A capacity market optimization clearing system and method that takes into account the need for flexibility adjustment

Abstract

This invention relates to a capacity market optimization and clearing system that considers flexibility adjustment requirements. It constructs a unit model with flexibility adjustment characteristics and combines this unit model with a capacity market optimization and clearing simulation. The capacity market optimization and clearing model achieves capacity market clearing by establishing mathematical models of the power supply side, load side, energy storage, and grid structure, and setting capacity market constraints. The unit model considering flexibility adjustment characteristics comprehensively considers unit participation in the electricity market, provision of standby services, energy storage participation in the electricity market, and ancillary services market, and establishes physical constraint models for each unit. The two models are combined through the system capacity market clearing price and the unit capacity bidding results, and different typical operating days can be selected. This ensures that the optimization results can intuitively reflect the capacity market clearing situation, while also guaranteeing that the simulation results meet the actual operational reliability requirements of the system on different typical operating days.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of capacity market optimization clearing, and particularly relates to a capacity market optimization clearing system and method considering flexible regulation demand. BACKGROUND

[0002] With the access of new energy units, the problem of insufficient flexibility of power system operation will become increasingly prominent. With the continuous improvement of medium and long-term market and spot market, as the core mechanism to ensure the long-term adequacy of the power system, the capacity market urgently needs to build a capacity market clearing system that integrates the characteristics of flexible regulation, in order to realize the optimal allocation of regulation resources. The traditional capacity market can provide sufficient electric energy in the face of gradually increasing new energy generating units, but the corresponding flexible capacity incentive is insufficient, which is difficult to cope with load mutations and the intermittency and volatility of new energy output, resulting in a greater operating regulation pressure of the system under the new energy penetration rate. SUMMARY

[0003] The purpose of the application is to overcome the shortcomings of the prior art, and to provide a capacity market optimization clearing system and method considering flexible regulation demand. In the clearing process, not only the static capacity demand corresponding to the annual maximum load is taken as the target, but also the demand for flexible regulation ability of the system to load fluctuations and new energy fluctuations in typical operating days is constrained.

[0004] The application solves the technical problems by adopting the following technical solutions:

[0005] A capacity market optimization clearing system considering flexible regulation demand comprises

[0006] A typical day generation module is configured to obtain given new energy generation data and load data, and select a typical day according to the principle of maximum flexible demand.

[0007] A capacity market simulation module is configured to obtain given typical day new energy generation data and load data, and given other generation side, grid side, and load side related data, and obtain capacity market simulation results of units, energy storage, and virtual power plants according to simulation optimization targets and constraint conditions.

[0008] A data processing module is configured to calculate total capacity market procurement costs and incomes of various flexible resources in the capacity market according to the capacity market simulation results.

[0009] Further, the system further comprises a login authentication module for user identity authentication.

[0010] A data input module is configured to input generation side, grid side, and load side related data required by the simulation calculation module, and power system related parameters required by the data processing module.

[0011] The data output module is configured to output the capacity market clearing result, the total capacity market procurement cost and the income of each flexible resource in the capacity market from the data processing module to form a report.

[0012] In addition, the data input module inputs data including the installed capacity of thermal power units, the bus, the ramp-up rate, the ramp-down rate, the start-up cost, the minimum output, the effective load carrying capacity coefficient, the effective capacity and the capacity bid, the maximum capacity, the maximum charge-discharge power, the charge-discharge efficiency, the effective load carrying capacity coefficient, the effective capacity and the capacity bid of the energy storage, the maximum regulation capacity, the effective load carrying capacity coefficient, the effective capacity and the capacity bid of the virtual power plant, the installed capacity of new energy power generation, the annual time sequence generation curve, the effective load carrying capacity coefficient, the effective capacity and the capacity bid, and the load side information including the load geographical location, the annual time sequence power, and the capacity-price curve of the variable resource demand.

