Multi-energy capacity market pricing method and system taking power generation capacity adequacy into consideration

Abstract

The present invention is applicable to the technical field of capacity market pricing. Provided are a multi-energy capacity market pricing method and system taking power generation capacity adequacy into consideration. The method comprises: constructing a first model with the objective of maximizing the revenue of power generation companies in a capacity market involved in power generation types of thermal power generation, wind power generation, photovoltaic power generation, hydropower and large-scale energy storage power stations; determining active power output constraints, total capacity constraints and power transmission line constraints of thermal power units, wind farms, photovoltaic power stations, hydropower units and energy storage power stations; constructing a second model with the objective of maximizing the difference between the consumer surplus and the cost of the power generation companies in a day-ahead spot trading market; determining a generator unit power constraint and a consumer energy constraint; using an improved genetic algorithm to optimize and solve the first model; and on the basis of a dynamic programming method based on a duality theory, optimizing and solving the second model, so as to obtain a clearing price corresponding to an active power capacity of the power generation companies. Therefore, the effective operation of a power market is improved.

Description

A capacity market multi-energy pricing method and system that takes into account generation capacity adequacy Technical Field

[0001] This invention relates to the field of capacity market pricing technology, and in particular to a capacity market multi-energy pricing method and system that takes into account the adequacy of power generation capacity. Background Technology

[0002] With the high proportion of renewable energy generation such as wind, solar, and hydropower being integrated into the power system, the low cost of renewable energy generation poses a significant challenge to the cost recovery of traditional thermal power units. Furthermore, renewable energy generation is heavily reliant on weather conditions and cannot serve as a reliable guarantee for electricity trading during peak load periods. Therefore, reasonable pricing in the capacity market is crucial to ensuring sufficient capacity.

[0003] In existing technologies, for traditional thermal power generation, unreasonable pricing in the capacity market may reduce the supply of generating capacity. During peak electricity demand or when renewable energy output is insufficient, power system stability will be threatened, increasing the risk of power outages. For wind and solar power, excessively high or low pricing will affect grid stability. Excessively high pricing will impose unnecessary costs on market participants purchasing capacity, while excessively low pricing will fail to incentivize wind and solar power companies to invest in energy storage or complementary power generation facilities. Hydropower can rapidly adjust its generating capacity to balance grid supply and demand. Unreasonable pricing fails to reflect this adjustment value, making it difficult for hydropower companies to obtain reasonable returns in the capacity market. This may lead to reduced participation in the capacity market, thereby affecting the grid's ability to respond to emergencies and regulate power balance. Energy storage will also be affected under such unreasonable pricing mechanisms, potentially lacking sufficient economic incentives for the development of the energy storage industry and failing to fully realize its role in improving the continuity and stability of energy supply. Ultimately, this will make the entire power system more vulnerable to energy supply fluctuations.

[0004] Therefore, there is a need for a capacity market multi-energy pricing method and system that takes into account the adequacy of power generation capacity.

[0005] Summary of the Invention

[0006] This application provides a method and system for multi-energy pricing in the capacity market that takes into account the adequacy of power generation capacity, in order to solve the problem of unstable reasonable pricing in the capacity market.

[0007] The first aspect of this application provides a capacity market multi-energy pricing method that takes into account the adequacy of power generation capacity, including:

[0008] The first model is constructed with the goal of maximizing the revenue of power generators in the capacity market. The types of power generation generated by these power generators include thermal power, wind power, photovoltaic power, hydropower, and large-scale energy storage power stations.

[0009] The constraints of the first model are determined, including active power output constraints, total capacity constraints, and transmission line constraints for thermal power units, wind farms, photovoltaic power stations, hydropower units, and energy storage power stations.

[0010] A second model is constructed with the objective of maximizing the difference between the user's revenue and the generator's cost in the daily spot trading market;

[0011] Determine the constraints of the second model, including generator power constraints and user power constraints;

[0012] An improved genetic algorithm is used to optimize the first model, and the optimal solution is used as a boundary condition to input into the second model.

