Medium and long term energy-capacity coupled market trading clearing method, device and system

Abstract

A method, device, and system for clearing transactions in a medium- to long-term electricity-capacity coupled market are disclosed. The method involves collecting electricity, capacity, and price data, and constructing a medium- to long-term electricity-capacity coupled market operation mechanism on an annual timescale. This mechanism includes early market opening and centralized bidding, capacity and electricity delivery, and capacity cost allocation. An objective function for the clearing optimization problem of the medium- to long-term electricity-capacity coupled market operation mechanism is constructed by minimizing the difference between generation-side costs and consumption-side utility, i.e., maximizing social welfare as the optimization objective. Constraints are established based on the objective function to ensure the balance of power system energy supply and demand and the adequacy of system capacity during peak load periods. The clearing optimization problem is solved based on these constraints to obtain the coupled clearing results of the medium- to long-term electricity market and capacity market, as well as a resource scheduling scheme.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a medium- and long-term power-capacity coupled market trading clearing method, equipment, and system. Background Technology

[0002] The clearing price in the spot electricity market is close to the marginal cost of power generation, while renewable energy power plants with near-zero marginal costs have become a major source of electricity, further depressing prices. This has led to a decrease in the utilization hours of traditional thermal power units, making it difficult to recover fixed costs, accelerating the retirement of thermal power units, and weakening investors' willingness to invest in thermal power units. At the same time, renewable energy output is highly random, volatile, and intermittent, posing challenges to the reliability and stability of the power system. In the future, with the large-scale retirement of existing thermal power units, the capacity adequacy of the power system cannot be guaranteed. Therefore, the problem of insufficient power system capacity adequacy caused by the high penetration rate of renewable energy and the withdrawal of traditional thermal power units urgently needs to be addressed.

[0003] Capacity guarantee mechanisms can be divided into three main categories: capacity subsidy mechanisms, scarcity pricing mechanisms, and capacity market mechanisms. As a market-based capacity guarantee mechanism, the capacity market is crucial for addressing the issue of power system capacity adequacy under high-proportion renewable energy integration. Currently, most capacity market mechanisms rely on grid managers, rather than the market, to determine future capacity demand in their jurisdictions. Such a "one-size-fits-all" approach to capacity demand determination can lead to inaccurate assessments and potential imbalances between system demand and supply. Furthermore, for new entities that are both power producers and consumers, such as integrated power generation, grid, load, and storage systems, virtual power plants, and microgrids, their net load is relatively small and their capacity demand is variable. However, the grid must prepare generation capacity based on their maximum load, which not only wastes capacity resources but also is unfair to other market participants sharing the capacity costs. How to construct or reserve capacity resources for these new entities and how they should bear their corresponding capacity costs are also urgent issues that need to be addressed.

[0004] The electricity market system comprises the medium- and long-term electricity market and the spot market. The majority of electricity consumption by users is determined in the medium- and long-term electricity market, making medium- and long-term electricity trading a crucial "ballast" and "stabilizer" for ensuring the stable operation of the power grid. Both the capacity market and the medium- and long-term electricity market are essential for long-term power system planning. The effective integration of capacity resource development with medium- and long-term electricity planning helps the power system maintain sufficient capacity adequacy in the long term, and the medium- and long-term electricity market helps traders lock in future prices and reduce price volatility risks. However, most existing research treats the capacity market and the energy market as independent electricity markets, with only a few studies exploring the coupling relationship between the capacity market and the spot market. Research on the coupling relationship between the capacity market and the medium- and long-term electricity market is currently lacking.

[0005] The information disclosed in the background section is only for enhancing the understanding of the background of this invention, and therefore may contain information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] This invention provides a method, device, and system for clearing transactions in a medium- and long-term electricity-capacity coupled market. This method not only effectively reduces arc-starting voltage and stabilizes arc initiation but also reduces contact erosion. Through the combined action of the medium- and long-term electricity market and the capacity market, it ensures sufficient system capacity and a stable supply of medium- and long-term electricity during peak load periods, while also balancing capacity investment and the development of new market players in an economically optimal manner.

[0007] A medium- to long-term electricity-capacity coupled market clearing method includes:

[0008] Step 1: Collect data on electricity, capacity, and price, and construct a medium- to long-term electricity-capacity coupling market operation mechanism on an annual time scale. This includes opening the market in advance and organizing centralized bidding, delivery of capacity and electricity, and sharing of capacity costs.

[0009] Step 2: By minimizing the difference between generation costs and consumption utility, i.e., with maximizing social welfare as the optimization objective, construct the objective function for the trading clearing optimization problem of the medium- and long-term electricity energy-capacity coupled market operation mechanism.

[0010] Step 3: Establish constraints based on the objective function to ensure the balance of energy supply and demand in the power system and the adequacy of system capacity during peak load periods;

[0011] Step 4: Solve the transaction clearing optimization problem based on the constraints to obtain the coupled clearing results of the medium- and long-term electricity market and capacity market, as well as the resource scheduling scheme.

[0012] In the aforementioned medium- and long-term electricity-capacity coupled market transaction clearing method, the early market opening and centralized bidding process includes the power grid designing capacity demand curves based on historical data, independently declaring capacity demand curves, and the power dispatching agency verifying the available natural capacity of capacity resources. Simultaneously, the capacity price, winning bid capacity, electricity price, and winning bid electricity volume are determined, contracts are signed, and total electricity costs are settled. During capacity and electricity delivery, when the winning bid capacity resources are utilized, they participate in the spot market for capacity fulfillment, simultaneously obtaining capacity and energy revenue, which is cleared daily and settled monthly. In capacity cost allocation, the main entity bears its own capacity costs, while other electricity users allocate capacity costs monthly based on their load contribution percentage during peak load periods.

[0013] In the aforementioned medium-to-long-term power-capacity coupled market clearing method, the shape of the capacity demand curve designed by the power grid based on historical data is determined by three points A, B, and C. The coordinates of each point are calculated based on the regional installed capacity reserve margin, the predicted annual peak load, and the power system's average equivalent forced outage rate. The expression for the regional predicted annual reliability demand, derived from the regional installed capacity reserve margin, the predicted annual peak load, and the power system's average equivalent forced outage rate, is as follows:

[0014] ;

[0015] In the formula: For the region's projected reliability requirements for the next year, in MW; For regional installed capacity reserve margin; To predict annual peak load, MW; The average equivalent forced outage rate of the power system.

[0016] The expressions for the coordinates of points A, B, and C are:

[0017] ;

[0018] ;

[0019] ;

[0020] In the formula: , and The prices for available natural capacity at points A, B, and C are respectively, in yuan / (MW·day); , and These are the available natural capacity at points A, B, and C, respectively, in MW; Net new unit cost for reference units, in yuan / (MW·day); , and These are the fine-tuning coefficients for the capacity at points A, B, and C, respectively. , and The percentages are 1.2%, 1.9%, and 7.8%, respectively.

[0021] In the aforementioned medium-to-long-term energy-capacity coupled market clearing method, the objective function is expressed as follows:

[0022] ;

[0023] In the formula: z is the region index; j is the month index; t is the time period index; l is the segment index of the capacity demand curve; Z is the region set; J is the number of months in a year, J=12; T is the number of optimization cycles per day, T=24; , , and These represent the number of thermal power units participating in the medium- and long-term electricity energy-capacity coupling market in region z, the total number of thermal power units and new energy units participating only in the medium- and long-term electricity energy market, the number of traditional power purchasers participating only in the medium- and long-term electricity energy market, and the number of new entities participating in the medium- and long-term electricity energy-capacity coupling market. New entities refer to resource aggregation-type new market operators that have dual market participation attributes on both the power consumption side and the power generation side and can participate in the electricity market as either loads or power sources.

