP _t ^demand are the flexibility regulation resource supply of energy storage operators, thermal power operators, and demand response aggregators in period t, respectively.

S3.2: The constraints of the incentive price optimization decision model of the upper-level wind power operator include:

Wind power output constraint: 0≤P _t ^w ≤P _t ^w,pre ;

Flexibility adjusts resource supply and demand balance constraints: ΔP _t ^w ＝ΔP _t ^store +ΔP _t ^thermal +ΔP _t ^demand ;

Flexibility adjustment resource adjustment direction state variable constraints:

Flexibility regulates resource incentives and price constraints:

Where: P _t ^w is the wind power in period t, P _t ^w,pre is the predicted wind power in period t; P _t ^w,equal is the ideal wind power in period t; The flexibility of wind power operators to adjust resource demand during period t, are the flexible resource supply of energy storage operators, thermal power operators, and demand response aggregators during period t; Set incentive prices for wind power operators to provide flexible regulation resources during period t; Maximum incentive prices for wind operators for flexibility regulation resources; is the state variable for adjusting the resource adjustment direction for the flexibility in period t, When it is 1, it means that the wind power operator needs upward flexibility to adjust resources. When it is 0, it means that wind power needs downward flexibility adjustment resources.

The step S4 is specifically as follows:

S4.1: Establish a decision-making model for the flexible regulation of resource supply by energy storage operators, whose objective function is:

Where: F _store is the revenue function of the energy storage operator; Energy revenue obtained from peak-valley arbitrage of energy storage; The operation and maintenance costs of energy storage; Providing flexibility to regulate the benefits of energy storage resources.

Where: _Ptstore ^,c and _Ptstore ^,d are the charging and discharging powers of the energy storage in period t; is the operation and maintenance cost coefficient of energy storage; ΔP _t ^store is the flexibility adjustment resource supply of the energy storage operator in period t, that is, the flexibility adjustment resource supply provided by the energy storage operator for energy storage in period t.

S4.2: Establish a decision-making model for the flexibility regulation of resource supply by thermal power operators, whose objective function is:

Where: F _thermal represents the revenue function of thermal power operators; Energy gain obtained by thermal power units from providing electricity to loads; Profits from providing flexible regulation resources for thermal power; is the operating cost of thermal power units.

Where: N is the number of thermal power units; represents the amount of flexible regulation resources provided by the i-th thermal power unit in period t; represents the actual output of the i-th thermal power unit during period t; The output of the i-th thermal power unit before providing flexibility regulation resources in period t. The output relationship of the thermal power units before and after the provision of flexibility regulation resources can be expressed as: in, The state variable for adjusting the resource adjustment direction for flexibility during period t;

The operating cost of deep peak load units varies according to the operating status and can be expressed as:

Where: They represent the coal consumption cost, life loss cost and oil investment cost of the i-th thermal power unit in period t respectively; Li _,t , Mi _,t and Ki _,t are 0-1 state variables indicating that the thermal power unit is in the basic peak regulation stage, the deep peak regulation stage without oil investment and the deep peak regulation stage with oil investment; The start-up and shutdown cost of the unit is: Among them, f _i ^g,on and f _i ^g,off are the startup and shutdown costs of the thermal power units respectively; It is the start and stop state variable of the thermal power unit.

The amount of flexible resources provided by thermal power operators to wind power operators is:

Where: It is the flexibility regulation resource supply of thermal power operators in period t, that is, the flexibility regulation resource supply provided by thermal power operators for wind power in period t.

S4.3: Establish a demand response aggregator flexibility adjustment resource supply decision model. The specific process is as follows:

S4.3.1: Differentiate and refine the transfer time of the transferable load, and divide the ideal transfer time period specified by the user within the time period in which the transferable load is allowed to be transferred. The degree to which the actual transfer time of the transferable load deviates from the ideal transfer time is defined as the transfer deviation degree. There is a negative correlation between the user's energy consumption experience and the transfer deviation degree of the transferable load. The time characteristics of the transferable load are modeled by constructing the following transferable load transfer deviation degree matrix:

Where: is the transfer deviation matrix when the transferable load j is transferred in; It represents the transfer deviation coefficient of the transferable load j in time period t, and its size can be calculated by the following formula:

Where: are the start time and end time of the ideal transfer-in time period of transferable load j; the transfer deviation matrix of transferable load j when transferring out Calculation method and similar.

