Flexible climbing and spinning standby market coupling system

Abstract

The invention relates to the technical field of electric power system market coupling, and discloses a flexible climbing and spinning standby market coupling system, which comprises a data acquisition module used for acquiring market clearing data of P units in T time periods before and within the day; the market clearing data comprises day-ahead market clearing data and intra-day market clearing data; the demand establishment module is used for determining spinning reserve and flexible climbing demands according to the preprocessed market clearing data; the coupled market clearing model construction module is used for constructing a coupled market clearing model according to the requirements of spinning reserve and flexible climbing and the preprocessed market clearing data in combination with a particle swarm algorithm; and the market coupling scheme generation module is used for generating a market coupling scheme by using the coupling market clearing model.

Description

Technical Field

[0001] The present invention relates to the technical field of power system market coupling, and more specifically, to a flexible ramping and spinning reserve market coupling system. Background Art

[0002] The existing technical solutions do not consider the market coordination design of flexible ramping and spinning reserve, but simply superimpose the flexible ramping market on the basis of the original spinning reserve market. Such solutions will have negative impacts on safety, green and economic levels. In terms of safety and green, there are few fast units in my country's power system, which will lead to an imbalance between supply and demand in the flexible ramping market, greatly increase the risk of system load shedding and abandonment of renewable energy, and affect the stability of power supply and the utilization rate of renewable energy. In terms of economy, the supply gap leads to price fluctuations in the spot market, while raising the clearing price of the flexible ramping market and increasing the procurement costs of system auxiliary services.

[0003] Therefore, how to achieve deep integration of the flexible ramping market with my country's actual situation and balance the needs of safety, greenness and economy has become the core challenge in improving the flexibility of the power system. Summary of the invention

[0004] The present invention provides a flexible ramping and rotating reserve market coupling system to solve the technical problems of unstable power supply system, low utilization rate of renewable energy and high procurement cost in related technologies.

[0005] The present invention provides a flexible ramping and spinning reserve market coupling system, comprising:

[0006] The data collection module 101 is used to collect the market clearing data of P units in T time periods before and during the day;

[0007] Market clearing data include: day-ahead market clearing data and intraday market clearing data;

[0008] The day-ahead clearing data include: the bids of the electric energy market based on segmented variable costs, the winning bids of the corresponding bid segments of electric energy, the operating status of the units, the no-load costs of the units, the startup status of the units, the startup costs of the units, the winning bids of the spinning reserve, the bids of the spinning reserve based on the reserve costs, the value of lost load, the load shedding amount and the amount of abandoned electricity of new energy;

[0009] Intraday clearing data include: load shedding, new energy curtailment, winning bids in corresponding quotation periods of electric energy, slack up-ramp purchases, slack down-ramp purchases and slack purchase prices in up-ramp and down-ramp markets;

[0010] A demand determination module 102, which is used to determine the demand for spinning reserve and flexible ramping according to the pre-processed market clearing data;

[0011] A coupled market clearing model building module 103, which is used to build a coupled market clearing model based on the demand for spinning reserve and flexible ramping and pre-processed market clearing data in combination with a particle swarm algorithm;

[0012] The market coupling scheme generating module 104 is used to generate a market coupling scheme using a coupled market clearing model.

[0013] Furthermore, P and T are both custom parameters, and P is set according to the actual number of units.

[0014] Furthermore, the market clearing data of P units in T time periods before and during the day are preprocessed, including the following steps:

[0015] Step S201, filling missing values ​​in the market clearing data by taking the average of the value of the previous period and the value of the next period;

[0016] Step S202, replacing the outliers in the market clearing data by calculating the mean of all non-outliers.

[0017] Furthermore, the demand for spinning reserve is determined by following the current method, where the market procurement demand is composed of the sum of the system's maximum possible accident capacity, the percentage of the next day's maximum predicted load, and the percentage of predicted output of new energy sources such as wind and solar power.