[0013] In addition, the typical day generation module calculates the time sequence curve of the annual net load according to the time sequence curve of the annual new energy power generation and load power, and selects the maximum net load day, the minimum net load day, the maximum peak-valley difference day, the maximum net load up-ramp day, the maximum net load down-ramp day, the maximum up-ramp day and the maximum down-ramp day.

[0014] In addition, the data processing module calculates the total capacity market procurement cost and the income of each flexible resource in the capacity market according to the capacity market simulation result, and the specific implementation method is as follows:

[0015]

[0016] In the formula, Q is the operation day capacity market income of each unit, P is the market clearing price of the time period, and B is the winning capacity of the unit in the time period. t i t

[0017] An optimization clearing method of a capacity market optimization clearing system considering flexible regulation demand, comprising the following steps:

[0018] According to the given new energy power generation data and load data, a typical day is selected according to the principle of maximum flexibility demand;

[0019] According to the new energy power generation data and load data of the typical day selected according to the principle of maximum flexibility demand, and the given other power generation side, power grid side and load side related data, and according to the simulation optimization target and constraint condition, the capacity market simulation result of the unit, the energy storage and the virtual power plant is solved;

[0020] ​​​​​​The total procurement cost of the capacity market and the income of various flexible resources in the capacity market are calculated according to the capacity market simulation results.

[0021] Moreover, the specific implementation method for selecting a typical day according to given new energy power generation data and load data in the principle of maximum flexibility demand is: obtaining the 24-hour time sequence curve of the wind power, photovoltaic and load system boundary data of the maximum net load day, minimum net load day, maximum peak-valley difference day, maximum net load up-climbing day and maximum net load down-climbing day;

[0022] The specific implementation method for calculating the daily peak value of the maximum net load day and the corresponding day selection is:

[0023]

[0024] In the formula, is the net load of the running day d hour t, is the daily maximum net load, is the daily peak value of the maximum net load day;

[0025] The specific implementation method for calculating the daily peak value of the minimum net load day and the corresponding day selection is:

[0026]

[0027] In the formula, is the minimum net load of the running day d, is the daily peak value of the minimum net load day;

[0028] The specific implementation method for calculating the daily peak value of the maximum peak-valley difference day and the corresponding day selection is:

[0029]

[0030] In the formula, is the net load peak-valley of the running day d, is the daily peak value of the maximum peak-valley difference day;

[0031] The specific implementation method for calculating the daily peak value of the maximum up-climbing day and the corresponding day selection is:

[0032]

[0033] In the formula, is the maximum up-climbing of the running day d, is the daily peak value of the maximum up-climbing day;

[0034] The specific implementation method for calculating the daily peak value of the maximum down-climbing day and the corresponding day selection is:

[0035]

[0036] In the formula, for the maximum down ramping day, for the maximum down ramping day peak.

[0037] Moreover, the specific implementation method for obtaining the capacity market simulation result of the unit, the energy storage and the virtual power plant according to the new energy power generation data and the load data of the typical day selected according to the principle of maximum flexibility demand, and the given other power generation side, power grid side and load side related data, and the simulation optimization target and the constraint condition is:

[0038] determining a target function of minimizing the system procurement capacity cost;

[0039] determining a safety constraint condition, and establishing a capacity market simulation model considering 24-hour time sequence simulation of the typical day according to the determined target function.

[0040] Moreover, the constraint condition includes a unit bid segment capacity limit constraint, a total capacity of the unit, a total capacity of the virtual power plant, a total demand of the variable resource demand, a maximum output limit value of the unit, a maximum output limit value of the virtual power plant, an upper limit of the unit output, a lower limit of the unit output, a lower limit of the virtual power plant, an upper limit of the virtual power plant, an upper standby limit of the unit, a lower standby limit of the unit, an upper standby limit of the virtual power plant, a lower standby limit of the virtual power plant, a unit start-stop variable constraint, a unit start response time constraint, a minimum operation time constraint of the unit, a minimum shutdown time constraint of the unit, a unit ramping constraint, a storage power balance constraint, a storage power balance constraint, a storage power upper limit constraint, a storage discharge power upper limit constraint, a storage charging power lower limit constraint, a storage charging-discharging state constraint, a storage power energy and auxiliary service coupling constraint, a system upper standby constraint, a system lower standby constraint and a power balance constraint.