[0013] The second model is optimized and solved using dynamic programming based on duality theory to obtain the clearing price corresponding to the active capacity of the power generator.

[0014] Furthermore, the first model is constructed with the objective of maximizing the revenue of power generators in the capacity market. The power generation types of these generators include thermal power, wind power, photovoltaic power, hydropower, and large-scale energy storage power stations, including:

[0015] The expression for the first model is as follows:

[0016] In the formula: C R This represents the economically maximized benefit of the power generator's investment in capacity construction, where i, j, k, f, and m represent the number of generating units, respectively. This indicates the price quote for thermal power unit i. This indicates the tendered sales capacity of thermal power unit i. This indicates the price quote for hydroelectric unit j. This indicates the bidding and sale capacity of hydropower unit j. This represents the price quote for wind farm k. This represents the bidding and sale capacity of wind farm k. This indicates the price quote for photovoltaic power plant f. This indicates the bidding and sale capacity of photovoltaic power plant f. This indicates the price quote for energy storage power station m. This indicates the bidding and sale capacity of the energy storage power station m. This represents the price quoted for the l-th segment of the capacity market demand curve at position x. This represents the capacity requirement of the l-th segment in the x-th location region.

[0017] Furthermore, the determination of the constraints of the first model includes active power output constraints, total capacity constraints, and transmission line constraints for thermal power units, wind farms, photovoltaic power stations, hydropower units, and energy storage power stations, including:

[0018] Active power output constraints of thermal power units:

[0019] In the formula: and These represent the minimum and maximum active power output of thermal power unit i, respectively. and Let represent the active power output of thermal power unit i at times t+1 and t, respectively. This represents the rate of decrease in power per unit of thermal power unit i. ΔT represents the rate of increase of thermal power unit i by a unit power, and ΔT represents the demand response time interval.

[0020] Active power output constraints of hydroelectric power plants:

[0021] In the formula: and These represent the minimum and maximum active power output of hydropower unit j, respectively. and Let represent the active power output of hydropower unit j at times t+1 and t, respectively. This represents the rate at which the hydroelectric generator j decreases in power per unit volume. This represents the rate of increase of hydroelectric generator j by a unit of power.

[0022] Active power output constraints of wind farms:

[0023] In the formula: This represents the maximum active power output of wind farm k, which is expressed by the function... The prediction is obtained through deep learning, where t represents time and w... k Indicates the confidence factor;

[0024] Active power output constraints of photovoltaic power plants:

[0025] In the formula: This represents the maximum active power output (f) of the photovoltaic power station, which is expressed by the function... The prediction is obtained; the prediction function is obtained through deep learning, G. f The credibility factor is a factor that is used to calculate the safety and reliability of participants in the capacity market.

[0026] Active power output constraints of energy storage power stations:

[0027] In the formula: and These are the minimum and maximum active power outputs of energy storage unit m, respectively. and Let represent the active power output of energy storage unit m at times t+1 and t, respectively. This represents the rate at which the energy storage unit m decreases in power per unit. This represents the rate of increase of energy storage unit m by a unit of power.

[0028] Active power output constraint of total capacity:

[0029] In the formula: γ l,x Indicates the required capacity margin;

[0030] Transmission line constraints:

[0031] In the formula: l represents the transmission line from region g to region d. This represents the active power on the line from region g to region d. and This indicates the minimum and maximum allowable active power on the line from region g to region d.

[0032] Furthermore, the second model, which aims to maximize the difference between the user's revenue and the generator's cost in the daily spot trading market, includes:

[0033] The expression for the second model is as follows:

[0034] In the formula: D represents the optimal objective, u represents the user identifier variable, and λ u,t P represents the price at which user u receives revenue during time period t. u,t λ represents the power of user u during time period t. u,t P u,t This indicates that user u receives actual revenue from using electricity during time period t. Variable i represents the generator identifier, which includes thermal power plants, hydropower units, wind farms, photovoltaic power plants, and large-scale energy storage power plants. P represents the electricity price of power generator i in the spot trading electricity market within region x during time period t. i,x,t This represents the active power sold by generator i within the time period x.