[0018] Furthermore, the determination of flexible ramping demand is based on the dynamic Copula theory to determine the upward and downward ramping demand in each period of the day. Based on the winning results of the spinning reserve, the coupling amount of each unit in the spinning reserve on the day before is determined to ensure the sufficiency of the emergency reserve. The difference between the upward ramping demand and the upward ramping demand is purchased by the flexible ramping market.

[0019] Furthermore, the coupled market clearing model includes two stages: day-ahead and intraday. The day-ahead stage clears the electric energy and spinning reserve, including two levels of unit combination and economic dispatch; the intraday stage rolling optimizes the electric energy and ramping based on the coupleable amount in the day-ahead spinning reserve bidding results to determine the bidding results of each unit and the additional procurement volume for ramping.

[0020] Furthermore, constructing a coupled market clearing model includes the following steps:

[0021] Step S301, randomly generate an initial particle population with a particle number U that meets the constraint conditions;

[0022] The constraints include: power balance constraints, operating status constraints, and upper and lower limits of unit output in the day-ahead unit combination and economic dispatch phase, as well as power balance constraints, upper and lower limits of unit output, and ramp demand constraints in the intraday phase;

[0023] Each particle includes a position code and a velocity code;

[0024] The position encoding is done by a vector X = {x1…x n} means that x1…x n They respectively represent the power transmission status from the first unit to the nth unit. The power transmission status is represented by real number codes. The integer part of the real number code represents the output power of the unit in one period, and the decimal part of the real number code represents the increase in the output power of the unit in one period.

[0025] The velocity is encoded by the vector V = {v1…v n} indicates that the value of each dimension of the velocity vector is K% of the value of each dimension of the position vector, where K is a custom parameter;

[0026] Step S302, constructing the objective function of the coupled market clearing model;

[0027] Step S303, calculating the fitness values ​​of all particles in the particle swarm according to the objective function;

[0028] Step S304, updating the particle positions and velocities in the next generation particle swarm according to the particle swarm algorithm update strategy;

[0029] Step S305, if the fitness value of each particle in the initialized particle swarm with the current iteration number j is determined to be greater than the maximum fitness value of the particle in history, the maximum fitness value of the particle in history is replaced by the fitness value of the particle with the current iteration number j;

[0030] The maximum fitness value in the initialized particle swarm with the current iteration number j is taken as the global optimal solution;

[0031] Step S306, the current number of iterations is increased by 1, and if it is determined that the current number of iterations reaches the maximum number of iterations, the global optimal solution is output as the market coupling solution, otherwise, the process returns to step S303, where the maximum number of iterations is a custom parameter.

[0032] Furthermore, the constraints are:

[0033] Day-ahead unit combination stage:

[0034] ⑴Power balance constraints:

[0035]

[0036] Where w, s and d represent the wind turbine index, photovoltaic unit index and node index respectively, and t is the time period index, where 1≤t≤T, and They represent the predicted output of the w-th wind turbine unit in the t-th period, the predicted output of the s-th photovoltaic unit in the t-th period, and the predicted load of the d-th node in the t-th period.

[0037] ⑵Operation status constraints:

[0038]

[0039] in represents the operating state variable of the i-th thermal power unit in the t-1th period;

[0040] ⑶ Upper and lower limits of unit output:

[0041]

[0042]

[0043] in and It represents the total amount of winning bids of the i-th thermal power unit in the electricity market in the t-th period and the maximum winning bid amount in the corresponding quotation period, and Respectively represent the maximum output and minimum output of the i-th thermal power unit;

[0044] The constraints in the day-ahead economic dispatch stage are basically the same as those in the day-ahead unit combination stage, except for the start-up and shutdown variables, which will not be elaborated here.

[0045] Intraday stage constraints:

[0046] ⑴Power balance constraints:

[0047]

[0048] in and They represent the predicted output of the w-th wind turbine in the t-th period of the day, the predicted output of the s-th photovoltaic unit in the t-th period of the day, and the predicted load of the d-th node in the t-th period of the day;

[0049] ⑵ Upper and lower limits of unit output:

[0050]

[0051] in and They represent the total amount of bids won by the i-th thermal power unit in the t-th period of the day in the electric energy market, the amount of bids won by the i-th thermal power unit in the up-ramp in the t-th period, and the amount of bids won by the i-th thermal power unit in the down-ramp in the t-th period;

[0052] ⑶Climbing demand constraints:

[0053]

[0054] in and They represent the upward climbing capacity and downward climbing capacity that all adjustable thermal power units can provide in the tth period, respectively. represents the system's up-slope demand in the tth period, It indicates the ramp coupling amount available for the winning spinning reserve in the tth period on the previous day.