[0041] Moreover, the specific implementation method for calculating the total procurement cost of the capacity market and the income of various types of flexibility resources in the capacity market according to the capacity market simulation result is:

[0042]

[0043] In the formula, is the operation day capacity market income of each unit, is the market clearing price of the time period, t is the bid capacity of the unit in the time period i . t

[0044] The advantages and positive effects of the present application are:

[0045] ​1、The application constructs a unit model with flexible adjustment characteristics, and combines the unit model with a capacity market optimization clearing simulation. Among them, the capacity market optimization clearing model realizes the clearing of the capacity market by establishing mathematical models of the power supply side, the load side, energy storage and the grid structure and setting capacity market constraint conditions; the unit model considering flexible adjustment characteristics comprehensively considers the participation of units in the electricity market, the provision of backup services, and the participation of energy storage in the electricity market and auxiliary service market, and establishes physical constraint models of each unit. The combination of the two models is realized through the system capacity market clearing price and the unit capacity bid-winning situation, and different typical operating days can be selected, which not only considers that the optimization result can directly reflect the capacity market clearing situation, but also ensures that the simulation result meets the actual operation reliability requirements in different typical operating days.

[0046] 2、The application constructs a capacity market clearing model considering multi-resource flexible adjustment characteristics, overcomes the problem that the traditional capacity market mechanism only focuses on static capacity adequacy and ignores the demand for dynamic flexible adjustment capability, effectively avoids the risk of power supply shortage of the system in multiple time and space scales; at the same time, a physical constraint model considering the participation of each unit in the electricity market and auxiliary service market is established; the capacity market clearing model and the physical constraint model of each unit are combined. Both the optimization result can directly reflect the requirement of minimum total capacity procurement cost of the system, and the simulation result can meet the system operation reliability requirement. Finally, the application establishes a typical day system time sequence curve generation model, and selects different typical operating days according to different scene curve characteristics. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1 It is a structural diagram of the system of the application;

[0048] Figure 2 It is a flowchart of the method of the application. DETAILED DESCRIPTION

[0049] The application will be further described in detail below in combination with the drawings.

[0050] The construction idea of the application is: taking the minimization of the electricity purchase cost of the system in the capacity market as the target, considering the physical constraints of each unit participating in the electricity market and auxiliary service market, using the mixed integer programming technology, and solving to realize the capacity clearing price meeting the capacity market safety constraints.

[0051] Firstly, two indicators that can fully reflect the minimum cost of capacity market electricity purchase, i.e. market capacity clearing price and each unit's winning capacity, need to be determined. A physical constraint model considering the participation of each unit in the electricity market and auxiliary service market is established; a storage model reflecting the operating characteristics of the storage, such as charging and discharging efficiency, maximum charging and discharging power, and maximum storage capacity, is established; a security constraint model based on the actual power system is constructed; and a grid structure is established by using transfer factors. After selecting typical operating days according to the characteristics of the 24-hour time series curve of the system boundary, 24-hour time series simulation of the operating day is performed to obtain the capacity market clearing result. The price simulation result that meets the system transmission security constraint is obtained, and the capacity market income of each unit and the total capacity procurement cost of the system are calculated.

[0052] A capacity market optimization clearing system considering flexible regulation demand, as shown in Figure 1 , comprises a login authentication module, a data input module, a typical day generation module, a capacity market simulation module, a data processing module and a data output module; the login authentication module, the data input module, the typical day generation module, the capacity market simulation module, the data processing module and the data output module are connected in sequence, the data input module is connected to the capacity market simulation module, and the capacity market simulation module is connected to the data output module.

