Capacity demand curve calculation method and device, medium and equipment

Abstract

The invention relates to the technical field of electric power calculation, and provides a capacity demand curve calculation method and device, a medium and equipment, and the method comprises the steps: obtaining unit parameters of a thermal power unit set and time sequence net load data in a target time period; the thermal power generating unit set comprises a plurality of thermal power generating units, and the unit parameters comprise the unit installed capacity, the equivalent forced outage rate and the operation cost of each thermal power generating unit; calculating the semi-invariant of the available capacity of each thermal power generating unit based on a semi-invariant formula and the unit installed capacity, the equivalent forced outage rate and the operation cost of each thermal power generating unit; calculating a capacity demand curve value of each installed capacity based on the time sequence net load data and semi-invariants of the installed capacity and the available capacity of each thermal power generating unit; and determining a capacity demand curve in the target time period. According to the method, the semi-invariant of the available capacity of the thermal power generating unit is calculated through the semi-invariant formula, so that the capacity demand curve value of each installed capacity is calculated, and the calculation accuracy of the capacity demand curve is improved.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of power calculation, in particular, to a capacity demand curve calculation method, device, medium and equipment. BACKGROUND

[0002] In the power industry, the forward capacity market is a market mechanism for ensuring sufficient generation capacity in future years, and the goal of the market is to ensure that the expected load demand can be met in the next year or three years. The capacity demand curve is formulated by the government or its designated agent according to the predicted load demand, and is intended to determine the total generation capacity required in the target year. In the capacity market, power plant operators can submit their generation capacity commitments and the required capacity compensation fees. According to the market rules, the price corresponding to the intersection of the capacity supply curve and the capacity demand curve is the transaction price of the capacity market, and the capacity traded will pay the capacity compensation fee to ensure that the power plant operators provide sufficient power during the peak demand period.

[0003] Currently, the calculation of the capacity demand curve usually relies on empirical design and engineering assumptions, such as replacing the value of lost load (VOLL) with the capacity value of a typical generator, which ignores the dynamic changes of VOLL and the spot market price of electricity, and replaces the instantaneous electricity price with the average electricity price, resulting in inaccurate calculation results. At the same time, the calculation method of the loss of load probability (LOLP) in the related art lacks a unified standard, and the calculation method based on the continuous load curve is often used, but this method cannot accurately reflect complex load changes. SUMMARY

[0004] The embodiments of the present disclosure at least provide a capacity demand curve calculation method, device, medium and equipment, which calculates the semi-invariant of the available capacity of thermal power units based on the semi-invariant formula, and then calculates the capacity demand curve value of each installed capacity, thereby improving the accuracy of the capacity demand curve calculation.

[0005] The embodiments of the present disclosure provide a capacity demand curve calculation method, comprising:

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

[0007] Based on the semi-invariant formula and the unit installed 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 sequence net load data of the power system in the target time period, the unit installed capacity of each thermal power unit, and the semi-invariant of the available capacity, calculate the capacity demand curve value of each installed capacity;

[0009] determine the capacity demand curve in the target time period according to the capacity demand curve value of each installed capacity.

[0010] The embodiment of the present disclosure provides a capacity demand curve calculation device, which comprises:

[0011] a data acquisition module, configured to acquire unit parameters of a set of thermal power units and time sequence net load data of a power system in a target time period; wherein the set of thermal power units comprises a plurality of thermal power units, and the unit parameters comprise unit installed capacity, equivalent forced outage rate, and operation cost corresponding to each thermal power unit;

[0012] a semi-invariant calculation module, configured to calculate, based on a semi-invariant formula, the semi-invariant of the available capacity of each thermal power unit according to the unit installed capacity, the equivalent forced outage rate, and the operation cost corresponding to each thermal power unit;

[0013] a curve value solving module, configured to calculate, based on the time sequence net load data of the power system in the target time period, the unit installed capacity of each thermal power unit, and the semi-invariant of the available capacity, the capacity demand curve value of each installed capacity;

[0014] a curve determination module, configured to determine the capacity demand curve in the target time period according to the capacity demand curve value of each installed capacity.

[0015] The embodiment of the present disclosure provides a computer device, which comprises 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 and the memory communicate through the bus, and the machine readable instructions are executed by the processor to perform the capacity demand curve calculation method in any possible embodiment described above.

[0016] The embodiment of the present disclosure provides a computer readable storage medium, which stores a computer program, and the computer program is run by a processor to implement the capacity demand curve calculation method in any possible embodiment described above.

