Capacity market optimization clearing system and method considering flexibility adjustment demand

Abstract

The invention relates to a capacity market optimization clearing system considering flexibility adjustment requirements, which constructs a unit model with flexibility adjustment characteristics and combines the unit model with capacity market optimization clearing simulation. Wherein the capacity market optimization clearing model realizes clearing of the capacity market by establishing mathematical models of a power supply side, a load side, energy storage and a power grid structure and setting capacity market constraint conditions; the unit model considering the flexibility adjustment characteristic comprehensively considers the participation of the unit in the electric energy market, provides standby service and stored energy to participate in the electric energy market and an auxiliary service market, and establishes a physical constraint model of each unit. The two models are combined through the system capacity market clearing price and the unit capacity bid winning condition, different typical operation days can be selected, the situation that the capacity market clearing condition can be visually reflected by the optimization result is considered, and it is guaranteed that the simulation result meets the actual operation reliability requirement of the system in the different typical operation days.

Description

Technical Field

[0001] The present invention belongs to the technical field of capacity market optimization and clearing, and in particular to a capacity market optimization and clearing system and method taking into account flexibility adjustment needs. Background Art

[0002] With the access of new energy units, the problem of insufficient flexibility in the operation of the power system will become increasingly prominent. With the continuous improvement of the medium- and long-term market and the spot market, the capacity market, as the core mechanism to ensure the long-term adequacy of the power system, urgently needs to build a capacity market clearing system that integrates flexibility regulation characteristics to achieve the optimal allocation of regulation resources. The traditional capacity market can provide sufficient electricity in the face of the increasing number of new energy power generation units, but its corresponding flexible capacity incentives are insufficient, making it difficult to cope with sudden load changes and the intermittent and fluctuating changes in new energy output, resulting in the system facing greater operational regulation pressure under the penetration of new energy. Summary of the Invention

[0003] The purpose of the present invention is to overcome the shortcomings of the existing technology and propose a capacity market optimization clearing system and method that takes into account the flexibility adjustment needs. During the clearing process, it is not only aimed at meeting the static capacity demand corresponding to the annual maximum load, but also constrains the system's flexible adjustment capability requirements for load fluctuations and new energy fluctuations during typical operating days.

[0004] The present invention solves the technical problem by adopting the following technical solutions: A capacity market optimal clearing system taking into account flexibility adjustment needs, including The typical day generation module is used to obtain the given renewable energy power generation data and load data and select the typical day based on the principle of maximizing flexibility demand; The capacity market simulation module is used to obtain given typical daily renewable energy generation and load data, as well as other given generation-side, grid-side, and load-side related data, and solves the capacity market simulation results of units, energy storage, and virtual power plants based on the simulation optimization objectives and constraints; The data processing module is used to calculate the total procurement cost of the capacity market and the income of various types of flexibility resources in the capacity market based on the capacity market simulation results.

[0005] Moreover, it also includes a login authentication module for user identity authentication; The data input module is used to input the power generation side, grid side, and load side related data required by the simulation calculation module and the power system related parameters required by the data processing module; The data output module is used to generate reports based on the capacity market clearing results, total capacity market procurement costs, and revenues of various types of flexibility resources in the capacity market transmitted from the data processing module.

[0006] Moreover, the data input by the data input module include the installed capacity of the thermal power units, the bus they are located on, the climbing rate, the landslide rate, the startup cost, the minimum output, the effective load capacity coefficient, the effective capacity and the capacity quotation, the maximum capacity, the maximum charge and discharge power, the charge and discharge efficiency, the effective load capacity coefficient, the effective capacity and the capacity quotation of the energy storage, the maximum regulation capacity, the effective load capacity coefficient, the effective capacity and the capacity quotation of the virtual power plant; the installed capacity of new energy power generation, the annual time-series power generation curve, the effective load capacity coefficient, the effective capacity and the capacity quotation, and the load side information including the geographical location of the load, the annual time-series power, and the capacity-price curve of the variable resource demand.

[0007] Moreover, the typical day generation module calculates the timing curve of the net load throughout the year based on the timing curve of new energy power generation and load power throughout the year, and selects the maximum net load day, minimum net load day, maximum peak-to-valley difference day, maximum net load up-climbing day, maximum net load down-climbing day, maximum up-climbing day and maximum down-climbing day.