[0053] The login authentication module is used for identity authentication of the user.

[0054] The data input module is used for inputting the relevant data of the power generation side, the grid side and the load side required by the simulation calculation module and the power system related parameters required by the data processing module. The data inputted by the data input module includes the installed capacity, the bus, the ramp-up rate, the ramp-down rate, the start-up cost, the minimum output, the effective load carrying capacity coefficient, the effective capacity and the capacity bid of the thermal power unit, the maximum capacity, the maximum charging and discharging power, the charging and discharging efficiency, the effective load carrying capacity coefficient, the effective capacity and the capacity bid of the storage, the maximum regulation capacity, the effective load carrying capacity coefficient, the effective capacity and the capacity bid of the virtual power plant, the installed capacity, the annual time series generation curve, the effective load carrying capacity coefficient, the effective capacity and the capacity bid of the new energy power generation, and the load side information including the load geographical location, the annual time series power, and the capacity-price curve of the variable resource demand.

[0055] The typical day generation module is used for obtaining the new energy generation data and the load data given in the data input module and selecting the typical day according to the principle of maximum flexibility demand. The specific implementation method is that the typical day generation module calculates the 24-hour time series curve of the annual 365-day net load according to the 24-hour time series curve of the annual 365-day new energy generation and load power, and selects the maximum net load day, the minimum net load day, the maximum peak-valley difference day, the maximum net load up-ramp day and the maximum net load down-ramp day therefrom.

[0056] The capacity market simulation module is configured to obtain the new energy power generation data and load data of a typical day given in the typical day generation module, and other power generation side, power grid side, and load side related data given in the data input module, and to obtain the capacity market simulation results of the units, energy storage, and virtual power plants according to the simulation optimization target and constraint conditions, and to transmit the simulation results to the data processing module; the capacity market simulation module establishes a capacity market simulation model, runs a typical day 24-hour time sequence program that meets the actual operation power transmission safety constraints of the system, and obtains the capacity market clearing price situation.

[0057] The data processing module is configured to calculate the capacity market clearing results transmitted by the capacity market simulation module, and to calculate the total procurement cost of the capacity market and the income of various flexible resources in the capacity market;

[0058] The data output module is configured to output the capacity market clearing results, the total procurement cost of the capacity market, and the income of various flexible resources in the capacity market transmitted by the data processing module to form a report.

[0059] An optimization clearing method of a capacity market optimization clearing system considering flexible regulation demand, as shown in Figure 2 includes the following steps:

[0060] Step 1, input the power generation side, power grid side, and load side related data required by the capacity market simulation module, and the power system related parameters required by the data processing module.

[0061] Step 2, obtain the 24-hour time sequence curves of the wind power, photovoltaic, and load system boundary data of the maximum net load day, the minimum net load day, the maximum peak-valley difference day, the maximum net load up-climbing day, and the maximum net load down-climbing day.

[0062] The specific implementation method for calculating the daily peak value of the maximum net load day and selecting the corresponding day is as follows:

[0063]

[0064] In the formula, is the net load of the running day d hour t, is the daily maximum net load, is the daily peak value of the maximum net load day;

[0065] The specific implementation method for calculating the daily peak value of the minimum net load day and selecting the corresponding day is as follows:

[0066]

[0067] In the formula, is the minimum net load of the running day d, is the daily peak value of the minimum net load day;

[0068] The specific implementation method for calculating the daily peak value of the maximum peak-valley difference day and selecting the corresponding day is as follows:

[0069]

[0070] In the formula, is the daily peak value of the maximum peak-valley difference day d,

[0071] The specific implementation method for calculating the daily peak value of the maximum up-ramp day and selecting the corresponding day is as follows:

[0072]

[0073] In the formula, is the maximum up-ramp of the operation day d, is the daily peak value of the maximum up-ramp day;

[0074] The specific implementation method for calculating the daily peak value of the maximum down-ramp day and selecting the corresponding day is as follows:

[0075]

[0076] In the formula, is the maximum down-ramp of the operation day d, is the daily peak value of the maximum down-ramp day.