[0017] The method, device, medium and equipment for calculating the capacity demand curve provided in the embodiments of the present disclosure can fully consider the characteristics of each thermal power generating unit and the dynamic change of the load by obtaining the unit parameters of the set of thermal power generating units and the time-series net load data of the power system; based on the semi-invariant formula, the semi-invariant of the available capacity of the thermal power generating unit is calculated, and then the capacity demand curve value of each installed capacity is calculated, which improves the accuracy of the capacity demand curve calculation compared with the method of replacing the load loss value with the typical generating unit capacity value and replacing the instantaneous electricity price with the average electricity price; at the same time, based on the calculation of the unit parameters and the time-series net load data, compared with the traditional method of calculating the load loss probability based on the continuous load curve, the complex load change can be more accurately reflected, and a reliable basis is provided for the accurate operation of the capacity market.

[0018] In this way, the embodiments of the present disclosure improve the accuracy of the capacity demand curve calculation, capture the dynamic change of the load through the time-series net load data, unify the calculation standard of the load loss probability, avoid the error based on the continuous load curve by using the semi-invariant formula, optimize the capacity compensation mechanism, reduce the deviation of using the average electricity price to replace the instantaneous electricity price by combining the unit operation cost and other parameters, enhance the adaptability of the forward capacity market to the load loss value and the spot price fluctuation, and ensure that the power generation capacity planning is more in line with the actual demand.

[0019] In order to make the above-mentioned purposes, features and advantages of the present disclosure more obvious and easy to understand, the following preferred embodiments are described in detail below, and the accompanying drawings are described in detail below. The accompanying drawings are incorporated into the specification and form a part of the specification, which show the embodiments consistent with the present disclosure, and are used to illustrate the technical solutions of the present disclosure. It should be understood that the following drawings only show some embodiments of the present disclosure, and therefore should not be considered as limiting the scope, and other related drawings can be obtained by those skilled in the art without creative labor. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings referred to in the embodiments will be briefly introduced, and the drawings are incorporated into the specification and form a part of the specification, which show the embodiments consistent with the present disclosure, and are used to illustrate the technical solutions of the present disclosure. It should be understood that the following drawings only show some embodiments of the present disclosure, and therefore should not be considered as limiting the scope, and other related drawings can be obtained by those skilled in the art without creative labor.

[0021] Figure 1 A flowchart of a method for calculating a capacity demand curve provided by an embodiment of the present disclosure is shown;

[0022] Figure 2 A flowchart of a method for calculating a capacity demand curve provided by an embodiment of the present disclosure is shown;

[0023] Figure 3 A flowchart of a method for calculating a capacity demand curve provided by an embodiment of the present disclosure is shown;

[0024] Figure 4 A structural diagram of a device for calculating a capacity demand curve is shown.

[0025] Figure 5 A structural diagram of a computer device is shown. DETAILED DESCRIPTION

[0026] In order to make the objects, technical solutions and advantages of the embodiments of the present disclosure clearer, the following will be combined with the accompanying drawings for the embodiments of the present disclosure to clearly and completely describe the technical solutions of the embodiments of the present disclosure. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all the embodiments. The components of the embodiments of the present disclosure described and shown in the accompanying drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present disclosure provided in the accompanying drawings is not intended to limit the scope of the claimed present disclosure, but only represents selected embodiments of the present disclosure. Based on the embodiments of the present disclosure, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present disclosure.

[0027] It should be noted that: similar labels and letters represent similar items in the following drawings, therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0028] The term "and / or" herein is only used to describe an associated relationship, which means that there can be three relationships, for example, A and / or B can represent three cases of A alone, A and B together, and B alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality, 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 for the adequacy of generation capacity to meet the net load demand in the next year or three years, aiming to ensure that there is enough generation capacity to meet the load demand in the target year. In the capacity market, power generation companies that commit to providing power supply services in the future target year can report the capacity of their power generation services and the required capacity fees. The capacity demand curve is determined by the government or its designated agent, reflecting the capacity demand in the target year. In the capacity market, the reported capacity and its price of the power generation companies are sorted by price from low to high to form the capacity supply curve. The intersection of the capacity supply curve and the capacity demand curve corresponds to the capacity price in the capacity market, and the intersection left of the reported capacity is the market transaction capacity. The transaction capacity will obtain the capacity compensation fee, which is equal to the transaction price multiplied by the transaction capacity. According to the power market theory, the capacity compensation price can be calculated according to the following formula:

[0030]

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

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

[0033] It is found through research that the calculation of the capacity demand curve usually depends on empirical design and engineering assumptions. For example, the capacity value of a typical generator is used to replace the value of lost load (VOLL), which ignores the dynamic changes of VOLL and the price of the power spot market, and the average price is used to replace the instantaneous price, resulting in inaccurate calculation results. At the same time, the calculation method of the loss of load probability (LOLP) in related technologies lacks a unified standard, and the calculation method based on the continuous load curve is often used, which cannot accurately reflect the complex load changes.