[0008] Furthermore, the data processing module calculates 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 results in a specific implementation method as follows:

[0009] Where, is the market revenue of the daily operating capacity of each unit, For the period t The market clearing price of For the crew i Time t The winning bid capacity.

[0010] An optimal clearing method for a capacity market optimal clearing system taking into account flexibility adjustment requirements comprises the following steps: Based on the given renewable energy generation data and load data, select a typical day based on the principle of maximizing flexibility demand; Based on the renewable energy generation and load data of a typical day selected according to the principle of maximizing flexibility demand, as well as other given generation-side, grid-side, and load-side related data, and according to the simulation optimization objectives and constraints, the capacity market simulation results of the units, energy storage, and virtual power plants are obtained; Based on the capacity market simulation results, the total procurement cost of the capacity market and the revenue of various types of flexibility resources in the capacity market are calculated.

[0011] Furthermore, the specific implementation method of selecting a typical day based on the given renewable energy power generation data and load data and the principle of maximizing flexibility demand is as follows: obtaining 24-hour time series curves of wind power, photovoltaic, and load system boundary data for the maximum net load day, the minimum net load day, the maximum peak-to-valley difference day, the maximum net load ramp-up day, and the maximum net load ramp-down day; The specific implementation method for calculating the daily peak value of the maximum net load and selecting the corresponding day is as follows:

[0012] In the formula is the net load at hour t on the operating day, is the maximum daily net load, is the daily peak value of the maximum net load; The specific implementation method for calculating the daily peak value of minimum net load and selecting the corresponding day is as follows:

[0013] In the formula is the minimum net load on operating day d, is the minimum net load daily peak value; The specific implementation method of calculating the daily peak value of the maximum peak-to-valley difference and selecting the corresponding day is:

[0014] In the formula is the net load peak and valley on operating day d, is the daily peak value of the maximum peak-to-valley difference; The specific implementation method of calculating the maximum daily peak value of the climbing slope and selecting the corresponding day is as follows:

[0015] In the formula is the maximum climbing grade on the running day, is the daily peak value of the maximum uphill climb; The specific implementation method of calculating the maximum downhill daily peak value and selecting the corresponding day is as follows:

[0016] In the formula is the maximum downhill slope on the operating day, It is the daily peak value of the maximum downhill climb.

[0017] Furthermore, the specific implementation method for obtaining the capacity market simulation results of the units, energy storage, and virtual power plants based on the new energy power generation data and load data of the typical day selected according to the principle of maximizing flexibility demand, as well as other given power generation side, grid side, and load side related data, and solving according to the simulation optimization objectives and constraints is as follows: Determine the objective function that minimizes the system procurement capacity cost; According to the determined objective function, the security constraints are determined and a capacity market simulation model considering the typical day 24-hour time series simulation is established.

[0018] Moreover, the constraints include unit quotation segment capacity limit constraint, unit total capacity, virtual power plant total capacity, total variable resource demand, unit maximum output limit, virtual power plant maximum 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 and stop variable constraint, unit start response time constraint, unit minimum operating time constraint, unit minimum shutdown time constraint, unit climbing constraint, energy storage power balance constraint, energy storage power balance constraint, energy storage power 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 power and auxiliary service coupling constraint, system upper reserve constraint, system lower reserve constraint and power balance constraint.

[0019] Furthermore, the specific implementation method for calculating the total procurement cost of the capacity market and the revenue of various types of flexibility resources in the capacity market based on the capacity market simulation results is as follows:

[0020] Where, is the market revenue of the daily operating capacity of each unit, For the period t The market clearing price of For the crew i Time t The winning bid capacity.

[0021] The advantages and positive effects of the present invention are: 1. The present invention constructs a unit model with flexibility regulation characteristics, and combines the unit model with the 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, load side, energy storage and grid structure and setting capacity market constraints; the unit model considering flexibility regulation characteristics comprehensively considers the unit's participation in the electric energy market, provision of backup services and energy storage participation in the electric energy market and auxiliary service market, and establishes a physical constraint model for each unit. The combination of the two models is achieved through the system capacity market clearing price and the unit capacity bidding situation, and different typical operating days can be selected, which not only takes into account that the optimization results can intuitively reflect the capacity market clearing situation, but also ensures that the simulation results on different typical operating days meet the actual operation reliability requirements of the system.