[0055] Furthermore, the objective function is:

[0056]

[0057] in:

[0058]

[0059]

[0060] Among them C SCUC , C SCED They represent the total power purchase cost of the system in the day-ahead unit combination stage and the economic dispatch stage, respectively. INN represents the total cost of electricity purchased by the system within the day, i and b represent the thermal power unit index and the quotation segment index respectively, and They represent the system load shedding cost, system wind and solar curtailment cost, load shedding amount and renewable energy curtailment amount in the t-th period a day ago, and represents the bidding cost, no-load cost, startup cost, bidding cost of spinning reserve, bidding price of electricity market based on segmented variable cost, winning amount of electricity corresponding bidding segment, bidding price of spinning reserve based on reserve cost and winning amount of spinning reserve of the i-th thermal power unit in the t-th period before the day, ξ represents the ratio of unit optimization period to hour, voll represents the load loss value, ρ new represents the penalty coefficient for abandonment of renewable energy, and They represent the startup and operation state variables of the i-th thermal power unit in the t-th period, and They represent the no-load cost and startup cost of the i-th thermal power unit, FRUS t and FRDS t They respectively represent the system load shedding cost, system wind and solar power abandonment cost, load shedding amount, renewable energy abandonment amount, slack up-ramp purchase amount and slack down-ramp purchase amount in the t-th period of the day.

[0061] Furthermore, the market coupling scheme derived from the coupled market clearing model determines the electric energy output plan, spinning reserve allocation and flexible ramping capacity arrangement of each unit.

[0062] The beneficial effects of the present invention are as follows: the present invention constructs a coupled market clearing model based on a particle swarm algorithm, and the generated market coupling scheme can improve the stability of the power supply system, increase the utilization rate of renewable energy, and reduce procurement costs under the premise of ensuring system reliability requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 is a schematic diagram of a flexible ramping and spinning reserve market coupling system of the present invention;

[0064] Figure 2 is a flow chart of the preprocessing of market clearing data of the present invention;

[0065] Figure 3 It is a flow chart of constructing a coupled market clearing model of the present invention.

[0066] In the figure: data collection module 101, demand establishment module 102, coupled market clearing model construction module 103, market coupling plan generation module 104. DETAILED DESCRIPTION

[0067] The subject matter described herein will now be discussed with reference to example embodiments. It should be understood that the discussion of these embodiments is only to enable those skilled in the art to better understand and implement the subject matter described herein, and the functions and arrangements of the elements discussed may be changed without departing from the scope of protection of the contents of this specification. Each example may omit, replace or add various processes or components as needed. In addition, the features described relative to some examples may also be combined in other examples.

[0068] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in one or more embodiments of the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in one or more embodiments of the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0069] like Figure 1 to Figure 3 As shown, a flexible ramping and spinning reserve market coupling system comprises:

[0070] The data collection module 101 is used to collect the market clearing data of P units in T time periods before and during the day;

[0071] Market clearing data include: day-ahead market clearing data and intraday market clearing data;

[0072] The day-ahead clearing data include: the bids of the electric energy market based on segmented variable costs, the winning bids of the corresponding bid segments of electric energy, the operating status of the units, the no-load costs of the units, the startup status of the units, the startup costs of the units, the winning bids of the spinning reserve, the bids of the spinning reserve based on the reserve costs, the value of lost load, the load shedding amount and the amount of abandoned electricity of new energy;

[0073] Intraday clearing data include: load shedding, new energy curtailment, winning bids in corresponding quotation periods of electric energy, slack up-ramp purchases, slack down-ramp purchases and slack purchase prices in up-ramp and down-ramp markets;

[0074] A demand determination module 102, which is used to determine the demand for spinning reserve and flexible ramping according to the pre-processed market clearing data;

[0075] A coupled market clearing model building module 103, which is used to build a coupled market clearing model based on the demand for spinning reserve and flexible ramping and pre-processed market clearing data in combination with a particle swarm algorithm;

[0076] The market coupling scheme generating module 104 is used to generate a market coupling scheme using a coupled market clearing model.