[0034] Meanwhile, in some regional markets, the method for determining the capacity market demand curve is based on the results of engineering design, which has the following characteristics: 1. Instead of using the value of lost load VOLL, a typical generator type is selected, such as using the capacity value of a thermal power generator to replace it; 2. Although the price p of the day-ahead market of the electricity market is used, the average electricity price concept is used instead of the instantaneous electricity price. t The value changes over time, but the average electricity price concept is used instead. Based on the two assumptions, the Net CONE is used to represent (VOLL-p t ) in the above calculation formula. However, the specific method for calculating LOLP and the capacity demand curve is mainly determined by experience, and then market simulation is performed to determine its feasibility. This method cannot accurately reflect the real demand of the capacity market and the reasonable level of the capacity price.

[0035] Based on the above research, the disclosure provides a capacity demand curve calculation method, device, medium and equipment in the embodiment, which inherits the method of using Net CONE to replace (VOLL-p t ) adopted by most markets at present, but gives a complete method for calculating LOLP t based on the load level L t and the installed capacity C, and finally obtains the capacity demand curve.

[0036] Specifically, by obtaining the unit parameters of the thermal power generator set and the time sequence net load data of the power system, the characteristics of each thermal power generator set and the dynamic changes of the load can be fully considered; based on the semi-invariant formula, the semi-invariant of the available capacity of the thermal power generator set is calculated, and then the capacity demand curve value of each installed capacity is calculated, which improves the accuracy of the capacity demand curve calculation compared with the method of using the capacity value of a typical generator to replace the value of lost load and using the average electricity price to replace the instantaneous electricity price; at the same time, based on the unit parameters and the time sequence net load data calculation, compared with the traditional method of calculating the value of lost load based on the continuous load curve, the complex load change can be more accurately reflected, and a reliable basis is provided for the accurate operation of the capacity market.

[0037] In this way, the disclosure improves the accuracy of the capacity demand curve calculation, captures the dynamic changes of the load through the time sequence net load data, unifies the calculation standard of the value of lost load, avoids the error based on the continuous load curve by using the semi-invariant formula, optimizes the capacity compensation mechanism, reduces the deviation of using the average electricity price to replace the instantaneous electricity price by combining the unit operation cost and other parameters, enhances the adaptability of the forward capacity market to the value of lost load and the fluctuation of the spot price, and ensures that the power generation capacity planning is more in line with the actual demand.

[0038] To facilitate the understanding of the present embodiment, first, the execution subject of the capacity demand curve calculation method provided by the present embodiment is introduced in detail. The execution subject of the capacity demand curve calculation method provided by the present embodiment is a computer device. The computer device can be a terminal device or a server. The terminal device can also be a mobile device, a user terminal, a terminal, a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. The server can be a standalone physical server, a server cluster or a distributed system composed of multiple physical servers, a cloud server providing cloud services, cloud databases, cloud computing, cloud storage, big data and artificial intelligence platforms, and other basic cloud computing services. Optionally, the method can also be applied to an implementation environment composed of a computer device and a server.

[0039] The capacity demand curve calculation method provided by the present embodiment will be described in detail below with reference to the accompanying drawings. Referring to FIG. 1, it is a flowchart of a capacity demand curve calculation method provided by the present embodiment, which includes the following S101-S104: Figure 1

[0040] S101, obtaining unit parameters of a thermal power unit set and time-series net load data of a power system in a target time period.

[0041] It can be understood that the thermal power unit set is a collection composed of multiple thermal power units, which work together in the power system to meet the power supply demand. The unit parameters include multiple key indicators corresponding to each thermal power unit, which can include unit installed capacity, equivalent forced outage rate, and operation cost corresponding to each thermal power unit, etc. Among them, the unit installed capacity represents the maximum power capacity that a single thermal power unit can stably output in a normal operating state, which is used to measure the power generation capacity of the thermal power unit; the equivalent forced outage rate is used to reflect the reliability of the thermal power unit, which considers the possibility of unplanned outage of the unit due to various faults, maintenance, etc., and obtains an equivalent outage probability value through a specific calculation method; the operation cost includes various costs generated during the operation of the thermal power unit, such as fuel cost, equipment maintenance cost, personnel salary, etc., which can be used to evaluate the economy of the thermal power unit and the overall operation cost of the power system.

