Method, device, medium and equipment for calculating capacity requirement curve

Abstract

The present disclosure relates to the technical field of power calculation, and provides a capacity demand curve calculation method, device, medium and equipment, which comprises the following steps: obtaining unit parameters of a thermal power unit set and time-series net load data in a target time period; the thermal power unit set comprises multiple thermal power units, and the unit parameters comprise unit installed capacity, equivalent forced outage rate and operation cost of each thermal power unit; calculating semi-invariants of available capacity of each thermal power unit based on a semi-invariant formula and the unit installed capacity, equivalent forced outage rate and operation cost of each thermal power unit; calculating capacity demand curve values of each installed capacity based on the time-series net load data, the unit installed capacity and the semi-invariants of available capacity of each thermal power unit; and further determining a capacity demand curve in the target time period. The semi-invariants of available capacity of the thermal power unit are calculated by the semi-invariant formula, and then the capacity demand curve values of each installed capacity are calculated, thereby improving the calculation accuracy of the capacity demand curve.

Description

Technical Field

[0001] This disclosure relates to the field of power calculation technology, and more specifically, to a method, apparatus, medium, and equipment for calculating capacity demand curves. Background Technology

[0002] In the power industry, the forward capacity market is a market mechanism used to ensure sufficient generating capacity for future years. The goal of this market is to guarantee that anticipated load demand can be met within the next one or three years. The capacity demand curve is developed by the government or its designated agent based on projected load demand, aiming to determine the total generating capacity required in the target year. In the capacity market, power generators can submit their generating capacity commitments and the required capacity compensation fees. According to market rules, the price at the intersection of the capacity supply curve and the capacity demand curve is the transaction price in the capacity market. The contracted capacity will be subject to capacity compensation fees to ensure that power generators can provide sufficient electricity during peak demand periods.

[0003] Currently, capacity demand curve calculations typically rely on empirical design and engineering assumptions. For example, they often substitute the value of lost load (VOLL) for the capacity value of typical generating units. This method ignores the dynamic changes in VOLL and spot market electricity prices, and uses average electricity prices instead of instantaneous prices, leading to inaccurate calculation results. Furthermore, there is a lack of unified standards for calculating the probability of load loss (LOLP) in related technologies. Many methods rely on calculations based on continuous load curves, but these methods cannot accurately reflect complex load changes. Summary of the Invention

[0004] This disclosure provides at least one method, apparatus, medium, and device for calculating capacity demand curves. By calculating the semi-invariant of the available capacity of thermal power units based on a semi-invariant formula, the capacity demand curve value of each installed capacity is calculated, thereby improving the accuracy of capacity demand curve calculation.

[0005] This disclosure provides a method for calculating a capacity demand curve, including:

[0006] Acquire the unit parameters of the thermal power unit set and the time-series net load data of the power system within the target time period; wherein, the thermal power unit set includes multiple thermal power units, and the unit parameters include the unit capacity, equivalent forced outage rate and operating cost corresponding to each thermal power unit;

[0007] Based on the semi-invariant formula, and the unit capacity, equivalent forced outage rate and operating cost corresponding to each thermal power unit, the semi-invariant of the available capacity of each thermal power unit is calculated.

[0008] Based on the time-series net load data of the power system within the target time period, the installed capacity of each thermal power unit, and the semi-invariant of the available capacity, calculate the capacity demand curve value for each installed capacity.

[0009] The capacity demand curve for the target time period is determined based on the capacity demand curve values ​​for each installed capacity.

[0010] This disclosure provides a device for calculating a capacity demand curve, comprising:

[0011] The data acquisition module is used to acquire the unit parameters of the thermal power unit set and the time-series net load data of the power system within the target time period; wherein, the thermal power unit set includes multiple thermal power units, and the unit parameters include the unit capacity, equivalent forced outage rate and operating cost corresponding to each thermal power unit;

[0012] The semi-invariant calculation module is used to calculate the semi-invariant of the available capacity of each thermal power unit based on the semi-invariant formula and the unit's installed capacity, equivalent forced outage rate and operating cost corresponding to each thermal power unit.

[0013] The curve value solving module is used to calculate the capacity demand curve value of each installed capacity based on the time-series net load data of the power system within the target time period, the installed capacity of each thermal power unit, and the semi-invariant of the available capacity.

[0014] The curve determination module is used to determine the capacity demand curve within the target time period based on the capacity demand curve values ​​of each installed capacity.

[0015] This disclosure provides a computer device including a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the computer device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, a method for calculating the capacity demand curve as described in any of the above possible embodiments is performed.

[0016] This disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for calculating the capacity demand curve as described in any of the possible embodiments above.

[0017] The capacity demand curve calculation method, apparatus, medium, and equipment provided in this disclosure, by acquiring the unit parameters of the thermal power unit set and the time-series net load data of the power system, can fully consider the characteristics of each thermal power unit and the dynamic changes in load. Based on a semi-invariant formula, the semi-invariant of the available capacity of the thermal power units is calculated, and then the capacity demand curve value of each installed capacity is calculated. Compared with methods such as using the capacity value of typical generator units to replace the value of load shedding and using average electricity price to replace instantaneous electricity price, the accuracy of capacity demand curve calculation is improved. Furthermore, the calculation based on unit parameters and time-series net load data, compared with traditional methods based on continuous load curves to calculate the probability of load shedding, can more accurately reflect complex load changes, providing a reliable basis for the accurate operation of the capacity market.

