Day-ahead electricity market random joint clearing method and system considering capacity value
By introducing a capacity pricing mechanism into the electricity market and constructing a stochastic capacity-energy joint clearing model, the problem of separation between the capacity market and the energy market has been solved, realizing the quantitative representation of capacity value and improving the stability of system operation, as well as enhancing the interpretability and response efficiency of market dispatch.
Patent Information
- Application Number
- CN202511598942.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-04
- Publication Date
- 2026-02-27
AI Technical Summary
Existing research has failed to effectively combine capacity market and energy market, resulting in nodal marginal electricity prices failing to accurately reflect the system's capacity strain and affecting the consistency between system dispatch signals and actual operating conditions.
By introducing a capacity pricing mechanism, a stochastic capacity and energy joint clearing model is constructed. K-means clustering is used to generate typical wind and solar scenarios and their probabilities, optimize unit output constraints and node power balance, establish a capacity-aware node marginal price (LMP), and consider the uncertainty of renewable power output.
It realizes the quantitative representation of capacity value, improves the reliability of the system and the rationality of price signals, enhances the interpretability and response efficiency of market scheduling, and reduces computational complexity.
Smart Images
Figure CN121584532A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of electricity market clearing technology, specifically to a day-ahead electricity market stochastic joint clearing method and system that takes capacity value into account. Background Technology
[0002] As the penetration rate of renewable energy sources such as wind and solar power in the power system continues to increase, the operation mechanism of the electricity market faces new challenges. The volatility and uncertainty of renewable energy output increase the complexity of system dispatching, while traditional energy market models, which only settle accounts based on power generation, cannot reflect the value of conventional units in terms of capacity guarantee, resulting in a decline in the revenue of dispatchable units such as thermal power and affecting the long-term reliability of the system. To ensure the resource sufficiency and investment signals of the power system, capacity market mechanisms have been introduced in many countries, compensating for the available capacity of generating units through capacity prices. However, existing studies often separate the capacity market from the energy market clearing process, failing to establish the inherent coupling relationship between the two, and lacking an understanding of how capacity value is expressed in the nodal marginal price (LMP) from the perspective of market clearing. Ultimately, due to the lack of explicit modeling of capacity constraints, the nodal marginal price (LMP) cannot accurately reflect the capacity tension of the system in different regions, resulting in inconsistencies between dispatch signals and actual operating conditions. Summary of the Invention
[0003] To address the shortcomings mentioned in the background section, the present invention aims to provide a day-ahead electricity market stochastic joint clearing method and system that takes into account capacity value, which can coordinate capacity compensation and energy dispatch during the day-ahead market phase, thereby improving system reliability and the rationality of price signals.
[0004] Firstly, the objective of this invention can be achieved through the following technical solution: a day-ahead electricity market stochastic joint clearing method considering capacity value, the method comprising the following steps: Receiver parameters, load data, line parameters, and historical renewable energy output data. Based on the historical renewable energy output data, K-means clustering is used to generate typical wind and solar scenes and their corresponding probabilities. The unit parameters, load data, line parameters, and typical wind and solar scenarios and their corresponding probabilities are input into a pre-established stochastic capacity and energy joint clearing model, and the optimized market joint clearing result is output. The pre-established stochastic capacity and energy joint clearing model is optimized with the goal of minimizing operating costs and capacity costs. It includes unit output constraints, node power balance constraints, capacity adequacy constraints, and power flow constraints. The optimized market joint clearing results include node capacity-aware LMP, total operating costs, capacity compensation costs, and the expected value and fluctuation range of node capacity-aware LMP under different typical wind and solar power scenarios, calculated based on the dual variables of node power balance constraints.
[0005] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: the process of generating typical landscape scenes and their corresponding probabilities using K-means clustering, comprising: The K-means clustering algorithm was used to cluster the annual renewable energy output curves of historical renewable energy data: Wind speed or light intensity data is divided into samples by day; based on the samples, the cluster error within each sample is calculated using SSE; based on the cluster error, the optimal number of clusters is selected according to the elbow rule. Based on the optimal number of clusters, representative scenes and their probabilities are determined, serving as typical scenic scenes and their corresponding probabilities.
[0006] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: selecting the optimal cluster number based on the elbow rule. The process includes: Set the number of clusters k The clustering interval is determined, and K-means clustering is performed sequentially within this interval; for each... k Value, calculate its SSE, and then let SSE(k) vary. The changes are plotted as an elbow curve; observe the inflection point where the steep drop transitions to a gentle drop, and the inflection point corresponds to... That is, the elbow position. This represents the trade-off between clustering accuracy and computational complexity, and is selected as the optimal number of clusters.