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

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

[0044] It can be understood that after obtaining the unit parameters (unit installed capacity, equivalent forced outage rate and operation cost) corresponding to each thermal power unit, based on the semi-invariant formula, combined with the unit installed capacity, the equivalent forced outage rate and the operation cost corresponding to each thermal power unit, the semi-invariant of the available capacity of each thermal power unit can be calculated. Here, the semi-invariant formula is used to describe the relationship between each order moment of random variable, and through the formula, the complex random variable distribution characteristics can be converted into a series of semi-invariant numerical values, so as to simplify the calculation process and facilitate analysis.

[0045] Specifically, for the variable of the available capacity of thermal power unit, its distribution is influenced by multiple factors, such as the failure probability of the unit, the operating state, etc. In the calculation method of the present application, the semi-invariant formula is used, the unit installed capacity and the equivalent forced outage rate of the thermal power unit are taken as input variables, and through mathematical operation, the semi-invariant of the available capacity of each thermal power unit can be obtained. The available capacity refers to the actual power capacity that the thermal power unit can provide for the power system after considering the forced outage and other factors, which is used to evaluate the contribution of the thermal power unit to the power supply capacity of the power system. By calculating the semi-invariant of the available capacity, the probability distribution characteristics of the available capacity of the thermal power unit can be understood, which provides a basis for subsequent capacity demand curve calculation.

[0046] Exemplarily, referring to FIG. 2, in solving the semi-invariant of the available capacity of each thermal power unit, the following S201-S203 can be included: Figure 2

[0047] S201, according to the operation cost corresponding to each thermal power unit, each thermal power unit in the set of thermal power units is sorted from small to large, and an ordered set of thermal power units arranged in ascending order of operation cost is obtained.

[0048] Here, the thermal power units are sorted according to the operation cost, which is helpful to consider the influence of the cost factor on the semi-invariant of the available capacity in the subsequent calculation, so that the calculation result is more in line with the actual economic operation situation.

[0049] S202, for each thermal power unit in the ordered set of thermal power units, based on the unit installed capacity and the equivalent forced outage rate of the thermal power unit, the moments of the thermal power unit are calculated.

[0050] ​Specifically, each moment is a statistical quantity describing the distribution characteristics of a random variable. By calculating the moments related to the unit capacity and the equivalent forced outage rate of the thermal power unit, the distribution characteristics of the available capacity of the thermal power unit can be further revealed. For example, the first moment can reflect the expected value of the available capacity, and the second moment can reflect the variance and other information of the available capacity.

[0051] Here, the calculation formula of the moments of the thermal power unit can be expressed as:

[0052]

[0053] wherein GM i,m represents the mth moment of the thermal power unit i; represents the probability of the available capacity of the thermal power unit i; EFOR i represents the equivalent forced outage rate of the thermal power unit i; C i represents the unit capacity of the thermal power unit i.

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

[0055] It can be understood that by substituting the moments of each thermal power unit calculated in the foregoing into the semi-invariant formula, the semi-invariant value of the available capacity of each thermal power unit can be obtained.

[0056] Here, the semi-invariant calculation formula is expressed as:

[0057]

[0058] wherein GC i,1 represents the mth semi-invariant of the thermal power unit i; GM i,m represents the mth moment of the thermal power unit i; is a binomial formula.

[0059] S103, calculating the capacity demand curve value of each unit capacity based on the time sequence net load data of the power system in the target time period, the unit capacity of each thermal power unit and the semi-invariant of the available capacity.

[0060] It can be understood that, according to the time sequence net load data of the power system in the target time period, the unit installed capacity of each thermal power unit and the semi-invariant of the available capacity calculated in the foregoing, the capacity demand curve value of each installed capacity can be further solved. Here, the capacity demand curve value is a quantitative index, which is used to reflect the degree of demand of the power system for the power generation capacity of the thermal power unit under different installed capacities. In actual power system operation, the size of the installed capacity and the uncertainty of the available capacity will affect the capacity demand of the system. For example, when the installed capacity is small, the system may have more urgent demand for the power generation capacity of the thermal power unit (the price may be higher); and the uncertainty of the available capacity, such as the fluctuation of the available capacity caused by unit failure and other factors, will also affect the demand assessment of the system for capacity.

