Reliability power capacity planning method and device, electronic equipment and storage medium

Abstract

The embodiment of the application relates to the technical field of medium and long term power market transaction, in particular to a reliability power capacity planning method and device, an electronic equipment and a storage medium, and the method comprises the following steps: firstly, based on the actual load curve of the peak load period, the reliable capacity of the multi-period charge-discharge characteristics of the energy storage resource is evaluated to obtain the reliable capacity of the energy storage resource; then, based on the reliable capacity of the energy storage resource and the mathematical optimization model of the market equilibrium theory, the reliability power capacity is planned. The embodiment of the application provides a reliability power capacity planning method, which quantitatively guides the multi-period medium and long term reliability evaluation of the energy storage resource and the medium and long term reliability power capacity planning, and according to the market equilibrium theory, the actual energy transaction is considered to provide a new idea for the medium and long term reliability capacity planning.

Description

Technical Field

[0001] This application relates to the field of semiconductor device technology for medium- and long-term electricity market transactions, and in particular to a reliable power capacity planning method, apparatus, electronic device, and storage medium. Background Technology

[0002] The output of renewable energy is random and volatile. Compared with fossil fuels, it can only serve as a substitute for electricity, not a substitute for power generation. Therefore, issues such as capacity adequacy and power supply reliability may arise in the future. From the perspective of market participants, the current variable-cost electricity market struggles to fully recover the fixed investment costs of generating units and is unlikely to stimulate investment in new power sources. A reliable power capacity mechanism aims to meet system capacity demands, uses reliable capacity as the trading instrument, connects to the electricity market, and ensures that all power generation resources have equal status upon clearing, based on a determined reliable capacity.

[0003] Meanwhile, as a flexible response resource, energy storage can smooth peak and valley loads, promote the consumption of renewable energy, and play a capacity support role, improving grid reliability. Capacity mechanisms can bring stable revenue to power generation companies, solve problems such as difficulty in recovering fixed costs, and promote the development of flexible power generation resources such as energy storage.

[0004] However, reliable power capacity planning and reliability assessment of energy storage resources still face many challenges. Unlike traditional generator sets, energy storage has finite energy capacity, a limited time for stable charging and discharging, and unique characteristics in terms of utility, cost, and scale. Currently, many countries and regions use methods for reliable capacity assessment of energy storage resources based on the average output of energy storage resources during peak load periods. This method is relatively crude and cannot meet actual needs. Summary of the Invention

[0005] This application provides a reliable power capacity planning method, apparatus, electronic device, and storage medium, which provides quantitative guidance for multi-period medium- and long-term reliability assessment of energy storage resources and medium- and long-term reliable power capacity planning. Based on market equilibrium theory and considering the actual situation of energy trading, it provides a new approach to medium- and long-term reliable capacity planning.

[0006] To address the aforementioned technical problems, in a first aspect, embodiments of this application provide a reliable power capacity planning method, comprising: evaluating the reliable capacity of energy storage resources based on the actual load curve during peak load periods to obtain the reliable capacity of the energy storage resources; and planning the reliable power capacity based on the reliable capacity of the energy storage resources and a mathematical optimization model of market equilibrium theory.

[0007] In some exemplary embodiments, the reliable capacity of the energy storage resource's multi-period charge-discharge characteristics is evaluated based on the actual load curve during peak load periods, including: establishing an evaluation model for the reliable capacity of the energy storage resource; adjusting the evaluation model based on the actual load curve during peak load periods to obtain a discrete model; and evaluating the reliable capacity of the energy storage resource's multi-period charge-discharge characteristics based on the discrete model.

[0008] In some exemplary embodiments, the evaluation model is:

[0009]

[0010] The evaluation model includes the first constraint formula (1), the second constraint formula (2), and the third constraint formula (3); in the evaluation model, t0 and t1 are the decision variables of the system, and t0 and t1 represent the discharge start and end times of the energy storage resources, respectively, with the unit being h; , These represent the load at the start and end of the discharge of the energy storage resource, respectively, both in MW; S ES The charging and discharging capacity of energy storage resources; t p,0 t p,1 The first constraint formula (1) and the second constraint formula (2) represent the start and end times of the peak load period, respectively, both in hours; the first constraint formula (1) and the second constraint formula (2) represent the peak reduction amount corresponding to different energy storage resource capacities.