[0077] In one embodiment of the present invention, P and T are both custom parameters, P is set according to the actual number of units, preferably, one time period is 15 minutes, and T is set to 96.

[0078] In one embodiment of the present invention, the market clearing data of P units in T time periods before and during the day are preprocessed, such as Figure 2 As shown, the following steps are included:

[0079] Step S201, filling missing values ​​in the market clearing data by taking the average of the value of the previous period and the value of the next period;

[0080] Step S202, replacing the outliers in the market clearing data by calculating the mean of all non-outliers.

[0081] In one embodiment of the present invention, the spinning reserve demand is determined by following the current method, that is, the market procurement demand is composed of the sum of the system's maximum possible accident capacity, the next day's maximum predicted load percentage, and the percentage of predicted output of new energy sources such as wind and solar power.

[0082] In one embodiment of the present invention, the determination of flexible ramping demand is based on the dynamic Copula theory to determine the upward and downward climbing demands in each period of the day, and based on the winning result of the spinning reserve, the coupling amount of each unit in the spinning reserve on the day before is determined to ensure the principle of sufficient emergency reserve, and the difference between the upward climbing demand and the upward climbing demand is purchased by the flexible ramping market.

[0083] In one embodiment of the present invention, the coupled market clearing model includes two stages: day-ahead and intraday. The day-ahead stage clears electric energy and spinning reserve, including two levels of unit combination and economic dispatch; the intraday stage rolling optimizes electric energy and ramping based on the coupling amount in the day-ahead spinning reserve bid results, and determines the bid results of each unit and the additional purchase amount for ramping.

[0084] In one embodiment of the present invention, Figure 3 As shown in Figure 2, constructing a coupled market clearing model includes the following steps:

[0085] Step S301, randomly generate an initial particle population with a particle number U that meets the constraint conditions;

[0086] The constraints include: power balance constraints, operating status constraints, and upper and lower limits of unit output in the day-ahead unit combination and economic dispatch phase, as well as power balance constraints, upper and lower limits of unit output, and ramp demand constraints in the intraday phase;

[0087] Each particle includes a position code and a velocity code;

[0088] The position encoding is done by a vector X = {x1…x n} means that x1…x n They respectively represent the power transmission status from the first unit to the nth unit. The power transmission status is represented by real number codes. The integer part of the real number code represents the output power of the unit in one period, and the decimal part of the real number code represents the increase in the output power of the unit in one period.

[0089] For example, x2 equals 60.13, which means that the output power of the second unit in one period is 60 MW, and the increase in output power in one period is 13 MW;

[0090] The velocity is encoded by the vector V = {v1…v n} indicates that the value of each dimension of the velocity vector is K% of the value of each dimension of the position vector, where K is a custom parameter. Preferably, the value of K is 15;

[0091] Step S302, constructing the objective function of the coupled market clearing model;

[0092] Step S303, calculating the fitness values ​​of all particles in the particle swarm according to the objective function;

[0093] Step S304, updating the particle positions and velocities in the next generation particle swarm according to the particle swarm algorithm update strategy;

[0094] Step S305, if the fitness value of each particle in the initialized particle swarm with the current iteration number j is determined to be greater than the maximum fitness value of the particle in history, the maximum fitness value of the particle in history is replaced by the fitness value of the particle with the current iteration number j;

[0095] The maximum fitness value in the initialized particle swarm with the current iteration number j is taken as the global optimal solution;

[0096] Step S306, the current number of iterations is increased by 1, and if it is determined that the current number of iterations reaches the maximum number of iterations, the global optimal solution is output as the market coupling solution, otherwise, the process returns to step S303, wherein the maximum number of iterations is a custom parameter, and preferably, the maximum number of iterations is set to 200.