[0035] Furthermore, the constraints of the second model are determined, including generator power constraints and user energy constraints, including:

[0036] Generator power constraints:

[0037] In the formula: ∑ u P u,t =∑ i η i,t P i,x,t This indicates that the load equals the total power generation, η i,t A Boolean variable representing whether generator unit i is in operation or out of operation at time t. It is 1 when in operation and 0 when out of operation. minP i,x,t and maxP i,x,t P i,x,t The minimum and maximum values;

[0038] User power constraints:

[0039] Where: maxP u,t P represents u,t The maximum value set, U u,t MinU represents the voltage level of user u during time period t. u,t and maxU u,t These represent the minimum and maximum allowed values, respectively.

[0040] Furthermore, the step of optimizing the first model using an improved genetic algorithm and inputting the obtained optimal solution as a boundary condition into the second model includes:

[0041] Population classification and individual settings yield preliminary candidate solutions:

[0042] In the formula: P represents the maximum ratio of the number of superior individuals to the total number of individuals, N represents the number of individuals in the population, and x max Indicates the number of successful candidates in the population;

[0043] The optimal solution is obtained by iterative optimization based on the cross-differentiation process:

[0044] Where: maxP a minP represents the maximum value during the genetic crossover process. a Let f represent the minimum value, and let f represent the two values ​​representing the maximum priority of an individual during the genetic crossover process. av This represents the average priority value across all populations;

[0045] Where: maxP b minP represents the maximum value in the process of genetic co-dissimilarity. b This represents the minimum value.

[0046] Furthermore, the dynamic programming method based on duality theory is used to optimize and solve the second model to obtain the clearing price corresponding to the active power capacity of the power generator, including:

[0047] The objective of the second model is rewritten in the dual programming solution format:

[0048] In the formula: ω represents the constraint duality.

[0049] A second aspect of this application provides a capacity market multi-energy pricing system that takes into account the adequacy of power generation capacity, including:

[0050] The first model construction unit is used to construct a first model with the goal of maximizing the revenue of power generators in the capacity market. The power generation types of the power generators include thermal power generation, wind power generation, photovoltaic power generation, hydropower, and large-scale energy storage power stations.

[0051] The constraint determination unit of the first model is used to determine the constraint conditions of the first model. The constraint conditions include the active power output constraints, total capacity constraints, and transmission line constraints of thermal power units, wind farms, photovoltaic power stations, hydropower units, and energy storage power stations.

[0052] The second model construction unit is used to construct a second model with the objective of maximizing the difference between the user's revenue and the generator's cost in the daily spot trading market.

[0053] The constraint determination unit of the second model is used to determine the constraint conditions of the second model, including generator set power constraints and user power constraints.

[0054] The first model optimization unit is used to optimize and solve the first model using an improved genetic algorithm, and input the obtained optimal solution as a boundary condition into the second model;

[0055] The clearing price determination unit corresponding to the active capacity is used to optimize and solve the second model based on the dynamic programming method of duality theory to obtain the clearing price corresponding to the active capacity of the power generator.

[0056] A third aspect of this application provides a computer device, including:

[0057] Memory, transceiver, processor, and bus system;

[0058] The memory is used to store programs;

[0059] The processor is used to execute programs in the memory, including executing a capacity market multi-energy pricing method that takes into account the adequacy of generating capacity as described in any of the above statements;

[0060] The bus system is used to connect the memory and the processor to enable communication between the memory and the processor.

[0061] A fourth aspect of this application provides a readable storage medium including instructions that, when executed on a computer, cause the computer to perform a capacity market multi-energy pricing method that takes into account the adequacy of generating capacity as described in any of the preceding embodiments.