[0061] Specifically, referring to FIG. 8, due to factors such as equipment failure, maintenance, etc., the fluctuation range of the actual available capacity of the unit is not always a constant, but its change range can be estimated, so according to these data, the calculation of the capacity demand curve value can be carried out through the following steps S301-S306: Figure 3

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

[0063] Here, first set the initial k value, let k = 1, this k value is used to represent the number of thermal power units currently considered to be loaded, and at the same time, a blank capacity demand curve value set is constructed, which will be used to store the capacity demand curve values corresponding to each installed capacity calculated subsequently.

[0064] S302, based on the unit installed capacity of each thermal power unit and the semi-invariant of the available capacity, calculate the semi-invariant and installed capacity of the effective capacity of the power system after loading of 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 capacity that can actually stably supply power to the power system after considering factors such as the available capacity of the thermal power unit and its uncertainty. By combining the unit installed capacity and the semi-invariant of the available capacity of the first k thermal power units, the semi-invariant and installed capacity of the effective capacity of the power system after loading of the first k thermal power units can be obtained.

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

[0067] (1) determining the installed capacity of the effective capacity of the power system after loading of the first k thermal power units based on the unit installed capacity corresponding to the first k thermal power units; ​

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

[0069] It can be understood that when the installed capacity and semi-invariant of the effective capacity of the power system are solved, a summation formula can be used, which is as follows:

[0070]

[0071] wherein, represents the installed capacity of the effective capacity of the power system after the first k thermal power units are loaded; C i represents the installed capacity of the thermal power unit i; EGC k,m represents 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, determining the probability distribution function of the effective capacity of the power system after the first k thermal power units are loaded based on the semi-invariant of the effective capacity of the power system after the first k thermal power units are loaded.

[0073] It can be understood that the probability distribution function is mainly used to describe the probability of the effective capacity of the power system in different value ranges. Through the mathematical conversion relationship between the semi-invariant and the probability distribution function, the function can be obtained, so as to further understand the distribution rule of the effective capacity of the system.

[0074] In some possible embodiments, since the semi-invariant values of the effective capacity of the power system can have different dimensions, orders of magnitude or distribution ranges, it can bring certain difficulties and errors to the subsequent solving of the probability distribution function, so before solving the probability distribution function of the effective capacity of the power system, the semi-invariant of the effective capacity of the power system after the first k thermal power units are loaded can also be normalized by using a normalization formula. Normalization is a common data preprocessing method, and its purpose is to convert data of different dimensions, orders of magnitude or distribution ranges into a form with uniform standard. For the semi-invariant of the effective capacity of the power system, normalization can eliminate the dimensional differences between data, so that the values of each semi-invariant are in 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] wherein, g n, n = 1, 2, 3, 4 are EGC k,m , m = 1, 2, 3, 4 are normalized results corresponding to EGC k,m is expressed as the mth order semi-invariant of the effective capacity of the power system after loading the first k thermal power units.

[0080] Further, after the semi-invariant of the effective capacity of the power system after loading the first k thermal power units is normalized, 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 normalized result, which can be expressed as:

[0081]

[0082]

[0083] wherein, is expressed as the probability distribution function of the effective capacity of the power system after loading the first k thermal power units; x is expressed as a variable of the probability distribution function.

[0084] S304, based on the time sequence net load data of the power system in the target time period, the installed capacity of the effective capacity of the power system after loading the first k thermal power units and the probability distribution function, determining the capacity demand curve value in the target time period corresponding to the installed capacity, and storing the capacity demand curve value in the target time period corresponding to the installed capacity in the capacity demand curve value set.

[0085] Specifically, the time sequence net load data reflects the actual demand situation of the power system, and in combination with the installed capacity of the system effective capacity and the probability distribution function, the capacity demand curve value in the target time period under the current installed capacity condition can be calculated. Subsequently, the calculated capacity demand curve value in the target time period corresponding to the installed capacity can be stored in the capacity demand curve value set constructed before.

[0086] Exemplarily, in determining the capacity demand curve value in the target time period corresponding to the installed capacity, the following (a) to (b) can be included:

[0087] (a) obtaining the net capacity investment cost of the thermal power unit;

[0088] (b) determining the capacity demand curve value in the target time period corresponding to the installed capacity based on the curve value solving formula, the net capacity investment cost of the thermal power unit, the time sequence net load data of the power system in the target time period, the installed capacity of the effective capacity of the power system after loading the first k thermal power units and the probability distribution function.