[0018] Thus, the embodiments of this disclosure improve the accuracy of capacity demand curve calculation, capture dynamic load changes through time-series net load data, unify the calculation standard for load shedding probability, and avoid errors based on continuous load curves by using semi-invariant formulas; optimize the capacity compensation mechanism, and reduce the deviation of using average electricity prices to replace instantaneous electricity prices by combining parameters such as unit operating costs; enhance the adaptability of the long-term capacity market to load shedding value and spot price fluctuations, and ensure that power generation capacity planning is more in line with actual needs.

[0019] To make the above-mentioned objects, features and advantages of this disclosure more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings referenced in the embodiments will be briefly described below. These drawings are incorporated in and constitute a part of this specification. They illustrate embodiments conforming to this disclosure and, together with the specification, serve to explain the technical solutions of this disclosure. It should be understood that the following drawings only show some embodiments of this disclosure and should not be considered as limiting the scope. Those skilled in the art can obtain other related drawings based on these drawings without creative effort.

[0021] Figure 1 A flowchart illustrating a method for calculating a capacity demand curve provided in an embodiment of this disclosure is shown;

[0022] Figure 2 A flowchart is shown illustrating a method for solving semi-invariants of available capacity provided in an embodiment of this disclosure;

[0023] Figure 3 A flowchart illustrating a method for calculating capacity demand curve values ​​provided in an embodiment of this disclosure is shown.

[0024] Figure 4 A schematic diagram of the structure of a capacity demand curve calculation device provided in an embodiment of the present disclosure is shown;

[0025] Figure 5 A schematic diagram of the structure of a computer device provided in an embodiment of this disclosure is shown. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. The components of the embodiments of this disclosure described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this disclosure provided in the accompanying drawings is not intended to limit the scope of the claimed disclosure, but merely represents selected embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without inventive effort are within the scope of protection of this disclosure.

[0027] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0028] In this document, the term "and / or" merely describes a relationship, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0029] In the power industry, the forward capacity market is a market established to ensure sufficient generation capacity to meet net load demand over the next one or three years, aiming to guarantee adequate generation capacity to meet the target load demand in future years. In the capacity market, power generators that commit to providing power supply services in the target year can declare the capacity they will provide and the capacity compensation they require. The capacity demand curve is determined by the government or a government-designated agent and reflects the capacity demand for the target year. In the capacity market, the capacity declared by power generators and their prices are sorted from low to high to form the capacity supply curve. The price corresponding to the intersection of the capacity supply curve and the capacity demand curve is the capacity price in the capacity market. The capacity declared to the left of the intersection is the market-traded capacity. The traded capacity will receive a capacity compensation fee, which is equal to the traded electricity price multiplied by the traded capacity. According to power market theory, the capacity compensation price can be calculated using the following formula:

[0030]

[0031] in, It is the capacity price that should be compensated for the load level at time t, in yuan / MW·h; VOLL is the value of lost load, in yuan / MWh; ρ t The price in the electricity spot market (usually the day-ahead market price) is expressed in yuan / MWh; LOLP t It is the probability of load loss at time t when the existing available generating capacity faces the load at time t.

[0032] Here, if the data on the left side of the formula is known, the corresponding compensation electricity price can be calculated. If the available power generation capacity is taken as a variable, the capacity price that changes with the installed power generation capacity can be obtained. Conceptually, this curve is the capacity demand curve of the capacity market.

[0033] Research has revealed that the calculation of capacity demand curves often relies on empirical design and engineering assumptions. For example, substituting the value of lost load (VOLL) for the capacity value of typical generating units ignores the dynamic changes in VOLL and spot market electricity prices, and uses average electricity prices instead of instantaneous prices, resulting in inaccurate calculations. Furthermore, there is a lack of unified standards for calculating the probability of load loss (LOLP) in related technologies, with many employing calculation methods based on continuous load curves, which fail to accurately reflect complex load changes.

[0034] Meanwhile, in some regional markets, the method for determining the capacity market demand curve is based on the results of artificial engineering design. Its characteristics include: first, it does not use the off-load value (VOLL), but instead selects a typical type of generating unit, such as using the capacity value of a thermal power unit as a substitute; second, although the day-ahead market price (ρ) of the electricity market... t This varies over time, but an average electricity price concept is used instead. Based on these two assumptions, net investment capacity cost (Net Cone) is used to represent (VOLL-ρ) in the above calculation formula. t However, the specific methods for determining LOLP and calculating the capacity demand curve mainly rely on experience to determine the capacity demand curve and then conduct market simulations to judge its feasibility. This method cannot accurately reflect the real demand situation in the capacity market and the reasonable level of capacity prices.

[0035] Based on the above research, this disclosure provides a method, apparatus, medium, and device for calculating capacity demand curves, inheriting the current market practice of using Net Cone instead of (VOLL-ρ) t The method is described, but a complete explanation is given based on the load level L. t Calculate LOLP with installed capacity C t The method, and the process of finally obtaining the capacity demand curve.