[0022] 2. The present invention constructs a capacity market clearing model that takes into account the flexible adjustment characteristics of multiple resources. It overcomes the problem that the traditional capacity market mechanism only focuses on static capacity redundancy and ignores the demand for dynamic flexible adjustment capabilities, and effectively avoids the risk of power supply shortages in the system at multiple time and space scales. At the same time, it establishes a physical constraint model that takes into account the participation of each unit in the electric energy market and the auxiliary service market; and combines the capacity market clearing model with the physical constraint model of each unit. It not only takes into account the requirement that the optimization results can intuitively reflect the minimum total capacity procurement cost of the system, but also ensures that the simulation results meet the system operation reliability requirements. Finally, the present invention establishes a typical day system time series curve generation model, and selects different typical operating days according to the characteristics of different scenario curves. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a structural diagram of the system of the present invention; Figure 2 Flowchart of the method of the present invention. DETAILED DESCRIPTION

[0024] The present invention is further described below in conjunction with the accompanying drawings.

[0025] The construction idea of ​​the present invention is: with the goal of minimizing the system's electricity purchase cost in the capacity market, considering the physical constraints of each unit participating in the electric energy market and ancillary service market, using mixed integer programming technology to solve the capacity clearing price that meets the safety constraints of the capacity market.

[0026] First, two indicators must be identified that fully reflect the minimization of the capacity market's electricity purchase costs: the market capacity clearing price and the winning bid capacity of each unit. A physical constraint model is established, taking into account each unit's participation in the electricity energy market and ancillary services market. An energy storage model is developed that reflects operational characteristics such as energy storage charging and discharging efficiency, maximum charge and discharge power, and maximum storage capacity. A security constraint model based on the actual power system is constructed, and the grid structure is established using transfer factors. After selecting a typical operating day based on the 24-hour time series curve characteristics of the system boundary, a 24-hour time series simulation is conducted to obtain capacity market clearing results. Price simulation results that meet the system's transmission security constraints are obtained, and the capacity market revenue of each unit and the total system capacity procurement cost are calculated.

[0027] A capacity market optimal clearing system taking into account the flexibility adjustment demand, such as Figure 1 As shown, it includes 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.

[0028] Among them, the login authentication module is used for user identity authentication; The data input module is used to input the generation-side, grid-side, and load-side data required by the simulation module, as well as the power system parameters required by the data processing module. This data includes the installed capacity, busbar location, ramp rate, ramp rate, startup cost, minimum output, effective load capacity coefficient, effective capacity, and capacity quote for thermal power units; the maximum capacity, maximum charge and discharge power, charge and discharge efficiency, effective load capacity coefficient, effective capacity, and capacity quote for energy storage; the maximum regulation capacity, effective load capacity coefficient, effective capacity, and capacity quote for virtual power plants; the installed capacity, annual sequential power generation curve, effective load capacity coefficient, effective capacity, and capacity quote for renewable energy power generation; and load-side information including the load's geographic location, annual sequential power output, and the capacity-price curve for variable resource demand.

[0029] The typical day generation module takes the renewable energy generation and load data given in the data input module and selects a typical day based on the principle of maximizing flexibility. This module calculates the 24-hour time series of net load for 365 days of the year based on the 24-hour time series of renewable energy generation and load power. It then selects the day with the maximum net load, the day with the minimum net load, the day with the largest peak-to-valley difference, the day with the maximum net load ramp-up, and the day with the maximum net load ramp-down.

[0030] The capacity market simulation module is used to obtain the typical day renewable energy power generation data and load data given in the typical day generation module, as well as other power generation side, grid side, and load side related data given in the data input module, and solve the capacity market simulation results of the units, energy storage, and virtual power plants based on the simulation optimization objectives and constraints, and 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 transmission security constraints of the system, and solves the capacity market clearing price.