[0011] In some exemplary embodiments, the discrete model is:

[0012]

[0013] The discrete model includes the fourth constraint formula (4), the fifth constraint formula (5), and the sixth constraint formula (6); in the discrete model, The unit time interval in the load curve is represented; in the discrete model, the first constraint formula (1) is adjusted to the fourth constraint formula (4), the second constraint formula (2) is adjusted to the fifth constraint formula (5) and the sixth constraint formula (6).

[0014] In some exemplary embodiments, the mathematical optimization model is as follows:

[0015]

[0016] The mathematical optimization model includes the seventh constraint formula (7), the eighth constraint formula (8), the ninth constraint formula (9), and the tenth constraint formula (10); in the mathematical optimization model, x and y represent binary variables indicating whether the market is cleared; PD0 and PD1 represent the initial and final values ​​of the elastic electricity demand, respectively, both in MW; PD TThe final cleared capacity is represented in MW; ΔPD represents the incremental micro-element of capacity demand in MW; PC represents the capacity bid amount obtained from the credible capacity assessment in MW; Pr i This represents the price when the capacity demand is PD0 + iΔPD, in yuan; Pr ceil denoted by , indicating the upper limit of demand price, in yuan; Pbj represents the bid price of power generation resource j, in yuan; m and n represent the incremental micro-element of capacity demand and the number of power generation resources participating in the auction, respectively; in the mathematical optimization model based on market equilibrium theory, the eighth constraint formula (8) represents the peak load demand constraint, which indicates that the total supply of the winning bid resources is greater than the total cleared load demand; the ninth constraint formula (9) is the base load demand constraint, which indicates that the sum of the minimum operating capacities of the winning bid resources is less than the basic capacity demand of the system; the eighth constraint formula (8) and the ninth constraint formula (9) are used to constrain the range of system capacity change within the overall capacity adjustment range of the winning bid resources.

[0017] In some exemplary embodiments, a mathematical optimization model based on market equilibrium theory is used to plan reliable power capacity, including: sorting the price-quantity pairs of power generation resources from lowest to highest price to form a supply curve, with lower-priced goods receiving priority in bidding, until the supply curve intersects the demand curve, resulting in equilibrium; and constructing a constraint model for the mathematical optimization model to achieve equilibrium based on the actual relationship between the supply curve and the demand curve formed by price-quantity pairs. The constraint model is as follows:

[0018]

[0019] The constraint model includes the eleventh constraint formula (11) and the twelfth constraint formula (12); in the constraint model, i represents the number of power generation resources traded, j+1 represents the total number of demand micro-elements satisfied at the time of clearing; B represents the power generation resource price array sorted from smallest to largest price, in yuan; C represents the power generation resource reliable capacity array sorted from smallest to largest price, in MW; C min The array of minimum operating capacity of power generation resources is sorted by price from smallest to largest, with units of MW; in the constraint model, the eleventh constraint formula (11) is used to represent the occurrence of the equilibrium point, and the twelfth constraint formula (12) is a transformation of the eighth constraint formula (8) and the ninth constraint formula (9); based on the discretization characteristics of the supply curve and demand curve in actual application and the load demand constraint, the mathematical optimization model and constraint model are solved in the simulation example using MATLAB language.

[0020] In some exemplary embodiments, after planning the reliability power capacity, the above method further includes: testing and verifying the reliability power capacity planning method based on actual power grid data.

[0021] Secondly, this application also provides a reliable power capacity planning device, including: an evaluation module and a planning module. The evaluation module is used to evaluate the reliable capacity of the energy storage resource's multi-period charging and discharging characteristics based on the actual load curve during peak load periods, and obtain the reliable capacity of the energy storage resource. The input end of the planning module is connected to the output end of the evaluation module. The planning module is used to plan the reliable power capacity based on the reliable capacity of the energy storage resource output by the evaluation module and the mathematical optimization model of market equilibrium theory.

[0022] In addition, embodiments of this application also provide an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the above-described reliable power capacity planning method.

[0023] In addition, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described reliable power capacity planning method.