[0036] Specifically, by acquiring the unit parameters of the thermal power unit set and the time-series net load data of the power system, the characteristics of each thermal power unit and the dynamic changes in load can be fully considered. Based on the semi-invariant formula, the semi-invariant of the available capacity of thermal power units is calculated, and then the capacity demand curve value of each installed capacity is calculated. Compared with methods such as using the capacity value of typical generator units to replace the value of load shedding and using average electricity price to replace instantaneous electricity price, the accuracy of capacity demand curve calculation is improved. At the same time, the calculation based on unit parameters and time-series net load data can more accurately reflect complex load changes compared with the traditional method of calculating the probability of load shedding based on continuous load curve, providing a reliable basis for the accurate operation of the capacity market.

[0037] Thus, the embodiments of this disclosure improve the accuracy of capacity demand curve calculation, capture dynamic load changes through time-series net load data, unify the calculation standard for load shedding probability, and avoid errors based on continuous load curves by using semi-invariant formulas; optimize the capacity compensation mechanism, and reduce the deviation of using average electricity prices to replace instantaneous electricity prices by combining parameters such as unit operating costs; enhance the adaptability of the long-term capacity market to load shedding value and spot price fluctuations, and ensure that power generation capacity planning is more in line with actual needs.

[0038] To facilitate understanding of this embodiment, the executing entity of the capacity demand curve calculation method provided in this disclosure embodiment will first be described in detail. The executing entity of the capacity demand curve calculation method provided in this disclosure embodiment is a computer device. This computer device can be a terminal device or a server. The terminal device can also be a mobile device, user terminal, terminal, handheld device, computing device, vehicle-mounted device, wearable device, etc. The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud storage, big data, and artificial intelligence platforms. Optionally, this method can also be applied to an implementation environment composed of computer devices and servers.

[0039] The method for calculating the capacity demand curve provided in the embodiments of this application will be described in detail below with reference to the accompanying drawings. See also Figure 1 The diagram shows a flowchart of a method for calculating a capacity demand curve according to an embodiment of this disclosure. The method includes the following steps S101 to S104:

[0040] S101, obtain the unit parameters of the thermal power unit set and the time-series net load data of the power system within the target time period.

[0041] It is understandable that a thermal power unit assembly is a collection of multiple thermal power units that work together in the power system to meet power supply demand. Unit parameters include several key indicators corresponding to each thermal power unit, such as the unit's installed capacity, equivalent forced outage rate, and operating costs. The installed capacity represents the maximum stable power output of a single thermal power unit under normal operating conditions, used to measure its power generation capacity. The equivalent forced outage rate reflects the reliability of the thermal power unit, comprehensively considering the possibility of unplanned outages due to various faults, maintenance, and other factors, and derives an equivalent outage probability value through a specific calculation method. Operating costs include various expenses incurred during the operation of the thermal power unit, such as fuel costs, equipment maintenance costs, and personnel wages. These cost factors can be used to evaluate the economics of the thermal power unit and the overall operating costs of the power system.

[0042] Here, the time-series net load data of the power system within the target time period refers to the sequence data of the actual power load that the power system needs to obtain from conventional power sources such as thermal power units over time within a pre-set specific time period, after deducting the influence of factors such as distributed power generation and energy storage system charging and discharging. It accurately reflects the dynamic changes in the power demand of the power system within this time period.

[0043] S102, based on the semi-invariant formula and the unit capacity, equivalent forced outage rate and operating cost corresponding to each thermal power unit, calculates the semi-invariant of the available capacity of each thermal power unit.

[0044] Understandably, after obtaining the unit parameters (installed capacity, equivalent forced outage rate, and operating cost) corresponding to each thermal power unit, the semi-invariant values ​​of the available capacity of each thermal power unit can be calculated based on the semi-invariant formula, combined with the installed capacity, equivalent forced outage rate, and operating cost of each thermal power unit. Here, the semi-invariant formula describes the relationship between the moments of a random variable. This formula can transform the complex distribution characteristics of random variables into a series of semi-invariant values, thereby simplifying the calculation process and facilitating analysis.

[0045] Specifically, the distribution of available capacity of thermal power units, a variable with randomness, is influenced by a combination of factors, such as the unit's failure probability and operating status. In the calculation method of this application, a semi-invariant formula is used, taking parameters such as the unit's installed capacity and equivalent forced outage rate as input variables. Through mathematical calculations, the semi-invariant of the available capacity of each thermal power unit can be obtained. Here, available capacity refers to the actual power capacity that a thermal power unit can provide to the power system after considering factors such as forced outages, and is used to assess the contribution of thermal power units to the power system's supply capacity. By calculating the semi-invariant of available capacity, the probability distribution characteristics of the available capacity of thermal power units can be understood, providing a basis for subsequent capacity demand curve calculations.

[0046] For example, refer to Figure 2 As shown, when solving for the semi-invariant of the available capacity of each thermal power unit, the following steps S201 to S203 can be included:

[0047] S201, Sort the thermal power units in the set of thermal power units from smallest to largest according to the operating cost of each thermal power unit, and obtain an ordered set of thermal power units arranged in ascending order of operating cost.

[0048] Here, sorting thermal power units according to their operating costs helps to consider the impact of cost factors on the semi-invariant of available capacity in subsequent calculations, making the calculation results more consistent with the actual economic operation.

[0049] S202, for each thermal power unit in the ordered set of thermal power units, calculate the torque of each thermal power unit based on the unit's installed capacity and the equivalent forced outage rate.