[0031] The data processing module is used to calculate the capacity market clearing results transmitted by the capacity market simulation module, and calculate the total procurement cost of the capacity market and the income of various types of flexibility resources in the capacity market; The data output module is used to generate reports based on the capacity market clearing results, total capacity market procurement costs, and revenues of various types of flexibility resources in the capacity market transmitted from the data processing module.

[0032] An optimal clearing method for the capacity market optimal clearing system taking into account the flexibility adjustment demand, such as Figure 2 As shown, the following steps are included: Step 1: Input the generation side, 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.

[0033] Step 2: Obtain 24-hour time series curves of system boundary data such as wind power, photovoltaic power, and load on the maximum net load day, minimum net load day, maximum peak-to-valley difference day, maximum net load ramp-up day, and maximum net load ramp-down day.

[0034] The specific implementation method of calculating the daily peak value of the maximum net load and selecting the corresponding day is as follows:

[0035] In the formula is the net load at hour t on the operating day, is the maximum daily net load, is the daily peak value of the maximum net load; The specific implementation method for calculating the daily peak value of minimum net load and selecting the corresponding day is as follows:

[0036] In the formula is the minimum net load on operating day d, is the minimum net load daily peak value; The specific implementation method of calculating the daily peak value of the maximum peak-to-valley difference and selecting the corresponding day is:

[0037] In the formula is the net load peak and valley on operating day d, is the daily peak value of the maximum peak-to-valley difference; The specific implementation method of calculating the maximum daily peak value of the climbing slope and selecting the corresponding day is as follows:

[0038] In the formula is the maximum climbing grade on the running day, is the daily peak value of the maximum uphill climb; The specific implementation method of calculating the maximum downhill daily peak value and selecting the corresponding day is as follows:

[0039] In the formula is the maximum downhill slope on the operating day, It is the daily peak value of the maximum downhill climb.

[0040] Step 3: Input the acquired system boundary data time series curve into the capacity market simulation module, and perform a 24-hour time series simulation on the power market operation day to obtain the capacity market clearing price.

[0041] Step 3.1: Determine the objective function that minimizes the system procurement capacity cost.

[0042] The clearing of the capacity market is essentially a question of optimizing the balance between economic efficiency and reliability. While ensuring that the power system has sufficient generation capacity, it aims to maximize economic efficiency. Its core objective is to minimize the system's procurement capacity costs:

[0043] Where, For all units, , 、 、 They are respectively the collection of all thermal power units, all new energy units and all energy storage units; Index for the unit; is the load index; is the quotation segment index; is a set of quotation segments; The effective capacity of the winning bid for unit i, section b; Quote the capacity of unit i, section b; For all load sets, . is the set of all fixed loads, Aggregate for all virtual power plants; is the effective capacity of the winning bid of section b of virtual power plant j; Quote the capacity of section b for unit j; Clearing capacity for segment b of the variable resource demand curve; is the price of segment b of the variable resource demand curve.

[0044] Step 3.2: Based on the objective function determined in Step 3.1, determine the safety constraints and establish a capacity market simulation model that takes into account the typical day 24-hour time series simulation. The constraints include unit quotation period capacity limit, total unit capacity, total virtual power plant capacity, total variable resource demand, unit maximum output limit, virtual power plant maximum 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 and stop variable constraints, unit start response time constraint, unit minimum operating time constraint, unit minimum shutdown time constraint, unit ramp constraint, energy storage power balance constraint, energy storage power balance constraint, energy storage power upper limit constraint, energy storage discharge power upper limit constraint, energy storage charging power lower limit constraint, energy storage charge and discharge state constraint, energy storage power and ancillary service coupling constraint, system upper reserve constraint, system lower reserve constraint, and power balance constraint.

[0045] (1) Unit quotation segment capacity limit:

[0046] Where, is the maximum effective capacity of section b of unit i.

[0047] (2) Total capacity of the unit

[0048] In the formula is the effective capacity of unit i.

[0049] (3) Total capacity of virtual power plants

[0050] In the formula is the effective capacity of the winning bid of virtual power plant j.

[0051] (4) Total variable resource demand

[0052] In the formula is the total demand for variable resources.

[0053] (5) Maximum output limit of the unit

[0054] In the formula is the maximum output of unit i, is the effective load factor of unit i.

