A backup market clearing model creation method, system, storage medium and device

Abstract

The application relates to a reserve market clearing model creation method, which comprises the following steps: obtaining actual net load and predicted net load of the same period of each day in a history year of a power dispatching system, obtaining a deviation rate distribution probability table, and drawing a net load prediction error distribution function according to the deviation rate distribution probability table; determining a loss load cost caused by insufficient reserve due to positive and negative errors of net load prediction, and drawing a demand value curve of insufficient reserve due to net load prediction error in combination with the net load prediction error distribution function; and establishing a joint clearing model according to bidding and winning information in an electricity market, reserve capacity in a reserve market, and the demand value curve. Compared with the prior art, the application considers the influence of net load prediction accuracy on reserve, combines reserve demand elasticity, clears the reserve market and the electricity market, makes the clearing result of the reserve market clearing model more feasible and reliable, and meets actual application requirements.

Description

Technical Field

[0001] This application relates to the field of power dispatching technology, and in particular to a method, system, storage medium, and power equipment for creating a standby market clearing model. Background Technology

[0002] The electricity reserve market is a crucial mechanism for ensuring the short-term operational security of the power system. From an economic perspective, the reserve market needs to compensate for the opportunity costs incurred by generating units that fail to participate in the electricity market due to reserved capacity. From a system perspective, the reserve market primarily ensures the adequacy of the system's short-term capacity. Load reserve refers to the ability of grid-connected entities to maintain power balance within a specified timeframe by utilizing reserved regulation capacity and regulation capabilities in response to dispatch instructions, but it is mainly used to address net load changes caused by load forecast deviations.

[0003] However, the current reserve market adopts a bidding model where generating units submit their capacity and offer prices, or submit capacity but not prices. Reserves lack demand elasticity, do not reflect actual electricity market conditions, and do not reflect the value of reserves at different times. Therefore, by combining reserve demand elasticity with the electricity market supply and demand situation, and jointly clearing the reserve market with the electricity market, the value of reserve services can be effectively reflected. This would make the reserve market clearing results more closely linked to the actual supply and demand situation of the power system, providing reserve capacity more accurately based on actual needs, thus helping to improve the operating efficiency of the power system and ensure its safe and stable operation. Summary of the Invention

[0004] Based on this, it is necessary to provide a method, system, storage medium, and power equipment for creating a reserve market clearing model that considers the impact of net load forecast accuracy on reserve, combines reserve demand elasticity, and jointly clears the reserve market with the electricity market, making the clearing results of the reserve market clearing model more feasible and reliable.

[0005] This invention provides a method for creating a reserve market clearing model, adapted to a power dispatching system. The method includes the following steps:

[0006] Obtain the actual net load and predicted net load data for the same time period of each day over a historical year from the power dispatching system;

[0007] The net load prediction deviation rate is calculated based on the actual net load and predicted net load data of the same time period of each day in the past year of the power dispatch system, and a deviation rate distribution probability table is obtained. The net load prediction error distribution function is then plotted based on the deviation rate distribution probability table.

[0008] Determine the load loss cost due to insufficient reserve due to positive or negative net load forecasting errors, and plot the demand value curve for insufficient reserve due to net load forecasting errors by combining the net load forecasting error distribution function.

[0009] A joint clearing model is established based on the bidding and winning information in the electricity market, the standby capacity in the standby market, and the aforementioned demand value curve.

[0010] Furthermore, the model for calculating the net load forecast deviation rate based on the actual net load and the predicted net load data is as follows:

[0011]

[0012] Where η represents the net load forecast deviation rate, L actual L represents the actual net load. forecast This indicates the predicted net load.

[0013] Furthermore, the method for obtaining the deviation rate distribution probability table specifically includes:

[0014] Multiple net load forecast error intervals are divided with a deviation rate of 1%.

[0015] The calculated net load forecast deviation rate for the historical year is categorized according to the net load forecast error range of different intervals to obtain a deviation rate distribution probability table.