[0024] The technical solution provided in this application has at least the following advantages:

[0025] This application addresses the problem that existing reliable power capacity planning methods cannot meet the actual needs of the planning process due to the inability of energy trading systems. This application provides a reliable power capacity planning method, apparatus, electronic device, and storage medium. The method includes the following steps: First, based on the actual load curve during peak load periods, the reliable capacity of the energy storage resource's multi-period charging and discharging characteristics is evaluated to obtain the reliable capacity of the energy storage resource; then, based on the reliable capacity of the energy storage resource and a mathematical optimization model of market equilibrium theory, the reliable power capacity is planned. This application provides quantitative guidance for the multi-period medium- and long-term reliability assessment and medium- and long-term reliable power capacity planning of energy storage resources by designing a reliable power capacity planning method. Based on market equilibrium theory and considering the actual energy trading situation, it provides a new approach to medium- and long-term reliable capacity planning.

[0026] This application first proposes a reliable capacity assessment method considering the multi-period charging and discharging characteristics of energy storage resources, providing a possible technical reference standard for reliable capacity assessment of energy storage resources participating in power capacity planning. This method effectively considers the actual load curve and the peak-shaving characteristics of energy storage resources during peak load periods, offering a new approach to solving the reliable capacity assessment problem of energy storage resources. Then, based on traditional market supply and demand theory, and aiming to maximize social welfare at the time of transaction, a medium- to long-term reliable power capacity planning method that meets the characteristics of power production planning is proposed, facilitating the matching of transactions between multiple types of power generation resources and the user side. This application, based on traditional market equilibrium theory, fully considers the physical specificities of reliable power capacity trading and the discrete characteristics of actual transactions, ensuring that market transaction results meet the actual needs of power production planning. It provides a framework for establishing a medium- to long-term reliable capacity planning technical model and has strong reference value for connecting energy storage resources' participation in power capacity planning with actual conditions. Attached Figure Description

[0027] One or more embodiments are illustrated by way of example with reference to the accompanying drawings. These illustrations do not constitute a limitation on the embodiments, and unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0028] Figure 1 A flowchart illustrating a reliability power capacity planning method provided in an embodiment of this application;

[0029] Figure 2 A flowchart for a reliable capacity assessment considering the reliability of energy storage across multiple time periods is provided as an embodiment of this application;

[0030] Figure 3 A flowchart of a medium- to long-term reliable power capacity planning technology that meets the characteristics of power production planning is provided as an embodiment of this application;

[0031] Figure 4 A reliability power capacity planning process diagram is provided as an embodiment of this application;

[0032] Figure 5 A flowchart illustrating a reliability power capacity planning method provided in another embodiment of this application;

[0033] Figure 6 A schematic diagram of the structure of a reliability power capacity planning device provided in an embodiment of this application;

[0034] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0035] As the background technology shows, current methods for assessing the reliable capacity of energy storage resources are relatively crude and neglect the peak-shaving characteristics of energy storage resources during peak load periods. Moreover, because traditional market equilibrium theory does not take into account actual physical constraints, the actual planned price may not perfectly match the market equilibrium price, and energy trading systems cannot meet the actual needs of the planning process during the design phase.

[0036] To address the aforementioned technical problems, embodiments of this application provide a reliable power capacity planning method, apparatus, electronic device, and storage medium. The method includes the following steps: First, based on the actual load curve during peak load periods, the reliable capacity of the energy storage resource's multi-period charging and discharging characteristics is evaluated to obtain the reliable capacity of the energy storage resource; then, based on the reliable capacity of the energy storage resource and a mathematical optimization model based on market equilibrium theory, the reliable power capacity is planned. Embodiments of this application provide a reliable power capacity planning method that provides quantitative guidance for multi-period medium- and long-term reliability assessment and medium- and long-term reliable power capacity planning of energy storage resources, and, based on market equilibrium theory and considering the realities of energy trading, provides a new approach to medium- and long-term reliable capacity planning.

[0037] The embodiments of this application will now be described in detail with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details have been provided in the embodiments of this application to facilitate a better understanding of the application. However, the technical solutions claimed in this application can be implemented even without these technical details and various variations and modifications based on the following embodiments.

[0038] See Figure 1 This application provides a reliable power capacity planning method, including the following steps:

[0039] Step S1: Based on the actual load curve during peak load periods, evaluate the reliable capacity of the energy storage resources based on the charging and discharging characteristics during multiple periods to obtain the reliable capacity of the energy storage resources.

[0040] Step S2: Based on the reliable capacity of energy storage resources and the mathematical optimization model of market equilibrium theory, plan the reliable power capacity.