[0024] , These represent the number of segments in the power grid's uniformly defined capacity demand curve and the user-defined capacity demand curve in region z, respectively.

[0025] , These represent the bid price and winning bid volume of thermal power unit g in region z for the j-th month t-th period in the medium- and long-term electricity market, respectively, in yuan / MWh and MWh; , These represent the bid price and winning bid volume of generator set s in region z during the t-th period of month j in the medium- and long-term electricity market, respectively, in yuan / MWh and MWh; , These represent the bid price and winning bid volume of traditional power purchaser k in region z during the t-th period of month j in the medium- and long-term electricity market, respectively, in yuan / MWh and MWh; , These represent the bid price and winning bid volume of the main new type of electricity in region z during the t-th period of month j in the medium- and long-term electricity market, respectively, in yuan / MWh and MWh.

[0026] , These represent the bid price and winning bid capacity of thermal power unit g in region z during the t-th period of month j in the capacity market, respectively, in yuan / (MW·day) and MW; , The capacity demand curve uniformly formulated for the power grid in region z. Price and winning bid capacity for the segment, in yuan / (MW·day), MW; , These represent the new entity u in region z and its position on the custom capacity demand curve. Price and winning bid capacity for the segment, in yuan / (MW·day), MW.

[0027] In the aforementioned medium-to-long-term electricity-capacity coupled market clearing method, the constraints established based on the objective function to ensure the balance of power system energy supply and demand and the adequacy of system capacity during peak load periods include:

[0028] Establish constraints on the balance between power supply and demand in the power system;

[0029] Establish capacity supply and demand constraints to ensure sufficient system capacity during peak load periods of the power system;

[0030] Establish capacity transfer constraints between different regions;

[0031] Based on the electricity declaration data of each market participant, establish the electricity volume constraints for each market participant in the bid;

[0032] Based on the capacity declaration data of each market entity, establish the winning capacity constraints for each market entity;

[0033] Based on the bidding data of new entities in the medium- and long-term electricity-capacity coupling market, a coupling constraint is established for the winning bids of capacity demand and electricity demand of new entities.

[0034] In the aforementioned medium- and long-term electricity-capacity coupled market clearing method, the expression for establishing the electricity supply-demand balance constraint of the power system is as follows:

[0035] ;

[0036] In the formula: Let represent the dual variable of the supply and demand balance constraint of electrical energy in region z, and represent the electricity clearing price in the medium- and long-term electrical energy market, expressed as yuan / MWh.

[0037] In the aforementioned medium- and long-term electricity-capacity coupled market clearing method, the expression for establishing the capacity supply and demand constraint to ensure the adequacy of system capacity during peak load periods is as follows:

[0038] ;

[0039] In the formula: n is the region index that has a transmission channel with region z; For the set of regions that have a transmission channel with region z; The capacity, expressed in MW, represents the system capacity adequacy during peak load periods for load transfer from region n to region z. A positive number indicates that region n provides capacity to region z, while a negative number indicates that region z provides capacity to region n. For the capacity supply and demand constraints of region z, the dual variable is the capacity price, which is used to ensure the adequacy of system capacity during peak load periods, in yuan / (MW·day).

[0040] In the aforementioned medium-to-long-term energy-capacity coupled market clearing method, the expression for establishing capacity transfer constraints between different regions is as follows:

[0041] ;

[0042] In the formula: This represents the maximum transmission capacity between region n and region z, expressed in MW.

[0043] The established power volume constraints for each market participant include: power volume constraints for thermal power units participating in the medium- and long-term power-capacity coupling market; power volume constraints for generator units participating only in the medium- and long-term power market; power demand constraints for traditional power purchasers; and power demand constraints for new entities.

[0044] The expression for the winning bid volume constraint of the thermal power units participating in the medium- and long-term electricity-capacity coupling market is as follows:

[0045] ;

[0046] ;

[0047] In the formula: This represents the maximum declared electricity generation of thermal power unit g in region z during period t of month j, in MWh; This represents the number of days in month j. The duration of power generation by thermal power units, in hours;

[0048] The expression for the winning bid volume constraint of the generator sets participating only in the medium- and long-term electricity market is as follows:

[0049] ;

[0050] In the formula: This represents the maximum declared electricity generation of generator unit s in region z during period t of month j, in MWh;

[0051] The expression for the electricity demand bidding constraint of the traditional electricity purchaser is as follows:

[0052] ;

[0053] In the formula: This represents the maximum electricity demand (MWh) of traditional electricity purchaser k in region z during period t of month j.

[0054] The expression for the power demand bid constraint of the new entity is as follows:

[0055] ;

[0056] In the formula: Let MWh represent the maximum electricity demand of the new entity u in region z during period t of month j.

[0057] The establishment of winning capacity constraints for each market entity based on their capacity declaration data includes: winning capacity constraints for thermal power units participating in the medium- and long-term electricity-capacity coupling market, winning capacity demand constraints for grid-agent capacity purchases, and winning capacity constraints for new entities.

[0058] The expression for the winning bid capacity constraint of the thermal power units participating in the medium- and long-term electricity-capacity coupling market is as follows:

[0059] ;

[0060] In the formula: This represents the maximum capacity, in MW, that thermal power unit g in region z can provide.

[0061] The expression for the capacity demand bid constraint of the power grid agent purchasing capacity is as follows:

[0062] ;

[0063] In the formula: The capacity demand curve for region z represents the first... The maximum capacity required for the segment, in MW;

[0064] The expression for the capacity bid constraint of the novel entity is:

[0065] ;

[0066] In the formula: The curve representing the capacity demand of new types of entities in region z is shown in section 1. Maximum capacity required for the segment, MW

[0067] Based on the bidding data of new entities in the medium- and long-term electricity-capacity coupling market, the expression for the coupling constraint of the new entities' capacity demand and electricity demand in the bidding is as follows:

[0068] ;

[0069] In the formula: This represents the maximum net load of the new main body u in region z, in MW.

[0070] A system for performing the method includes:

[0071] The data acquisition module collects electricity, capacity, and price data to build a medium- to long-term electricity-capacity coupled market operation mechanism on an annual time scale.

[0072] The module constructs an objective function for the trading clearing optimization problem of the medium- and long-term electricity energy-capacity coupled market operation mechanism by minimizing the difference between generation costs and consumption utility, i.e., maximizing social welfare as the optimization objective.

[0073] The calculation module establishes constraints based on the objective function to ensure the balance of energy supply and demand in the power system and the adequacy of system capacity during peak load periods.

[0074] The processing module solves the transaction clearing optimization problem based on constraints to obtain the coupled clearing results of the medium- and long-term electricity market and capacity market, as well as the resource scheduling scheme.

[0075] An electronic device, the electronic device comprising:

[0076] Memory, processor, and computer programs stored in memory and executable on the processor, wherein,

[0077] The processor implements the method when executing the program.

[0078] Compared with existing technologies, this invention has the following advantages: Through an optimization model aimed at maximizing social welfare, this invention ensures sufficient system capacity and stable medium- and long-term power supply during peak load periods, while providing traditional generator sets with stable capacity and energy revenue and recovering their fixed costs. Furthermore, under this method, the new entity locks in most of the grid-connected electricity in the medium- and long-term power market and declares capacity demand based on the maximum net load. While fully bearing its own capacity costs, it saves its own electricity costs and grid capacity construction costs, thus promoting the improvement of social welfare. Attached Figure Description

[0079] Various other advantages and benefits of the present invention will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiments below. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. It is obvious that the drawings described below are merely some embodiments of the invention, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. Furthermore, the same reference numerals denote the same parts throughout the drawings.