[0050] Specifically, moments are statistics that describe the distribution characteristics of random variables. By analyzing the moments related to the unit capacity and the equivalent forced outage rate using computer-generated data, the distribution characteristics of the available capacity of thermal power units can be further revealed. For example, the first moment can reflect the expected value of the available capacity, while the second moment can reflect information such as its variance.

[0051] Here, the formulas for calculating the torques of each order of a thermal power unit can be expressed as:

[0052]

[0053] Among them, GM i,m Represented as the m-th order of thermal power unit i; EFOR represents the probability of the available capacity of thermal power unit i; i C represents the equivalent forced outage rate of thermal power unit i; i This represents the installed capacity of thermal power unit i.

[0054] S203, For each thermal power unit, calculate the semi-invariant of the available capacity of the thermal power unit according to the semi-invariant calculation formula and the moments of each order of the thermal power unit.

[0055] Understandably, by substituting the calculated moments of each thermal power unit into the semi-invariant formula, the semi-invariant values ​​of the available capacity of each thermal power unit can be obtained.

[0056] Here, the formula for calculating semi-invariants is expressed as:

[0057]

[0058] Among them, GC i,1 Let GM be the m-th semi-invariant of thermal power unit i; i,m Represented as the m-th order of thermal power unit i; This is a binomial formula.

[0059] S103. Based on the time-series net load data of the power system within the target time period, the installed capacity of each thermal power unit, and the semi-invariant of the available capacity, calculate the capacity demand curve value for each installed capacity.

[0060] Understandably, based on the time-series net load data of the power system within the target time period, the installed capacity of each thermal power unit, and the semi-invariant of the available capacity calculated earlier, the capacity demand curve value for each installed capacity can be further solved. Here, the capacity demand curve value is a quantitative indicator used to reflect the degree of demand of the power system for the power generation capacity of thermal power units under different installed capacity conditions. In actual power system operation, the size of the installed capacity and the uncertainty of the available capacity will affect the system's capacity demand. For example, when the installed capacity is small, the system may have a more urgent need for the power generation capacity of thermal power units (whose price may be higher); while the uncertainty of the available capacity, such as fluctuations in available capacity caused by factors such as unit failures, will also affect the system's assessment of capacity demand.

[0061] Specifically, refer to Figure 3 As shown, due to factors such as equipment failure and maintenance, the actual available capacity of the unit fluctuates within a range that is usually not a complete constant, but its range of variation can be estimated. Therefore, based on these data, the capacity demand curve value can be calculated through the following steps S301 to S306:

[0062] S301, set the initial k value, k=1; and construct a blank set of capacity demand curve values.

[0063] Here, we first set an initial k value, let k=1. This k value is used to represent the number of thermal power units currently being considered for loading. At the same time, we construct a blank set of capacity demand curve values, which will be used to store the capacity demand curve values ​​corresponding to each installed capacity obtained in subsequent calculations.

[0064] S302, based on the semi-invariants of the installed capacity and available capacity of each thermal power unit, calculate the semi-invariants of the effective capacity and installed capacity of the power system after loading the first k thermal power units in the ordered set of thermal power units.

[0065] Here, the effective capacity of the power system refers to the actual capacity that can stably supply power to the power system after considering the available capacity of thermal power units and their uncertainties. By combining the semi-invariants of the installed capacity and available capacity of the first k thermal power units, the semi-invariants of the effective capacity and the installed capacity of the power system after loading the first k thermal power units can be obtained.

[0066] Specifically, when solving for the semi-invariants and installed capacity of the effective capacity of the power system after the loading of the first k thermal power units, the following (1) to (2) can be included:

[0067] (1) Based on the installed capacity of the units corresponding to the first k thermal power units, determine the installed capacity of the effective capacity of the power system after loading the first k thermal power units;

[0068] (2) Based on the semi-invariant of the available capacity corresponding to the first k thermal power units, determine the semi-invariant of the effective capacity of the power system after the first k thermal power units are loaded.

[0069] Understandably, when solving for the installed capacity and semi-invariants of the effective capacity of a power system, a summation formula can be used, as follows:

[0070]

[0071] in, C represents the installed capacity of the power system after the first k thermal power units are loaded; i The unit capacity of thermal power unit i is represented by EGC. k,m It is represented as the m-th order semi-invariant of the effective capacity of the power system after the first k thermal power units are loaded.

[0072] S303, based on the semi-invariant of the effective capacity of the power system after the loading of the first k thermal power units, determine the probability distribution function of the effective capacity of the power system after the loading of the first k thermal power units.

[0073] Understandably, the probability distribution function is mainly used to describe the probability of the effective capacity of a power system within different value ranges. By using the mathematical transformation relationship between semi-invariants and the probability distribution function, this function can be obtained, thereby gaining a deeper understanding of the distribution law of the effective capacity of the system.

[0074] In some possible implementations, since the semi-invariant values ​​of the effective capacity of the power system may have different dimensions, magnitudes, or distribution ranges, this may introduce difficulties and errors into the subsequent solution of the probability distribution function. Therefore, before solving for the probability distribution function of the effective capacity of the power system, a normalization formula can be used to normalize the semi-invariant values ​​of the effective capacity of the power system after the loading of the first k thermal power units. Normalization is a common data preprocessing method, the purpose of which is to convert data with different dimensions, magnitudes, or distribution ranges into a form with a unified standard. For the semi-invariant values ​​of the effective capacity of the power system, normalization can eliminate the dimensional differences between data, making the values ​​of each semi-invariant fall within the same order of magnitude and distribution range.