[0077] Step 3, input the obtained system boundary data time series curve into the capacity market simulation module, and perform 24-hour time series simulation calculation of the power market operation day to obtain the capacity market clearing price.

[0078] Step 3.1, determine the objective function of minimizing the system procurement capacity cost.

[0079] The clearing of the capacity market is essentially an optimization balance problem between economy and reliability. Under the premise of ensuring that the power system has sufficient generation capacity adequacy, the economy is maximized. The core is to minimize the system procurement capacity cost as the optimization target:

[0080]

[0081] In the formula, is the set of all units, , , , are the sets of all thermal power units, all new energy units and all energy storage units respectively; is the unit index; is the load index; is the bid segment index; is the set of bid segments; is the winning effective capacity of the unit i in the b segment.​ is the winning capacity of unit i in the b-th segment. is the set of all loads, . is the set of all fixed loads, is the set of all virtual power plants. is the winning capacity of virtual power plant j in the b-th segment. is the capacity bid of unit j in the b-th segment. is the dispatch capacity of the variable resource demand curve in the b-th segment. is the price of the variable resource demand curve in the b-th segment.

[0082] Step 3.2. According to the objective function determined in step 3.1, determine the security constraint conditions, and establish a capacity market simulation model considering 24-hour time sequence simulation of typical days. The constraint conditions include unit bid segment capacity limit constraint, total unit capacity, total virtual power plant capacity, total variable resource demand, maximum unit output limit, maximum virtual power plant output limit, unit output upper limit, unit output lower limit, virtual power plant lower limit, virtual power plant upper limit, unit upper reserve limit, unit lower reserve limit, virtual power plant upper reserve limit, virtual power plant lower reserve limit, unit start-stop variable constraint, unit start-up response time constraint, unit minimum operation time constraint, unit minimum downtime constraint, unit ramp constraint, energy storage power balance constraint, energy storage energy balance constraint, energy storage energy upper limit constraint, energy storage discharge power upper limit constraint, energy storage charging power lower limit constraint, energy storage charging and discharging state constraint, energy storage energy and ancillary service coupling constraint, system upper reserve constraint, system lower reserve constraint, and power balance constraint.

[0083] (1) Unit bid segment capacity limit:

[0084]

[0085] In the formula, is the maximum winning capacity of unit i in the b-th segment.

[0086] (2) Total unit capacity

[0087]

[0088] In the formula is the winning capacity of unit i.

[0089] (3) Total virtual power plant capacity

[0090]

[0091] In the formula is the winning capacity of virtual power plant j.

[0092] (4) Total demand for variable resources

[0093]

[0094] In the formula This represents the total demand for variable resources.

[0095] (5) Maximum output limit of the unit

[0096]

[0097] In the formula For the maximum output of unit i, is the effective load factor for unit i.

[0098] (6) Maximum output limit of virtual power plant

[0099]

[0100] In the formula To maximize the output of the virtual power plant j, Let j be the effective load factor of the virtual power plant.

[0101] (7) Maximum output of the unit

[0102]

[0103] In the formula For the unit i Time period t Total output; For the unit i Time period t spare

[0104] (8) Lower limit of unit output

[0105]

[0106] In the formula For the unit i Time period t For future use; For the unit i Lower limit of output

[0107] (9) Lowering the lower limit for virtual power plants

[0108]

[0109] In the formula Total output for the virtual power plant; Virtual power plant j Time period t For future use; For virtual power plantsj Lower limit of down-regulation

[0110] (10) Upper limit of virtual power plant up-regulation

[0111]

[0112] wherein virtual power plant j time period t upper reserve; for virtual power plant j upper limit of up-regulation.