[0055] (6) Maximum output limit of virtual power plant

[0056] In the formula is the maximum output of virtual power plant j, is the effective load factor of virtual power plant j.

[0057] (7) Upper limit of unit output

[0058] In the formula For the crew i Time t Total output; For the crew i Time t The upper spare (8) Lower limit of unit output

[0059] In the formula For the crew i Time t The next spare; For the crew i Lower limit of output (9) Virtual power plants lower their lower limit

[0060] In the formula The total output of the virtual power plant; Virtual Power Plant j Time t The next spare; Virtual Power Plant j Lower the lower limit (10) Increase in the upper limit of virtual power plants

[0061] In the formula Virtual Power Plant j Time t The upper spare; Virtual Power Plant j Raise the upper limit.

[0062] (11) Unit standby limit

[0063] In the formula For the crew i The upper spare capacity limit.

[0064] (12) Unit standby limit

[0065] In the formula For the crew i The capacity limit of the lower reserve.

[0066] (13) Reserve restrictions on virtual power plants

[0067] In the formula Virtual Power Plant j Upper spare limit.

[0068] (14) Reserve restrictions under virtual power plants

[0069] In the formula Virtual Power Plant j Lower backup limit.

[0070] (15) Unit start-stop variable constraints

[0071] In the formula Start 0-1 variables for the unit; 0-1 variable for unit shutdown; The initial state of the unit needs to last for a certain period of time.

[0072] (16) Unit startup response time constraint

[0073] (17) Minimum operating time constraints of the unit

[0074] In the formula The minimum operating time of the unit; is the total number of time periods; (18) Minimum downtime constraints for units

[0075] In the formula The minimum downtime of the unit.

[0076] (19) Unit climbing constraints

[0077] In the formula It is the limit for the unit to climb downward; It is the climbing limit of the unit.

[0078] (20) Energy storage power balance constraints

[0079] In the formula It is the energy storage discharge and charging power.

[0080] (21) Energy storage power balance constraints

[0081] In the formula For energy storage i Time t of electricity; It is the energy storage discharge and charging power. It is a 0-1 variable for energy storage action.

[0082] (22) Energy storage capacity upper limit constraint

[0083] In the formula For energy storage i The upper limit of power.

[0084] (23) Upper limit constraint on energy storage discharge power

[0085] In the formula 0-1 variable for discharging the energy storage unit; For energy storage i The upper limit of discharge power.

[0086] (24) Energy storage charging power lower limit constraint

[0087] In the formula Charging the energy storage unit 0-1 variable; For energy storage i The charging power limit.

[0088] (25) Energy storage charging and discharging state constraints

[0089] (26) Coupling constraints between energy storage and ancillary services

[0090] (27) Spare constraints on the system

[0091] In the formula The spare capacity requirement on the system.

[0092] (28) System backup constraints

[0093] In the formula The spare capacity requirement for the system.

[0094] (29) Power balance constraints

[0095] In the formula is the load relaxation variable; System network loss.

[0096] Step 4: The data processing module calculates the capacity market clearing results transmitted by the capacity market simulation module, calculates the total procurement cost of the capacity market and the income of various types of flexibility resources in the capacity market, and transmits the results to the data output module.

[0097] The revenue calculation method for each unit in the capacity market on the operating day is:

[0098] Where, is the market revenue of the daily operating capacity of each unit, For the period t The market clearing price of For the crew i Time t The winning bid capacity.

[0099] Step 5: Data output includes the capacity market clearing results, the total procurement cost of the capacity market, and the revenue of various types of flexibility resources in the capacity market.

[0100] According to the above-mentioned optimized clearing method of the capacity market optimized clearing system taking into account the flexibility regulation demand, data calculation is performed by constructing a power system to verify the effect of the present invention.

[0101] The constructed power system includes 4 thermal power units, 1 energy storage power station, 1 photovoltaic station and 1 wind power station.

[0102] Step 1: Obtain relevant data and parameters required by the calculation module as shown in Table 1.