[0075] Here, the normalization formula can be expressed as:

[0076] g1 = 0;

[0077] g2 = 1;

[0078]

[0079] Among them, g nn = 1, 2, 3, 4 are EGC k,m The normalized results corresponding to m = 1, 2, 3, 4; EGC k,m It is represented as the m-th order semi-invariant of the effective capacity of the power system after the first k thermal power units are loaded.

[0080] Furthermore, after normalizing the semi-invariants of the effective capacity of the power system after loading the first k thermal power units, the probability distribution function of the effective capacity of the power system after loading the first k thermal power units can be obtained based on the normalization result, which can be expressed as:

[0081]

[0082]

[0083] in, Let be the probability distribution function of the effective capacity of the power system after the first k thermal power units are loaded; x represents the variable of the probability distribution function.

[0084] S304, based on the time-series net load data of the power system within the target time period, the installed capacity of the effective capacity of the power system after the loading of the first k thermal power units, and the probability distribution function, determine the capacity demand curve value within the target time period corresponding to the installed capacity, and store the capacity demand curve value within the target time period corresponding to the installed capacity into the capacity demand curve value set.

[0085] Specifically, time-series net load data reflects the actual demand of the power system. By combining the installed capacity and probability distribution function of the system's effective capacity, the capacity demand curve value for the target time period under the current installed capacity can be calculated. Subsequently, the calculated capacity demand curve value for the target time period corresponding to the installed capacity can be stored in the previously constructed set of capacity demand curve values.

[0086] For example, when determining the capacity demand curve value for a target time period corresponding to the installed capacity, the following (a) to (b) may be included:

[0087] (a) Obtaining the net capacity investment cost of thermal power units;

[0088] (b) Based on the curve value solution formula, the net capacity investment cost of the thermal power unit, the time-series net load data of the power system in the target time period, the installed capacity of the effective capacity of the power system after the first k thermal power units are loaded, and the probability distribution function, determine the capacity demand curve value in the target time period corresponding to the installed capacity.

[0089] Here, the net capacity investment cost of thermal power units represents the net cost invested in acquiring the power generation capacity of thermal power units. It can cover a series of costs directly related to capacity acquisition, such as the construction of thermal power units, the purchase of equipment, installation and commissioning, while possibly deducting some related subsidies or residual value.

[0090] Furthermore, after obtaining the net capacity investment cost of thermal power units, based on the curve value calculation formula, the net capacity investment cost of thermal power units, the time-series net load data of the power system within the target time period, the installed capacity and probability distribution function of the effective capacity of the power system after the loading of the first k thermal power units, the capacity demand curve value within the target time period corresponding to the installed capacity can be determined. Specifically, the curve value calculation formula can organically combine the actual demand reflected by the time-series net load data, the power supply scale represented by the installed capacity of the system's effective capacity, and the probabilistic characteristics of power supply capacity revealed by the probability distribution function. By substituting the net capacity investment cost of thermal power units and other relevant data into this formula, the capacity demand curve value within the target time period under the current installed capacity condition can be calculated. Here, the curve value calculation formula can be expressed as:

[0091]

[0092] Among them, L t NetCONE represents the net load data at time t in the time-series net load data; NetCONE represents the net capacity investment cost of the thermal power unit. Indicated as installed capacity The corresponding capacity demand curve value within the target time period; Indicated as the target time period; This represents the number of all moments within the target time period.

[0093] S305, k = k + 1, determine whether k is not greater than the total number of thermal power units in the ordered set of thermal power units; if yes, proceed to step S302; if no, proceed to step S306.

[0094] Here, the value of k is updated to k = k + 1, and then it is determined whether k is not greater than the total number of thermal power units in the ordered set of thermal power units. If the result is yes, it means that there are still thermal power units that have not been considered for loading, so the process returns to step S302 to continue calculating the relevant data after loading the next number of thermal power units; if the result is no, it means that the loading situation of all thermal power units has been considered, so the process proceeds to step S306.

[0095] S306, Based on the set of capacity demand curve values, determine the capacity demand curve value for each installed capacity.

[0096] Specifically, the capacity demand curve value set stores the capacity demand curve values ​​corresponding to different installed capacities. By organizing these data, the capacity demand curve value for each installed capacity can be determined.

[0097] In this embodiment, the time-series net load data of the power system within the target time period can more accurately reflect the dynamic changes in the actual power demand of the power system within the target time period. The installed capacity and probability distribution function of the effective capacity of the power system after the loading of the first k thermal power units can characterize the power supply capacity of the power system from both deterministic and probabilistic dimensions. By determining the capacity demand curve value within the target time period corresponding to the installed capacity based on these values, the actual demand and various characteristics of the power system's power supply capacity can be more fully considered, thereby deriving a more accurate and realistic capacity demand curve value and avoiding calculation errors caused by a single data dimension or one-sided analysis.

[0098] S104, determine the capacity demand curve for the target time period based on the capacity demand curve values ​​of each installed capacity.