[0113] (11) Unit upper reserve limit

[0114]

[0115] wherein is the capacity limit of unit i upper reserve.

[0116] (12) Unit lower reserve limit

[0117]

[0118] wherein is the capacity limit of unit i lower reserve.

[0119] (13) Virtual power plant upper reserve limit

[0120]

[0121] wherein is the limit of virtual power plant j upper reserve.

[0122] (14) Virtual power plant lower reserve limit

[0123]

[0124] wherein is the limit of virtual power plant j lower reserve.

[0125] (15) Unit start-stop variable constraint

[0126]

[0127] wherein is the unit start 0-1 variable; is the unit stop 0-1 variable; is the time length that the initial state of unit needs to be sustained.

[0128] (16) Unit start-up response time constraints

[0129]

[0130] (17) Minimum operating time constraint of the unit

[0131]

[0132] In the formula This is the minimum operating time of the unit; This represents the total number of time periods;

[0133] (18) Minimum downtime constraint of the unit

[0134]

[0135] In the formula This is the minimum downtime for the generator unit.

[0136] (19) Unit ramping constraints

[0137]

[0138] In the formula For the unit's downhill climbing limit; This is a limitation on the unit's uphill climb.

[0139] (20) Energy storage power balance constraints

[0140]

[0141] In the formula This refers to the power for energy storage discharge and charging.

[0142] (21) Energy storage power balance constraints

[0143]

[0144] In the formula For energy storage i Time period t The amount of electricity; This refers to the power for energy storage discharge and charging. For energy storage actions, 0-1 variables.

[0145] (22) Upper limit constraint on energy storage capacity

[0146]

[0147] In the formula For energy storage i The maximum battery capacity.

[0148] (23) Upper limit constraint on energy storage discharge power

[0149]

[0150] where is the discharge 0-1 variable of the energy storage unit; is the discharge power of the energy storage unit; i is the upper limit of the discharge power of the energy storage unit.

[0151] (24) Energy storage charging power lower limit constraint

[0152]

[0153] where is the charging 0-1 variable of the energy storage unit; is the charging power of the energy storage unit; i is the upper limit of the charging power of the energy storage unit.

[0154] (25) Energy storage charging and discharging state constraint

[0155]

[0156] (26) Energy storage electric energy and ancillary service coupling constraint

[0157]

[0158] (27) System on standby constraint

[0159]

[0160] where is the system on standby capacity requirement.

[0161] (28) System off standby constraint

[0162]

[0163] where is the system off standby capacity requirement.

[0164] (29) Power balance constraint

[0165]

[0166] where is the load slack variable; is the system network loss.

[0167] Step 4, the data processing module calculates the capacity market clearing result transmitted by the capacity market simulation module, calculates the total procurement cost of the capacity market and the income of various flexible resources in the capacity market, and transmits the result to the data output module.

[0168] The calculation method of the income of each unit in the capacity market on the operation day is:

[0169]

[0170] wherein, is the operation day capacity market income of each unit, is the market clearing price of the time period t is the winning capacity of the unit in the time period i . t

[0171] Step 5, data output capacity market clearing results, capacity market total procurement cost and the income of each type of flexible resource in the capacity market.

[0172] According to the optimization clearing method of the capacity market optimization clearing system considering the flexible adjustment demand, the effect of the application is verified by constructing a power system for data calculation.

[0173] The constructed power system includes four thermal power units, one energy storage power station, one photovoltaic power station and one wind power station.

[0174] Step 1, obtain the relevant data required by the calculation module and the relevant parameters shown in Table 1.