S4.3.2: Taking into account the impact of the power characteristics and time characteristics of the transferable load on user comfort, the response cost of the transferable load user participating in demand response can be expressed as:

Where: The response costs for users of shiftable loads to participate in demand response; are the transfer power of transferable load and the loss of user comfort caused by transfer deviation; _cj is the transfer cost coefficient of transferable load j; is the deviation cost coefficient of unit electricity of transferable load j; is the transfer power of transferable load j in each period; is the transfer power of transferable load j in each period; Respectively The square of Respectively The transpose of

S4.3.3: Establish a demand response aggregator decision model based on profit maximization, and its objective function is:

Where: F _demand is the revenue function of the demand response aggregator; Revenue from providing flexibility to demand response aggregators; the cost of incentives for demand response aggregators for shiftable load; Revenue earned by demand response aggregators for providing energy to shiftable loads.

Where: It is the supply of flexibility regulation resources of demand response aggregator in period t, that is, the flexibility regulation resources provided by demand response aggregator for wind power in period t.

S4.4: Add constraints to the decision model. The specific steps are as follows:

S4.4.1: Add constraints to the energy storage operator optimization model

Energy storage charging and discharging power constraints:

Energy storage capacity constraints:

Energy storage charging and discharging state variable constraints:

Where: are the maximum charging and discharging power of energy storage respectively; is the state variable of energy storage charging and discharging during period t; represents the energy storage capacity at time t; is the maximum storage capacity of energy storage; η _store represents the charging and discharging efficiency of energy storage.

S4.4.2: Add constraints to the optimization model for thermal power operators

Output constraints of thermal power units:

Thermal power unit climbing constraints:

Minimum start and stop time constraints:

Peak load state variable constraints: 0≤L _i,t +M _i,t +K _i,t ≤1;

Where: They are the upper and lower limits of conventional peak-shaving output of thermal power units respectively; It is the lower limit of deep peak load regulation without oil injection; It is the lower limit of peak output of oil injection depth; represents the ramp rate of the i-th unit; They are the continuous operation time and continuous shutdown time of the thermal power unit at time t-1 respectively; They are respectively the minimum continuous operation time and continuous shutdown time allowed for the unit.

S4.4.3: Add demand response aggregator optimization model constraints

Transferable load transfer power constraints:

Transferable load transfer time constraints:

Transition in and out state variable constraints:

Where: are the maximum transfer-in and transfer-out powers of transferable load j respectively; Transfer in and out state variables for transferable loads; is the start time and end time of the time period that the transferable load j is allowed to transfer into; are the start time and end time of the time period during which transferable load j is allowed to transfer out.

S4.4.4: Add system operation constraints

Power balance constraints:

Flexibility adjustment resource adjustment amount constraint: 0≤ΔP _t ^store +ΔP _t ^themal +ΔP _t ^demand ≤ΔP _t ^w ;

Flexibility adjustment resource adjustment direction constraints:

The step S5 is specifically as follows:

S5.1. Combine the incentive price optimization decision model established above with the flexibility regulation resource supply decision model established above to jointly form a new power system flexibility regulation resource supply and demand game optimization model containing a high proportion of wind power;

S5.2. The process of solving the flexible resource supply and demand double-layer game optimization model using the Gurobi solver nested particle swarm algorithm is as shown in Figure 2. The detailed process is as follows:

S5.2.1. Input original data and parameters, including wind power forecast data, load forecast data, real-time electricity price data, wind power operation parameters, thermal power operation parameters, energy storage operation parameters, and demand response operation parameters;

S5.2.2. Set the maximum number of iterations of the particle swarm algorithm _kmax = 200, initialize the number of iterations k = 0, and initialize the particle swarm of the wind power operator strategy That is, when the number of iterations is k

S5.2.3. Set the wind power volatility index threshold R=0, and calculate the ideal wind power sequence P _t ^{w ,equal} according to step S2.2;

S5.2.4. Calculate the demand for flexibility regulation resources and the direction of flexibility regulation resource regulation of wind power operators That is, when the number of iterations is k

S5.2.5. Energy storage operators receive incentive information from wind power operators According to the energy storage operator flexibility regulation resource supply decision model established in S4.1, the Gurobi solver is used to solve the optimal flexibility regulation resource supply of the energy storage operator. That is, when the number of iterations is k+1