[0099] Understandably, based on the capacity demand curve values ​​for each installed capacity obtained from the previous calculations, the capacity demand curve for the target time period can be determined. The capacity demand curve is a curve with installed capacity as the horizontal axis and capacity demand curve value (such as price) as the vertical axis. It depicts the pattern of how the power system's demand for the generating capacity of thermal power units changes with the installed capacity during the target time period.

[0100] Specifically, by connecting and fitting the capacity demand curves corresponding to each installed capacity, a complete capacity demand curve can be obtained. This curve can help planners rationally determine the installed capacity and layout of thermal power units, optimize the resource allocation of the power system, and ensure that the power system can achieve economical, reliable, and efficient operation while meeting electricity demand. For power system operators, the capacity demand curve can serve as an important reference for dispatching decisions. Based on the trend of the curve, the start-up, shutdown, and power generation plans of thermal power units can be rationally arranged, improving the operational flexibility and stability of the power system.

[0101] The capacity demand curve calculation method, apparatus, medium, and equipment provided in this disclosure improve the accuracy of capacity demand curve calculation. By capturing dynamic load changes through time-series net load data, it unifies the calculation standard for load shedding probability and avoids errors based on continuous load curves by using semi-invariant formulas. It optimizes the capacity compensation mechanism and reduces the deviation of using average electricity prices to replace instantaneous electricity prices by combining parameters such as unit operating costs. It enhances the adaptability of the long-term capacity market to load shedding value and spot price fluctuations, ensuring that power generation capacity planning is more in line with actual needs.

[0102] Those skilled in the art will understand that, in the above-described method of the specific implementation, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0103] Based on the same inventive concept, this disclosure also provides a capacity demand curve calculation device corresponding to the capacity demand curve calculation method. Since the principle of the device in this disclosure for solving the problem is similar to the capacity demand curve calculation method described above in this disclosure, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0104] Reference Figure 4 The diagram shown is a schematic representation of a capacity demand curve calculation device 400 provided in an embodiment of this disclosure. The device includes:

[0105] The data acquisition module 401 is used to acquire the unit parameters of the thermal power unit set and the time-series net load data of the power system within the target time period; wherein, the thermal power unit set includes multiple thermal power units, and the unit parameters include the unit capacity, equivalent forced outage rate and operating cost corresponding to each thermal power unit;

[0106] The semi-invariant calculation module 402 is used to calculate the semi-invariant of the available capacity of each thermal power unit based on the semi-invariant formula and the unit's installed capacity, equivalent forced outage rate and operating cost corresponding to each thermal power unit.

[0107] The curve value solving module 403 is used to calculate the capacity demand curve value of each installed capacity based on the time-series net load data of the power system within the target time period, the installed capacity of each thermal power unit, and the semi-invariant of the available capacity.

[0108] The curve determination module 404 is used to determine the capacity demand curve within the target time period based on the capacity demand curve values ​​of each installed capacity.

[0109] In some possible embodiments, the semi-invariant calculation module 402 is specifically used for:

[0110] Based on the operating cost of each thermal power unit, the thermal power units in the set are sorted from smallest to largest to obtain an ordered set of thermal power units arranged in ascending order of operating cost.

[0111] For each thermal power unit in the ordered set of thermal power units, the moments of the thermal power unit are calculated based on the unit's installed capacity and the equivalent forced outage rate.

[0112] For each thermal power unit, the semi-invariant of the available capacity of the thermal power unit is calculated based on the semi-invariant calculation formula and the moments of each order of the thermal power unit.

[0113] In some possible embodiments, the semi-invariant calculation formula is expressed as:

[0114]

[0115] Among them, GC i,1 Let GM be the m-th semi-invariant of thermal power unit i; i,m Represented as the m-th order of thermal power unit i; This is a binomial formula.

[0116] In some possible embodiments, the curve value solving module 403 is specifically used to perform:

[0117] Step 1: Set the initial k value, k=1; and construct a blank set of capacity demand curve values;

[0118] Step 2: Based on the semi-invariants of the installed capacity and available capacity of each thermal power unit, calculate the semi-invariants of the effective capacity of the power system after loading the first k thermal power units in the ordered set of thermal power units and the installed capacity.

[0119] Step 3: Based on the semi-invariants of the effective capacity of the power system after the loading of the first k thermal power units, determine the probability distribution function of the effective capacity of the power system after the loading of the first k thermal power units;

[0120] Step 4: Based on the time-series net load data of the power system within the target time period, the installed capacity of the effective capacity of the power system after the loading of the first k thermal power units, and the probability distribution function, determine the capacity demand curve value within the target time period corresponding to the installed capacity, and store the capacity demand curve value within the target time period corresponding to the installed capacity into the capacity demand curve value set.

[0121] Step 5: k = k + 1. If k ≤ N, return to step 2. N represents the total number of thermal power units in the ordered set of thermal power units. Otherwise, proceed to step 6.

[0122] Step 6: Based on the set of capacity demand curve values, determine the capacity demand curve value for each installed capacity.

[0123] In some possible embodiments, the curve value solving module 403 is specifically used for:

[0124] Based on the installed capacity of the units corresponding to the first k thermal power units, the effective capacity of the power system after loading the first k thermal power units is determined by the installed capacity.

[0125] Based on the semi-invariants of the available capacity corresponding to the first k thermal power units, the semi-invariants of the effective capacity of the power system after the first k thermal power units are loaded are determined.