[0041] The method provided in this application is a reliability-based power capacity planning method that takes into account the multi-period coupling constraints of energy storage. Specifically, step S1 mainly provides a reliable capacity assessment method that considers the reliability of energy storage over multiple periods. By considering the actual load curve during peak load periods, it provides a possible technical reference standard for the reliable capacity assessment of energy storage resources participating in power capacity planning, and finally obtains the reliable capacity of energy storage resources. The reliable capacity assessment method that considers the reliability of energy storage over multiple periods provided in step S1 can effectively consider the actual situation of the load curve and the peak shaving characteristics of energy storage resources during peak load periods, providing a new approach to solving the reliable capacity assessment problem of energy storage resources.

[0042] Step S2 primarily uses traditional market supply and demand theory, aiming to maximize social welfare at the time of transaction, to design a medium- to long-term reliable power capacity planning method that meets the characteristics of power production planning. This method facilitates the matching of transactions between multiple types of power generation resources and the user side. The planning method provided in Step S2, based on the reliable capacity of energy storage resources provided in Step S1 and traditional market equilibrium theory, fully considers the physical specificities of reliable power capacity trading and the discrete nature of actual transactions. This ensures that market transaction results meet the actual needs of power production planning, providing a framework for establishing a medium- to long-term reliable capacity planning technical model.

[0043] In some embodiments, step S1, evaluating the reliable capacity of energy storage resources based on the actual load curve during peak load periods, includes the following steps:

[0044] Step S101: Establish an assessment model for the reliable capacity of energy storage resources.

[0045] Step S102: Based on the actual load curve during peak load periods, adjust the evaluation model to obtain a discrete model.

[0046] Step S103: Based on the discrete model, evaluate the reliable capacity of the energy storage resource's multi-period charge and discharge characteristics.

[0047] Specifically, step S1 provides a reliable capacity assessment method that considers the reliability of energy storage over multiple time periods. First, step S101 is executed to establish a reliable capacity assessment model for energy storage resources.

[0048] Traditional methods for assessing the reliable capacity of energy storage resources based on their average output during peak load periods typically only consider the duration of the peak load period, neglecting the actual shape of the load curve during that period. However, the actual peak-shaving effect of energy storage resources is closely related to the actual shape of the load curve; therefore, the actual shape of the load curve must be considered when assessing the reliable capacity of energy storage resources. The traditional reliable capacity assessment method for energy storage resources can be expressed as formula (1):

[0049] (1)

[0050] in, The system represents the reliable capacity of the energy storage resources; ESP is the output power of the energy storage resources during peak load periods; ECP is the absorbed power of the energy storage resources during peak load periods; (ESP-ECP) represents the net output power of the energy storage resources during peak load periods; NHP is the duration of peak load periods; FOES is the forced outage rate of the energy storage resources; and the system load and energy production are also represented by these parameters.

[0051] However, this assessment method clearly only considers the duration of peak load periods and does not reflect the peak-shaving characteristics of energy storage resources. Therefore, it is necessary to consider the actual shape of the load curve when conducting reliable capacity assessments, resulting in the following assessment model, the entire flowchart of which is shown below. Figure 2 As shown. First, determine the storage capacity (MWh) of the energy storage resources, then determine the peak shaving duration NL from the daily load curve; next, the capacity factor CF can be obtained from a table based on the peak shaving duration NL. ES Based on the above formula (1), a reliable capacity assessment is performed.

[0052] In some embodiments, the evaluation model in step S101 is:

[0053]

[0054] The evaluation model includes the first constraint formula (1), the second constraint formula (2), and the third constraint formula (3); in the evaluation model, t0 and t1 are the decision variables of the system, and t0 and t1 represent the discharge start and end times of the energy storage resources, respectively, with the unit being h; , These represent the load at the start and end of the discharge of the energy storage resource, respectively, both in MW; S ES The charging and discharging capacity of energy storage resources; t p,0 t p,1 The first constraint formula (1) and the second constraint formula (2) represent the start and end times of the peak load period, respectively, both in hours; the first constraint formula (1) and the second constraint formula (2) represent the peak reduction amount corresponding to different energy storage resource capacities.

[0055] Considering that the actual load curve is discrete, the above evaluation model is rewritten and adjusted. Therefore, after obtaining the evaluation model, step S102 is executed: based on the actual load curve during peak load periods, the evaluation model is adjusted to obtain a discrete model. In some embodiments, the discrete model is:

[0056]

[0057] The above discrete model includes the fourth constraint formula (4), the fifth constraint formula (5), and the sixth constraint formula (6); in the discrete model, The unit time interval in the load curve is represented; in the discrete model above, the first constraint formula (1) is rewritten as the fourth constraint formula (4), the second constraint formula (2) is rewritten as the fifth constraint formula (5) and the sixth constraint formula (6).