[0007] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: the pre-established stochastic capacity and energy joint clearing model is based on the day-ahead energy market clearing model, the capacity price term is introduced into the objective function of the day-ahead energy market clearing model to obtain the capacity and energy joint clearing model, and renewable energy uncertainty and stochastic optimization mechanism are introduced on the basis of the capacity and energy joint clearing model to finally obtain the stochastic capacity and energy joint clearing model.
[0008] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: the day-ahead energy market clearing model is as follows: In the formula, The generator set represents the set of all dispatchable generator unit numbers in the system. The node set represents the set of all bus nodes in the system. Branch set, representing all transmission lines in the network. A time set represents all time periods within a scheduling period. unit During the period Power output of electricity, dynamo During the period The marginal cost of generating electricity; The maximum output is the upper limit of the power generation of the unit at any given time. Maximum transmission capacity node During the period The load demand, branch road susceptivity, node During the period voltage phase angle, The elements of the node-branch association matrix, if the branch Connect to node The sending end, If connected to the receiving end, then Otherwise, it is 0; node During the period The power balance constraint dual variables, System reserve rate The reference node has its voltage phase angle set to... .
[0009] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: the capacity and energy joint clearing model is as follows: in Indicates the unit capacity cost. Unit start-up and shutdown status, when Indicates the unit Commissioning; when This indicates that the generator unit is out of service. For generator nodes The rated capacity value.
[0010] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: the joint clearing model of random capacity and energy is as follows: in As a typical scenario, This represents the probability of a scenario.
[0011] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: the unit parameters include: 6 generators, 41 lines, 30 nodes, and corresponding generator rated capacity, cost coefficient, and node coordinate information.
[0012] Load data includes: standard load distribution parameters in the IEEE 30-node system, and the load of each node. Time-series processing is performed based on typical daily load curves; Line parameters include: nominal impedance data and power flow model parameters for the IEEE 30-bus system, including line susceptance. Rated capacity Node-line association matrix ; Historical data on renewable energy output includes characteristic parameters such as wind speed, power output, and temperature.
[0013] Secondly, in order to achieve the above objectives, the present invention discloses a day-ahead electricity market stochastic joint clearing system that takes into account capacity value, comprising: The scene generation module is used to receive unit parameters, load data, line parameters and historical renewable energy output data, and generate typical wind and solar scenes and their corresponding probabilities based on the historical renewable energy output data using K-means clustering. The joint clearing module is used to input unit parameters, load data, line parameters, and typical wind and solar scenarios and their corresponding probabilities into a pre-established stochastic capacity and energy joint clearing model, and output optimized market joint clearing results. The pre-established stochastic capacity and energy joint clearing model is optimized with the goal of minimizing operating costs and capacity costs. It includes unit output constraints, node power balance constraints, capacity adequacy constraints, and power flow constraints. The optimized market joint clearing results include node capacity-aware LMP, total operating costs, capacity compensation costs, and the expected value and fluctuation range of node capacity-aware LMP under different typical wind and solar power scenarios, calculated based on the dual variables of node power balance constraints.
[0014] In another aspect of the present invention, in order to achieve the above-mentioned objective, a terminal device is disclosed, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The memory stores the computer program capable of running on the processor, and when the processor loads and executes the computer program, it employs the day-ahead electricity market stochastic joint clearing method that takes into account capacity value as described above.
[0015] The beneficial effects of this invention are: This invention introduces a capacity pricing mechanism into the traditional energy market clearing model, achieving unified and coordinated clearing of the capacity and energy markets, effectively overcoming the shortcomings of the separation between the capacity and energy markets in existing technologies. This method adds a capacity cost term to the objective function, quantifying the available capacity of generating units during the market clearing process, thus intrinsically reflecting capacity value and ensuring the system maintains sufficient reserve and regulation capacity even with a high proportion of renewable energy penetration. Furthermore, by constructing a stochastic capacity-energy joint clearing model, this invention considers the uncertainties of renewable energy output such as wind power during optimization, significantly improving the robustness of the clearing results and the stability of system operation. By utilizing K-means clustering and the elbow rule to select typical scenarios, the dimensionality of scenarios is effectively reduced, lowering computational complexity while maintaining accuracy, enabling the model to be applied in engineering. This invention introduces capacity constraints into the model, intrinsically reflecting the degree of capacity scarcity in node prices, thereby obtaining a capacity-aware marginal node price (Capacity-aware LMP). This price signal can accurately reflect the local capacity constraints of the system and the adjustment pressure caused by fluctuations in renewable energy output, which helps to improve the interpretability and response efficiency of market scheduling. Attached Figure Description
[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 This is a schematic diagram of the system structure of the present invention. Detailed Implementation
[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0018] Example 1: like Figure 1 As shown, a day-ahead electricity market stochastic joint clearing method considering capacity value includes the following steps: S101: Receiver parameters, load data, line parameters and historical renewable energy output data. Based on the historical renewable energy output data, use K-means clustering to generate typical wind and solar scenes and their corresponding probabilities. The unit parameters are obtained based on the IEEE 30-node system standard dataset provided with MATPOWER, including 6 generators, 41 lines, 30 nodes, and corresponding generator rated capacity, cost factor, and node coordinate information. The unit's maximum output is also specified. Cost coefficient and start / stop state variables All parameters are derived from the case30.m example file provided by MATPOWER, and the capacity cost coefficient is set according to the model requirements. With reserve rate This data is representative of a wide range of studies and can be used to verify the applicability of different market clearing algorithms.