[0016] Furthermore, the formula for calculating the demand value in the demand value curve is as follows:

[0017] θ P =min{RPRC,A%·PC}

[0018] θ N =min{RNRC,A%·PF}

[0019] Where, θ P and θ N These represent the demand costs for positive and negative reserve capacity, respectively; RPRC and RNRC represent the reference costs when positive and negative reserves are insufficient, respectively; A% represents the probability of positive and negative reserve insufficiency, corresponding to the probability of occurrence in different net load forecast error ranges; PC is the upper limit of bidding costs in the electricity spot market; PF is the upper limit of costs in the deep peak-shaving ancillary service market or the upper limit of deep peak-shaving compensation costs. In the later stages of the electricity market, if negative electricity prices are allowed to be declared in the electricity spot market, then PF will be the lower limit of bidding costs in the electricity spot market.

[0020] Furthermore, the joint clearing model is as follows:

[0021]

[0022]

[0023]

[0024]

[0025]

[0026]

[0027] Among them, EN k,i,t θ represents the output of unit i in time period t corresponding to price segment k; k,i,t p represents the cost reported by unit i for quotation segment k during time period t; t (e) represents the cumulative probability value; PRC i,t The positive reserve capacity of unit i during time period t; PRC t For the positive reserve capacity requirement during time period t, NRC i,t NRC represents the negative reserve capacity of unit i during time period t. t The negative reserve capacity requirement for period t; EN i,t The output of unit i during time period t; and These represent the upper and lower limits of unit i's output during time period t, respectively.

[0028] Another embodiment of the present invention proposes a reserve market clearing model creation system, adapted to a power dispatching system, the system comprising:

[0029] The load data acquisition module is used to acquire the actual net load and predicted net load data of the power dispatching system for the same period of each day over the past year.

[0030] The error distribution plotting function module is used to calculate the net load prediction deviation rate based on the actual net load and predicted net load data of the same time period of each day in the past year of the power dispatching system, obtain the deviation rate distribution probability table, and plot the net load prediction error distribution function based on the deviation rate distribution probability table.

[0031] The demand value curve plotting module is used to determine the load loss cost due to insufficient reserve due to positive or negative net load forecasting errors, and plots the demand value curve due to insufficient reserve due to net load forecasting errors by combining the net load forecasting error distribution function.

[0032] The joint clearing module creation module is used to establish a joint clearing model based on the bidding and winning information in the electricity market, the standby capacity in the standby market, and the demand value curve.

[0033] Furthermore, the model for calculating the net load forecast deviation rate based on the actual net load and the predicted net load data is as follows:

[0034]

[0035] Where η represents the net load forecast deviation rate, L actual L represents the actual net load. forecast This indicates the predicted net load.

[0036] Furthermore, the joint clearing model is as follows:

[0037]

[0038]

[0039]

[0040]

[0041]

[0042]

[0043] Among them, EN k,i,t θ represents the output of unit i in time period t corresponding to price segment k; k,i,t p represents the cost reported by unit i for quotation segment k during time period t; t (e) represents the cumulative probability value; PRC i,t The positive reserve capacity of unit i during time period t; PRC t For the positive reserve capacity requirement during time period t, NRC i,t NRC represents the negative reserve capacity of unit i during time period t. t The negative reserve capacity requirement for period t; EN i,t The output of unit i during time period t; and These represent the upper and lower limits of unit i's output during time period t, respectively.

[0044] Another embodiment of the present invention provides a computer-readable storage medium comprising a stored computer program; wherein, when the computer program is executed, it controls the device on which the computer-readable storage medium resides to perform the standby market clearing model creation method as described above.

[0045] Another embodiment of the present invention also provides a power device including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the standby market clearing model creation method as described above.

[0046] The aforementioned method for creating a reserve market clearing model is applicable to a power dispatching system. First, it acquires the actual and predicted net load data for the same time period each day over the past year. Then, it calculates the net load prediction deviation rate based on these data, obtaining a probability table of the deviation rate distribution, and plots a net load prediction error distribution function based on this table. Next, it determines the load loss cost due to positive or negative errors in net load prediction and plots a demand value curve for insufficient reserve due to net load prediction errors, using the net load prediction error distribution function. Finally, it establishes a joint clearing model based on bidding and winning information in the energy market, reserve capacity in the reserve market, and the demand value curve. Compared to existing technologies, this invention considers the impact of net load prediction accuracy on reserve capacity, combines reserve demand elasticity, and jointly clears the reserve market and the energy market, making the clearing results of this reserve market clearing model more feasible and reliable, meeting practical application needs. Attached Figure Description