S5.2.6. Thermal power operators receive incentive information from wind power operators According to the decision-making model of the flexibility regulation resource supply of thermal power operators established in S4.2, the Gurobi solver is used to solve the optimal flexibility regulation resource supply of thermal power operators. That is, ΔP _t ^thermal when the number of iterations is k+1;

S5.2.7 Demand response aggregators receive incentive information from wind power operators According to the demand response aggregator flexibility regulation resource supply decision model established in S4.3, the Gurobi solver is used to solve the optimal flexibility regulation resource supply of the demand response aggregator. That is, ΔP _t ^demand when the number of iterations is k+1;

S5.2.8. Wind power operators receive decision information from follower energy storage operators, thermal power operators, and demand response aggregators Calculate the fitness of each particle according to the objective function of the incentive price optimization decision model of wind power operators established in S3.1, and update the particle swarm according to the particle fitness The number of iterations k = k + 1;

S5.2.9. Determine whether the master-slave game model has reached the game equilibrium point, that is, determine whether the subject and optimization results of the k+1th round are consistent with those of the kth round. If yes:

This means that the equilibrium solution of the master-slave game is found Otherwise, go to S5.2.4 and continue iterating until the game equilibrium solution is found or the maximum number of iterations is reached.

The specific experimental data are given below:

1. Parameter settings

The present invention conducts simulation research on a new power system with a high proportion of wind power, in which the entities participating in the flexible regulation resource transaction include 4 thermal power units, 1 energy storage power station, and 5 types of transferable loads. Two of the 4 thermal power units on the source side have deep peak regulation capabilities, and the detailed parameters of the units are shown in Table 1; the rated capacity of the energy storage is 300MW, and the specific parameters are shown in Table 2; the load side includes 5 types of transferable loads, and the detailed parameters are shown in Table 3; the wind power installed capacity is 840MW, accounting for 45% of the system installed capacity, and the wind power generation accounts for 49% of the total system load. The wind power abandonment penalty coefficient is 250 yuan/MW·h, and the wind power output volatility index is R=0. The system load forecast curve, wind power forecast curve, and real-time electricity price curve are shown in Figure 4.

Table 1 Thermal power unit parameters

project Thermal power unit 1 Thermal power unit 2 Thermal power unit 3 Thermal power unit 4 Output limit/MW 340 320 255 148 Output lower limit/MW 160 152 128 65 Peak load regulation without oil injection/MW 144 137 - - Lower limit of oil peak load/MW 85 80 - - Climbing rate/(MW·h) 145 125 90 70 a/yuan/(MW ² ·h) 0.28665 0.3087 0.40635 1.31229 b/yuan/(MW·h) 126 126 126 126 c/(yuan/h) 1209.6 1146.6 963.9 559.44 Loss cost/(yuan/h) 665 631 - - Oil input cost/(yuan/h) 22000 18500 - - Start-stop cost/(yuan/time) 12600 9450 7875 6930 Minimum start/stop time/h 10/10 10/10 6/6 6/6

Table 2 Energy storage equipment parameters

project Numeric Energy storage equipment capacity/MW·h 300 Energy storage charging and discharging efficiency 0.95 Upper and lower limits of energy storage state of charge/MW·h 0.95S _oc 、0.15S _oc Energy storage initial charge state/MW·h 0.1S _oc Maximum and minimum values of energy storage charging power/MW 0.4S _oc 、0.1S _oc Maximum and minimum value of energy storage discharge power/MW 0.4S _oc 、0.1S _oc Energy storage charging and discharging operation and maintenance cost coefficient/(yuan/MW·h) 75

Table 3 Transferable load parameters

2. Simulation results and comparative analysis

In order to verify the effectiveness of the strategy proposed in the present invention, this application sets up two scenarios for comparative analysis. The specific settings of the scenarios are as follows:

Scenario 1: The system contains three flexible resources: source, load, and storage. The optimal economic dispatch strategy is used to coordinate and optimize the source, load, and storage.

Scenario 2: The system contains three flexible resources: source, load, and storage. The flexible resource supply and demand game strategy proposed in this application is used to coordinately optimize the source, load, and storage.