[0175] Table 1: Data on the generation side

[0176]

[0177] Step 2, according to the maximum climbing day peak value calculation and the corresponding day selection method, the typical day is obtained as shown in Tables 2 and 3:

[0178] Table 2: Market boundary time sequence data

[0179]

[0180] Table 3: Other data

[0181]

[0182] Step 3, according to the model setting, the traditional capacity market model and the capacity market model considering the flexible adjustment characteristics are set. The clearing results are shown in Tables 4 to 7.

[0183] Table 4: Traditional capacity market clearing data

[0184]

[0185] Table 5: Capacity market clearing data considering flexible adjustment characteristics

[0186] ​

[0187] Table 6 Load loss data of two capacity markets

[0188]

[0189] Table 7 Unit output of capacity market considering flexibility regulation characteristics

[0190]

[0191] From the comparison of Table 4 and Table 5, it can be seen that the traditional capacity market clearing method obtains a higher capacity procurement cost in calculation, and Table 6 shows that this method leads to insufficient system flexibility regulation capability due to excessive procurement of new energy capacity, and load loss occurs in the 8th to 10th and 18th to 19th time periods. In contrast, the capacity market clearing method considering flexibility regulation characteristics proposed in the present application effectively improves the system flexibility regulation capability by appropriately reducing the procurement of photovoltaic capacity and increasing the configuration of thermal power capacity, realizes no load loss in the whole time period, and fully embodies the advantages of the present application in ensuring the safety of system operation.

[0192] It should be emphasized that the embodiments described in the present application are illustrative rather than restrictive, and therefore the present application includes but is not limited to the embodiments described in the specific embodiments, and any other embodiments derived by those skilled in the art according to the technical solutions of the present application also belong to the scope of protection of the present application.

Claims

1. A capacity market optimization dispatch system that accounts for flexibility regulation needs, characterized by: Comprising a typical day generation module for obtaining given new energy generation data and load data, and selecting a typical day according to the principle of maximum flexibility demand; the typical day generation module calculates the time sequence curve of the annual net load according to the time sequence curve of the annual new energy generation and load power, and selects the maximum net load day, the minimum net load day, the maximum peak valley difference day, the maximum net load up ramp day, the maximum net load down ramp day, the maximum up ramp day and the maximum down ramp day; a capacity market simulation module for obtaining given typical day new energy generation data and load data, and given other generation side, grid side, load side related data, and obtaining the capacity market simulation results of units, energy storage and virtual power plants according to simulation optimization objectives and constraint conditions; a data processing module for calculating the total procurement cost of the capacity market and the income of various flexible resources in the capacity market according to the capacity market simulation results. ; wherein is the operating day capacity market revenue for each unit, is the market clearing price for the period t is the market clearing price for the period is the winning capacity for the unit i is the period t is the winning capacity for the period A data processing module is used to calculate the total procurement cost of the capacity market and the income of various flexible resources in the capacity market according to the capacity market simulation results. 2.The capacity market optimization dispatch system considering flexibility regulation demand according to claim 1, wherein: It also includes a login authentication module for user identity authentication; a data input module for inputting the generation side, grid side, load side related data required by the simulation calculation module and the power system related parameters required by the data processing module; a data output module for outputting the capacity market clearing results, the total procurement cost of the capacity market and the income of various flexible resources in the capacity market transmitted by the data processing module to form a report. 3.The capacity market optimization dispatch system considering flexibility regulation demand according to claim 2, wherein: The data inputted by the data input module includes the installed capacity of thermal power units, the bus, the ramp rate, the sliding rate, the start-up cost, the minimum output, the effective load carrying capacity coefficient, the effective capacity and the capacity bid, the maximum capacity, the maximum charge and discharge power, the charge and discharge efficiency, the effective load carrying capacity coefficient, the effective capacity and the capacity bid of the energy storage, the maximum adjustment capacity, the effective load carrying capacity coefficient, the effective capacity and the capacity bid of the virtual power plant; The installed capacity of new energy generation, the annual time sequence generation curve, the effective load carrying capacity coefficient, the effective capacity and the capacity bid, and the load side information including the load geographical location, the annual time sequence power, and the capacity-price curve of variable resource demand.