[0019] Load data includes: standard load distribution parameters for the IEEE 30-node system. Load at each node. The load is processed according to a typical daily load curve and the load power varies according to the time step (1 hour) for day-ahead market clearing simulation.
[0020] Line parameters include: nominal impedance data and power flow model parameters for the IEEE 30-bus system, including line susceptance. Rated capacity Node-line association matrix This parameter is used to establish the DC power flow equations and nodal power balance constraints: This is used to simulate the power flow distribution and transmission limitations of each branch during operation. The reference node is set as the bus. Voltage phase angle .
[0021] Historical renewable energy output data includes: a full-year measured wind power dataset for a certain city, covering the period from January to December 2024. The data is recorded at hourly resolution, capturing characteristic parameters such as wind speed, power output, and temperature. To match the IEEE 30-bus system, wind power output is mapped using a wind speed-power curve model, and normalization is applied to generate a wind power output time series. .in For a moment wind speed, This is the wind speed-power characteristic function.
[0022] The process of generating typical landscape scenes and their corresponding probabilities using K-means clustering includes: To reduce the dimensionality of the stochastic model, this invention employs the K-means clustering algorithm to cluster the annual renewable energy output curves. First, wind speed / sunlight data are divided into daily samples; second, the Sum of Squared Errors (SSE) is used to calculate the error of each cluster; finally, the optimal number of clusters is selected based on the elbow rule. This method identifies representative scenarios and their probabilities. It preserves the characteristics of power output fluctuations while reducing computational burden.
[0023] Selecting the optimal number of clusters based on the elbow rule The process includes: setting the number of clusters. k The clustering interval is determined, and K-means clustering is performed sequentially within this interval; for each... k Value, calculate its SSE, and then let SSE(k) vary. The changes are plotted as an "elbow curve"; observe the inflection point where the steep drop turns into a gentle drop, and the inflection point corresponds to... That is, the "elbow" position. This represents the trade-off between clustering accuracy and computational complexity, and is selected as the optimal number of clusters.
[0024] S102: Input the unit parameters, load data, line parameters, and typical wind and solar scenarios and their corresponding probabilities into the pre-established stochastic capacity and energy joint clearing model, and output the optimized market joint clearing result; The pre-established stochastic capacity and energy joint clearing model is optimized with the goal of minimizing operating costs and capacity costs. It includes unit output constraints, node power balance constraints, capacity adequacy constraints, and power flow constraints. The optimized market joint clearing results include node capacity-aware LMP, total operating costs, capacity compensation costs, and the expected value and fluctuation range of node capacity-aware LMP under different typical wind and solar power scenarios, calculated based on the dual variables of node power balance constraints.
[0025] The pre-established stochastic capacity and energy joint clearing model is based on the day-ahead energy market clearing model. The capacity price term is introduced into the objective function of the day-ahead energy market clearing model to obtain the capacity and energy joint clearing model. Based on the capacity and energy joint clearing model, renewable energy uncertainty and stochastic optimization mechanism are introduced to finally obtain the stochastic capacity and energy joint clearing model.
[0026] The day-ahead energy market clearing model is as follows: With the goal of minimizing the total system operating cost, and considering the upper and lower limits of unit output, power flow constraints, and node power balance constraints, a standard DC power flow optimization model is formed: This model provides the infrastructure for joint clearing.
[0027] In the formula, The generator set represents the set of all schedulable generator unit numbers in the system. The node set represents the set of all bus nodes in the system. Branch set represents all transmission lines in the network. The time set represents all time periods within the scheduling cycle. unit During the period Power output of electricity, dynamo During the period The marginal cost of generating electricity.