[0089] Here, the net capacity investment cost of the thermal power unit represents the net cost invested for obtaining the power generation capacity of the thermal power unit, which can cover a series of costs directly related to capacity acquisition, such as construction of the thermal power unit, purchase of equipment, installation and commissioning, and can also deduct some related subsidies or residual value and other factors.

[0090] Further, after obtaining the net capacity investment cost of the thermal power unit, based on the curve value solving 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 power system effective capacity after loading of the first k thermal power units, and the probability distribution function, the capacity demand curve value corresponding to the installed capacity in the target time period can be determined. Specifically, the curve value solving 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 effective capacity, and the probability characteristics of the power supply capacity revealed by the probability distribution function. By substituting the net capacity investment cost of the thermal power unit and other related data into the formula, the capacity demand curve value in the target time period under the current installed capacity condition can be calculated. Here, the curve value solving formula can be expressed as:

[0091]

[0092] Wherein, L t represents the net load data corresponding to time t in the time-series net load data; NetCONE represents the net capacity investment cost of the thermal power unit; represents the installed capacity corresponding to the capacity demand curve value in the target time period; represents the target time period; represents the number of all time points in 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, execute step S302, and if no, execute step S306.

[0094] Here, the value of k is updated, i.e. 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 of the determination is yes, it means that there are still thermal power units that have not been considered for loading, and then the step S302 is returned to execute, and the relevant data after loading of the next number of thermal power units is calculated; if the result of the determination is no, it means that the loading of all thermal power units has been considered, and then the step S306 is executed.

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

[0096] Specifically, the capacity demand curve value set stores capacity demand curve values corresponding to different installed capacities. Through sorting of these data, the capacity demand curve values of various installed capacities can be finally determined.

[0097] In the embodiments of the present disclosure, the time sequence net load data of the power system in the target time period can more accurately reflect the actual power demand dynamic change of the power system in the target time period, and the installed capacity and the probability distribution function of the effective capacity of the power system after loading of the first k thermal power units can respectively depict the power supply capacity of the power system from the two dimensions of certainty and probability. By determining the capacity demand curve value corresponding to the installed capacity in the target time period based on them, various characteristics of the actual demand and power supply capacity of the power system can be more fully considered, so that more accurate and actual capacity demand curve values can be obtained, and calculation errors caused by single data dimension or one-sided analysis can be avoided.

[0098] In S104, a capacity demand curve in the target time period is determined according to the capacity demand curve values of the various installed capacities.

[0099] It can be understood that the capacity demand curve in the target time period can be determined according to the capacity demand curve values of the various installed capacities calculated in the foregoing. The capacity demand curve is a curve with installed capacity as the horizontal coordinate and capacity demand curve value (such as price) as the vertical coordinate, which depicts the law of the demand of the power system for the power generation capacity of the thermal power unit with the change of installed capacity in the target time period.

[0100] Specifically, by connecting and fitting the capacity demand curve values corresponding to the various installed capacities, a complete capacity demand curve can be obtained. This curve can help the planner to reasonably determine the installed scale and layout of the thermal power unit, optimize the resource allocation of the power system, and ensure that the power system meets the power demand while realizing economic, reliable and efficient operation; for the operation personnel of the power system, the capacity demand curve can be used as an important reference for dispatching decision, and according to the change trend of the curve, the start-stop and power generation plan of the thermal power unit can be reasonably arranged, and the operation flexibility and stability of the power system can be improved.

[0101] The calculation method, device, medium and equipment of the capacity demand curve provided in the embodiments of the present disclosure improve the accuracy of the calculation of the capacity demand curve, capture the dynamic change of the load through the time sequence net load data, unify the calculation standard of the loss of load probability, avoid the error based on the continuous load curve by using the semi-invariant formula, optimize the capacity compensation mechanism, combine the unit operation cost and other parameters, reduce the deviation of using the average electricity price to replace the instantaneous electricity price, enhance the adaptability of the forward capacity market to the loss of load value and the fluctuation of the spot price, and ensure that the power generation capacity planning is more in line with the actual demand.

[0102] Those skilled in the art can understand that, in the above method of the specific embodiment, the writing order of each step does not mean a strict execution order and does not constitute any limitation on the implementation process, and the specific execution order of each step should be determined according to its function and possible internal logic.

[0103] Based on the same inventive concept, the disclosure embodiments also provide a capacity demand curve calculation device corresponding to the capacity demand curve calculation method. Since the device in the disclosure embodiments solves the problem in the same principle as the above-mentioned capacity demand curve calculation method of the disclosure embodiments, the implementation of the device can be referred to the implementation of the method, and the repeated parts will not be described here.