[0062] As can be seen from the above technical solutions, the embodiments of this application have the following advantages:

[0063] This invention first constructs a first model, the upper-level model, with the objective of maximizing the revenue of power generators in the capacity market. The power generation types of these generators include thermal power, wind power, photovoltaic power, hydropower, and large-scale energy storage power stations. Constraints are then defined. Next, a second model, the lower-level model, is constructed with the objective of maximizing the difference between user revenue and power generator costs in the daily spot market. Constraints are defined again. An improved genetic algorithm is used to optimize and solve the upper-level model, and the optimal solution is input as boundary conditions into the lower-level model. Simultaneously, a dynamic programming method based on duality theory is used to optimize and solve the lower-level model, yielding the clearing price corresponding to the active power capacity of the power generators. This invention, through the coordinated use of multiple energy pricing mechanisms, ensures sufficient power generation capacity while improving the effective operation of the electricity market. Attached Figure Description

[0064] Figure 1 is a schematic flowchart of an embodiment of a capacity market multi-energy pricing method that takes into account the adequacy of power generation capacity in this invention. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0066] This embodiment employs a capacity market multi-energy pricing method that considers generation capacity adequacy to improve the efficient operation of the electricity market. The implementation method in this embodiment can be implemented in the system, on a server, or on a terminal; no specific limitation is made.

[0067] Example 1

[0068] Please refer to Figure 1. An embodiment of a capacity market multi-energy pricing method that takes into account the adequacy of power generation capacity in this invention includes the following steps:

[0069] S11. The first model is constructed with the goal of maximizing the revenue of power generators in the capacity market. The types of power generation generated by power generators include thermal power generation, wind power generation, photovoltaic power generation, hydropower, and large-scale energy storage power stations.

[0070] In this embodiment, the upper-level model (first model) for time-of-use load demand and capacity characteristic pricing of thermal, wind, solar, hydro, and energy storage at the capacity market level is set as an optimization model that maximizes the revenue of the five types of power generators in the capacity market, as expressed below:

[0071] In the formula: C R This represents the economically maximized benefit of the power generator's investment in capacity construction, where i, j, k, f, and m represent the number of generating units, respectively. This indicates the price quote for thermal power unit i. This indicates the tendered sales capacity of thermal power unit i. This indicates the price quote for hydroelectric unit j. This indicates the bidding and sale capacity of hydropower unit j. This represents the price quote for wind farm k. This represents the bidding and sale capacity of wind farm k. This indicates the price quote for photovoltaic power plant f. This indicates the bidding and sale capacity of photovoltaic power plant f. This indicates the price quote for energy storage power station m. This indicates the bidding and sale capacity of the energy storage power station m. This represents the price quoted for the l-th segment of the capacity market demand curve at position x. This represents the capacity requirement of the l-th segment in the x-th location region.

[0072] S12. Determine the constraints of the first model. The constraints include the active power output constraints, total capacity constraints, and transmission line constraints of thermal power units, wind farms, photovoltaic power stations, hydropower units, and energy storage power stations.

[0073] To achieve the optimization objective described above, corresponding constraints must be met. The constraints for five types of power generators—thermal power, wind power, photovoltaic power, hydropower, and large-scale energy storage power stations—are given below:

[0074] 1. Active power output constraints of thermal power units:

[0075] In the formula: and These represent the minimum and maximum active power output of thermal power unit i, respectively. and Let represent the active power output of thermal power unit i at times t+1 and t, respectively. This represents the rate of decrease in power per unit of thermal power unit i. ΔT represents the rate of increase of thermal power unit i by a unit power, and ΔT represents the demand response time interval.

[0076] 2. Active power output constraints of hydroelectric power plants:

[0077] In the formula: and These represent the minimum and maximum active power output of hydropower unit j, respectively. and Let represent the active power output of hydropower unit j at times t+1 and t, respectively. This represents the rate at which the hydroelectric generator j decreases in power per unit volume. This represents the rate of increase of hydroelectric generator unit j by a unit power output.

[0078] 3. Active power output constraints of wind farms:

[0079] In the formula: This represents the maximum active power output of wind farm k, which is expressed by the function... The prediction is obtained through deep learning, where t represents time and w... k Indicates the confidence factor.

[0080] 4. Active power output constraints of photovoltaic power plants:

[0081] In the formula: This represents the maximum active power output (f) of the photovoltaic power station, which is expressed by the function... The prediction is obtained; the prediction function is obtained through deep learning, G. f The credibility factor is a factor that is used to calculate the safety and reliability of participants in the capacity market.