[0126] In some possible embodiments, the curve value solving module 403 is specifically used for:

[0127] Based on the normalization formula, the semi-invariant of the effective capacity of the power system after the loading of the first k thermal power units is normalized, and the probability distribution function of the effective capacity of the power system after the loading of the first k thermal power units is determined based on the normalization result.

[0128] The normalization formula is expressed as follows:

[0129] g1 = 0;

[0130] g2 = 1;

[0131]

[0132] Among them, g n n = 1, 2, 3, 4 are EGC k,m The normalized results corresponding to m = 1, 2, 3, 4; EGC k,m It is represented as the m-th semi-invariant of the effective capacity of the power system after the first k thermal power units are loaded;

[0133] The probability distribution function is expressed as:

[0134]

[0135] in, Let be the probability distribution function of the effective capacity of the power system after the first k thermal power units are loaded; x represents the variable of the probability distribution function.

[0136] In some possible embodiments, the curve value solving module 403 is specifically used for:

[0137] Obtain the net capacity investment cost of thermal power units;

[0138] Based on the curve value solution formula, the net capacity investment cost of the thermal power unit, the time-series net load data of the power system in the target time period, the installed capacity of the effective capacity of the power system after the first k thermal power units are loaded, and the probability distribution function, the capacity demand curve value in the target time period corresponding to the installed capacity is determined.

[0139] The formula for solving the curve value is expressed as follows:

[0140]

[0141] Among them, L t NetCONE represents the net load data at time t in the time-series net load data; NetCONE represents the net capacity investment cost of the thermal power unit. Indicated as installed capacity The corresponding capacity demand curve value within the target time period; Indicated as the target time period; This represents the number of all moments within the target time period.

[0142] Based on the same technical concept, this disclosure also provides a computer device. (See also...) Figure 5 The diagram shows the structure of a computer device 500 provided in this embodiment of the present disclosure, including a processor 501, a memory 502, and a bus 503. The memory 502 is used to store execution instructions and includes a main memory 5021 and an external memory 5022. The main memory 5021, also called internal memory, is used to temporarily store computational data in the processor 501, as well as data exchanged with external memory 5022 such as a hard disk. The processor 501 exchanges data with the external memory 5022 through the main memory 5021.

[0143] In this embodiment, the memory 502 is specifically used to store application code that executes the solution of this application, and its execution is controlled by the processor 501. That is, when the computer device 500 is running, the processor 501 communicates with the memory 502 through the bus 503, so that the processor 501 executes the application code stored in the memory 502, and then executes the method described in any of the foregoing embodiments.

[0144] The memory 502 may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0145] Processor 501 may be an integrated circuit chip with signal processing capabilities. The aforementioned processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor.

[0146] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the computer device 500. In other embodiments of this application, the computer device 500 may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0147] This disclosure also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program performs the steps of the capacity demand curve calculation method described in the above-described method embodiments. The storage medium can be a volatile or non-volatile computer-readable storage medium.

[0148] This disclosure also provides a computer program product carrying program code. The program code includes instructions that can be used to execute the steps of the capacity demand curve calculation method described in the above method embodiments. For details, please refer to the above method embodiments, which will not be repeated here.

[0149] The aforementioned computer program product can be implemented through hardware, software, or a combination thereof. In one optional embodiment, the computer program product is specifically embodied in a computer storage medium; in another optional embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0150] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and devices described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. In the several embodiments provided in this disclosure, it should be understood that the disclosed systems and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division; in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection may be through some communication interfaces; the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms.

[0151] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0152] In addition, the functional units in the various embodiments of this disclosure can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0153] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the embodiments of this disclosure. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0154] Finally, it should be noted that the above-described embodiments are merely specific implementations of this disclosure, used to illustrate the technical solutions of this disclosure, and not to limit it. The protection scope of this disclosure is not limited thereto. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this disclosure. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure, and should all be covered within the protection scope of this disclosure. Therefore, the protection scope of this disclosure should be determined by the protection scope of the claims.

Claims

1. A method for calculating a capacity demand curve, characterized in that, include: Acquire the unit parameters of the thermal power unit set and the time-series net load data of the power system within the target time period; wherein, the thermal power unit set includes multiple thermal power units, and the unit parameters include the unit capacity, equivalent forced outage rate and operating cost corresponding to each thermal power unit; Based on the operating cost of each thermal power unit, the thermal power units in the set are sorted from smallest to largest to obtain an ordered set of thermal power units arranged in ascending order of operating cost. For each thermal power unit in the ordered set of thermal power units, based on the unit's installed capacity and the equivalent forced outage rate, the moments of each thermal power unit are calculated, and the semi-invariants of the available capacity of each thermal power unit are calculated according to the semi-invariant calculation formula and the moments of each thermal power unit. Based on the time-series net load data of the power system within the target time period, the installed capacity of each thermal power unit, and the semi-invariant of the available capacity, the capacity demand curve value of each installed capacity is calculated, specifically including the following steps 1 to 6: Step 1: Obtain the net capacity investment cost of thermal power units, set an initial k value (k=1), and construct a blank set of capacity demand curve values; Step 2: Based on the semi-invariants of the installed capacity and available capacity of each thermal power unit, calculate the semi-invariants of the effective capacity of the power system after loading the first k thermal power units in the ordered set of thermal power units and the installed capacity. Step 3: Based on the semi-invariants of the effective capacity of the power system after the loading of the first k thermal power units, determine the probability distribution function of the effective capacity of the power system after the loading of the first k thermal power units; Step 4: Based on the curve value solution formula, the net capacity investment cost of the thermal power unit, the time-series net load data of the power system within the target time period, the installed capacity of the effective capacity of the power system after the loading of the first k thermal power units, and the probability distribution function, determine the capacity demand curve value within the target time period corresponding to the installed capacity; and store the capacity demand curve value within the target time period corresponding to the installed capacity into the capacity demand curve value set. Step 5: k = k + 1, if Then return to step 2, where N represents the total number of thermal power units in the ordered set of thermal power units; otherwise, proceed to step 6. Step 6: Based on the set of capacity demand curve values, determine the capacity demand curve value for each installed capacity; The capacity demand curve for the target time period is determined based on the capacity demand curve values ​​for each installed capacity.