[0028] The maximum output is the upper limit of the power generation of the unit at any given time. The upper limit of transmission capacity. node During the period The load demand. branch road susceptivity. node During the period The voltage phase angle. The elements of the node-branch association matrix, if the branch Connect to node The sending end, If connected to the receiving end, then Otherwise, it is 0.
[0029] node During the period The dual variable of the power balance constraint is the nodal marginal electricity price. System reserve ratio is used to ensure that the system has sufficient reserve capacity to cope with uncertainties. The reference node has its voltage phase angle set to... .
[0030] Construct a capacity-energy joint clearing model: Introduce a capacity price term into the objective function of the traditional energy model: in Indicates the unit capacity cost. This indicates the start-up and shutdown status of the generating unit. The model achieves coordinated optimization of capacity and energy through capacity compensation constraints, and derives a capacity-aware LMP from the dual variables of the node balance constraints. For generator nodes The rated capacity value.
[0031] Introducing a mechanism for uncertainty and stochastic optimization in renewable energy: To address the power output fluctuations in wind and solar power, this invention is extended to a stochastic capacity-energy joint clearing model: in As a typical scenario, The probability represents the scenario. Constraints for each scenario include: unit output constraints, node power balance constraints, capacity adequacy constraints, and power flow constraints. Scenario-based modeling allows for more robust market clearing results under uncertain conditions.
[0032] Example 2: To achieve the above objective, such as Figure 2 As shown, based on Embodiment 1, this invention discloses a day-ahead electricity market stochastic joint clearing method that considers capacity value, characterized by comprising: Scene generation module 11 is used to receive unit parameters, load data, line parameters and historical renewable energy output data, and generate typical wind and solar scenes and their corresponding probabilities based on the historical renewable energy output data using K-means clustering. The joint clearing module 12 is used to input unit parameters, load data, line parameters, and typical wind and solar scenarios and their corresponding probabilities into a pre-established stochastic capacity and energy joint clearing model, and output the optimized market joint clearing result. The pre-established stochastic capacity and energy joint clearing model is optimized with the goal of minimizing operating costs and capacity costs. It includes unit output constraints, node power balance constraints, capacity adequacy constraints, and power flow constraints. The optimized market joint clearing results include node capacity-aware LMP, total operating costs, capacity compensation costs, and the expected value and fluctuation range of node capacity-aware LMP under different typical wind and solar power scenarios, calculated based on the dual variables of node power balance constraints.
[0033] Based on the same inventive concept, this invention also provides a computer device, comprising: one or more processors, and a memory for storing one or more computer programs; the programs include program instructions, and the processor executes the program instructions stored in the memory. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, used to implement one or more instructions, specifically for loading and executing one or more instructions stored in a computer storage medium to implement the above-described method.
[0034] It should be further explained that, based on the same inventive concept, the present invention also provides a computer storage medium storing a computer program, which, when executed by a processor, performs the above-described method. This storage medium can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.
[0035] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.
[0036] The foregoing has shown and described the basic principles, main features, and advantages of this disclosure. Those skilled in the art should understand that this disclosure is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of this disclosure. Various changes and modifications can be made to this disclosure without departing from its spirit and scope, and all such changes and modifications fall within the scope of this disclosure as claimed.
Claims
1. A day-ahead electricity market stochastic joint clearing method considering capacity value, characterized in that, The method includes the following steps: Receiver parameters, load data, line parameters, and historical renewable energy output data. Based on the historical renewable energy output data, K-means clustering is used to generate typical wind and solar scenes and their corresponding probabilities. The unit parameters, load data, line parameters, and typical wind and solar scenarios and their corresponding probabilities are input into a pre-established stochastic capacity and energy joint clearing model, and the optimized market joint clearing result is output. The pre-established stochastic capacity and energy joint clearing model is optimized with the goal of minimizing operating costs and capacity costs. It includes unit output constraints, node power balance constraints, capacity adequacy constraints, and power flow constraints. The optimized market joint clearing results include node capacity-aware LMP, total operating costs, capacity compensation costs, and the expected value and fluctuation range of node capacity-aware LMP under different typical wind and solar power scenarios, calculated based on the dual variables of node power balance constraints.
2. The day-ahead electricity market stochastic joint clearing method considering capacity value according to claim 1, characterized in that, The process of generating typical landscape scenes and their corresponding probabilities using K-means clustering includes: The K-means clustering algorithm was used to cluster the annual renewable energy output curves of historical renewable energy data: Wind speed or light intensity data is divided into samples by day; based on the samples, the cluster error within each sample is calculated using SSE; based on the cluster error, the optimal number of clusters is selected according to the elbow rule. Based on the optimal number of clusters, representative scenes and their probabilities are determined, serving as typical scenic scenes and their corresponding probabilities.