[0103] Table 1 Power generation side data

[0104] Step 2: According to the method of calculating the peak value of the maximum climbing day and selecting the corresponding day, the typical days are obtained as shown in Table 2 and Table 3: Table 2 Market boundary time series data

[0105] Table 3 Other data

[0106] Step 3: Set up the traditional capacity market model and the capacity market model with flexibility adjustment characteristics according to the model. The clearing results are shown in Tables 4 to 7.

[0107] Table 4 Traditional capacity market clearing data

[0108] Table 5 Capacity market clearing data taking into account flexibility adjustment characteristics

[0109] Table 6 Load shedding data for the two capacity markets

[0110] Table 7 Unit output in the capacity market taking into account flexibility regulation characteristics

[0111] A comparison of Tables 4 and 5 shows that the traditional capacity market clearing method results in higher capacity procurement costs. Table 6 also shows that this method, due to excessive procurement of renewable energy capacity, results in insufficient system flexibility and adjustment capabilities, resulting in load losses in periods 8-10 and 18-19, respectively. In contrast, the capacity market clearing method proposed in this invention, which takes flexibility adjustment characteristics into account, effectively improves the system's flexibility and adjustment capabilities by appropriately reducing PV capacity procurement and increasing thermal power capacity allocation, achieving zero load loss throughout the entire period. This fully demonstrates the advantages of this invention in ensuring system operational safety.

[0112] It should be emphasized that the embodiments described in the present invention are illustrative rather than restrictive. Therefore, the present invention includes but is not limited to the embodiments described in the specific embodiments. Any other embodiments derived by those skilled in the art based on the technical solutions of the present invention also fall within the scope of protection of the present invention.

Claims

1. A capacity market optimization clearing system that takes into account flexibility adjustment needs, characterized by: include The typical day generation module is used to obtain the given renewable energy power generation data and load data and select the typical day based on the principle of maximizing flexibility demand; The capacity market simulation module is used to obtain given typical daily renewable energy generation and load data, as well as other given generation-side, grid-side, and load-side related data, and solves the capacity market simulation results of units, energy storage, and virtual power plants based on the simulation optimization objectives and constraints; The data processing module is used to calculate the total procurement cost of the capacity market and the income of various types of flexibility resources in the capacity market based on the capacity market simulation results.

2. The capacity market optimization clearing system taking into account flexibility adjustment needs according to claim 1, characterized in that: It also includes a login authentication module for user identity authentication; The data input module is used to input the power generation side, grid side, and load side related data required by the simulation calculation module and the power system related parameters required by the data processing module; The data output module is used to generate reports based on the capacity market clearing results, total capacity market procurement costs, and revenues of various types of flexibility resources in the capacity market transmitted from the data processing module.

3. The capacity market optimization clearing system taking into account flexibility adjustment needs according to claim 2, characterized in that: The data input by the data input module includes the installed capacity of the thermal power unit, the bus it is located on, the climbing rate, the landslide rate, the startup cost, the minimum output, the effective load capacity coefficient, the effective capacity and the capacity quotation, the maximum capacity, the maximum charge and discharge power, the charge and discharge efficiency, the effective load capacity coefficient, the effective capacity and the capacity quotation of the energy storage, and the maximum regulation capacity, the effective load capacity coefficient, the effective capacity and the capacity quotation of the virtual power plant; The installed capacity of renewable energy power generation, annual time-series power generation curve, effective load capacity coefficient, effective capacity and capacity quotation; load-side information includes the geographical location of the load, annual time-series power, and the capacity-price curve of variable resource demand.

4. The capacity market optimization clearing system taking into account flexibility adjustment needs according to claim 1, characterized in that: The typical day generation module calculates the timing curve of the net load throughout the year based on the timing curve of renewable energy power generation and load power throughout the year, and selects the maximum net load day, minimum net load day, maximum peak-to-valley difference day, maximum net load up-climbing day, maximum net load down-climbing day, maximum up-climbing day and maximum down-climbing day.

5. The capacity market optimization clearing system taking into account flexibility adjustment needs according to claim 1, characterized in that: The specific implementation method of the data processing module to calculate the total procurement cost of the capacity market and the income of various types of flexibility resources in the capacity market based on the capacity market simulation results is: Where, is the market revenue of the daily operating capacity of each unit, For the period t The market clearing price of For the crew i Time t The winning bid capacity.