[0058] After obtaining the discrete model, step S103 is executed: based on the discrete model, the reliable capacity of the energy storage resource's multi-period charge-discharge characteristics is evaluated. Step S103 mainly considers the design of a reliable capacity calculation algorithm for the reliability of energy storage over multiple periods.

[0059] First, define each parameter:

[0060] i. The longest duration required to reduce the capacity of energy storage resources during peak load periods.

[0061] ii. : The unit time interval in the load curve ( h ).

[0062] iii. : The start time of energy storage resource discharge ( h ).

[0063] iv. : The time when the energy storage resource discharge ends ( h ).

[0064] v. The start time of peak load period ( h ).

[0065] vi. The end time of peak load period ( h ).

[0066] vii. : Load at any given time (MW).

[0067] Input: Energy storage capacity S ES (MWh), daily load curve, energy storage resource capacity factor table.

[0068] Output: Capacity factor (CF) of energy storage resources ES .

[0069] initialization: ;

[0070] implement:

[0071] 1 for :

[0072] 2 for :

[0073] 3. If the following conditions are met simultaneously

[0074] ,

[0075] ,

[0076] ,

[0077] ,

[0078] Then we have:

[0079] 4 ;

[0080] 5 If Then we have:

[0081] 6 ;

[0082] 7 by You can find the table.

[0083] End of 8

[0084] After executing step S1, the reliable capacity of the energy storage resources is obtained. Next, step S2 is executed, using a mathematical optimization model based on the reliable capacity of the energy storage resources and market equilibrium theory to plan the reliable power capacity. Step S2 primarily provides a medium- to long-term reliable power capacity planning method that meets the characteristics of power production planning.

[0085] First, a planning technique aimed at maximizing social welfare is established. Specifically, based on market equilibrium theory, a mathematical optimization model is developed. In some embodiments, the mathematical optimization model is as follows:

[0086]

[0087] The mathematical optimization model includes the seventh constraint formula (7), the eighth constraint formula (8), the ninth constraint formula (9), and the tenth constraint formula (10); in the mathematical optimization model, x and y represent binary variables indicating whether the market is cleared; PD0 and PD1 represent the initial and final values ​​of the elastic electricity demand, respectively, both in MW; PD T The final cleared capacity is represented in MW; ΔPD represents the incremental micro-element of capacity demand in MW; PC represents the capacity bid amount obtained from the credible capacity assessment in MW; Pr i This represents the price when the capacity demand is PD0 + iΔPD, in yuan; Pr ceilrepresents the upper limit of demand price, in yuan; Pbj represents the bid price of power generation resource j, in yuan; m and n represent the incremental micro-element of capacity demand and the number of power generation resources participating in the auction, respectively; in the mathematical optimization model based on market equilibrium theory, the eighth constraint formula (8) represents the peak load demand constraint, which means that the total supply of the winning bid resources is greater than the total cleared load demand; the ninth constraint formula (9) is the base load demand constraint, which means that the sum of the minimum operating capacities of the winning bid resources is less than the basic capacity demand of the system; the eighth constraint formula (8) and the ninth constraint formula (9) are used to constrain the range of system capacity change within the overall capacity adjustment range of the winning bid resources.

[0088] Based on a mathematical optimization model based on market equilibrium theory, a solution algorithm is designed that takes into account practical applications.

[0089] Figure 3 A flowchart illustrating the technical process of medium- to long-term reliability power capacity planning that meets the characteristics of power production planning is presented. (For example...) Figure 3 As shown, first, the parameters of each power generation resource and demand curve are input. Then, the price and quantity pairs provided by the power generation resources are sorted in ascending order of price to form a supply curve. After the two lines (supply curve and demand curve) intersect and reach market equilibrium, the market clearing result is obtained from the demand constraints of power capacity trading.