3. The day-ahead electricity market stochastic joint clearing method considering capacity value according to claim 2, characterized in that, The optimal cluster number is selected based on the elbow rule. The process includes: setting the number of clusters. k The clustering interval is determined, and K-means clustering is performed sequentially within this interval; for each... k Value, calculate its SSE, and then let SSE(k) vary. The changes are plotted as an elbow curve; observe the inflection point where the steep drop transitions to a gentle drop, and the inflection point corresponds to... That is, the elbow position. This represents the trade-off between clustering accuracy and computational complexity, and is selected as the optimal number of clusters.
4. The day-ahead electricity market stochastic joint clearing method considering capacity value according to claim 1, characterized in that, The pre-established stochastic capacity and energy joint clearing model is based on the day-ahead energy market clearing model. The capacity price term is introduced into the objective function of the day-ahead energy market clearing model to obtain the capacity and energy joint clearing model. Based on the capacity and energy joint clearing model, renewable energy uncertainty and stochastic optimization mechanism are introduced to finally obtain the stochastic capacity and energy joint clearing model.
5. The day-ahead electricity market stochastic joint clearing method considering capacity value according to claim 4, characterized in that, The day-ahead energy market clearing model is as follows: In the formula, The generator set represents the set of all dispatchable generator unit numbers in the system. The node set represents the set of all bus nodes in the system. Branch set, representing all transmission lines in the network. A time set represents all time periods within a scheduling period. unit During the period Power output of electricity, dynamo During the period The marginal cost of generating electricity; The maximum output is the upper limit of the power generation of the unit at any given time. Maximum transmission capacity node During the period The load demand, branch road susceptivity, node During the period voltage phase angle, The elements of the node-branch association matrix, if the branch Connect to node The sending end, ; If connected to the receiving end, then Otherwise, it is 0; node During the period The power balance constraint dual variables, System reserve rate The reference node has its voltage phase angle set to... .
6. The day-ahead electricity market stochastic joint clearing method considering capacity value according to claim 5, characterized in that, The combined capacity and energy clearing model is as follows: in Indicates the unit capacity cost. Unit start-up and shutdown status, when Indicates the unit Commissioning; when This indicates that the generator unit is out of service. For generator nodes The rated capacity value.
7. The day-ahead electricity market stochastic joint clearing method considering capacity value according to claim 6, characterized in that, The joint clearing model of stochastic capacity and energy is as follows: in As a typical scenario, This represents the probability of a scenario.
8. The day-ahead electricity market stochastic joint clearing method considering capacity value according to claim 1, characterized in that, The unit parameters include: 6 generators, 41 lines, 30 nodes, and corresponding generator rated capacity, cost coefficient, and node coordinate information; Load data includes: standard load distribution parameters in the IEEE 30-node system, and the load of each node. Time-series processing is performed based on typical daily load curves; Line parameters include: nominal impedance data and power flow model parameters for the IEEE 30-bus system, including line susceptance. Rated capacity Node-line association matrix ; Historical data on renewable energy output includes characteristic parameters such as wind speed, power output, and temperature.
9. A day-ahead electricity market stochastic joint clearing system considering capacity value, employing the day-ahead electricity market stochastic joint clearing method considering capacity value as described in any one of claims 1 to 8, characterized in that, include: The scene generation module is used to receive unit parameters, load data, line parameters and historical renewable energy output data, and generate typical wind and solar scenes and their corresponding probabilities based on the historical renewable energy output data using K-means clustering. The joint clearing module is used to input unit parameters, load data, line parameters, and typical wind and solar scenarios and their corresponding probabilities into a pre-established stochastic capacity and energy joint clearing model, and output optimized market joint clearing results. The pre-established stochastic capacity and energy joint clearing model is optimized with the goal of minimizing operating costs and capacity costs. It includes unit output constraints, node power balance constraints, capacity adequacy constraints, and power flow constraints. The optimized market joint clearing results include node capacity-aware LMP, total operating costs, capacity compensation costs, and the expected value and fluctuation range of node capacity-aware LMP under different typical wind and solar power scenarios, calculated based on the dual variables of node power balance constraints.
10. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, The memory stores a computer program that can run on a processor, and when the processor loads and executes the computer program, it employs the day-ahead electricity market stochastic joint clearing method that takes into account capacity value, as described in any one of claims 1 to 8.