[0080] In the attached diagram:

[0081] Figure 1 This is a flowchart of a medium-to-long-term energy-capacity coupled market transaction clearing method according to an embodiment of the present invention;

[0082] Figure 2 This is a flowchart illustrating the process of opening the market ahead of schedule and organizing centralized bidding in an embodiment of the present invention;

[0083] Figure 3 This is a flowchart illustrating the capacity and electrical energy delivery process in an embodiment of the present invention;

[0084] Figure 4 It is a capacity demand curve;

[0085] Figure 5 This is a comparison chart of the costs for new entities in Regions 1, 2 and 3 under the two mechanisms in the year of delivery;

[0086] Figure 6 This is a graph showing the 24-hour electricity clearing results for Region 1 and Region 2 in August of the delivery year;

[0087] Figure 7 This is a graph showing the total revenue of coal-fired power units in Regions 1, 2, and 3 for the year of delivery.

[0088] The present invention will be further explained below with reference to the accompanying drawings and embodiments. Detailed Implementation

[0089] Specific embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While specific embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0090] It should be noted that certain terms are used in the specification and claims to refer to specific components. Those skilled in the art will understand that different terms may be used to refer to the same component. This specification and claims do not distinguish components based on differences in terminology, but rather on differences in function. The terms "comprising" or "including" used throughout the specification and claims are open-ended and should be interpreted as "comprising but not limited to." The following descriptions are preferred embodiments for carrying out the invention; however, these descriptions are for the purpose of understanding the general principles of the specification and are not intended to limit the scope of the invention. The scope of protection of this invention is determined by the appended claims.

[0091] To facilitate understanding of the embodiments of the present invention, further explanations and descriptions will be provided below with reference to the accompanying drawings and specific embodiments. The accompanying drawings do not constitute a limitation on the embodiments of the present invention.

[0092] like Figures 1 to 7 As shown, the medium- and long-term electricity-capacity coupling market clearing method includes the following steps:

[0093] Step 1: Collect data on electricity, capacity, and price, and construct a medium- to long-term electricity-capacity coupling market operation mechanism on an annual time scale. This includes opening the market in advance and organizing centralized bidding, delivery of capacity and electricity, and sharing of capacity costs.

[0094] Step 2: By minimizing the difference between generation costs and consumption utility, i.e., with maximizing social welfare as the optimization objective, construct the objective function for the trading clearing optimization problem of the medium- and long-term electricity energy-capacity coupled market operation mechanism.

[0095] Step 3: Establish constraints based on the objective function to ensure the balance of energy supply and demand in the power system and the adequacy of system capacity during peak load periods;

[0096] Step 4: Solve the transaction clearing optimization problem based on the constraints to obtain the coupled clearing results of the medium- and long-term electricity market and capacity market, as well as the resource scheduling scheme.

[0097] In a preferred embodiment of the medium- and long-term electricity-capacity coupled market transaction clearing method, the early market opening and centralized bidding includes the power grid designing capacity demand curves based on historical data, independently declaring capacity demand curves, and the power dispatching agency verifying the available natural capacity of capacity resources. Simultaneously, the capacity price, winning bid capacity, electricity price, and winning bid electricity volume are determined, contracts are signed, and total electricity costs are settled. During capacity and electricity delivery, when the winning bid capacity resources are called up, they participate in the spot market for capacity fulfillment, simultaneously obtaining capacity revenue and energy revenue, which are cleared daily and settled monthly. In capacity cost allocation, the main entity bears its own capacity costs, while other power users allocate capacity costs monthly based on their load contribution ratio during peak load periods.

[0098] In a preferred embodiment of the medium-to-long-term power-capacity coupled market clearing method, the shape of the capacity demand curve designed by the power grid based on historical data is determined by three points A, B, and C. The coordinates of each point are calculated based on the regional installed capacity reserve margin, the predicted annual peak load, and the power system's average equivalent forced outage rate. The expression for the regional predicted annual reliability demand, based on the regional installed capacity reserve margin, the predicted annual peak load, and the power system's average equivalent forced outage rate, is as follows:

[0099] ;

[0100] In the formula: For the region's projected reliability requirements for the next year, in MW; For regional installed capacity reserve margin; To predict annual peak load, MW; The average equivalent forced outage rate of the power system.

[0101] The expressions for the coordinates of points A, B, and C are:

[0102] ;

[0103] ;

[0104] ;

[0105] In the formula: , and The prices for available natural capacity at points A, B, and C are respectively, in yuan / (MW·day); , and These are the available natural capacity at points A, B, and C, respectively, in MW; Net new unit cost for reference units, in yuan / (MW·day); , and These are the fine-tuning coefficients for the capacity at points A, B, and C, respectively. , and The percentages are 1.2%, 1.9%, and 7.8%, respectively.

[0106] In a preferred embodiment of the medium-to-long-term energy-capacity coupled market clearing method, the objective function is expressed as follows:

[0107] ;

[0108] In the formula: z is the region index; j is the month index; t is the time period index; l is the segment index of the capacity demand curve; Z is the region set; J is the number of months in a year, J=12; T is the number of optimization cycles per day, T=24; , , and These represent the number of thermal power units participating in the medium- and long-term electricity energy-capacity coupling market in region z, the total number of thermal power units and new energy units participating only in the medium- and long-term electricity energy market, the number of traditional power purchasers participating only in the medium- and long-term electricity energy market, and the number of new entities participating in the medium- and long-term electricity energy-capacity coupling market. New entities refer to resource aggregation-type new market operators that have dual market participation attributes on both the power consumption side and the power generation side, and can participate in the electricity market as loads or power sources. For example, integrated source-grid-load-storage projects, virtual power plants, and microgrids are typical representatives.

[0109] , These represent the number of segments in the power grid's uniformly defined capacity demand curve and the user-defined capacity demand curve in region z, respectively.

[0110] , These represent the bid price and winning bid volume of thermal power unit g in region z for the j-th month t-th period in the medium- and long-term electricity market, respectively, in yuan / MWh and MWh; , These represent the bid price and winning bid volume of generator set s in region z during the t-th period of month j in the medium- and long-term electricity market, respectively, in yuan / MWh and MWh; , These represent the bid price and winning bid volume of traditional power purchaser k in region z during the t-th period of month j in the medium- and long-term electricity market, respectively, in yuan / MWh and MWh; , These represent the bid price and winning bid volume of the new entity u in region z during the t-th period of month j in the medium- and long-term electricity market, respectively, in yuan / MWh and MWh.

[0111] , These represent the bid price and winning bid capacity of thermal power unit g in region z during the t-th period of month j in the capacity market, respectively, in yuan / (MW·day) and MW; , The capacity demand curve uniformly formulated for the power grid in region z. Price and winning bid capacity for the segment, in yuan / (MW·day), MW; , These represent the new entity u in region z and its position on the custom capacity demand curve. Price and winning bid capacity for the segment, in yuan / (MW·day), MW.