4. An optimization dispatch method of a capacity market optimization dispatch system considering flexibility regulation demand, characterized in that: The method comprises the following steps: According to the given new energy generation data and load data, a typical day is selected according to the principle of maximum flexibility demand; According to the new energy generation data and load data of the typical day selected according to the principle of maximum flexibility demand, and given other generation side, grid side, load side related data, and according to the simulation optimization objectives and constraint conditions, the capacity market simulation results of units, energy storage and virtual power plants are obtained; According to the capacity market simulation results, the total procurement cost of the capacity market and the income of various flexible resources in the capacity market are calculated; The specific implementation method of selecting a typical day according to the given new energy generation data and load data according to the principle of maximum flexibility demand is to obtain the 24-hour time sequence curve of the wind power, photovoltaic and load system boundary data of the maximum net load day, the minimum net load day, the maximum peak valley difference day, the maximum net load up ramp day and the maximum net load down ramp day. The specific implementation method of the maximum net load day peak value calculation and the corresponding day selection is as follows: ; wherein is the net load for the day d at the hour t, is the daily maximum net load, is the daily peak value of the maximum net load; The specific implementation method of the minimum net load day peak value calculation and the corresponding day selection is as follows: ; In the formula is the minimum net load for the day d, is the peak value of the minimum net load day d. The specific implementation method of the maximum peak valley difference day peak value calculation and the corresponding day selection is as follows: ; In the formula is the daily net load peak valley, is the maximum peak valley difference daily peak value; The specific implementation method of the maximum up ramp day peak value calculation and the corresponding day selection is as follows: ; In the formula is the maximum uphill climb for day d, is the daily peak value for the maximum uphill climb. The specific implementation method of the maximum down ramp day peak value calculation and the corresponding day selection is as follows: ; wherein is the maximum downhill slope for day d, is the daily peak value of the maximum downhill slope. The specific implementation method of the capacity market simulation result of the unit, the energy storage, and the virtual power plant based on the new energy power generation data and the load data of the typical day selected according to the principle of the maximum flexibility demand, the given other power generation side, power grid side, and load side related data, and the simulation optimization target and the constraint condition is as follows: Determine the objective function of minimizing the system procurement capacity cost; According to the determined objective function, determine the safety constraint condition, and establish a capacity market simulation model considering 24-hour time sequence simulation of a typical day.

5. The optimization dispatch method of the capacity market optimization dispatch system considering flexibility regulation demand according to claim 4, characterized in that: The constraint conditions include unit bidding segment capacity limit constraint, total unit capacity, total virtual power plant capacity, total variable resource demand, maximum unit output limit, maximum virtual power plant output limit, unit output upper limit, unit output lower limit, virtual power plant lower limit, virtual power plant upper limit, unit upper standby limit, unit lower standby limit, virtual power plant upper standby limit, virtual power plant lower standby limit, unit start-stop variable constraint, unit start-up response time constraint, unit minimum operation time constraint, unit minimum shutdown time constraint, unit ramp constraint, energy storage power balance constraint, energy storage electric quantity balance constraint, energy storage electric quantity upper limit constraint, energy storage discharge power upper limit constraint, energy storage charging power lower limit constraint, energy storage charging and discharging state constraint, energy storage electric energy and auxiliary service coupling constraint, system upper standby constraint, system lower standby constraint, and power balance constraint.

6. The optimization dispatch method of the capacity market optimization dispatch system considering flexibility regulation demand according to claim 4, characterized in that: The specific implementation method of calculating the total capacity market procurement cost and the income of various types of flexibility resources in the capacity market based on the capacity market simulation result is as follows: ; wherein is the operating day capacity market revenue for each unit, is the market clearing price for the period t is the market clearing price for the period is the winning capacity for the unit i is the period t is the winning capacity for the period

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