[0082] 5. Active power output constraints of energy storage power stations:

[0083] In the formula: and These are the minimum and maximum active power outputs of energy storage unit m, respectively. and Let represent the active power output of energy storage unit m at times t+1 and t, respectively. This represents the rate at which the energy storage unit m decreases in power per unit. This represents the rate of increase of energy storage unit m by a unit of power.

[0084] 6. Active power output constraint of total capacity:

[0085] In the formula: γ l,x This indicates the required capacity margin.

[0086] 7. Transmission line constraints:

[0087] In the formula: l represents the transmission line from region g to region d. This represents the active power on the line from region g to region d. and This represents the minimum and maximum allowable active power on the line from region g to region d.

[0088] S13. Construct a second model with the objective of maximizing the difference between the revenue of users and the cost of generators in the daily spot trading market;

[0089] In this embodiment, the objective of the lower-level model (second model) for the joint optimization scheduling of thermal, wind, solar, hydro, and energy storage at the spot trading market level is to maximize the revenue of the five types of suppliers on the buyer side (i.e., the user side) and minimize the generator costs. The expression is as follows:

[0090] In the formula: D represents the optimal objective, u represents the user identifier variable, and λ u,t P represents the price at which user u receives revenue during time period t. u,t λ represents the power of user u during time period t. u,t P u,t This indicates that user u receives actual revenue from using electricity during time period t. Variable i represents the generator identifier, which includes thermal power plants, hydropower units, wind farms, photovoltaic power plants, and large-scale energy storage power plants. P represents the electricity price of power generator i in the spot trading electricity market within region x during time period t. i,x,t This represents the active power sold by generator i within the time period x.

[0091] S14. Determine the constraints of the second model, including generator power constraints and user energy constraints;

[0092] The optimization objective in the above formula needs to satisfy certain constraints, which are detailed below:

[0093] 1. Generator set power constraints:

[0094] In the formula: ∑ u P u,t =∑ i η i,t P i,x,tThis indicates that the load equals the total power generation, η i,t A Boolean variable representing whether generator unit i is in operation or out of operation at time t. It is 1 when in operation and 0 when out of operation. minP i,x,t and maxP i,x,t P i,x,t The minimum and maximum values.

[0095] 2. User power constraints:

[0096] Where: maxP u,t P represents u,t The maximum value set, U u,t MinU represents the voltage level of user u during time period t. u,t and maxU u,t These represent the minimum and maximum allowed values, respectively.

[0097] S15. The improved genetic algorithm is used to optimize the first model, and the optimal solution is used as the boundary condition input into the second model;

[0098] As can be seen from the upper and lower optimization objective functions (first model and second model) in the above steps, nonlinear function calculations are involved, which cannot be achieved using conventional linear programming methods. Therefore, this embodiment proposes to solve the problem using an improved dynamic programming method combined with an intelligent optimization approach. Genetic algorithms are processes that search for the optimal solution through initial population classification, selection, crossover, and cooperative differentiation. Initial population classification is the foundation and key to ensuring the subsequent search for the optimal solution. However, traditional genetic algorithms typically select the first node as the initial population, which easily leads to getting trapped in local optima and failing to obtain the global optimum. This invention improves upon this approach.

[0099] First, prioritize each individual in the population under various conditions, classify the population, and configure the individuals to obtain preliminary candidate solutions:

[0100] In the formula: P represents the maximum ratio of the number of superior individuals to the total number of individuals, N represents the number of individuals in the population, and x max This indicates the number of eugenics in the population.

[0101] By optimizing the above formula, we can see that n gradually increases in the results of multiple iterations, and the desired value of n can be obtained by appropriately adjusting the value of P.

[0102] Traditional genetic algorithms use pre-defined crossover and cooperative heterogeneity, which cause subsequent populations to search at the same rate. However, efficiency cannot be adjusted as needed, and sometimes the speed is slow and unable to escape local iterations. This invention makes corresponding improvements to address these issues.