[0097] In one embodiment of the present invention, the particle swarm algorithm update strategy is a conventional technical means and will not be described in detail here.

[0098] In one embodiment of the present invention, the constraints are:

[0099] Day-ahead unit combination stage:

[0100] 1. Power balance constraints

[0101]

[0102] Where w, s and d represent the wind turbine index, photovoltaic unit index and node index respectively, and t is the time period index, where 1≤t≤T, and They represent the predicted output of the w-th wind turbine unit in the t-th period, the predicted output of the s-th photovoltaic unit in the t-th period, and the predicted load of the d-th node in the t-th period.

[0103] 2. Operation status constraints

[0104]

[0105] in represents the operating state variable of the i-th thermal power unit in the t-1-th period, preferably, the state variable is 0 or 1;

[0106] 3. Upper and lower limits of unit output

[0107]

[0108] in and It represents the total amount of winning bids of the i-th thermal power unit in the electricity market in the t-th period and the maximum winning bid amount in the corresponding quotation period, and Respectively represent the maximum output and minimum output of the i-th thermal power unit;

[0109] The constraints in the day-ahead economic dispatch stage are basically the same as those in the day-ahead unit combination stage, except for the start-up and shutdown variables, which will not be elaborated here.

[0110] Intraday stage constraints:

[0111] 1. Power balance constraints

[0112]

[0113] in and They represent the predicted output of the w-th wind turbine in the t-th period of the day, the predicted output of the s-th photovoltaic unit in the t-th period of the day, and the predicted load of the d-th node in the t-th period of the day;

[0114] 2. Upper and lower limits of unit output

[0115]

[0116] in and They represent the total amount of bids won by the i-th thermal power unit in the t-th period of the day in the electric energy market, the amount of bids won by the i-th thermal power unit in the up-ramp in the t-th period, and the amount of bids won by the i-th thermal power unit in the down-ramp in the t-th period;

[0117] 3. Climbing demand constraints

[0118]

[0119] in and They represent the upward climbing capacity and downward climbing capacity that all adjustable thermal power units can provide in the tth period, represents the system's up-slope demand in the tth period, It indicates the ramp coupling amount available for the winning spinning reserve in the tth period on the previous day.

[0120] In one embodiment of the present invention, the objective function is:

[0121]

[0122] in:

[0123]

[0124]

[0125] Among them C SCUC , C SCED They represent the total power purchase cost of the system in the day-ahead unit combination stage and the economic dispatch stage, respectively. INN represents the total cost of electricity purchased by the system within the day, i and b represent the thermal power unit index and the quotation segment index respectively, and They represent the system load shedding cost, system wind and solar curtailment cost, load shedding amount and renewable energy curtailment amount in the t-th period a day ago, and represents the bidding cost, no-load cost, startup cost, bidding cost of spinning reserve, bidding price of electric energy market based on segmented variable cost, winning amount of electric energy corresponding bidding segment, bidding price of spinning reserve based on reserve cost and winning amount of spinning reserve of the i-th thermal power unit in the t-th period before the day, ξ represents the ratio of unit optimization period to hour, preferably, the unit optimization period is 15 minutes, then ξ is 1 / 4, voll represents the load loss value, ρ new represents the penalty coefficient for abandonment of renewable energy, and They represent the startup and operation state variables of the i-th thermal power unit in the t-th period, preferably, the state variable is 0 or 1, and They represent the no-load cost and startup cost of the i-th thermal power unit, FRUS t and FRDS t They respectively represent the system load shedding cost, system wind and solar power abandonment cost, load shedding amount, renewable energy abandonment amount, slack up-ramp purchase amount and slack down-ramp purchase amount in the t-th period of the day.

[0126] In one embodiment of the present invention, the electric energy output plan, spinning reserve allocation and flexible ramp capacity arrangement of each unit are determined according to the market coupling scheme derived from the coupled market clearing model.

[0127] The above describes an embodiment of the present embodiment, but the present embodiment is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present embodiment, ordinary technicians in this field can also make many forms, all of which are within the protection of the present embodiment.