[0104] Referring to Figure 4 As shown in the figure, a schematic diagram of a capacity demand curve calculation device 400 provided by the disclosure embodiments, the device comprises:

[0105] A data acquisition module 401 is configured to acquire unit parameters of a thermal power unit set and time-series net load data of a power system in a target time period, wherein the thermal power unit set comprises a plurality of thermal power units, and the unit parameters comprise unit installed capacity, equivalent forced outage rate and operation cost corresponding to each thermal power unit.

[0106] A semi-invariant calculation module 402 is configured to calculate 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 corresponding to each thermal power unit.

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

[0108] A curve determination module 404 is configured to determine a capacity demand curve in the target time period according to the capacity demand curve values of each installed capacity.

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

[0110] Sort each thermal power unit in the thermal power unit set according to the operation cost corresponding to each thermal power unit in ascending order to obtain a thermal power unit ordered set arranged in ascending order of operation cost;

[0111] For each thermal power unit in the thermal power unit ordered set, calculate each order moment of the thermal power unit based on the unit installed capacity and the equivalent forced outage rate of the thermal power unit.

[0112] For each thermal power unit, according to a semi-invariant calculation formula and each order moment of the thermal power unit, a semi-invariant of an available capacity of the thermal power unit is calculated.

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

[0114]

[0115] wherein GC i,1 represents the mth order semi-invariant of the thermal power unit i; GM i,m represents the mth order distance of the thermal power unit i; is a binomial formula.

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

[0117] Step 1: setting an initial k value, k = 1; and constructing an empty capacity demand curve value set;

[0118] Step 2: based on the unit installed capacity of each thermal power unit and the semi-invariant of the available capacity, calculating the semi-invariant and installed capacity of the effective capacity of the power system after loading of the first k thermal power units in the ordered set of thermal power units;

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

[0120] Step 4: based on the time sequence net load data of the power system in the target time period, the installed capacity of the effective capacity of the power system after loading of the first k thermal power units and the probability distribution function, determining the capacity demand curve value in the target time period corresponding to the installed capacity, and storing the capacity demand curve value in 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, returning to Step 2, N representing the total number of thermal power units in the ordered set of thermal power units, otherwise executing Step 6;

[0122] Step 6: based on the capacity demand curve value set, determining the capacity demand curve value of each installed capacity.

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

[0124] based on the unit installed capacity corresponding to the first k thermal power units, determining the installed capacity of the effective capacity of the power system after loading of the first k thermal power units;

[0125] determine the semi-invariant of the effective capacity of the power system after the first k thermal power generating units are loaded based on the semi-invariant of the available capacity corresponding to the first k thermal power generating units.

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

[0127] normalize the semi-invariant of the effective capacity of the power system after the first k thermal power generating units are loaded based on a normalization processing formula, and determine a probability distribution function of the effective capacity of the power system after the first k thermal power generating units are loaded based on a normalization processing result.

[0128] The normalization processing formula is represented as:

[0129] g1=0;

[0130] g2=1;

[0131]

[0132] wherein g n , n=1, 2, 3, 4 are the normalized results of the EGC k,m , m=1, 2, 3, 4 corresponding to the EGC k,m is represented as the mth-order semi-invariant of the effective capacity of the power system after the first k thermal power generating units are loaded.

[0133] The probability distribution function is represented as:

[0134]

[0135] wherein, is represented as the probability distribution function of the effective capacity of the power system after the first k thermal power generating units are loaded; and x is represented as a variable of the probability distribution function.

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

[0137] obtain a net capacity investment cost of the thermal power generating unit;

[0138] determine the capacity demand curve value of the target time period corresponding to the installed capacity based on a curve value solving formula, the net capacity investment cost of the thermal power generating unit, time sequence 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 generating units are loaded, and the probability distribution function.

[0139] wherein the curve value solving formula is represented as:

[0140]

[0141] wherein, L t represents the net load data corresponding to time t in the time sequence net load data; NetCONE represents the net capacity investment cost of the thermal power generating unit; represents the installed capacity corresponding to the capacity demand curve value in the target time period; represents the target time period; represents the number of all time points in the target time period.

[0142] Based on the same technical concept, the embodiments of the present disclosure further provide a computer device. Referring to FIG. 5, Figure 5 As shown in FIG. 5, a structural schematic diagram of a computer device 500 provided by the embodiments of the present disclosure includes a processor 501, a memory 502, and a bus 503. The memory 502 is used for storing execution instructions, including an internal memory 5021 and an external memory 5022; the internal memory 5021 is also called an internal storage, and is used for temporarily storing operation data in the processor 501 and data exchanged with the external memory 5022 such as a hard disk, and the processor 501 exchanges data with the external memory 5022 through the internal memory 5021.