[0103] The optimal solution is obtained by iterative optimization based on the cross-differentiation process:

[0104] Where: maxP a minP represents the maximum value during the genetic crossover process. a Let f represent the minimum value, and let f represent the two values ​​representing the maximum priority of an individual during the genetic crossover process. av This represents the average priority value across all populations.

[0105] Where: maxP b minP represents the maximum value in the process of genetic co-dissimilarity. b This represents the minimum value.

[0106] The genetic algorithm improved by the above formula is used to solve the upper-level optimization objective. First, the population is classified and individuals are set to obtain preliminary candidate solutions. Second, the optimal solution is iterated according to the crossover and differentiation process to obtain the updated solution, which is substituted into the lower-level optimization objective as the boundary condition.

[0107] S16. The second model is optimized and solved using dynamic programming based on duality theory to obtain the clearing price corresponding to the active capacity of the power generator.

[0108] In this embodiment, dynamic programming is a commonly used method in power grid planning. However, traditional methods have problems such as slow convergence speed and the existence of local solutions. To address these issues, this invention adopts a dynamic programming method based on duality theory to solve the lower-level optimization objective.

[0109] First, the lower-level optimization objective is rewritten into a dual programming solution format:

[0110] In the formula: ω represents the duality of the constraint in the above formula.

[0111] This can be expressed as a dual solution format:

[0112] Based on the above formula, the M-method can be used to transform the multilinearity in the objective function product into a single-level linearization problem, and then solved using linearization programming.

[0113] The above embodiment optimizes the active power capacity of power generators as a coordination quantity at both the capacity market and spot trading market levels. It then uses multiple iterations of the two-layer model to solve the model and obtain the active power capacity of power generation, thereby deriving the clearing price. This method avoids power shortages, thus ensuring the safety and reliability of the power system and providing a foundation for the stable operation of the electricity market.

[0114] Example 2

[0115] An embodiment of a capacity market multi-energy pricing system that takes into account the adequacy of power generation capacity in this invention includes the following steps:

[0116] The first model building unit is used to build the first model with the goal of maximizing the revenue of power generators in the capacity market. The power generation types of power generators include thermal power generation, wind power generation, photovoltaic power generation, hydropower and large-scale energy storage power stations.

[0117] The constraint determination unit of the first model is used to determine the constraint conditions of the first model. The constraint conditions include the active power output constraints, total capacity constraints, and transmission line constraints of thermal power units, wind farms, photovoltaic power stations, hydropower units, and energy storage power stations.

[0118] The second model building unit is used to build a second model with the goal of maximizing the difference between the user's revenue and the generator's cost in the daily spot trading market.

[0119] The constraint determination unit of the second model is used to determine the constraint conditions of the second model, including generator power constraints and user power constraints.

[0120] The first model optimization unit is used to optimize and solve the first model using an improved genetic algorithm, and input the obtained optimal solution as a boundary condition into the second model;

[0121] The clearing price determination unit corresponding to the active capacity is used to optimize the second model using dynamic programming based on duality theory, so as to obtain the clearing price corresponding to the active capacity of the power generator.

[0122] For specific limitations regarding the system, please refer to the method limitations described above, which will not be repeated here. Each module in the above system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0123] Example 3

[0124] The present invention provides a computer device, including a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, wherein the processor executes the computer-readable instructions to perform the steps of the method described above.

[0125] Those skilled in the art will recognize that the units of the various examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of the invention.

[0126] In the embodiments provided by this invention, it should be understood that the division of units is merely a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units may be combined into one unit, one unit may be split into multiple units, or some features may be ignored. Furthermore, the functional units in the various embodiments of this invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The integrated unit described above can be implemented in hardware or as a software functional unit.