[0112] In a preferred embodiment of the medium-to-long-term power-capacity coupled market clearing method, the constraints established based on the objective function to ensure the balance of power system energy supply and demand and the adequacy of system capacity during peak load periods include:

[0113] Establish constraints on the balance between power supply and demand in the power system;

[0114] Establish capacity supply and demand constraints to ensure sufficient system capacity during peak load periods of the power system;

[0115] Establish capacity transfer constraints between different regions;

[0116] Based on the electricity declaration data of each market participant, establish the electricity volume constraints for each market participant in the bid;

[0117] Based on the capacity declaration data of each market entity, establish the winning capacity constraints for each market entity;

[0118] Based on the bidding data of new entities in the medium- and long-term electricity-capacity coupling market, a coupling constraint is established for the winning bids of capacity demand and electricity demand of new entities.

[0119] In a preferred embodiment of the medium-to-long-term electricity-capacity coupled market clearing method, the expression for establishing the electricity supply-demand balance constraint of the power system is:

[0120] ;

[0121] In the formula: Let represent the dual variable of the supply and demand balance constraint of electrical energy in region z, and represent the electricity clearing price in the medium- and long-term electrical energy market, expressed as yuan / MWh.

[0122] In a preferred embodiment of the medium- and long-term electricity-capacity coupled market clearing method, the expression for establishing the capacity supply and demand constraint to ensure the adequacy of system capacity during peak load periods is as follows:

[0123] ;

[0124] In the formula: n is the region index that has a transmission channel with region z; For the set of regions that have a transmission channel with region z; The capacity, expressed in MW, represents the system capacity adequacy during peak load periods for load transfer from region n to region z. A positive number indicates that region n provides capacity to region z, while a negative number indicates that region z provides capacity to region n. For the capacity supply and demand constraints of region z, the dual variable is the capacity price, which is used to ensure the adequacy of system capacity during peak load periods, in yuan / (MW·day).

[0125] In a preferred embodiment of the medium-to-long-term energy-capacity coupled market clearing method, the expression for establishing capacity transfer constraints between different regions is:

[0126] ;

[0127] In the formula: This represents the maximum transmission capacity between region n and region z, expressed in MW.

[0128] In a preferred embodiment of the medium- and long-term electricity-capacity coupling market transaction clearing method, the winning bid electricity constraints for each market participant include: winning bid electricity constraints for thermal power units participating in the medium- and long-term electricity-capacity coupling market, winning bid electricity constraints for generator units participating only in the medium- and long-term electricity market, winning bid electricity demand constraints for traditional electricity purchasers, and winning bid electricity demand constraints for new participants.

[0129] The expression for the winning bid volume constraint of the thermal power units participating in the medium- and long-term electricity-capacity coupling market is as follows:

[0130] ;

[0131] ;

[0132] In the formula: This represents the maximum declared electricity generation of thermal power unit g in region z during period t of month j, in MWh; This represents the number of days in month j. The duration of power generation by thermal power units, in hours;

[0133] The expression for the winning bid volume constraint of the generator sets participating only in the medium- and long-term electricity market is as follows:

[0134] ;

[0135] In the formula: This represents the maximum declared electricity generation of generator unit s in region z during period t of month j, in MWh;

[0136] The expression for the electricity demand bidding constraint of the traditional electricity purchaser is as follows:

[0137] ;

[0138] In the formula: This represents the maximum electricity demand (MWh) of traditional electricity purchaser k in region z during period t of month j.

[0139] The expression for the power demand bid constraint of the new entity is as follows:

[0140] ;

[0141] In the formula: Let MWh represent the maximum electricity demand of the new entity u in region z during period t of month j.

[0142] In a preferred embodiment of the medium- and long-term electricity-capacity coupling market transaction clearing method, the step of establishing the winning bid capacity constraints for each market participant based on the capacity declaration data of each market participant includes: the winning bid capacity constraints for thermal power units participating in the medium- and long-term electricity-capacity coupling market, the winning bid constraints for capacity demand of grid agents purchasing capacity, and the winning bid constraints for capacity of new participants.

[0143] The expression for the winning bid capacity constraint of the thermal power units participating in the medium- and long-term electricity-capacity coupling market is as follows:

[0144] ;

[0145] In the formula: This represents the maximum capacity, in MW, that thermal power unit g in region z can provide.

[0146] The expression for the capacity demand bid constraint of the power grid agent purchasing capacity is as follows:

[0147] ;

[0148] In the formula: The capacity demand curve for region z represents the first... The maximum capacity required for the segment, in MW;

[0149] The expression for the capacity bid constraint of the novel entity is:

[0150] ;

[0151] In the formula: The curve representing the capacity demand of new types of entities in region z is shown in section 1. Maximum capacity required for the segment, MW

[0152] Based on the bidding data of new entities in the medium- and long-term electricity-capacity coupling market, the expression for the coupling constraint of the new entities' capacity demand and electricity demand in the bidding is as follows:

[0153] ;

[0154] In the formula: This represents the maximum net load of the new main body u in region z, in MW.

[0155] In one embodiment, the clearing method includes the following steps:

[0156] Step 1: Collect data on electricity, capacity, and price, and construct a medium- to long-term electricity-capacity coupled market operation mechanism on an annual time scale;

[0157] Step 2: By minimizing the difference between generation costs and consumption utility, i.e., maximizing social welfare as the optimization objective, construct the objective function for the medium- and long-term electricity-capacity coupled market transaction clearing optimization problem.

[0158] Step 3: Establish constraints based on the objective function to ensure the balance of energy supply and demand in the power system and the adequacy of system capacity during peak load periods;

[0159] Step 4: Solve the transaction clearing optimization problem based on the constraints to obtain the coupled clearing results of the medium- and long-term electricity market and capacity market, as well as the resource scheduling scheme.

[0160] In this optional embodiment, the establishment of a medium- and long-term electricity-capacity coupling market operation mechanism in step 1 mainly includes: opening the market in advance and organizing centralized bidding, capacity and electricity delivery, and capacity cost sharing;

[0161] The flowchart for opening the market ahead of schedule and organizing centralized bidding is as follows: Figure 2 As shown, power dispatching agencies hold capacity markets and medium- and long-term energy markets some time before capacity and energy delivery;

[0162] In the capacity market, new entities independently declare capacity demand curves, and the power grid designs capacity demand curves based on historical data. Parameters such as capacity resource declaration prices, installed capacity, and equivalent forced outage rates are used, and the power dispatching agency verifies the available natural capacity of capacity resources. In the medium- and long-term electricity market, the power grid conducts qualification reviews of market entities, power generation companies submit quantity and price quotations, and power purchasers submit quantity and price quotations. The clearing capacity price, winning bid capacity, medium- and long-term electricity price, and winning bid volume are coupled; capacity and medium- and long-term electricity contracts are signed, and the total electricity cost is settled.

[0163] The flowchart for capacity and power delivery is as follows: Figure 3 As shown, in the capacity market, when the awarded capacity resources are called up, they participate in the spot market to fulfill the capacity contract and obtain both capacity revenue and energy revenue, which are cleared daily and settled monthly. In the medium- and long-term electricity market, the awarded units participate in the spot market with all their electricity, and the difference between the contracted electricity and the actual electricity delivered to the grid is used for deviation settlement. The electricity purchaser also participates in the spot market, and the difference between the contracted electricity and the actual electricity delivered to the grid is used for deviation settlement, which is cleared daily and settled monthly.

[0164] The capacity cost sharing involves the new entity bearing its own capacity cost, while other electricity users share the capacity cost monthly based on their load contribution percentage during peak load periods.

