Rapid calculation method and device for dynamic operation standby demand curve and medium

Abstract

The invention relates to a rapid calculation method and device for a dynamic operation standby demand curve and a medium. The method comprises the steps of collecting the discrete probability of a power loss load probability calculation parameter random variable; calculating each moment of each power loss load probability calculation variable, and converting each moment into a corresponding semi-invariant; obtaining a semi-invariant of the total uncertainty net demand; carrying out normalization processing, and generating a probability distribution function of the total uncertainty net demand based on an Edgeworth series expansion method; calculating a power loss load probability; generating a scarce additional price based on the power loss-of-load probability, the loss-of-load value and the reserve market clearing price by combining the relationship between the available reserve capacity and the minimum reserve capacity; and traversing the available reserve capacity of different values, and generating a dynamic operation reserve demand curve. Compared with the prior art, the method has the advantages of high efficiency, high real-time performance, high reliability and the like.

Description

Technical Field

[0001] The present invention relates to a train signal control system, and in particular to a method, device and medium for quickly calculating a dynamic operation standby demand curve. Background Art

[0002] In the electricity spot market, the scarcity price mechanism is one of the core tools to ensure the reliability of the power system and market efficiency. Its core logic is to dynamically reflect the degree of power supply and demand tension in different periods through the Operating Reserve Demand Curve (ORDC), and generate additional electricity prices based on the available reserve capacity to encourage the power generation side to provide sufficient reserve capacity and guide the user side to adjust demand. In the existing technology, the generation of the operating reserve demand curve mainly relies on the correlation model of the loss of load probability (LOLP) and the value of lost load (VOLL). Specifically, when the available reserve capacity of the system is lower than the minimum threshold, the additional electricity price approaches VOLL, and vice versa, it decreases in a step-by-step manner as the reserve capacity increases. This type of method has been put into practical application in some market-oriented areas.

[0003] However, existing technologies have drawbacks: existing ORDC generation methods typically only consider the impact of renewable energy forecast errors on reserve demand, while key reliability parameters such as the forced outage rate (EFOR) of thermal power units are not incorporated into the calculation model. This leads to overly optimistic reserve capacity assessments that fail to truly reflect the potential load loss risks facing the system. Furthermore, existing methods often generate fixed ORDC curves based on broad timeframes such as seasonal or annual periods and directly apply them to all daily periods. Due to significant differences in load characteristics, renewable energy output, and unit availability across different time periods, a single curve cannot accurately depict short-term supply and demand fluctuations, such as hourly levels, resulting in lags and deviations in scarcity electricity prices. Furthermore, existing ORDC generation requires multi-dimensional convolution operations on complex probability distributions, which is computationally time-consuming. In power systems with high renewable energy penetration, the randomness of forecast errors and unit states further exacerbates the computational complexity, making it difficult for existing methods to support the dynamic generation of ORDC curves by time period. The above-mentioned defects make the existing scarcity electricity price mechanism difficult to adapt to the power system environment with an increasing proportion of highly volatile power sources. It is unable to accurately incentivize investment in power generation capacity and may also lead to market manipulation or supply and demand imbalance risks due to distorted electricity price signals.

[0004] Chinese application CN118826163A discloses a joint clearing method and system that takes into account peak shaving and standby. It integrates the peak shaving market with the spot market, combines the peak shaving resources on the user side and the power generation side, and reflects the scarcity value of standby services through the standby demand curve. However, this application does not dynamically generate standby demand curves for different time periods. Its model relies on fixed or coarse-grained curves, resulting in a lag in real-time electricity price signals and an inability to accurately reflect short-term supply and demand changes. Therefore, how to efficiently integrate multi-source uncertainties and support a method for generating an operating standby demand curve that supports refined time period modeling to improve the timeliness and accuracy of the scarcity electricity price mechanism is a technical problem that needs to be solved. Summary of the Invention

[0005] The purpose of the present invention is to overcome the defects of the above-mentioned existing technologies and provide a method for quickly calculating a dynamic operating standby demand curve. By considering random factors such as load forecast error, wind power generation forecast error, photovoltaic power generation forecast error and the available capacity of thermal power generating units, the operating standby demand curve is quickly generated, and the corresponding scarcity electricity price can be obtained in real time, thereby generating a more accurate market price and promoting the efficient operation of the power market.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] According to one aspect of the present invention, a method for quickly calculating a dynamic operation standby demand curve is provided, the specific steps comprising:

[0008] S1. Collect the discrete probabilities of random variables of power load loss probability calculation parameters, including the discrete probabilities of the load forecast error random variable, the discrete probabilities of the wind power generation forecast error random variable, the discrete probabilities of the photovoltaic power generation forecast error random variable, and the discrete probabilities of the available power generation capacity of the thermal power generation unit;

[0009] S2. Calculating the moments of the power load failure probability calculation variables based on the discrete probability distribution data of the power load failure probability calculation parameter random variables, and converting the moments into corresponding semi-invariants using a semi-invariant conversion formula;

[0010] S3. Obtain the semi-invariant of the total uncertainty net demand based on the semi-invariant of the load forecast error, the semi-invariant of the wind power generation forecast error, the semi-invariant of the photovoltaic power generation forecast error, and the semi-invariant of the available power generation capacity of the thermal power units;

[0011] S4. Normalizing the semi-invariant of the total uncertain net demand, and generating a probability distribution function of the total uncertain net demand based on an Edgeworth series expansion method;

[0012] S5. Calculate the probability of power load loss based on the probability distribution function of the total uncertain net demand; combine the relationship between available reserve capacity and minimum reserve capacity, and generate a scarcity price premium based on the probability of power load loss, the value of load loss, and the reserve market clearing price; traverse different values of available reserve capacity to generate a corresponding set of scarcity price premium points, and fit them into a dynamic operating reserve demand curve.

[0013] Furthermore, the discrete probability of the load forecast error random variable in S1 is expressed as:

[0014]

[0015] in, is the kth power value of the load forecast error random variable, is the probability corresponding to the kth power value of the load forecast error random variable, NL is the total number of load forecast error values, and

[0016] The discrete probability of the wind power generation prediction error random variable is expressed as:

[0017]

[0018] in, is the kth power value of the wind power generation prediction error random variable, is the probability corresponding to the kth power value of the wind power generation prediction error random variable, NW is the total number of wind power generation prediction error values, and

[0019] The discrete probability of the photovoltaic power generation prediction error random variable is expressed as:

[0020]

[0021] in, is the kth power value of the photovoltaic power generation prediction error random variable, is the probability corresponding to the kth power value of the photovoltaic power generation forecast error random variable, NPV is the total number of load forecast error values, and

[0022] The discrete probability of the available generating capacity of a thermal power generator is expressed as:

[0023]

[0024] in: is the available generating capacity of thermal power generating unit i, i = 1, ..., NG; NG is the total number of thermal power generating units; C iis the installed capacity of thermal power generating unit i, in MW; is the probability of availability of thermal power generating unit i; The unavailability probability of thermal power generation unit i; EFOR i is the forced outage rate of thermal power generating unit i.

[0025] Furthermore, in S2, the moments of the power load failure probability calculation variables include moments of load forecast error, moments of wind power generation forecast error, moments of photovoltaic power generation forecast error, and moments of available power generation capacity of thermal power generating units;

[0026] The expressions of the load forecast error moments are as follows:

[0027]

[0028] Among them, GM L,m is the mth moment of system load forecast error; is the kth power value of the load forecast error, is the probability corresponding to the kth power value of the load forecast error, NL is the total number of load forecast error values, and

[0029] The expressions of the moments of wind power generation prediction error are as follows:

[0030]

[0031] Among them, GM W,m is the mth moment of the system wind power generation prediction error; is the kth power value of wind power generation prediction error, is the probability corresponding to the kth power value of wind power generation prediction error, NW is the total number of wind power generation prediction error values, and

[0032] The expressions of the moments of photovoltaic power generation prediction error are as follows:

[0033]

[0034] Among them, GM PV,m is the mth order moment of the system photovoltaic power generation prediction error; is the kth power value of photovoltaic power generation prediction error, is the probability corresponding to the kth power value of the load forecast error, NPV is the total number of load forecast error values, and

[0035] The expressions of the moments of the available power generation capacity of the thermal power generating unit i based on the two-state model are as follows:

[0036]

[0037] Among them: GM i,m is the mth-order moment of thermal power generator set i; is the probability of available capacity of generator set i, EFOR i is the front-row outage rate of generator set i; C i is the installed capacity of generator set i.