2.1 Analysis of power and electricity balance results

Figures 5 and 6 show the power balance results of scenario 1 and scenario 2. As shown in Figure 5, in scenario 1, during the high wind power generation periods of 1:00-6:00 and 22:00-24:00, energy storage and demand response use their own time and space translation capabilities to transfer 584MW·h of low-cost wind power to the peak load period, reducing the system power generation cost while reducing the peak load depth of thermal power units during the low load period, saving the deep peak load cost of thermal power units, and realizing the economy of system operation. However, due to the lack of incentive mechanism, thermal power units only operate in the deep peak load state without oil injection, and the enthusiasm of energy storage and demand response to participate in flexibility regulation is not high. There is 10% wind power abandonment during 1:00-6:00 and 22:00-24:00, and the flexibility regulation resource potential of the system has not been fully tapped. In scenario 2, analysis of Figure 6 shows that compared with scenario 1, the thermal power units operate in the deep peak load state of oil injection during 1:00-6:00, and the peak load depth is deeper. At the same time, the regulation power of energy storage and demand response increased by 86% and 7% respectively, and the wind abandonment rate dropped to 0.5%, basically achieving the efficient consumption of high-proportion wind power. This shows that the strategy proposed in this application can improve the flexibility of system operation and effectively promote the consumption of high-proportion wind power.

2.2 Analysis of the results of the game of flexible regulation of resource supply and demand

Figure 7 shows the game results between wind power operators and thermal power operators, energy storage operators, and demand response aggregators. The net wind power output curve in the figure is the ideal wind power output curve after adjustment by flexible resources. In the supply and demand game of flexible resources, the level of flexibility incentive price is related to the demand for flexible adjustment resources of wind power operators and the operating status of flexible adjustment resources. Analysis of Figure 7 shows that from 1:00 to 7:00 and 21:00 to 24:00, wind power operators need to incentivize flexible resources to provide them with downward flexible adjustment resources. Since the cost of thermal power units entering the deep peak adjustment state is high, wind power operators need to incentivize flexible resource operators with higher flexibility incentive prices to obtain sufficient downward flexible adjustment resources. At the same time, with the decrease in the demand for flexible adjustment resources, the flexibility incentive prices at 7:00 and 21:00 also declined accordingly. Between 8:00 and 20:00, wind power operators need upward flexibility regulation resources. During this period, the real-time electricity price of the power grid is high. The energy value obtained by thermal power units, energy storage and demand response to provide upward regulation of electricity is higher than their operating costs. Flexibility regulation resources tend to actively provide upward flexibility services, and the supply of flexibility regulation resources in the system exceeds demand. Therefore, the flexibility incentive price provided by wind power operators from 12:00 to 18:00 is close to 0. Through the above analysis, it can be seen that the flexibility resource supply and demand game constructed in this application can effectively reflect the supply and demand relationship of flexibility regulation resources in the system and reasonably compensate for the flexibility value of flexibility regulation resources.

2.3 Comparative analysis of economic performance

Table 4 Costs and benefits of each entity

Table 4 shows the costs and benefits of each subject in scenario 1 and scenario 2. Analysis of Table 4 shows that in scenario 1, the net benefits obtained by wind power operators are much higher than the net benefits obtained by other subjects, and the net benefits of energy storage operators and demand response aggregators are negative. The reason is that the generation cost of wind power is low but the volatility is strong. In the process of wind power consumption, thermal power operators, energy storage operators, and demand response aggregators need to bear the additional balancing costs caused by wind power volatility while tracking load fluctuations. Due to the lack of a reasonable balancing cost sharing mechanism and flexibility adjustment compensation mechanism, the real-time electricity price cannot accurately reflect the supply and demand relationship of the system's flexibility adjustment resources and the true value of flexibility adjustment services, resulting in the goal of flexible operation of the system deviating from the realization of individual values of energy storage operators and demand response aggregators. In scenario 2, the reduction in wind power abandonment increased the energy income of wind power operators by 173,200 yuan. However, wind power operators need to bear their own volatility flexibility adjustment costs, and their net income has decreased by 13.58%. At the same time, under the incentive of the incentive price, the flexibility value of thermal power operators, energy storage operators, and demand response aggregators is reasonably compensated, and the net income is increased by 98,000, 153,500, and 128,300 yuan respectively. Compared with scenario 1, the allocation of volatility balancing costs in scenario 2 is more reasonable, and the benefits of each entity are more balanced. In summary, the volatility assessment of wind power output can achieve a reasonable allocation of balancing costs. At the same time, under the influence of the flexibility incentive mechanism, the realization of individual values of members is consistent with the goal of flexible operation of the system, which effectively promotes deep collaboration among members with different characteristics.