[0127] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0128] It is understood that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A capacity market multi-energy pricing method that takes into account the adequacy of power generation capacity, characterized in that, include: The first model is constructed with the goal of maximizing the revenue of power generators in the capacity market. The types of power generation generated by these power generators include thermal power, wind power, photovoltaic power, hydropower, and large-scale energy storage power stations. The constraints of the first model are determined, including active power output constraints, total capacity constraints, and transmission line constraints for thermal power units, wind farms, photovoltaic power stations, hydropower units, and energy storage power stations. A second model is constructed with the objective of maximizing the difference between the user's revenue and the generator's cost in the daily spot trading market; Determine the constraints of the second model, including generator power constraints and user power constraints; An improved genetic algorithm is used to optimize the first model, and the optimal solution is used as a boundary condition to input into the second model. The second model is optimized and solved using dynamic programming based on duality theory to obtain the clearing price corresponding to the active capacity of the power generator.

2. The capacity market multi-energy pricing method considering power generation capacity adequacy according to claim 1, characterized in that, The first model is constructed with the objective of maximizing the revenue of power generators in the capacity market. The power generation types of these generators include thermal power, wind power, photovoltaic power, hydropower, and large-scale energy storage power stations, including: The expression for the first model is as follows: In the formula: C R This represents the economically maximized benefit of the power generator's investment in capacity construction, where i, j, k, f, and m represent the number of generating units, respectively. This indicates the price quote for thermal power unit i. This indicates the tendered sales capacity of thermal power unit i. This indicates the price quote for hydroelectric unit j. This indicates the bidding and sale capacity of hydropower unit j. The report indicates that the wind farm k price, This represents the bidding and sale capacity of wind farm k. This indicates the price quote for photovoltaic power plant f. This indicates the bidding and sale capacity of photovoltaic power plant f. This indicates the price quote for energy storage power station m. This indicates the bidding and sale capacity of the energy storage power station m. This represents the price quoted for the l-th segment of the capacity market demand curve at position x. This represents the capacity requirement of the l-th segment in the x-th location region.

3. The capacity market multi-energy pricing method considering power generation capacity adequacy according to claim 1, characterized in that, The constraints for determining the first model include active power output constraints, total capacity constraints, and transmission line constraints for thermal power units, wind farms, photovoltaic power stations, hydropower units, and energy storage power stations, including: Active power output constraints of thermal power units: In the formula: and These represent the minimum and maximum active power output of thermal power unit i, respectively. and Let represent the active power output of thermal power unit i at times t+1 and t, respectively. This represents the rate of decrease in power per unit of thermal power unit i. ΔT represents the rate of increase of thermal power unit i by a unit power, and ΔT represents the demand response time interval. Active power output constraints of hydroelectric power plants: In the formula: and These represent the minimum and maximum active power output of hydropower unit j, respectively. and Let represent the active power output of hydropower unit j at times t+1 and t, respectively. This represents the rate at which the hydroelectric generator j decreases in power per unit volume. This represents the rate of increase of hydroelectric generator j by a unit of power. Active power output constraints of wind farms: In the formula: This represents the maximum active power output of wind farm k, which is expressed by the function... Pre The prediction function was obtained through deep learning, where t represents time and w... k Indicates the confidence factor; Active power output constraints of photovoltaic power plants: In the formula: This represents the maximum active power output (f) of the photovoltaic power station, which is expressed by the function... The prediction is obtained; the prediction function is obtained through deep learning, G. f The credibility factor is a factor that is used to calculate the safety and reliability of participants in the capacity market. Active power output constraints of energy storage power stations: In the formula: and These are the minimum and maximum active power outputs of energy storage unit m, respectively. and Let represent the active power output of energy storage unit m at times t+1 and t, respectively. This represents the rate at which the energy storage unit m decreases in power per unit. This represents the rate of increase of energy storage unit m by a unit of power. Active power output constraint of total capacity: In the formula: γ l,x Indicates the required capacity margin; Transmission line constraints: In the formula: l represents the transmission line from region g to region d. This represents the active power on the line from region g to region d. and This represents the minimum and maximum allowable active power on the line from region g to region d.