[0038] Furthermore, in said S2, the load forecast error random variable, the wind power generation forecast error random variable, the photovoltaic power generation forecast error random variable and the semi-invariant of the available power generation capacity of the thermal power generation unit are calculated.

[0039] The semi-invariant expression of the load forecast error random variable is:

[0040]

[0041] Among them, GC L,m is the mth order semi-invariant of the system load forecast error; GM L,m is the mth moment of system load forecast error; is the binomial formula;

[0042] The semi-invariant expression of the wind power generation prediction error random variable is:

[0043]

[0044] Among them, GC W,m is the mth order semi-invariant of the system wind power generation prediction error; GM W,m is the mth moment of the system wind power generation prediction error; is the binomial formula;

[0045] The semi-invariant expression of the photovoltaic power generation prediction error random variable is:

[0046]

[0047] Among them, GC PV,m is the mth order semi-invariant of the system photovoltaic power generation prediction error; GM PV,m is the mth order moment of the system photovoltaic power generation prediction error; is the binomial formula.

[0048] Furthermore, in S3, the semi-invariant of the total uncertainty net demand is:

[0049]

[0050] Among them: GC S,m is the mth order semi-invariant of the total uncertainty net demand; GC L,m is the mth order semi-invariant of the system load forecast error; GC W,m is the mth order semi-invariant of the system wind power generation prediction error; GC PV,m is the mth order semi-invariant of the system photovoltaic power generation prediction error; GC i,m is the mth-order semi-invariant of thermal power unit i, and NG is the number of thermal power units.

[0051] Furthermore, in S4, normalization processing is performed on the semi-invariants of each order of the total uncertain net demand to obtain a first normalized value, a second normalized value, a third normalized value, a fourth normalized value, and a fifth normalized value, and a probability distribution function of the total uncertain net demand is further generated based on the Edgeworth series expansion.

[0052] The expression of the normalization process is:

[0053]

[0054] Among them, g1 is the first normalized value; g2 is the second normalized value; g3 is the third normalized value; g4 is the fourth normalized value; GC S,2 is the second-order semi-invariant of the total uncertainty net demand; GC S,3 is the third-order semi-invariant of the total uncertainty net demand; GC S,4 is the fourth-order semi-invariant of the total uncertainty net demand;

[0055] The fifth normalized value is the normalized value of the available reserve capacity R(r), which is obtained based on the frequency regulation, spinning reserve and non-spinning reserve capacity reported by the market. The fifth normalized value The expression is:

[0056]

[0057] Among them, GC S,1 is the first-order semi-invariant of the total uncertainty net demand, GC S,2 is the second-order semi-invariant of the total uncertain net demand, and r is the available spare capacity parameter.

[0058] Furthermore, the probability distribution function of the total uncertainty net demand is The expression is:

[0059]

[0060] in, is the fifth normalized value, is the cumulative distribution function of the standard normal distribution, g3 is the third normalized value; g4 is the fourth normalized value;

[0061] is the second-order form of the Edgeworth series expansion, expressed as:

[0062]

[0063] is the third-order form of the Edgeworth series expansion, expressed as:

[0064]

[0065] is the fifth-order form of the Edgeworth series expansion, expressed as:

[0066]

[0067] Furthermore, in S5, the horizontal axis of the operating reserve demand curve ORDC is the available reserve capacity R, and the vertical axis is the scarcity additional price, and the expression is:

[0068]

[0069] in, is the scarcity price added at time t under the ORDC mechanism, λ represents the reserve market clearing price, R is the available reserve capacity; X is the minimum available reserve capacity, LOLP t (R) is the probability of power load loss when the available reserve capacity is R at time t;

[0070] The probability of power load loss when the available reserve capacity at time t is R is given by the total uncertainty net demand The expression is:

[0071]

[0072] According to a second aspect of the present invention, an electronic device is provided, comprising a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the method when executing the program.

[0073] According to a third aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the method described above is implemented.