According to another aspect of an embodiment of the present invention, a new type of power system flexibility resource supply and demand game optimization and dispatching system containing a high proportion of wind power is also provided, including: a first establishment module, used to establish a two-layer game architecture of flexibility regulation resource supply and demand based on master-slave game, including upper-level wind power operators and lower-level energy storage operators, thermal power operators, and demand response aggregators; a second establishment module, used to propose quantitative indicators of wind power volatility; based on the quantitative indicators of wind power volatility, a quantitative model of wind power operator flexibility regulation resource demand is established; a third establishment module, used to adjust the resource demand according to the flexibility of the wind power operator, with the goal of maximizing benefits. The first module is used to establish an incentive price optimization decision model for upper-level wind power operators to lower-level energy storage operators, thermal power operators, and demand response aggregators; the fourth module is used to establish a flexibility regulation resource supply decision model for lower-level energy storage operators, thermal power operators, and demand response aggregators with the goal of maximizing benefits; the solution module is used to combine the incentive price optimization decision model established by the third module with the flexibility regulation resource supply decision model established by the fourth module, to jointly form a flexibility regulation resource supply and demand game optimization model for a new power system containing a high proportion of wind power, and to solve the flexibility regulation resource supply and demand game optimization model.

The specific implementation modes of the present invention are described in detail above in conjunction with the accompanying drawings, but the present invention is not limited to the above implementation modes, and various changes can be made within the knowledge scope of ordinary technicians in this field without departing from the purpose of the present invention.

Claims (9)