[0165] In this optional embodiment, the capacity demand curve of the power grid designed based on historical data is as follows: Figure 4 As shown;

[0166] Based on the regional installed capacity reserve margin, the projected annual peak load, and the power system's average equivalent forced outage rate, the expression for the regional reliability requirement for the projected year is as follows:

[0167] ;

[0168] In the formula: For the region's projected reliability requirements for the next year, in MW; For regional installed capacity reserve margin; To predict annual peak load, MW; This represents the average equivalent forced outage rate of the power system.

[0169] Figure 3 The shape of the medium-capacity demand curve is determined by three points: A, B, and C. The coordinates of each point are expressed as follows:

[0170] ;

[0171] ;

[0172] ;

[0173] In the formula: , and The prices for available natural capacity at points A, B, and C are respectively, in yuan / (MW·day); , and These are the available natural capacity at points A, B, and C, respectively, in MW; Net new unit cost for reference units, in yuan / (MW·day); , and These are the fine-tuning coefficients for the capacity at points A, B, and C, respectively. , and The percentages are 1.2%, 1.9%, and 7.8%, respectively.

[0174] In this optional embodiment, the objective function of the transaction clearing optimization problem of the medium- and long-term electricity-capacity coupled market operation mechanism, which aims to minimize the difference between generation-side costs and consumption-side utility in step 2 (i.e., maximize social welfare), is expressed as follows:

[0175] ;

[0176] In the formula: z is the region index; j is the month index; t is the time period index; l is the segment index of the capacity demand curve; Z is the region set; J is the number of months in a year, J=12; T is the number of optimization cycles per day, T=24; , , and These represent the number of thermal power units participating in the medium- and long-term electricity energy-capacity coupling market in region z, the total number of thermal power units and new energy units participating only in the medium- and long-term electricity energy market, the number of traditional power purchasers participating only in the medium- and long-term electricity energy market, and the number of new entities participating in the medium- and long-term electricity energy-capacity coupling market. Among them, new entities refer to resource aggregation-type new market operators that have dual market participation attributes on the electricity consumption side and the power generation side, and can participate in the electricity market as loads or power sources. Typical representatives include integrated source-grid-load-storage projects, virtual power plants, and microgrids.

[0177] , These represent the number of segments in the power grid's uniformly defined capacity demand curve and the user-defined capacity demand curve in region z, respectively.

[0178] , These represent the bid price and winning bid volume of thermal power unit g in region z for the j-th month t-th period in the medium- and long-term electricity market, respectively, in yuan / MWh and MWh; , These represent the bid price and winning bid volume of generator set s in region z during the t-th period of month j in the medium- and long-term electricity market, respectively, in yuan / MWh and MWh; , These represent the bid price and winning bid volume of traditional power purchaser k in region z during the t-th period of month j in the medium- and long-term electricity market, respectively, in yuan / MWh and MWh; , These represent the bid price and winning bid volume of the new entity u in region z during the t-th period of month j in the medium- and long-term electricity market, respectively, in yuan / MWh and MWh.

[0179] , These represent the bid price and winning bid capacity of thermal power unit g in region z during the t-th period of month j in the capacity market, respectively, in yuan / (MW·day) and MW; , The capacity demand curve uniformly formulated for the power grid in region z. Price and winning bid capacity for the segment, in yuan / (MW·day), MW; , These represent the new entity u in region z and its position on the custom capacity demand curve. Price and winning bid capacity for the segment, in yuan / (MW·day), MW.

[0180] In this optional embodiment, step 3, which involves establishing constraints based on the objective function to ensure the balance of energy supply and demand in the power system and the adequacy of system capacity during peak load periods, includes the following steps:

[0181] Establish constraints on the balance between power supply and demand in the power system;

[0182] Establish capacity supply and demand constraints to ensure sufficient system capacity during peak load periods of the power system;

[0183] Establish capacity transfer constraints between different regions;

[0184] Based on the electricity declaration data of each market participant, establish the electricity volume constraints for each market participant in the bid;

[0185] Based on the capacity declaration data of each market entity, establish the winning capacity constraints for each market entity;

[0186] Based on the bidding data of new entities in the medium- and long-term electricity-capacity coupling market, a coupling constraint is established for the winning bids of capacity demand and electricity demand of new entities.

[0187] In this optional embodiment, the expression for establishing the power system's power supply and demand balance constraint is:

[0188] ;

[0189] In the formula: Let represent the dual variable of the supply and demand balance constraint of electrical energy in region z, and represent the electricity clearing price in the medium- and long-term electrical energy market, expressed as yuan / MWh.

[0190] In this optional embodiment, the expression for establishing the capacity supply and demand constraint to ensure the adequacy of system capacity during peak load periods is:

[0191] ;

[0192] In the formula: n is the region index that has a transmission channel with region z; For the set of regions that have a transmission channel with region z; The capacity, expressed in MW, represents the system capacity adequacy during peak load periods for load transfer from region n to region z. A positive number indicates that region n provides capacity to region z, while a negative number indicates that region z provides capacity to region n. For the capacity supply and demand constraints of region z, the dual variable is the capacity price, which is used to ensure the adequacy of system capacity during peak load periods, in yuan / (MW·day).

[0193] In this optional embodiment, the expression for establishing capacity transfer constraints between different regions is:

[0194] ;

[0195] In the formula: This represents the maximum transmission capacity between region n and region z, expressed in MW.

[0196] In this optional embodiment, the step of establishing the winning bid volume constraints for each market entity based on the electricity declaration data of each market entity includes: winning bid volume constraints for thermal power units participating in the medium- and long-term electricity-capacity coupling market, winning bid volume constraints for generator units participating only in the medium- and long-term electricity market, winning bid volume constraints for the electricity demand of traditional electricity purchasers, and winning bid volume constraints for the electricity demand of new entities.

[0197] The expression for the winning bid volume constraint of the thermal power units participating in the medium- and long-term electricity-capacity coupling market is as follows:

[0198] ;

[0199] ;

[0200] In the formula: This represents the maximum declared electricity generation of thermal power unit g in region z during period t of month j, in MWh; This represents the number of days in month j. The duration of power generation by thermal power units, in hours;

[0201] The expression for the winning bid volume constraint of the generator sets participating only in the medium- and long-term electricity market is as follows:

[0202] ;

[0203] In the formula: This represents the maximum declared electricity generation of generator unit s in region z during period t of month j, in MWh;

[0204] The expression for the electricity demand bidding constraint of the traditional electricity purchaser is as follows:

[0205] ;

[0206] In the formula: This represents the maximum electricity demand (MWh) of traditional electricity purchaser k in region z during period t of month j.

[0207] The expression for the power demand bid constraint of the new entity is as follows:

[0208] ;

[0209] In the formula: Let MWh represent the maximum electricity demand of the new entity u in region z during period t of month j.

[0210] In this optional embodiment, the step of establishing the winning bid capacity constraints for each market entity based on the capacity declaration data of each market entity includes: the winning bid capacity constraints for thermal power units participating in the medium- and long-term electricity-capacity coupling market, the winning bid constraints for capacity demand of grid agents purchasing capacity, and the winning bid constraints for capacity of new entities.

[0211] The expression for the winning bid capacity constraint of the thermal power units participating in the medium- and long-term electricity-capacity coupling market is as follows:

[0212] ;

[0213] In the formula: This represents the maximum capacity, in MW, that thermal power unit g in region z can provide.

[0214] The expression for the capacity demand bid constraint of the power grid agent purchasing capacity is as follows:

[0215] ;

[0216] In the formula: The capacity demand curve for region z represents the first... The maximum capacity required for the segment, in MW;

[0217] The expression for the capacity bid constraint of the novel entity is:

[0218] ;

[0219] In the formula: The curve representing the capacity demand of new types of entities in region z is shown in section 1. The maximum capacity required for the segment, in MW.