[0074] Compared with the prior art, the present invention has the following beneficial effects:

[0075] (1) Accurate generation of dynamic time-of-day electricity prices: By integrating multiple sources of uncertainty factors such as load forecast error, wind power / photovoltaic power forecast error, and forced outage rate of thermal power units, and based on the semi-invariant method and Edgeworth series expansion, the time-of-day operation reserve demand curve (ORDC) is quickly generated to achieve dynamic calculation of the scarcity electricity price at each moment t, accurately reflecting the real-time supply and demand tension, and avoiding the electricity price lag and deviation caused by the fixed time range curve.

[0076] (2) Computational efficiency is significantly improved: The semi-invariant method is used to simplify the complex convolution operation of multi-source random variables into semi-invariant linear superposition. The Edgeworth series is combined to perform high-order corrections to the standard normal distribution, quickly generating the net demand probability distribution function, reducing computational complexity, and meeting the day-ahead market's demand for real-time generation of hourly ORDC curves.

[0077] (3) Enhanced system reliability: A two-state model of thermal power units, available / unavailable and their forced outage rate, is introduced into ORDC generation to quantify the impact of traditional unit outages on reserve capacity, ensure that reserve demand assessment is closer to actual risks, reduce the probability of load loss, and improve the safety of power system operation and the reliability of market signals. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 A flowchart of a fast calculation method for dynamically running alternative demand curves;

[0079] Figure 2 Schematic diagram of the alternative demand curve for dynamic operation;

[0080] Figure 3 Schematic diagram of the probability distribution function curve of the total uncertain net demand. DETAILED DESCRIPTION

[0081] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0082] In the electricity spot market, existing methods for generating operating reserve demand curves (ORDCs) have drawbacks, such as failing to incorporate key parameters such as the forced outage rate of thermal power units, using fixed curves that result in delayed electricity prices, and complex and time-consuming calculations. These methods make them difficult to adapt to power system environments where the proportion of highly volatile power sources is increasing. To address these issues, the present invention provides a method for rapidly calculating a dynamic operating reserve demand curve. By integrating multiple sources of uncertainty, such as load forecast error, wind power / photovoltaic forecast error, and the forced outage rate of thermal power units, and based on the semi-invariant method and Edgeworth series expansion, this method can rapidly generate a time-segmented operating reserve demand curve, enabling dynamic calculation of scarcity electricity prices at each moment, accurately reflecting the real-time level of supply and demand tension, improving computational efficiency and system reliability, and providing strong support for the efficient operation of the electricity market.

[0083] like Figure 1 As shown in Figure 1, a fast calculation method for a dynamic operation backup demand curve is provided. The specific steps include:

[0084] S1. Collect the discrete probabilities of random variables of power load loss probability calculation parameters, including the discrete probabilities of the load forecast error random variable, the discrete probabilities of the wind power generation forecast error random variable, the discrete probabilities of the photovoltaic power generation forecast error random variable, and the discrete probabilities of the available power generation capacity of the thermal power generation unit;

[0085] S2. Based on the discrete probability distribution data of the random variables of the power load failure probability calculation parameters, calculate the moments of each power load failure probability calculation variable respectively, and convert each moment into a corresponding semi-invariant using a semi-invariant conversion formula;

[0086] S3. Obtain the semi-invariant of the total uncertainty net demand based on the semi-invariant of the load forecast error, the semi-invariant of the wind power generation forecast error, the semi-invariant of the photovoltaic power generation forecast error, and the semi-invariant of the available power generation capacity of the thermal power units;

[0087] S4. Normalize the semi-invariant of the total uncertain net demand and generate the probability distribution function of the total uncertain net demand based on the Edgeworth series expansion method;

[0088] S5. Calculate the probability of power load loss based on the probability distribution function of the total uncertain net demand; combine the relationship between available reserve capacity and minimum reserve capacity, generate a scarcity price premium based on the probability of power load loss, the value of load loss, and the reserve market clearing price; traverse different values of available reserve capacity, generate corresponding scarcity price premium point sets, and fit them into a dynamic operation reserve demand curve, such as Figure 2 shown.