Claims

1. A flexible ramping and spinning reserve market coupling system, characterized in that: include: A data collection module, which is used to collect market clearing data of P units in T time periods before and during the day; Market clearing data include: day-ahead market clearing data and intraday market clearing data; The day-ahead clearing data include: the bids of the electric energy market based on segmented variable costs, the winning bids of the corresponding bid segments of electric energy, the operating status of the units, the no-load costs of the units, the startup status of the units, the startup costs of the units, the winning bids of the spinning reserve, the bids of the spinning reserve based on the reserve costs, the value of lost load, the load shedding amount and the amount of abandoned electricity of new energy; Intraday clearing data include: load shedding, new energy curtailment, winning bids in corresponding quotation periods of electric energy, slack up-ramp purchases, slack down-ramp purchases and slack purchase prices in up-ramp and down-ramp markets; A demand determination module, which is used to determine the demand for spinning reserve and flexible ramping based on pre-processed market clearing data; A coupled market clearing model building module, which is used to build a coupled market clearing model based on the demand for spinning reserve and flexible ramping and pre-processed market clearing data combined with a particle swarm algorithm; A market coupling scheme generation module is used to generate a market coupling scheme using a coupled market clearing model.

2. A flexible ramping and spinning reserve market coupling system according to claim 1, characterized in that: P and T are custom parameters.

3. The flexible ramping and spinning reserve market coupling system according to claim 1, characterized in that: The market clearing data of P units in the day-ahead and T periods within the day are preprocessed, including the following steps: Step S201, filling missing values ​​in the market clearing data by taking the average of the value of the previous period and the value of the next period; Step S202, replacing the outliers in the market clearing data by calculating the mean of all non-outliers.

4. The flexible ramping and spinning reserve market coupling system according to claim 1, characterized in that: The demand for spinning reserve is determined using the current method, that is, the market procurement demand is composed of the sum of the system's maximum possible accident capacity, the percentage of the next day's maximum predicted load, and the percentage of predicted output of new energy such as wind and solar power.

5. The flexible ramping and spinning reserve market coupling system according to claim 1, characterized in that: The determination of flexible ramping demand is based on the dynamic Copula theory to determine the upward and downward ramping demand in each period of the day. Based on the winning results of the spinning reserve, the coupling amount of each unit in the spinning reserve on the day before is determined to ensure the sufficiency of the emergency reserve. The difference between the upward ramping demand and the upward ramping demand is purchased from the flexible ramping market.

6. The flexible ramping and spinning reserve market coupling system according to claim 1, characterized in that: The coupled market clearing model includes two stages: day-ahead and intraday. The day-ahead stage clears the electric energy and spinning reserve, including two levels of unit combination and economic dispatch; the intraday stage rolling optimizes the electric energy and ramping based on the coupleable amount in the day-ahead spinning reserve bidding results to determine the bidding results of each unit and the additional procurement volume for ramping.

7. The flexible ramping and spinning reserve market coupling system according to claim 1, characterized in that: Constructing a coupled market clearing model includes the following steps: Step S301, randomly generate an initial particle population with a particle number U that meets the constraint conditions; The constraints include: power balance constraints, operating status constraints, and upper and lower limits of unit output in the day-ahead unit combination and economic dispatch phase, as well as power balance constraints, upper and lower limits of unit output, and ramp demand constraints in the intraday phase; Each particle includes a position code and a velocity code; The position encoding is done by a vector X = {x1…x n } means that x1…x n They respectively represent the power transmission status from the first unit to the nth unit. The power transmission status is represented by real number codes. The integer part of the real number code represents the output power of the unit in one period, and the decimal part of the real number code represents the increase in the output power of the unit in one period. The velocity is encoded by the vector V = {v1…v n } indicates that the value of each dimension of the velocity vector is K% of the value of each dimension of the position vector, where K is a custom parameter; Step S302, constructing the objective function of the coupled market clearing model; Step S303, calculating the fitness values ​​of all particles in the particle swarm according to the objective function; Step S304, updating the particle positions and velocities in the next generation particle swarm according to the particle swarm algorithm update strategy; Step S305, if the fitness value of each particle in the initialized particle swarm with the current iteration number j is determined to be greater than the maximum fitness value of the particle in history, the maximum fitness value of the particle in history is replaced by the fitness value of the particle with the current iteration number j; The maximum fitness value in the initialized particle swarm with the current iteration number j is taken as the global optimal solution; Step S306, the current number of iterations is increased by 1, and if it is determined that the current number of iterations reaches the maximum number of iterations, the global optimal solution is output as the market coupling solution, otherwise, the process returns to step S303, where the maximum number of iterations is a custom parameter.