2. The method according to claim 1, characterized in that, The formula for calculating the semi-invariant is expressed as follows: ； in, Let m be the mth semi-invariant of thermal power unit i; Represented as the m-th order of thermal power unit i; This is a binomial formula.

3. The method according to claim 1, characterized in that, The calculation of the semi-invariants of the effective power system capacity and installed capacity after loading the first k thermal power units in the ordered set of thermal power units, based on the semi-invariants of the installed capacity and available capacity of each thermal power unit, includes: Based on the installed capacity of the units corresponding to the first k thermal power units, the effective capacity of the power system after loading the first k thermal power units is determined by the installed capacity. Based on the semi-invariants of the available capacity corresponding to the first k thermal power units, the semi-invariants of the effective capacity of the power system after the first k thermal power units are loaded are determined.

4. The method according to claim 3, characterized in that, The probability distribution function for determining the effective capacity of the power system after loading the first k thermal power units, based on the semi-invariants of the effective capacity of the power system after loading the first k thermal power units, includes: Based on the normalization formula, the semi-invariant of the effective capacity of the power system after the loading of the first k thermal power units is normalized, and the probability distribution function of the effective capacity of the power system after the loading of the first k thermal power units is determined based on the normalization result. The normalization formula is expressed as follows: ； ； ； ； in, n=1,2,3,4 are respectively The normalized results corresponding to m=1,2,3,4; It is represented as the m-th semi-invariant of the effective capacity of the power system after the first k thermal power units are loaded; The probability distribution function is expressed as: ； ； ； ； ； in, Let be the probability distribution function of the effective capacity of the power system after the first k thermal power units are loaded; x represents the variable of the probability distribution function.

5. The method according to claim 4, characterized in that, The formula for solving the curve value is expressed as follows: ； ； in, This is represented as the net load data at time t in the time-series net load data; This represents the net capacity investment cost of thermal power units. Indicated as installed capacity The corresponding capacity demand curve value within the target time period; Indicated as the target time period; This represents the number of all moments within the target time period.

6. A device for calculating capacity demand curves, characterized in that, include: The data acquisition module is used to acquire the unit parameters of the thermal power unit set and the time-series net load data of the power system within the target time period; wherein, the thermal power unit set includes multiple thermal power units, and the unit parameters include the unit capacity, equivalent forced outage rate and operating cost corresponding to each thermal power unit; and, according to the operating cost corresponding to each thermal power unit, the thermal power units in the thermal power unit set are sorted from smallest to largest to obtain an ordered set of thermal power units arranged in ascending order of operating cost; The semi-invariant calculation module is used to calculate the moments of each thermal power unit in the ordered set of thermal power units based on the unit's installed capacity and the equivalent forced shutdown rate, and to calculate the semi-invariant of the available capacity of each thermal power unit according to the semi-invariant calculation formula and the moments of each thermal power unit. The curve value solving module is used to calculate the capacity demand curve value for each installed capacity based on the time-series net load data of the power system within the target time period, the installed capacity of each thermal power unit, and the semi-invariant of the available capacity; specifically, it is used to perform the following steps 1 to 6: Step 1: Obtain the net capacity investment cost of thermal power units, set an initial k value (k=1), and construct a blank set of capacity demand curve values; Step 2: Based on the semi-invariants of the installed capacity and available capacity of each thermal power unit, calculate the semi-invariants of the effective capacity of the power system after loading the first k thermal power units in the ordered set of thermal power units and the installed capacity. Step 3: Based on the semi-invariants of the effective capacity of the power system after the loading of the first k thermal power units, determine the probability distribution function of the effective capacity of the power system after the loading of the first k thermal power units; Step 4: Based on the curve value solution formula, the net capacity investment cost of the thermal power unit, the time-series net load data of the power system within the target time period, the installed capacity of the effective capacity of the power system after the loading of the first k thermal power units, and the probability distribution function, determine the capacity demand curve value within the target time period corresponding to the installed capacity; and store the capacity demand curve value within the target time period corresponding to the installed capacity into the capacity demand curve value set. Step 5: k = k + 1, if Then return to step 2, where N represents the total number of thermal power units in the ordered set of thermal power units; otherwise, proceed to step 6. Step 6: Based on the set of capacity demand curve values, determine the capacity demand curve value for each installed capacity; The curve determination module is used to determine the capacity demand curve within the target time period based on the capacity demand curve values ​​of each installed capacity.

7. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 5.

8. A computer device, comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 5.

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