[0090] In some embodiments, a mathematical optimization model based on the reliable capacity of energy storage resources and market equilibrium theory is used to plan reliable power capacity. This includes: sorting the price-quantity pairs provided by power generation resources from lowest to highest price to form a supply curve; prioritizing the bidding for lower-priced goods until the supply curve intersects with the demand curve, achieving equilibrium; and constructing a constraint model for the mathematical optimization model to reach equilibrium based on the actual relationship between the supply curve and demand curve formed by price-quantity pairs. The constraint model is as follows:

[0091]

[0092] The constraint model includes the eleventh constraint formula (11) and the twelfth constraint formula (12); in the constraint model, i represents the number of power generation resources traded, j+1 represents the total number of demand micro-elements satisfied at the time of clearing; B represents the power generation resource price array sorted from smallest to largest price, in yuan; C represents the power generation resource reliable capacity array sorted from smallest to largest price, in MW; C minThe array of minimum operating capacity of power generation resources is sorted by price from smallest to largest, with units of MW; in the constraint model, the eleventh constraint formula (11) is used to represent the occurrence of the equilibrium point, and the twelfth constraint formula (12) is a transformation of the eighth constraint formula (8) and the ninth constraint formula (9); based on the discretization characteristics of the supply curve and demand curve in actual application and the load demand constraint, the mathematical optimization model and constraint model are solved in the simulation example using MATLAB language.

[0093] Based on mathematical optimization and constraint models, a medium- to long-term reliability power capacity planning algorithm is designed. First, the parameters are defined:

[0094] Bid price (RMB);

[0095] : Price of demand micro-element (yuan);

[0096] : Confidential capacity array (MW) for power generation resources;

[0097] : Minimum operating capacity array (MW) of power generation resources;

[0098] Total number of power generation resources (units);

[0099] : Total number of demand micro-elements (units);

[0100] : An array of power generation resource prices (in yuan) sorted from smallest to largest by price;

[0101] : Array of power generation resource locations;

[0102] : A reliable array of power generation capacity (MW) sorted by price from smallest to largest;

[0103] : The minimum operating capacity (MW) of power generation resources sorted by price from smallest to largest;

[0104] Market equilibrium capacity (MW);

[0105] Price at market equilibrium (in yuan);

[0106] Total clearing capacity (MW);

[0107] Clearing price (RMB).

[0108] Input: credible capacity of each power generation resource and market demand curve.

[0109] Output: Clearing price, winning resources, total cost.

[0110] initialization: ;

[0111] implement:

[0112] 1. Array The array is obtained by sorting the bidding prices of power generation resources from low to high. and position array ;

[0113] 2. From the position array Confidential capacity array for bidding on power generation resources and minimum running capacity array Get the corresponding array ;

[0114] 3 For :

[0115] 4 for :

[0116] if

[0117] Then we have:

[0118] 5 Break out of the loop;

[0119] Or if

[0120] Then we have:

[0121] 6 Break out of the loop;

[0122] 7 If Then we have:

[0123] 8 resources won in the bid The total supply cost is ;

[0124] End of 9

[0125] In some embodiments, such as Figure 5 As shown, after planning the reliability power capacity, the above method also includes step S3: testing and verifying the reliability power capacity planning method based on actual power grid data.

[0126] Specifically, the reliability power capacity planning method described above is tested and verified through numerical examples.

[0127] The method designed in this invention was applied to a simulation example, setting up the following scenario: Eleven power generation resources were set up, including three thermal power resources, two hydropower resources, two onshore wind power resources, two photovoltaic power resources, and two energy storage resources. Their specific price-quantity pair parameters are shown in the table below. The minimum elastic demand was 1580MW, the maximum elastic demand was 3590MW, and the basic demand was 1811MW. The price ceiling was set at 1.1 yuan, simulating the regulatory state during a time of power scarcity. The verification results are shown in Tables 1, 2, 3, and 4.

[0128] Table 1 Power Generation Resource Parameter Information

[0129]

[0130] Table 2 Energy Storage Resource Parameter Information

[0131]

[0132] Table 3 Reliable capacity assessment results of energy storage resources

[0133]

[0134] Table 4 Planning Technical Results

[0135]

[0136] As can be seen from the above clearing results, five resources were ultimately awarded in the entire process, with the transaction volume and price listed in the order of the transactions as follows: Figure 4 As shown, as buyers enter the market, low-priced capacity resources are sold out one after another, and the transaction price gradually rises. The transaction volume is determined by the demand of both buyers and sellers, and is cleared out in stages.