[0047] Figure 1 A flowchart illustrating a method for creating a backup market clearing model provided in an embodiment of the present invention;

[0048] Figure 2 This is a schematic diagram of the net load forecast error distribution function;

[0049] Figure 3 A structural block diagram of the backup market clearing model creation system provided in this embodiment of the invention;

[0050] Figure 4 This is a structural diagram of a power equipment provided in an embodiment of the present invention. Detailed Implementation

[0051] 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.

[0052] It should be noted that the step numbers in this document are only for the convenience of explaining the specific embodiments and are not intended to limit the order in which the steps are executed. The method provided in this embodiment can be executed by a relevant server, and the following description will use a server as the execution subject.

[0053] like Figures 1 to 2As shown, the standby market clearing model creation method provided in this embodiment of the invention includes steps S11 to S14:

[0054] Step S11: Obtain the actual net load and predicted net load data for the same time period of each day over the past year from the power dispatch system.

[0055] As mentioned above, in the data preparation stage, by acquiring the actual net load and predicted net load data for the same time period of each day over the past year of the power dispatch system, as well as constraints such as system demand capacity and upper and lower limits of unit output, data support is provided for the subsequent calculation of net load prediction deviation rate and the creation of joint clearing model.

[0056] Step S12: Calculate the net load prediction deviation rate based on the actual net load and predicted net load data for the same time period of each day in the past year of the power dispatch system, obtain the deviation rate distribution probability table, and draw the net load prediction error distribution function based on the deviation rate distribution probability table.

[0057] As described above, by calculating the net load prediction deviation rate based on the actual net load and predicted net load data for the same time period of each day in the historical year in step S11, a deviation rate distribution probability table can be obtained based on the net load prediction deviation rate, thereby enabling the plotting of the net load prediction error distribution function based on the deviation rate distribution probability table.

[0058] Specifically, the model for calculating the net load forecast deviation rate based on actual net load and predicted net load data is as follows:

[0059]

[0060] Where η represents the net load forecast deviation rate, L actual L represents the actual net load. forecast This indicates the predicted net load.

[0061] Furthermore, the method for obtaining the deviation rate distribution probability table includes:

[0062] Multiple net load forecast error intervals are divided with a deviation rate of 1%.

[0063] The calculated net load forecast deviation rate for the historical year is categorized according to the net load forecast error range of different intervals to obtain a deviation rate distribution probability table.

[0064] Specifically, using a deviation rate of 1%, eight net load forecast error intervals were defined. The calculated net load forecast deviations for the historical year were then categorized according to the net load forecast error range of each interval to obtain the distribution of the deviation rate across the intervals. The division method is shown below:

[0065]

[0066] Among them, P r This represents the probability that the prediction error falls within the corresponding interval, with p1 to p8 representing the probability values ​​corresponding to the eight net load prediction error intervals.

[0067] As shown in the table below, consider a set of probability distributions:

[0068]

[0069]

[0070] Based on the obtained deviation rate distribution probability table, the net load forecast error distribution function is plotted. The net load forecast error distribution function is as follows: Figure 2 As shown.

[0071] Step S13: Determine the load loss cost due to insufficient reserve due to positive or negative net load forecast errors, and plot the demand value curve for insufficient reserve due to net load forecast errors by combining the net load forecast error distribution function.

[0072] As described above, by determining the load loss cost due to insufficient reserve due to positive or negative net load forecasting errors, and combining the net load forecasting error distribution function drawn in step S12, a demand value curve for insufficient reserve due to net load forecasting errors is drawn, providing data support for the creation of the joint clearing model.