6. A method for optimizing the clearing of a capacity market optimal clearing system taking into account flexibility adjustment requirements, characterized by: The following steps are involved: Based on the given renewable energy generation data and load data, select a typical day based on the principle of maximizing flexibility demand; Based on the renewable energy generation and load data of a typical day selected according to the principle of maximizing flexibility demand, as well as other given generation-side, grid-side, and load-side related data, and according to the simulation optimization objectives and constraints, the capacity market simulation results of the units, energy storage, and virtual power plants are obtained; Based on the capacity market simulation results, the total procurement cost of the capacity market and the revenue of various types of flexibility resources in the capacity market are calculated.

7. The optimized clearing method for a capacity market optimized clearing system taking into account flexibility adjustment requirements according to claim 6, characterized in that: The specific implementation method of selecting a typical day based on the given renewable energy power generation data and load data and the principle of maximizing flexibility demand is as follows: obtaining 24-hour time series curves of wind power, photovoltaic, and load system boundary data for the maximum net load day, the minimum net load day, the maximum peak-to-valley difference day, the maximum net load ramp-up day, and the maximum net load ramp-down day; The specific implementation method for calculating the daily peak value of the maximum net load and selecting the corresponding day is as follows: ; In the formula is the net load at hour t on the operating day, is the maximum daily net load, is the daily peak value of the maximum net load; The specific implementation method for calculating the daily peak value of minimum net load and selecting the corresponding day is as follows: ; In the formula is the minimum net load on operating day d, is the minimum daily peak value of net load; The specific implementation method of calculating the daily peak value of the maximum peak-to-valley difference and selecting the corresponding day is: ; In the formula is the net load peak and valley on operating day d, is the daily peak value of the maximum peak-to-valley difference; The specific implementation method of calculating the maximum daily peak value of the climbing slope and selecting the corresponding day is as follows: ; In the formula is the maximum climbing grade on the running day, is the daily peak value of the maximum uphill climb; The specific implementation method of calculating the maximum downhill daily peak value and selecting the corresponding day is as follows: ; In the formula is the maximum downhill slope on the operating day, It is the daily peak value of the maximum downhill climb.

8. The optimized clearing method for a capacity market optimized clearing system taking into account flexibility adjustment requirements according to claim 6, characterized in that: The specific implementation method for obtaining the capacity market simulation results of the units, energy storage, and virtual power plants based on the new energy power generation data and load data of the typical day selected according to the principle of maximizing flexibility demand, as well as other given power generation side, grid side, and load side related data, and solving according to the simulation optimization objectives and constraints is as follows: Determine the objective function that minimizes the system procurement capacity cost; According to the determined objective function, the security constraints are determined and a capacity market simulation model considering the typical day 24-hour time series simulation is established.

9. The optimized clearing method for a capacity market optimized clearing system taking into account flexibility adjustment requirements according to claim 6, characterized in that: The constraints include the unit quotation segment capacity limit constraint, the total unit capacity, the total capacity of the virtual power plant, the total variable resource demand, the unit maximum output limit, the virtual power plant maximum output limit, the unit output upper limit, the unit output lower limit, the virtual power plant lower limit, the virtual power plant upper limit, the unit upper reserve limit, the unit lower reserve limit, the virtual power plant upper reserve limit, the virtual power plant lower reserve limit, the unit start and stop variable constraint, the unit start response time constraint, the unit minimum operating time constraint, the unit minimum shutdown time constraint, the unit climbing constraint, the energy storage power balance constraint, the energy storage power balance constraint, the energy storage power upper limit constraint, the energy storage discharge power upper limit constraint, the energy storage charging power lower limit constraint, the energy storage charging and discharging state constraint, the energy storage power and auxiliary service coupling constraint, the system upper reserve constraint, the system lower reserve constraint and the power balance constraint.

10. The optimized clearing method of the capacity market optimized clearing system taking into account flexibility adjustment requirements according to claim 6, characterized in that: The specific implementation method for calculating the total procurement cost of the capacity market and the revenue of various types of flexibility resources in the capacity market based on the capacity market simulation results is as follows: ; Where, is the market revenue of the daily operating capacity of each unit, For the period t The market clearing price of For the crew i Time t The winning bid capacity.

Images