4. The capacity market multi-energy pricing method considering power generation capacity adequacy according to claim 1, characterized in that, The second model, which aims to maximize the difference between the user's revenue and the generator's cost in the daily spot trading market, includes: The expression for the second model is as follows: In the formula: D represents the optimal objective, u represents the user identifier variable, and λ u,t P represents the price at which user u receives revenue during time period t. u,t λ represents the power of user u during time period t. u,t P u,t This indicates that user u receives actual revenue from using electricity during time period t. Variable i represents the generator identifier, which includes thermal power plants, hydropower units, wind farms, photovoltaic power plants, and large-scale energy storage power plants. P represents the electricity price of power generator i in the spot trading electricity market within region x during time period t. i,x,t This represents the active power sold by generator i within the time period x.

5. The capacity market multi-energy pricing method considering power generation capacity adequacy according to claim 1, characterized in that, The constraints of the second model are determined, including generator power constraints and user energy constraints, including: Generator power constraints: In the formula: ∑ u P u,t =∑ i η i,t P i,x,t This indicates that the load equals the total power generation, η i,t A Boolean variable representing whether generator unit i is in operation or out of operation at time t. It is 1 when in operation and 0 when out of operation. minP i,x,t and max P i,x,t P i,x,t The minimum and maximum values; User power constraints: Where: maxP u,t P represents u,t The maximum value set, U u,t MinU represents the voltage level of user u during time period t. u,t and maxU u,t These represent the minimum and maximum allowed values, respectively.

6. The capacity market multi-energy pricing method considering power generation capacity adequacy according to claim 1, characterized in that, The step of optimizing the first model using an improved genetic algorithm and inputting the resulting optimal solution as boundary conditions into the second model includes: Population classification and individual settings yield preliminary candidate solutions: In the formula: P represents the maximum ratio of the number of superior individuals to the total number of individuals, N represents the number of individuals in the population, and x max Indicates the number of successful candidates in the population; The optimal solution is obtained by iterative optimization based on the cross-differentiation process: Where: maxP a minP represents the maximum value during the genetic crossover process. a Let f represent the minimum value, and let f represent the two values ​​representing the maximum priority of an individual during the genetic crossover process. av This represents the average priority value across all populations; Where: maxP b minP represents the maximum value in the process of genetic co-dissimilarity. b This represents the minimum value.

7. The capacity market multi-energy pricing method considering power generation capacity adequacy according to claim 1, characterized in that, The dynamic programming method based on duality theory is used to optimize and solve the second model to obtain the clearing price corresponding to the active power capacity of the power generator, including: The objective of the second model is rewritten in the dual programming solution format: In the formula: ω represents the constraint duality.

8. A capacity market multi-energy pricing system that takes into account the adequacy of power generation capacity, characterized in that, include: The first model construction unit is used to construct a first model with the objective of maximizing the revenue of power generators in the capacity market. The power generation types of these power generators include thermal power, wind power, photovoltaic power, hydropower, and... Large-scale energy storage power stations; The constraint determination unit of the first model is used to determine the constraint conditions of the first model. The constraint conditions include the active power output constraints, total capacity constraints, and transmission line constraints of thermal power units, wind farms, photovoltaic power stations, hydropower units, and energy storage power stations. The second model construction unit is used to construct a second model with the objective of maximizing the difference between the user's revenue and the generator's cost in the daily spot trading market. The constraint determination unit of the second model is used to determine the constraint conditions of the second model, including generator set power constraints and user power constraints. The first model optimization unit is used to optimize and solve the first model using an improved genetic algorithm, and input the obtained optimal solution as a boundary condition into the second model; The clearing price determination unit corresponding to the active capacity is used to optimize and solve the second model based on the dynamic programming method of duality theory to obtain the clearing price corresponding to the active capacity of the power generator.

9. A computer device, characterized in that, include: Memory, transceiver, processor, and bus system; The memory is used to store programs; The processor is used to execute a program in the memory, including executing a capacity market multi-energy pricing method that takes into account the adequacy of generating capacity as described in any one of claims 1-7; The bus system is used to connect the memory and the processor to enable communication between the memory and the processor.

10. A readable storage medium, characterized in that, Includes instructions that, when executed on a computer, cause the computer to perform a capacity market multi-energy pricing method that takes into account the adequacy of generating capacity as described in any one of claims 1-7.

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