[0143] In the embodiments of the present application, the memory 502 is specifically used for storing application program codes for executing the schemes of the present application, and is controlled to execute by the processor 501. That is, when the computer device 500 is running, the processor 501 and the memory 502 communicate through the bus 503, so that the processor 501 executes the application program codes stored in the memory 502, and further executes the method described in any of the preceding embodiments.

[0144] The memory 502 can be, but is not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read only memory (PROM), an erasable programmable read only memory (EPROM), an electric erasable programmable read only memory (EEPROM), and the like.

[0145] The processor 501 can be an integrated circuit chip having a signal processing capability. The processor described above can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), or the like; or can be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic device, discrete gate or transistor logic device, discrete hardware component. The methods, steps, and logical block diagrams disclosed in the embodiments of the present application can be implemented or executed by the processor. The general-purpose processor can be a microprocessor, or the processor can also be any conventional processor or the like.

[0146] It can be understood that the structure illustrated in the embodiments of the present application does not constitute a specific limitation on the computer device 500. In some other embodiments of the present application, the computer device 500 can include more or fewer components than those illustrated, or combine certain components, or split certain components, or different arrangement of components. The illustrated components can be implemented in hardware, software, or a combination of software and hardware.

[0147] The embodiments of the present disclosure further provide a computer readable storage medium, which stores a computer program. When the computer program is run by a processor, the steps of the capacity demand curve calculation method described in the above method embodiments are executed. The storage medium can be a volatile or non-volatile computer readable storage medium.

[0148] The embodiments of the present disclosure further provide a computer program product, which carries a program code. The instructions included in the program code can be used to execute the steps of the capacity demand curve calculation method described in the above method embodiments. For details, refer to the above method embodiments, which will not be described here.

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

[0150] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system and device described above can refer to the corresponding process in the foregoing method embodiment, and will not be repeated here. In several embodiments provided in the present disclosure, it should be understood that the disclosed system and method can be implemented in other ways. The foregoing device embodiments are merely schematic, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, and for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some communication interfaces, devices or units, and can be electrical, mechanical or other forms.

[0151] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, can be located in one place, or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0152] In addition, the functional units in each embodiment of the present disclosure can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit.

[0153] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a non-volatile computer readable storage medium executable by a processor. Based on this understanding, the technical solutions of the present disclosure essentially or the part of the prior art or the part of the technical solutions can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the method described in the embodiments of the present disclosure. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various program code storage media.

[0154] Finally, it should be noted that the above-described embodiments are merely specific embodiments of the present disclosure, used to illustrate the technical solutions of the present disclosure, and are not intended to limit the present disclosure. The protection scope of the present disclosure is not limited thereto. Although the present 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 make modifications or easy changes to the technical solutions described in the foregoing embodiments, or easily think of changes or equivalent replacements for some of the technical features; and these modifications, changes or replacements do not cause the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure, and should be covered within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be subject to 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 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. 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. 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 semi-invariant formula, based on the installed capacity, equivalent forced outage rate, and operating cost of each thermal power unit, is used to calculate the available capacity of each thermal power unit. This includes: 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, the moments of the thermal power unit are calculated based on the unit's installed capacity and the equivalent forced outage rate. 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.

3. The method according to claim 2, characterized in that, The formula for calculating the semi-invariant is expressed as follows: 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.

4. The method according to claim 2, characterized in that, The calculation of 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 value of the available capacity includes: Step 1: Set the 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 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 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. Step 6: Based on the set of capacity demand curve values, determine the capacity demand curve value for each installed capacity.

5. The method according to claim 4, 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-invariant of the available capacity corresponding to the first k thermal power units, the semi-invariant of the effective capacity of the power system after the first k thermal power units are loaded is determined.

6. The method according to claim 4, 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: g1＝0； g2＝1； 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; 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.

7. The method according to claim 6, characterized in that, The determination of the capacity demand curve value within the target time period corresponding to the installed capacity, based on the time-series net load data of the power system within the target time period, the effective capacity of the power system after the loading of the first k thermal power units, and the probability distribution function, includes: Obtain the net capacity investment cost of thermal power units; 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. The formula for solving the curve value is expressed as follows: 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.

8. 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; 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. 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. 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.

9. 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 7.

10. 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 7.

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