[0073] In this invention, the demand value reflects the varying degrees of demand for reserves allocated to address different forecast deviations. When the probability of a net load forecast deviation occurring within a certain range is high, it means that the demand for reserves is greater in that range, and therefore the demand value for reserves should also be higher. The formula for calculating the demand value is as follows:

[0074] θ P =min{RPRC,A%·PC}

[0075] θ N =min{RNRC,A%·PF}

[0076] Where, θ P and θ NThese represent the demand costs for positive and negative reserve capacity, respectively; RPRC and RNRC represent the reference costs when positive and negative reserves are insufficient, respectively; A% represents the probability of positive and negative reserve insufficiency (corresponding to the probability of occurrence of different net load forecast error ranges in step S12); PC is the upper limit of bidding cost in the spot market for electricity; PF can be the upper limit of negative deep peak-shaving ancillary service market cost or deep peak-shaving compensation cost. In the later stages of the electricity market, if the spot market for electricity allows the bidding of negative electricity prices, then PF can be the lower limit of bidding cost in the spot market for electricity.

[0077] Step S14: Establish a joint clearing model based on the bidding and winning information in the electricity market, the standby capacity in the standby market, and the demand value curve.

[0078] As described above, by combining the bidding and winning bids in the power market with the standby capacity in the standby market and the demand value curve in step S13, a joint clearing model for the standby and power markets is established.

[0079] Specifically, the joint clearing model is as follows:

[0080]

[0081]

[0082]

[0083]

[0084]

[0085]

[0086] Among them, EN k,i,t θ represents the output of unit i in time period t corresponding to price segment k; k,i,t p represents the cost reported by unit i for quotation segment k during time period t; t (e) represents the cumulative probability value; PRC i,t The positive reserve capacity of unit i during time period t; PRC t For the positive reserve capacity requirement during time period t, NRC i,t NRC represents the negative reserve capacity of unit i during time period t. t The negative reserve capacity requirement for period t; EN i,t The output of unit i during time period t; and These represent the upper and lower limits of unit i's output during time period t, respectively.

[0087] Furthermore, the constraints listed in the above model only include those related to reserve. The constraints related to the operation of lines, sections, and generating units are the same as those in conventional power dispatching models and will not be repeated here.

[0088] Furthermore, the method also includes outputting results, namely, outputting the electrical energy and reserve bidding results for each unit.

[0089] The aforementioned method for creating a reserve market clearing model is applicable to a power dispatching system. First, it acquires the actual and predicted net load data for the same time period each day over the past year. Then, it calculates the net load prediction deviation rate based on these data, obtaining a probability table of the deviation rate distribution, and plots a net load prediction error distribution function based on this table. Next, it determines the load loss cost due to positive or negative errors in net load prediction and plots a demand value curve for insufficient reserve due to net load prediction errors, using the net load prediction error distribution function. Finally, it establishes a joint clearing model based on bidding and winning information in the energy market, reserve capacity in the reserve market, and the demand value curve. Compared to existing technologies, this invention considers the impact of net load prediction accuracy on reserve capacity, combines reserve demand elasticity, and jointly clears the reserve market and the energy market, making the clearing results of this reserve market clearing model more feasible and reliable, meeting practical application needs.

[0090] It should be understood that although the steps in the flowchart above are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart above may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0091] Please see Figure 3 The present invention also provides a backup market clearing model creation system, the system comprising:

[0092] The load data acquisition module 21 is used to acquire the actual net load and predicted net load data of the same period of each day for a historical year of the power dispatch system.

[0093] As mentioned above, in the data preparation stage, by acquiring the actual net load and predicted net load data for the same time period of each day over the past year of the power dispatch system, as well as constraints such as system demand capacity and upper and lower limits of unit output, data support is provided for the subsequent calculation of net load prediction deviation rate and the creation of joint clearing model.

[0094] The error distribution plotting function module 22 is used to calculate the net load prediction deviation rate based on the actual net load and predicted net load data of the same time period of each day in the past year of the power dispatching system, obtain the deviation rate distribution probability table, and plot the net load prediction error distribution function based on the deviation rate distribution probability table.

[0095] As described above, the net load prediction deviation rate is calculated by using the actual net load and predicted net load data for the same period of each day in the past year from the error distribution plotting function module. This allows for the generation of a deviation rate distribution probability table based on the net load prediction deviation rate, thereby enabling the plotting of the net load prediction error distribution function based on the deviation rate distribution probability table.

[0096] Specifically, the model for calculating the net load forecast deviation rate based on actual net load and predicted net load data is as follows:

[0097]

[0098] Where η represents the net load forecast deviation rate, L actual L represents the actual net load. forecast This indicates the predicted net load.