1. A new power system flexibility resource supply and demand game optimization scheduling method containing a high proportion of wind power, characterized by: including: Step S1, establishing a two-layer game architecture for flexible resource supply and demand regulation based on master-slave game, including upper-layer wind power operators and lower-layer energy storage operators, thermal power operators, and demand response aggregators; Step S2: Propose a quantitative index of wind power volatility; and establish a quantitative model of wind power operator's flexibility adjustment resource demand based on the quantitative index of wind power volatility; Step S3: adjusting resource demand according to the flexibility of wind power operators, and establishing an incentive price optimization decision model for upper-level wind power operators to lower-level energy storage operators, thermal power operators, and demand response aggregators with the goal of maximizing benefits; Step S4: with the goal of maximizing benefits, a flexible resource supply decision model for lower-level energy storage operators, thermal power operators, and demand response aggregators is established; Step S5, combining the incentive price optimization decision model established in step S3 and the flexibility regulation resource supply decision model established in step S4 to jointly form a flexibility regulation resource supply and demand game optimization model for a new power system containing a high proportion of wind power, and solving the flexibility regulation resource supply and demand game optimization model; The step S2 comprises: S2.1. Given wind power and load power sequence and Define the change rate sequence of wind power and load power and The Euclidean distance is used as a quantitative indicator of wind power volatility, and the calculation model of wind power volatility quantitative indicators is: Where: is the rate of change of wind power in period t, and is the tth element in the wind power rate of change sequence ^Rw ; _Ptw is the wind ^power in period t, and is the tth element in the wind power sequence ^Pw ; is the rate of change of load power in period t, and is the tth element in the load power rate of change sequence R ^d ; P _t ^d is the load power in period t, and is the tth element in the load power sequence P ^d ; r(P ^w ,P ^d ) represents the volatility index of the wind power sequence P ^w when the load power sequence is P ^d ; T is the total scheduling period; S2.2. Wind power prediction based on ideal model method Carry out power equivalence, aiming at minimizing the demand for flexible resource regulation, and construct an ideal wind power sequence that meets the preset wind power volatility index. The construction model of the ideal wind power sequence is expressed as: Where: P _t ^flexible represents the flexible adjustment resource demand of the wind power operator in period t. When it is positive, the wind power operator needs to adjust the power flexibly upward. When it is negative, the wind power needs to adjust the power flexibly downward. r(P ^w,equal ,P ^d ) is the volatility index of the ideal wind power sequence P ^w,equal when the load power sequence is P ^d . R is the threshold of the wind power volatility index. P _t ^w,pre represents the predicted power of wind power in period t, which is the tth element in the predicted power sequence P ^w,pre . P _t ^w,equal represents the ideal power of wind power in period t, which is the tth element in the ideal wind power sequence P ^w,equal . S2.3. By calculating the difference between the ideal wind power and the planned wind power on-grid power formulated by the wind power operator, the quantification of the flexibility adjustment resource demand of the wind power operator is realized. The quantification model of the flexibility adjustment resource demand of the wind power operator is expressed as: Where: ΔP _t ^w is the flexibility regulation resource demand of the wind power operator in period t. 2. According to claim 1, the flexibility resource supply and demand game optimization scheduling method for a new power system containing a high proportion of wind power is characterized in that: the two-layer game architecture of flexibility regulation resource supply and demand based on master-slave game is to use the upper-layer wind power operator as the leader of the master-slave game, and the lower-layer thermal power operator, energy storage operator, and demand response aggregator as the followers of the master-slave game; wherein the upper-layer leader formulates the optimal incentive price according to its own flexibility regulation resource demand, and transmits the incentive price information and flexibility regulation resource demand information to the followers; the lower-layer followers make optimal flexibility regulation resource supply decisions according to the information transmitted by the upper layer, and feed back the optimal flexibility regulation resource supply information to the upper layer; through continuous game iterations between the upper-layer leader and the lower-layer followers, the supply and demand balance of flexibility regulation resources is finally achieved. 3. The method for optimizing the dispatching of flexible resources supply and demand game of a new power system with a high proportion of wind power according to claim 1 is characterized in that: step S3 comprises: S3.1. The objective function of the incentive price optimization decision model of the upper-level wind power operator is: Where: _Fw is the profit function of the wind power operator; Gain income from wind power grid access; The operation and maintenance costs of wind power; The cost of wind power abandonment; Adjust resource incentive costs for wind operators’ flexibility; S3.2: The constraints of the incentive price optimization decision model of the upper-level wind power operator include: wind power output constraints, flexibility regulation resource supply and demand balance constraints, flexibility regulation resource regulation direction state variable constraints, and flexibility regulation resource incentive price constraints. 4. The method for optimizing the dispatching of flexible resource supply and demand game of a new power system with a high proportion of wind power according to claim 3 is characterized in that: the income from the access to the grid of wind power is the operation and maintenance cost of wind power, the cost of wind power abandonment, and the incentive cost of flexible adjustment resources of wind power operators, and the expression is as follows: Where: π _t is the market electricity price during period t; P _t ^w is the wind power during period t; is the operation and maintenance cost coefficient of wind power; is the wind power abandonment cost coefficient; P _t ^w,pre represents the predicted wind power in period t; An incentive price for flexibility regulation resources is set for wind power operators in period t; ΔP _t ^store , ΔP _t ^thermal , and ΔP _t ^demand are the flexibility regulation resource supply of energy storage operators, thermal power operators, and demand response aggregators in period t, respectively. 