[0137] refer to Figure 6 This application embodiment also provides a reliable power capacity planning device, including: an evaluation module 10 and a planning module 11. The evaluation module 10 is used to evaluate the reliable capacity of the energy storage resource's multi-period charging and discharging characteristics based on the actual load curve during peak load periods, and obtain the reliable capacity of the energy storage resource. The input terminal of the planning module 11 is connected to the output terminal of the evaluation module 10. The planning module 11 is used to plan the reliable power capacity based on the reliable capacity of the energy storage resource output by the evaluation module 10 and the mathematical optimization model of market equilibrium theory.

[0138] like Figure 6As shown, in some embodiments, the evaluation module 10 includes an evaluation model construction module 101, a discrete model construction module 102, and a calculation module 103; wherein, the evaluation model construction module 101 is used to establish an evaluation model for the reliable capacity of energy storage resources; the discrete model construction module 102 is used to adjust the evaluation model constructed by the evaluation model construction module 101 according to the actual load curve during peak load periods to obtain a discrete model; the calculation module 103 is used to evaluate the reliable capacity of the energy storage resources based on the discrete model constructed by the discrete model construction module 102, thereby obtaining the reliable capacity of the energy storage resources.

[0139] Please continue reading. Figure 6 In some embodiments, the planning module 11 includes a mathematical optimization model construction module 111, a constraint model construction module 112, and a processing module 113; wherein, the mathematical optimization model construction module 111 is used to construct a mathematical optimization model based on market equilibrium theory; the constraint model construction module 112 is used to constrain the mathematical optimization model based on the actual relationship between the supply curve and the demand curve formed by price and quantity pairs to obtain a constraint model; the processing module 113 is used to plan the reliable power capacity based on the reliable capacity of energy storage resources calculated by the evaluation module 10 and the constraint model.

[0140] Please continue reading. Figure 6 In some embodiments, the above-mentioned reliability power capacity planning device further includes a verification module 12, which is used to test and verify the reliability power capacity planning method based on actual power grid data.

[0141] Another embodiment of this application relates to an electronic device, such as... Figure 7 As shown, it includes at least one processor 13; and a memory 14 communicatively connected to at least one processor 13; wherein the memory 14 stores instructions executable by at least one processor 13, the instructions being executed by at least one processor 13 to enable at least one processor 13 to perform any of the above method embodiments.

[0142] The memory 14 and processor 13 are connected via a bus, which may include any number of interconnecting buses and bridges, connecting various circuits of one or more processors 13 and memory 14 together. The bus may also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. A bus interface provides an interface between the bus and the transceiver. The transceiver may be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by processor 13 is transmitted over a wireless medium via an antenna, which further receives data and transmits it to processor 13.

[0143] Processor 13 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory 14 can be used to store data used by processor 13 during operation.

[0144] Another embodiment of this application relates to a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the method embodiments described above.

[0145] That is, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0146] Based on the above technical solutions, this application addresses the problem that existing reliable power capacity planning methods cannot meet the actual needs of the planning process due to the inability of energy trading systems. This application provides a reliable power capacity planning method, apparatus, electronic device, and storage medium. The method includes the following steps: First, based on the actual load curve during peak load periods, the reliable capacity of the energy storage resource's multi-period charging and discharging characteristics is evaluated to obtain the reliable capacity of the energy storage resource. Then, based on the reliable capacity of the energy storage resource and a mathematical optimization model of market equilibrium theory, the reliable power capacity is planned. This application, by designing a reliable power capacity planning method, provides quantitative guidance for the multi-period medium- and long-term reliability assessment and medium- and long-term reliable power capacity planning of energy storage resources. Based on market equilibrium theory and considering the realities of energy trading, it provides a new approach to medium- and long-term reliable capacity planning.

[0147] This application first proposes a reliable capacity assessment method considering the multi-period charging and discharging characteristics of energy storage resources, providing a possible technical reference standard for reliable capacity assessment of energy storage resources participating in power capacity planning. This method effectively considers the actual load curve and the peak-shaving characteristics of energy storage resources during peak load periods, offering a new approach to solving the reliable capacity assessment problem of energy storage resources. Then, based on traditional market supply and demand theory, and aiming to maximize social welfare at the time of transaction, a medium- to long-term reliable power capacity planning method that meets the characteristics of power production planning is proposed, facilitating the matching of transactions between multiple types of power generation resources and the user side. This application, based on traditional market equilibrium theory, fully considers the physical specificities of reliable power capacity trading and the discrete characteristics of actual transactions, ensuring that market transaction results meet the actual needs of power production planning. It provides a framework for establishing a medium- to long-term reliable capacity planning technical model and has strong reference value for connecting energy storage resources' participation in power capacity planning with actual conditions.