[0099] Furthermore, obtaining the deviation rate distribution probability table includes:

[0100] Multiple net load forecast error intervals are divided with a deviation rate of 1%.

[0101] The calculated net load forecast deviation rate for the historical year is categorized according to the net load forecast error range of different intervals to obtain a deviation rate distribution probability table.

[0102] Specifically, using a deviation rate of 1%, eight net load forecast error intervals were defined. The calculated net load forecast deviations for the historical year were then categorized according to the net load forecast error range of each interval to obtain the distribution of the deviation rate across the intervals. The division method is shown below:

[0103]

[0104] Among them, P r This represents the probability that the prediction error falls within the corresponding interval, with p1 to p8 representing the probability values ​​corresponding to the eight net load prediction error intervals.

[0105] As shown in the table below, consider a set of probability distributions:

[0106]

[0107] Based on the obtained deviation rate distribution probability table, the net load forecast error distribution function is plotted. The net load forecast error distribution function is as follows: Figure 2 As shown.

[0108] The demand value curve plotting module 23 is used to determine the load loss cost due to insufficient reserve due to positive or negative net load forecast errors, and plot the demand value curve due to insufficient reserve due to net load forecast errors in conjunction with the net load forecast error distribution function.

[0109] As described above, by determining the load loss cost due to insufficient reserve due to positive and negative errors in net load forecasting, and combining the net load forecasting error distribution function plotted in the error distribution plotting function module, a demand value curve for insufficient reserve due to net load forecasting error is plotted, providing data support for the creation of the joint clearing model.

[0110] In this invention, the demand value reflects the varying degrees of demand for reserves allocated to address different forecast deviations. When the probability of a net load forecast deviation occurring within a certain range is high, it means that the demand for reserves is greater in that range, and therefore the demand value for reserves should also be higher. The formula for calculating the demand value is as follows:

[0111] θ P =min{RPRC,A%·PC}

[0112] θ N =min{RNRC,A%·PF}

[0113] Where, θ P and θ N These represent the demand costs for positive and negative reserve capacity, respectively; RPRC and RNRC represent the reference costs for insufficient positive and negative reserves, respectively; A% represents the probability of positive and negative reserve shortages occurring (corresponding to the probability of occurrence of different net load forecast error intervals in the error distribution plotting function module); PC is the upper limit of bidding costs in the spot market for electricity; PF can be the upper limit of negative deep peak-shaving ancillary service market costs or the upper limit of deep peak-shaving compensation costs. In the later stages of the electricity market, if negative electricity prices are allowed to be declared in the spot market for electricity, then PF can be the lower limit of bidding costs in the spot market for electricity.

[0114] The joint clearing module creation module 24 is used to establish a joint clearing model based on the bidding and winning information in the electricity market, the standby capacity in the standby market, and the demand value curve.

[0115] As mentioned above, by combining the bidding and winning bids in the power market with the demand value curve in the backup capacity and demand value curve plotting module in the backup market, a joint clearing model for the backup and power markets is established.

[0116] Specifically, the joint clearing model is as follows:

[0117]

[0118]

[0119]

[0120]

[0121]

[0122]

[0123] Among them, EN k,i,t θ represents the output of unit i in time period t corresponding to price segment k; k,i,t p represents the cost reported by unit i for quotation segment k during time period t; t (e) represents the cumulative probability value; PRC i,t The positive reserve capacity of unit i during time period t; PRC t For the positive reserve capacity requirement during time period t, NRC i,t NRC represents the negative reserve capacity of unit i during time period t. t The negative reserve capacity requirement for period t; EN i,t The output of unit i during time period t; and These represent the upper and lower limits of unit i's output during time period t, respectively.

[0124] Furthermore, the constraints listed in the above model only include those related to reserve. The constraints related to the operation of lines, sections, and generating units are the same as those in conventional power dispatching models and will not be repeated here.

[0125] Furthermore, the joint clearing module creation module is also used to output results, namely, to output the electrical energy and reserve bidding results of each unit.