5. The method for optimizing the dispatching of flexible resource supply and demand game of a new power system with a high proportion of wind power according to claim 1 is characterized in that: the decision model for the supply of flexible resources for lower-level energy storage operators, thermal power operators, and demand response aggregators is established with the goal of maximizing benefits, including: Establish a decision model and constraints for the flexible resource supply adjustment of energy storage operators; Establish a resource supply decision model and constraints for thermal power operators to adjust flexibility; Establish a demand response aggregator flexibility regulation resource supply decision model and constraints; Add system operation constraints; system operation constraints include: power balance constraints, flexibility adjustment resource adjustment amount constraints, and flexibility adjustment resource adjustment direction constraints. 6. The method for optimizing the dispatching of the flexible resource supply and demand game of a new power system with a high proportion of wind power according to claim 5 is characterized in that: the decision model for the flexible resource supply adjustment of the energy storage operator is established, and its objective function is: Where: F _store is the revenue function of the energy storage operator; Energy revenue obtained from peak-valley arbitrage of energy storage; The operation and maintenance costs of energy storage; The benefits gained from providing energy storage with flexibility regulation resources; Where: π _t is the market electricity price in period t; P _t ^store,c and P _t ^store,d are the charging and discharging power of the energy storage in period t; is the operation and maintenance cost coefficient of energy storage; The incentive price for the flexible regulation resources is set for the wind power operator in period t; ΔP _t ^store is the flexible regulation resource supply of the energy storage operator in period t; Add energy storage operator optimization model constraints, including energy storage charging and discharging power constraints, energy storage capacity constraints, and energy storage charging and discharging state variable constraints. 7. The method for optimizing the dispatching of the flexibility resource supply and demand game of the new power system with a high proportion of wind power according to claim 5 is characterized in that: the decision model for the flexibility regulation resource supply of the thermal power operator is established, and its objective function is: Where: F _thermal represents the revenue function of thermal power operators; Energy gain obtained by thermal power units from providing electricity to loads; Profits from providing flexible regulation resources for thermal power; is the operating cost of thermal power units; Where: N is the number of thermal power units; π _t is the market electricity price during period t; represents the actual output of the i-th thermal power unit during period t; represents the amount of flexible regulation resources provided by the i-th thermal power unit in period t; Set incentive prices for wind power operators to provide flexible regulation resources during period t; The operating cost of a thermal power unit varies according to the operating status, and is expressed as: Where: They represent the coal consumption cost, life loss cost, oil input cost, and start-up and shutdown cost of the i-th thermal power unit in period t respectively; Li _,t , Mi _,t , and Ki _,t are 0-1 state variables indicating that the thermal power unit is in the basic peak-shaving stage, the deep peak-shaving stage without oil input, and the deep peak-shaving stage with oil input; The amount of flexible resources provided by thermal power operators to wind power operators is: Where: Adjust resource supply for the flexibility of thermal power operators during period t; Add constraints to the thermal power operator optimization model: thermal power unit output constraints, thermal power unit ramp constraints, minimum start and stop time constraints, and peak load state variable constraints. 8. The method for optimizing the dispatching of the flexibility resource supply and demand game of a new power system with a high proportion of wind power according to claim 5 is characterized in that: the establishment of a demand response aggregator flexibility regulation resource supply decision model includes: S4.3.1. Differentiate and refine the transfer time of the transferable load, and divide the ideal transfer time period specified by the user within the time period in which the transferable load is allowed to be transferred; define the degree to which the actual transfer time of the transferable load deviates from the ideal transfer time as the transfer deviation degree, and there is a negative correlation between the user's energy consumption experience and the transfer deviation degree of the transferable load; model the time characteristics of the transferable load by constructing the following transferable load transfer deviation degree matrix: Where: is the transfer deviation matrix when the transferable load j is transferred in; It represents the transfer deviation coefficient of the transferable load j in time period t, and its size can be calculated by the following formula: Where: are the start time and end time of the ideal transfer-in time period of transferable load j; the transfer deviation matrix of transferable load j when transferring out Calculation method and similar; S4.3.2. Taking into account the impact of the power characteristics and time characteristics of the transferable load on user comfort, the response cost of the transferable load user participating in demand response is expressed as: Where: The response costs for users of shiftable loads to participate in demand response; are the transfer power of transferable load and the loss of user comfort caused by transfer deviation; _cj is the transfer cost coefficient of transferable load j; is the deviation cost coefficient of unit electricity of transferable load j; is the transfer power of transferable load j in each period; is the transfer power of transferable load j in each period; Respectively The transpose of S4.3.3. Establish a demand response aggregator decision model based on profit maximization, and its objective function is: Where: F _demand is the revenue function of the demand response aggregator; Revenue from providing flexibility to demand response aggregators; Revenues for demand response aggregators to provide power for shiftable loads; The response costs for users of shiftable loads to participate in demand response; Where: ΔP _t ^demand is the flexible resource supply of the demand response aggregator in period t; Respectively The tth element in ; S4.3.4. Add demand response aggregator optimization model constraints: transfer power constraints for transferable loads, transfer time constraints for transferable loads, and transfer-in and transfer-out state variable constraints. 9. The method for optimizing the dispatching of flexible resources supply and demand game of a new power system with a high proportion of wind power according to claim 1, characterized in that: step S5 comprises: S5.1. The resource supply and demand game optimization model of the new power system flexibility regulation with a high proportion of wind power is expressed as follows: Where: They represent the optimal flexibility regulation resource supply of energy storage operators, thermal power operators, and demand response aggregators during period t respectively; It means that when the incentive price set by the wind power operator is The current strategic combination of the lower level; represents the optimal incentive price set for wind power operation during period t; S5.2. The Gurobi solver nested particle swarm algorithm is used to solve the two-level game optimization model of flexibility regulation resource supply and demand.