[0220] In this optional embodiment, the expression for establishing the coupling constraint between the capacity demand and electricity demand of the new entity based on the application data of the new entity in the medium- and long-term electricity-capacity coupling market is as follows:

[0221] ;

[0222] In the formula: This represents the maximum net load of the new main body u in region z, in MW.

[0223] A system for performing the method includes:

[0224] The data acquisition module collects electricity, capacity, and price data to build a medium- to long-term electricity-capacity coupled market operation mechanism on an annual time scale.

[0225] The module constructs an objective function for the trading clearing optimization problem of the medium- and long-term electricity energy-capacity coupled market operation mechanism by minimizing the difference between generation costs and consumption utility, i.e., maximizing social welfare as the optimization objective.

[0226] The calculation module establishes constraints based on the objective function to ensure the balance of energy supply and demand in the power system and the adequacy of system capacity during peak load periods.

[0227] The processing module solves the transaction clearing optimization problem based on constraints to obtain the coupled clearing results of the medium- and long-term electricity market and capacity market, as well as the resource scheduling scheme.

[0228] An electronic device, the electronic device comprising:

[0229] Memory, processor, and computer programs stored in memory and executable on the processor, wherein,

[0230] The processor implements the method when executing the program.

[0231] In this optional embodiment, the optimization problem of medium- and long-term electricity-capacity coupled market transaction clearing considering the new subjects is solved to obtain the coupled clearing results of the medium- and long-term electricity market and capacity market, as well as the resource scheduling scheme.

[0232] In this optional embodiment, based on actual data from a regional power grid, three regions are defined. Regions 2 and 3 have identical internal construction resources, differing only in transmission capacity limits from Region 1. Specific parameters are shown in Table 1. Regarding the setting of the net new unit cost for the reference unit, given that China's power structure is dominated by coal-fired power, coal-fired power units are chosen as the reference unit for setting the capacity price ceiling. According to the "Notice on Establishing a Coal-fired Power Capacity Price Mechanism" issued by the National Development and Reform Commission in November 2023, the nationally unified fixed cost of coal-fired power units is 330 yuan per kilowatt per year. Referring to this data, we set the net new unit cost of the reference unit at 904 yuan / (MW·day), hoping to recover the fixed cost of coal-fired power units in the capacity market. The transmission capacity limit between Region 1 and Region 2 is 500MW, and the transmission capacity limit between Region 1 and Region 3 is 100MW. Regions 2 and 3 are independent of each other.

[0233] The experimental testing environment was a computer with the following specifications: CPU: Intel(R) Core(TM) i5-13600K; clock speed: 3.50GHz; memory: 32GB; programming environment: Python 3.9.7; solver: Gurobi 12.0.3.

[0234] Table 1 Regional Installation Status and Capacity Demand Curve Parameters

[0235]

[0236] All new entities adopt a three-tiered bidding curve for capacity allocation in the capacity market. In the medium- and long-term electricity market, the bidding price is limited to 0-450 yuan / MWh in the peak period, 0-380 yuan / MWh in the flat period, and 0-280 yuan / MWh in the off-peak period. To verify the feasibility of the proposed method, the price data in the medium- and long-term electricity market are generated through simulation, using data from a typical month (August) of the delivery year to simulate bidding data for the 12 months of the delivery year. Furthermore, coal-fired power units bid at their maximum generating capacity in each bidding segment of the medium- and long-term electricity market.

[0237] Only the total capacity demand within the power grid's jurisdiction is cleared; the settlement of capacity transmission costs between different regions is not considered at this time. Table 2 shows the capacity market clearing results for the three regions.

[0238] Table 2. Capacity Market Clearing Results

[0239]

[0240] According to Table 2, Region 1 has the most abundant capacity resources within the power grid's jurisdiction, with a cleared capacity of 6066 MW and a relatively low capacity clearing price of 1100 yuan / (MW·day). This ample resource capacity allows Region 1 to not only meet its own capacity needs but also support the capacity needs of other regions. Region 1 provided 500 MW and 100 MW of capacity to Regions 2 and 3, respectively. Through resource allocation in Region 1, the capacity gap between different regions can be balanced to some extent, optimizing system resource allocation and thus improving overall operational efficiency.

[0241] However, there is a significant difference in capacity clearing prices between Region 2 and Region 3. The capacity clearing price for Region 2 is 1150 yuan / (MW·day), while the price for Region 3 has reached the upper limit of 1356 yuan / (MW·day). This is due to capacity transmission limitations between Region 1 and Region 3; the abundant capacity resources of Region 1 cannot be effectively transmitted to Region 3, resulting in the capacity clearing price for Region 3 reaching its upper limit and zero clearing capacity for new entities. Therefore, when planning and constructing the power grid, the capacity transmission needs between different regions must be considered, and the layout and expansion of power transmission lines must be rationally optimized to better support inter-regional resource allocation and ensure the stability and economy of the power system.

[0242] Table 3 presents a comparison of the operational results of the general jurisdiction in the existing energy market and the medium- and long-term electricity-capacity coupled market trading clearing method proposed in this invention.

[0243] Table 3 Comparison of Operational Results of the General Jurisdiction

[0244]

[0245] As shown in Table 3, the method proposed in this invention brings social welfare of RMB 18.79693 billion and capacity fees of RMB 6.71625 billion. It generates an average daily capacity revenue of RMB 1192.835 per megawatt for the winning coal-fired power units upon delivery, fully covering the fixed recovery cost of RMB 904.12 per megawatt per year, and generating additional income. Under China's existing mechanism, coal-fired power units can only recover 30% to 50% of their fixed costs annually, and the compensation standard is the same for all units, which is insufficient to incentivize capital entities to invest in coal-fired power units.

[0246] Figure 5 This displays the electricity clearing price and quantity for Region 1 and Region 2 during the 24-hour period in August of the delivery year, and shows the electricity supply and demand situation within the region at different time periods. According toFigure 5 Data from the two regions shows that in the morning and evening, renewable energy generation is very low while electricity demand is high, and the power system mainly relies on traditional thermal power units to meet this demand. However, the high marginal cost of traditional thermal power generation leads to high clearing prices and low clearing volumes. During midday and early morning, solar and wind power generation surges, allowing the power system to meet demand using low-cost renewable energy. At this time, clearing prices are lower, and clearing volumes are higher. This characteristic of power clearing and price fluctuations accurately reflects the impact of the volatility of renewable energy generation on system dispatch in the electricity market.

[0247] Figure 6 The chart shows the revenue of coal-fired power units participating in both the capacity market and the medium- and long-term electricity market in three regions during the delivery year. The percentages in the chart represent the proportion of capacity revenue to the sum of capacity revenue and energy revenue for coal-fired power units. Figure 6 It can be seen that all coal-fired power units achieved capacity revenue, with capacity revenue averaging 28.5% of total revenue. Particularly in Region 1, which has abundant capacity resources, coal-fired power units G4, G7, and G12, although failing to clear their electricity supply in the medium- and long-term electricity market, still achieved considerable capacity revenue. In Region 2, the average capacity revenue of coal-fired power units accounted for 30.27% of total revenue, slightly higher than the average level across the entire region. However, in Region 3, where capacity resources are extremely scarce, the average capacity revenue of coal-fired power units accounted for 33.65% of total revenue, an increase of 3.38 percentage points compared to Region 2. This demonstrates that the availability of regional capacity has a significant impact on the revenue of coal-fired power units.