[0126] The standby market clearing model creation system provided in this invention is adapted to a power dispatching system. First, it acquires the actual net load and predicted net load data for the same time period each day over the past year. Then, it calculates the net load prediction deviation rate based on these data, obtaining a deviation rate distribution probability table, and plots a net load prediction error distribution function based on this table. Next, it determines the load loss cost due to positive or negative errors in net load prediction and plots a demand value curve for insufficient standby due to net load prediction errors, combining this with the net load prediction error distribution function. Finally, it establishes a joint clearing model based on bidding and winning information in the energy market, standby capacity in the standby market, and the demand value curve. Compared to existing technologies, this invention considers the impact of net load prediction accuracy on standby capacity, combines standby demand elasticity, and jointly clears the standby market and the energy market, making the clearing result of the standby market clearing model more feasible and reliable, meeting practical application needs.

[0127] This invention also provides a computer-readable storage medium, which includes a stored computer program; wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the standby market clearing model creation method as described above.

[0128] This invention also provides an electrical device, see [link to relevant documentation]. Figure 4 The diagram shown is a structural block diagram of a preferred embodiment of a power device provided by the present invention. The power device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the standby market clearing model creation method as described above.

[0129] Preferably, the computer program can be divided into one or more modules / units (such as computer program 1, computer program 2, ...), and the one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the power equipment.

[0130] The processor can 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. The general-purpose processor can be a microprocessor, or the processor can be any conventional processor. The processor is the control center of the power equipment, connecting various parts of the power equipment through various interfaces and lines.

[0131] The memory mainly includes a program storage area and a data storage area. The program storage area can store the operating system, applications required for at least one function, etc., while the data storage area can store related data, etc. Furthermore, the memory can be a high-speed random access memory, or a non-volatile memory, such as a plug-in hard drive, a SmartMedia Card (SMC), a Secure Digital (SD) card, and a Flash Card, or other volatile solid-state storage devices.

[0132] It should be noted that the aforementioned power equipment may include, but is not limited to, processors and memory, as will be understood by those skilled in the art. Figure 4 The structural block diagram is merely an example of electrical equipment and does not constitute a limitation on the electrical equipment. It may include more or fewer components than shown, or combine certain components, or use different components.

[0133] In summary, the standby market clearing model creation method, system, storage medium, and power equipment provided by this invention are adapted to a power dispatching system. First, the actual net load and predicted net load data for the same time period each day over a historical year are obtained from the power dispatching system. Then, based on the actual and predicted net load data for the same time period each day over a historical year, the net load prediction deviation rate is calculated, resulting in a deviation rate distribution probability table. A net load prediction error distribution function is then plotted based on this table. Next, the load loss cost due to positive or negative errors in net load prediction and insufficient reserve is determined. Combining this with the net load prediction error distribution function, a demand value curve for insufficient reserve due to net load prediction errors is plotted. Finally, a joint clearing model is established based on bidding and winning information in the energy market, reserve capacity in the standby market, and the demand value curve. Compared to existing technologies, this invention considers the impact of net load prediction accuracy on reserve capacity, combines reserve demand elasticity, and jointly clears the standby market and the energy market, making the clearing result of the standby market clearing model more feasible and reliable, meeting practical application needs.

[0134] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for creating a reserve market clearing model, adapted to a power dispatching system, characterized in that, The method includes the following steps: Obtain the actual net load and predicted net load data for the same time period of each day over a historical year from the power dispatching system; The net load prediction deviation rate is calculated based on the actual net load and predicted net load data of the same time period of each day in the past year of the power dispatch system, and a deviation rate distribution probability table is obtained. The net load prediction error distribution function is then plotted based on the deviation rate distribution probability table. Determine the load loss cost due to insufficient reserve due to positive or negative net load forecasting errors, and plot the demand value curve for insufficient reserve due to net load forecasting errors by combining the net load forecasting error distribution function. The formula for calculating the demand value in the demand value curve is as follows: in, and These are the required costs for positive and negative reserve capacity, respectively. RPRC and RNRC These represent the reference costs when positive and negative reserves are insufficient, respectively. A% This indicates the probability of positive or negative reserve shortages occurring, corresponding to the probability of occurrence in different net load forecast error ranges; PC This represents the upper limit for bidding costs in the spot market for electrical energy. PF To determine the upper limit of the market cost for negative deep peak-shaving ancillary services or the upper limit of deep peak-shaving compensation costs, in the later stages of the electricity market, if the spot market for electricity allows the submission of negative electricity prices, then... PF This represents the lower limit of bidding costs in the spot market for electrical energy. A joint clearing model is established based on the bid and winning information in the power market, the reserve capacity in the reserve market, and the demand value curve to determine the power and reserve winning results of each unit. The joint clearing model is as follows: in, For the unit i During the period t Corresponding price range k contribution; For the unit i During the period t For the quotation segment k The cost of filing a claim; This represents the cumulative probability value. For the unit i During the period t Corresponding positive reserve capacity; for t Positive and reserve capacity requirements for a given period of time. For the unit i During the period t The corresponding negative reserve capacity; for t Negative reserve capacity requirements for a given period; For the unit i exist t Efforts during a specific time period; and The units i During the period t The upper and lower limits of output.