8. The flexible ramping and spinning reserve market coupling system according to claim 7, characterized in that: The constraints are: Day-ahead unit combination stage: ⑴Power balance constraints: Where w, s and d represent the wind turbine index, photovoltaic unit index and node index respectively, and t is the time period index, where 1≤t≤T, and They represent the predicted output of the w-th wind turbine unit in the t-th period, the predicted output of the s-th photovoltaic unit in the t-th period, and the predicted load of the d-th node in the t-th period. ⑵Operation status constraints: in represents the operating state variable of the i-th thermal power unit in the t-1th period; ⑶ Upper and lower limits of unit output: in and It represents the total amount of winning bids of the i-th thermal power unit in the electricity market in the t-th period and the maximum winning bid amount in the corresponding quotation period, and Respectively represent the maximum output and minimum output of the i-th thermal power unit; The constraints in the day-ahead economic dispatch stage are basically the same as those in the day-ahead unit combination stage, except for the start-up and shutdown variables, which will not be elaborated here. Intraday stage constraints: ⑴Power balance constraints: in and They represent the predicted output of the w-th wind turbine in the t-th period of the day, the predicted output of the s-th photovoltaic unit in the t-th period of the day, and the predicted load of the d-th node in the t-th period of the day; ⑵ Upper and lower limits of unit output: in and They represent the total amount of bids won by the i-th thermal power unit in the t-th period of the day in the electric energy market, the amount of bids won by the i-th thermal power unit in the up-ramp in the t-th period, and the amount of bids won by the i-th thermal power unit in the down-ramp in the t-th period; ⑶Climbing demand constraints: in and They represent the upward climbing capacity and downward climbing capacity that all adjustable thermal power units can provide in the tth period, respectively. represents the system's up-slope demand in the tth period, It indicates the ramp coupling amount available for the winning spinning reserve in the tth period on the previous day.

9. The flexible ramping and spinning reserve market coupling system according to claim 7, characterized in that: The objective function is: in: Among them C SCUC , C SCED They represent the total power purchase cost of the system in the day-ahead unit combination stage and the economic dispatch stage, respectively. INN represents the total cost of electricity purchased by the system within the day, i and b represent the thermal power unit index and the quotation segment index respectively, and They represent the system load shedding cost, system wind and solar curtailment cost, load shedding amount and renewable energy curtailment amount in the t-th period a day ago, and represents the bidding cost, no-load cost, startup cost, bidding cost of spinning reserve, bidding price of electricity market based on segmented variable cost, winning amount of electricity corresponding bidding segment, bidding price of spinning reserve based on reserve cost and winning amount of spinning reserve of the i-th thermal power unit in the t-th period before the day, ξ represents the ratio of unit optimization period to hour, voll represents the load loss value, ρ new represents the penalty coefficient for abandoning electricity from new energy sources, and They represent the startup and operation state variables of the i-th thermal power unit in the t-th period, and They represent the no-load cost and startup cost of the i-th thermal power unit, FRUS t and FRDS t They respectively represent the system load shedding cost, system wind and solar power abandonment cost, load shedding amount, renewable energy abandonment amount, slack up-ramp purchase amount and slack down-ramp purchase amount in the t-th period of the day.

10. The flexible ramping and spinning reserve market coupling system according to claim 1, characterized in that: The market coupling scheme derived from the coupled market clearing model determines the power output plan, spinning reserve allocation and flexible ramp capacity arrangement of each unit.

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