[0248] Tables 4 and 5 show the winning capacity and electricity volume of the new entities in Region 1 and Region 2, respectively. Since the capacity clearing price in Region 3 reached the upper limit, none of the new entities in Region 3 won the bid for capacity.

[0249] Table 4. Winning bid capacity and electricity consumption of new entities in Region 1

[0250]

[0251] Table 5. Winning bid capacity and electricity consumption of new entities in Region 2

[0252]

[0253] In Regions 1 and 2, new energy entities cleared an average of 74.58% of the annual grid-connected electricity, with a maximum clearing rate of 98.67% and a minimum clearing rate of 54.07%. These new entities were able to lock in a large portion of the grid-connected electricity in the medium- and long-term electricity market, allowing them to bid for the required capacity at the most economical rate. A detailed analysis of Tables 4 and 5 reveals differences in the performance of new energy entities in the capacity market between Regions 1 and 2. For example, two different U4 entities in Regions 1 and 2 both won bids for 100 MW of capacity. However, the U4 entity in Region 1 had a larger net load and a larger annual grid-connected electricity volume. Therefore, the U4 entity in Region 1 needed to clear more electricity to compensate for the shortfall in its bid capacity and ensure that demand was met. This differentiated market mechanism allows the power system to flexibly adjust capacity and electricity clearing strategies based on the resource conditions and demands of different regions, ensuring that new energy entities achieve optimal economic benefits in their respective market environments.

[0254] This study compares the electricity purchase costs for the new entity participating in the method proposed in this invention with those for participating only in the energy market. Referring to a certain region, the actual average grid-connected electricity price for this new entity, which integrates power generation, grid, load, and storage, is 380 yuan / MWh, including a capacity fee of 50 yuan / MWh. The electricity volume won by the new entity in the medium- and long-term electricity market is settled according to the contract price, while the remaining grid-connected electricity volume is settled at 330 yuan / MWh, along with the capacity fee.

[0255] Figure 7 The figure shows a comparison of costs for new entities in Regions 1, 2, and 3 under two transaction methods in the delivery year. The red data in the figure represents the difference in electricity purchase costs for the new entities under the proposed method and the existing mechanism, as well as the percentage decrease. Figure 7 As can be seen, under the method proposed in this invention, the total electricity purchase cost for the new entities is lower than that under the existing mechanism. In Regions 1 and 2, the annual electricity purchase cost for the new entities decreased by an average of 3.4%, with the highest decrease reaching 8.8%, saving 95.677 million yuan. This clearly demonstrates the good economic benefits the method proposed in this invention brings to the new entities. Compared to Region 1, the capacity price in Region 2 is higher, resulting in higher costs for the new entities when purchasing capacity, and a lower overall cost reduction rate. In Region 3, due to the excessively high clearing capacity price, the new entities were unable to clear capacity, only clearing electricity in the medium- and long-term electricity market. This resulted in the new entities not bearing the corresponding capacity costs, affecting the grid's capacity construction and resource allocation in that region.

[0256] Furthermore, this invention constructs a unified optimization model to simultaneously solve for electricity price / volume and capacity price / capacity under the same objective function, thus coordinating the clearing results of the two markets. This avoids resource waste or mismatch caused by the fragmented operation of the energy and capacity markets, ensuring that capacity investment signals accurately reflect medium- and long-term electricity demand and load characteristics, especially adapting to the system operation requirements under a high proportion of renewable energy access. This invention allows new entities to submit multi-segment capacity demand curves based on their maximum net load (rather than maximum electricity consumption), allowing power dispatching agencies to verify their required capacity resources and construct them accordingly. This mechanism accurately identifies the actual capacity contribution of new entities during peak load periods, avoiding resource waste caused by constructing capacity resources based on maximum load and improving the efficiency of capacity resource allocation. New entities bear the full cost of the capacity corresponding to their bid capacity, while other electricity users share the remaining capacity cost according to their electricity consumption proportion during peak load periods. This mechanism realizes the fairness principle of "whoever uses it, pays for it," incentivizing new entities to optimize their energy consumption behavior while reducing unreasonable payments by ordinary electricity users. When the awarded capacity resources are utilized, they must participate in the spot market to complete energy delivery, thus linking capacity obligations with energy production; medium- and long-term power deviations are handled through a daily and monthly settlement mechanism in the spot market. This design strengthens the performance constraints on market participants and enhances the consistency between the financial attributes and physical execution of medium- and long-term contracts.

[0257] Although embodiments of the present invention have been described above in conjunction with the accompanying drawings, the present invention is not limited to the specific embodiments and application fields described above. The specific embodiments described above are merely illustrative and instructive, and not restrictive. Those skilled in the art can make many other forms based on the guidance of this specification and without departing from the scope of protection of the claims of the present invention, and all of these are within the scope of protection of the present invention.

Claims

1. A medium- to long-term electricity-capacity coupled market clearing method, characterized in that, Includes the following steps: Step 1: Collect data on electricity, capacity, and price, and construct a medium- to long-term electricity-capacity coupling market operation mechanism on an annual time scale. This includes opening the market in advance and organizing centralized bidding, delivery of capacity and electricity, and sharing of capacity costs. Step 2: By minimizing the difference between generation costs and consumption utility, i.e., with maximizing social welfare as the optimization objective, construct the objective function for the trading clearing optimization problem of the medium- and long-term electricity energy-capacity coupled market operation mechanism. Step 3: Establish constraints based on the objective function to ensure the balance of energy supply and demand in the power system and the adequacy of system capacity during peak load periods; Step 4: Solve the transaction clearing optimization problem based on the constraints to obtain the coupled clearing results of the medium- and long-term electricity market and capacity market, as well as the resource scheduling scheme.

2. The medium-to-long-term energy-capacity coupled market clearing method according to claim 1, characterized in that, Preferably, the early market opening and centralized bidding includes the power grid designing capacity demand curves based on historical data, independently declaring capacity demand curves, and the available natural capacity of capacity resources verified by the power dispatching agency, while determining the capacity price, the winning bid capacity, the electricity price and the winning bid electricity, signing contracts and settling the total electricity cost; In the delivery of capacity and electricity, when the bid-winning capacity resources are used, they participate in the spot market to fulfill the capacity contract and obtain capacity revenue and energy revenue at the same time. The revenue is cleared on a daily basis and settled on a monthly basis. In the allocation of capacity costs, the main entity bears its own capacity costs, and the remaining power users allocate the capacity costs on a monthly basis according to the proportion of the power user's load contribution during the peak load period.

3. The medium-to-long-term energy-capacity coupled market clearing method according to claim 2, characterized in that, The shape of the capacity demand curve designed by the power grid based on historical data is determined by three points, A, B, and C. The coordinates of each point are calculated based on the regional installed capacity reserve margin, the predicted annual peak load, and the power system's average equivalent forced outage rate. The expression for the regional predicted annual reliability demand, derived from the regional installed capacity reserve margin, the predicted annual peak load, and the power system's average equivalent forced outage rate, is as follows: ； In the formula: For the region's projected reliability requirements for the next year, in MW; For regional installed capacity reserve margin; To predict annual peak load, MW; The average equivalent forced outage rate of the power system. The expressions for the coordinates of points A, B, and C are: ； ； ； In the formula: , and The prices for available natural capacity at points A, B, and C are respectively, in yuan / (MW·day); , and These are the available natural capacity at points A, B, and C, respectively, in MW; Net new unit cost for reference units, in yuan / (MW·day); , and These are the fine-tuning coefficients for the capacity at points A, B, and C, respectively. , and The percentages are 1.2%, 1.9%, and 7.8%, respectively.