2. The method for creating a standby market clearing model according to claim 1, characterized in that, The model for calculating the net load forecast deviation rate based on actual net load and forecast net load data is as follows: in, η This indicates the net load forecast deviation rate. L actual Indicates the actual net load. L forecast This indicates the predicted net load.

3. The method for creating a standby market clearing model according to claim 2, characterized in that, The method for obtaining the deviation rate distribution probability table specifically includes: Multiple net load forecast error intervals are divided with a deviation rate of 1%. The calculated net load forecast deviation rate for the historical year is categorized according to the net load forecast error range of different intervals to obtain a deviation rate distribution probability table.

4. A backup market clearing model creation system, characterized in that, Adapted to a power dispatching system, the system comprising: The load data acquisition module is used to acquire the actual net load and predicted net load data of the power dispatching system for the same period of each day over the past year. The error distribution plotting function module is used to calculate the net load prediction deviation rate based on the actual net load and predicted net load data of the same time period of each day in the past year of the power dispatching system, obtain the deviation rate distribution probability table, and plot the net load prediction error distribution function based on the deviation rate distribution probability table. The demand value curve plotting module is used to determine the load loss cost due to insufficient reserve due to positive or negative net load forecasting errors, and plots the demand value curve due to insufficient reserve due to net load forecasting errors by combining the net load forecasting error distribution function. The formula for calculating the demand value in the demand value curve is as follows: in, and These are the required costs for positive and negative reserve capacity, respectively. RPRC and RNRC These represent the reference costs when positive and negative reserves are insufficient, respectively. A% This indicates the probability of positive or negative reserve shortages occurring, corresponding to the probability of occurrence in different net load forecast error ranges; PC This represents the upper limit for bidding costs in the spot market for electrical energy. PF To determine the upper limit of the market cost for negative deep peak-shaving ancillary services or the upper limit of deep peak-shaving compensation costs, in the later stages of the electricity market, if the spot market for electricity allows the submission of negative electricity prices, then... PF This represents the lower limit of bidding costs in the spot market for electrical energy. The joint clearing module creation module is used to establish a joint clearing model based on the bid and winning information in the power market, the standby capacity in the standby market, and the demand value curve, so as to determine the power and standby winning results of each unit. The joint clearing model is as follows: in, For the unit i During the period t Corresponding price range k contribution; For the unit i During the period t For the quotation segment k The cost of filing a claim; This represents the cumulative probability value. For the unit i During the period t Corresponding positive reserve capacity; for t Positive and reserve capacity requirements for a given period of time. For the unit i During the period t The corresponding negative reserve capacity; for t Negative reserve capacity requirements for a given period; For the unit i exist t Efforts during a specific time period; and The units i During the period t The upper and lower limits of output.

5. The standby market clearing model creation system according to claim 4, characterized in that, The model for calculating the net load forecast deviation rate based on actual net load and forecast net load data is as follows: in, η This indicates the net load forecast deviation rate. L actual Indicates the actual net load. L forecast This indicates the predicted net load.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program; wherein, when the computer program is executed, it controls the device on which the computer-readable storage medium is located to perform the standby market clearing model creation method as described in any one of claims 1 to 5.

7. An electrical device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the alternative market clearing model creation method as described in any one of claims 1 to 5.

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