[0148] Those skilled in the art will understand that the above-described embodiments are specific examples of implementing this application, and in practical applications, various changes in form and detail may be made without departing from the spirit and scope of this application. Any person skilled in the art can make their own modifications and alterations without departing from the spirit and scope of this application; therefore, the scope of protection of this application should be determined by the scope defined in the claims.

Claims

1. A reliability-based power capacity planning method, characterized in that, include: Based on the actual load curve during peak load periods, the reliable capacity of energy storage resources is evaluated based on the charging and discharging characteristics of energy storage resources in multiple time periods, and the reliable capacity of energy storage resources is obtained. Based on the reliable capacity of the energy storage resources and the mathematical optimization model of market equilibrium theory, the reliable power capacity is planned. The reliable capacity assessment of the multi-period charge-discharge characteristics of energy storage resources is based on the actual load curve during peak load periods, including: Establish an assessment model for the reliable capacity of energy storage resources; Based on the actual load curve during peak load periods, the evaluation model is adjusted to obtain a discrete model; Based on the discrete model, the reliable capacity of the energy storage resource's multi-period charge and discharge characteristics is evaluated; The evaluation model is as follows: In the evaluation model t 0、 t 1 is the system's decision variable, and t0 and t1 represent the start and end times of the energy storage resource's discharge, respectively, both in hours. , These represent the load at the start and end of the discharge of the energy storage resource, respectively, both in MW; Indicates energy storage resources in t Load at any given moment; S ES The charging and discharging capacity of energy storage resources; t p,0 t p,1 represent the start and end times of the peak load period, respectively, both in hours; the first constraint formula (1) and the second constraint formula (2) represent the peak reduction amount corresponding to the energy storage resource capacity to be calculated, respectively; The discrete model is as follows: In the discrete model This represents the unit time interval in the load curve; , These represent the load of energy storage resources in the period before the initial state and the load in the period before the end, respectively. The mathematical optimization model is as follows: In the mathematical optimization model x , y These are binary variables representing whether the data has been cleared. PD 0 represents the initial value of elastic electricity demand, in MW; Pr ceil This indicates the upper limit of the demand price, in yuan. i Indicates the capacity requirement sequence number; x i Indicates the first i Has the capacity demand been cleared? Pr i This indicates the capacity requirement is PD 0+ Δ PD The price at that time is in yuan; Δ PD The incremental micro-element represents the capacity demand, with the unit being MW; j Indicates the capacity bid number; y j They represent the first j Whether the bidding for individual power generation resources has been cleared; Pb j Indicates power generation resources j The bid price is in yuan; m , n These represent the incremental capacity demand in micro-element and the number of power generation resources participating in the auction, respectively. PC j Indicates power generation resources j The bidding power is in MW; PD T This indicates the final cleared capacity, expressed in MW. PC j,min Indicates power generation resources j The minimum power rating for the bid, in MW; BD The system's basic capacity requirement is represented by the eighth constraint formula (8) in the mathematical optimization model of the market equilibrium theory. The peak load demand constraint means that the total supply of the winning bid resources is not less than the total load demand after clearing. The ninth constraint formula (9) is a capacity demand calculation method, which means that the system capacity demand is equal to the sum of the cleared capacity demands; The tenth constraint formula (10) is the base load demand constraint, which means that the sum of the minimum operating capacity of the winning bid resources is not greater than the basic capacity requirement of the system.

2. The reliability-based power capacity planning method according to claim 1, characterized in that, After planning the reliability power capacity, the method further includes: testing and verifying the reliability power capacity planning method based on actual power grid data.

3. A reliability power capacity planning device, the device being used to implement the reliability power capacity planning method as described in any one of claims 1 to 2, characterized in that, The device includes: An evaluation module is used to evaluate the reliable capacity of energy storage resources based on the actual load curve during peak load periods, thereby obtaining the reliable capacity of the energy storage resources. The planning module has its input connected to the output of the evaluation module. The planning module is used to plan the reliable power capacity based on the reliable capacity of the energy storage resources output by the evaluation module and the mathematical optimization model of market equilibrium theory.

4. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the reliability power capacity planning method as described in any one of claims 1 to 2.

5. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the reliable power capacity planning method according to any one of claims 1 to 2.

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