4. The medium-to-long-term energy-capacity coupled market clearing method according to claim 1, characterized in that, The expression for the objective function is: ； In the formula: z is the region index; j is the month index; t is the time period index; l is the segment index of the capacity demand curve; Z is the region set; J is the number of months in a year, J=12; T is the number of optimization cycles per day, T=24; , , and These represent the number of thermal power units participating in the medium- and long-term electricity energy-capacity coupling market in region z, the total number of thermal power units and new energy units participating only in the medium- and long-term electricity energy market, the number of traditional power purchasers participating only in the medium- and long-term electricity energy market, and the number of new entities participating in the medium- and long-term electricity energy-capacity coupling market. New entities refer to resource aggregation-type new market operators that have dual market participation attributes on both the power consumption side and the power generation side and can participate in the electricity market as either loads or power sources. , These represent the number of segments in the power grid's uniformly defined capacity demand curve and the user-defined capacity demand curve in region z, respectively. , These represent the bid price and winning bid volume of thermal power unit g in region z for the j-th month t-th period in the medium- and long-term electricity market, respectively, in yuan / MWh and MWh; , These represent the bid price and winning bid volume of generator set s in region z during the t-th period of month j in the medium- and long-term electricity market, respectively, in yuan / MWh and MWh; , These represent the bid price and winning bid volume of traditional power purchaser k in region z during the t-th period of month j in the medium- and long-term electricity market, respectively, in yuan / MWh and MWh; , These represent the bid price and winning bid volume of the new entity u in region z during the t-th period of month j in the medium- and long-term electricity market, respectively, in yuan / MWh and MWh. , These represent the bid price and winning bid capacity of thermal power unit g in region z during the t-th period of month j in the capacity market, respectively, in yuan / (MW·day) and MW; , The capacity demand curve uniformly formulated for the power grid in region z. Price and winning bid capacity for the segment, in yuan / (MW·day), MW; , These represent the new entity u in region z and its position on the custom capacity demand curve. Price and winning bid capacity for the segment, in yuan / (MW·day), MW.

5. A medium- to long-term energy-capacity coupled market clearing method according to claim 1, characterized in that, Based on the objective function, the constraints for ensuring the balance of energy supply and demand in the power system and the adequacy of system capacity during peak load periods include: Establish constraints on the balance between power supply and demand in the power system; Establish capacity supply and demand constraints to ensure sufficient system capacity during peak load periods of the power system; Establish capacity transfer constraints between different regions; Based on the electricity declaration data of each market participant, establish the electricity volume constraints for each market participant in the bid; Based on the capacity declaration data of each market entity, establish the winning capacity constraints for each market entity; Based on the bidding data of new entities in the medium- and long-term electricity-capacity coupling market, a coupling constraint is established for the winning bids of capacity demand and electricity demand of new entities.

6. A medium- to long-term energy-capacity coupled market clearing method according to claim 5, characterized in that, The expression for establishing the power system's power supply and demand balance constraint is as follows: ； In the formula: Let represent the dual variable of the supply and demand balance constraint of electrical energy in region z, and represent the electricity clearing price in the medium- and long-term electrical energy market, expressed as yuan / MWh.

7. A medium- to long-term energy-capacity coupled market clearing method according to claim 5, characterized in that, The expression for establishing the capacity supply and demand constraint to ensure the adequacy of system capacity during peak load periods is as follows: ； In the formula: n is the region index that has a transmission channel with region z; For the set of regions that have a transmission channel with region z; The capacity, expressed in MW, represents the system capacity adequacy during peak load periods for load transfer from region n to region z. A positive number indicates that region n provides capacity to region z, while a negative number indicates that region z provides capacity to region n. For the capacity supply and demand constraints of region z, the dual variable is the capacity price, which is used to ensure the adequacy of system capacity during peak load periods, in yuan / (MW·day).

8. A medium- to long-term energy-capacity coupled market clearing method according to claim 5, characterized in that, The expression for establishing capacity transfer constraints between different regions is as follows: ； In the formula: The maximum transmission capacity between region n and region z is represented in MW. The winning bid electricity constraints for each market entity include: the winning bid electricity constraints for thermal power units participating in the medium- and long-term electricity-capacity coupling market, the winning bid electricity constraints for generator units participating only in the medium- and long-term electricity market, the winning bid electricity demand constraints for traditional electricity purchasers, and the winning bid electricity demand constraints for new entities. The expression for the winning bid volume constraint of the thermal power units participating in the medium- and long-term electricity-capacity coupling market is as follows: ； ； In the formula: This represents the maximum declared electricity generation of thermal power unit g in region z during period t of month j, in MWh; This represents the number of days in month j. The duration of power generation by thermal power units, in hours; The expression for the winning bid volume constraint of the generator sets participating only in the medium- and long-term electricity market is as follows: ； In the formula: This represents the maximum declared electricity generation of generator unit s in region z during period t of month j, in MWh; The expression for the electricity demand bidding constraint of the traditional electricity purchaser is as follows: ； In the formula: This represents the maximum electricity demand (MWh) of traditional electricity purchaser k in region z during period t of month j. The expression for the power demand bid constraint of the new entity is as follows: ； In the formula: This represents the maximum electricity demand of the new energy source u in region z during period t of month j, in MWh. The establishment of winning capacity constraints for each market participant based on their capacity declaration data includes: winning capacity constraints for thermal power units participating in the medium- and long-term electricity-capacity coupling market, winning capacity demand constraints for grid-agent capacity purchases, and winning capacity constraints for new market participants. The expression for the winning bid capacity constraint of the thermal power units participating in the medium- and long-term electricity-capacity coupling market is as follows: ； In the formula: This represents the maximum capacity, in MW, that thermal power unit g in region z can provide. The expression for the capacity demand bid constraint of the power grid agent purchasing capacity is as follows: ； In the formula: The capacity demand curve for region z represents the first... The maximum capacity required for the segment, in MW; The expression for the capacity bid constraint of the novel entity is: ； In the formula: The curve representing the capacity demand of new types of entities in region z is shown in the figure. Maximum capacity required for the segment, MW Based on the bidding data of new entities in the medium- and long-term electricity-capacity coupling market, the expression for the coupling constraint of the new entities' capacity demand and electricity demand in the bidding is as follows: ； In the formula: This represents the maximum net load of the new main body u in region z, in MW.

9. A system for performing the method as described in any one of claims 1-8, characterized in that, It includes: The data acquisition module collects electricity, capacity, and price data to construct a medium- to long-term electricity-capacity coupled market operation mechanism on an annual timescale. The module constructs an objective function for the trading clearing optimization problem of the medium- and long-term electricity energy-capacity coupled market operation mechanism by minimizing the difference between generation costs and consumption utility, i.e., maximizing social welfare as the optimization objective. The calculation module establishes constraints based on the objective function to ensure the balance of energy supply and demand in the power system and the adequacy of system capacity during peak load periods. The processing module solves the transaction clearing optimization problem based on constraints to obtain the coupled clearing results of the medium- and long-term electricity market and capacity market, as well as the resource scheduling scheme.

10. An electronic device, characterized in that, The electronic device includes: Memory, processor, and computer programs stored in memory and executable on the processor, wherein, When the processor executes the program, it implements the method as described in any one of claims 1-8.

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