[0089] Due to the rapid calculation of the operating reserve demand curve, an operating reserve demand curve based on time t can be generated. This allows for a scarcity electricity price corresponding to a different operating reserve demand curve at each time t, thereby generating more accurate market prices and promoting efficient operation of the electricity market. The operating reserve demand curve is a key function of electricity market software, and the content of this embodiment also plays an important role in electricity market-related management systems.

[0090] In S1, the discrete probability of the load forecast error random variable is expressed as:

[0091]

[0092] in, is the kth power value of the load forecast error random variable, is the probability corresponding to the kth power value of the load forecast error random variable, NL is the total number of load forecast error values, and

[0093] The discrete probability of the wind power generation forecast error random variable is expressed as:

[0094]

[0095] in, is the kth power value of the wind power generation prediction error random variable, is the probability corresponding to the kth power value of the wind power generation prediction error random variable, NW is the total number of wind power generation prediction error values, and

[0096] The discrete probability of the photovoltaic power generation prediction error random variable is expressed as:

[0097]

[0098] in, is the kth power value of the photovoltaic power generation prediction error random variable, is the probability corresponding to the kth power value of the photovoltaic power generation forecast error random variable, NPV is the total number of load forecast error values, and

[0099] The discrete probability of the available generating capacity of a thermal power generator is expressed as:

[0100]

[0101] in: is the available generating capacity of thermal power generating unit i, i = 1, ..., NG; NG is the total number of thermal power generating units; C iis the installed capacity of thermal power generating unit i, in MW; is the probability of availability of thermal power generating unit i; The unavailability probability of thermal power generation unit i; EFOR i is the forced outage rate of thermal power generating unit i.

[0102] In S2, the moments of the variables for calculating the probability of power failure include the moments of load forecast error, wind power generation forecast error, photovoltaic power generation forecast error, and available power generation capacity of thermal power generation units.

[0103] The expressions of the moments of load forecast error are as follows:

[0104]

[0105] Among them, GM L,m is the mth moment of system load forecast error; is the kth power value of the load forecast error, is the probability corresponding to the kth power value of the load forecast error, NL is the total number of load forecast error values, and

[0106] The expressions of each order moment of wind power generation prediction error are:

[0107]

[0108] Among them, GM W,m is the mth moment of the system wind power generation prediction error; is the kth power value of wind power generation prediction error, is the probability corresponding to the kth power value of wind power generation prediction error, NW is the total number of wind power generation prediction error values, and

[0109] The expressions of each order moment of photovoltaic power generation prediction error are:

[0110]

[0111] Among them, GM PV,m is the mth order moment of the system photovoltaic power generation prediction error; is the kth power value of photovoltaic power generation prediction error, is the probability corresponding to the kth power value of the load forecast error, NPV is the total number of load forecast error values, and

[0112] The expressions of the moments of available generating capacity of thermal power generating units based on the two-state model of thermal power generating unit i are:

[0113]

[0114] Among them: GM i,m is the mth-order moment of thermal power generator set i; is the probability of available capacity of generator set i, EFOR i is the front-row outage rate of generator set i; C i is the installed capacity of generator set i.

[0115] In addition, the semi-invariants of load forecast error random variables, wind power generation forecast error random variables, photovoltaic power generation forecast error random variables and available power generation capacity of thermal power generation units are calculated.

[0116] The semi-invariant expression of the load forecast error random variable is:

[0117]

[0118] Among them, GC L,m is the mth order semi-invariant of the system load forecast error; GM L,m is the mth moment of system load forecast error; is the binomial formula;

[0119] The semi-invariant expression of the wind power generation forecast error random variable is:

[0120]

[0121] Among them, GC W,m is the mth order semi-invariant of the system wind power generation prediction error; GM W,m is the mth moment of the system wind power generation prediction error; is the binomial formula;

[0122] The semi-invariant expression of the random variable of photovoltaic power generation prediction error is:

[0123]

[0124] Among them, GC PV,m is the mth order semi-invariant of the system photovoltaic power generation prediction error; GM PV,m is the mth order moment of the system photovoltaic power generation prediction error; is the binomial formula.

[0125] In S3, the semi-invariant of the total uncertainty net demand is:

[0126]

[0127] Among them: GC S,mis the mth order semi-invariant of the total uncertainty net demand; GC L,m is the mth order semi-invariant of the system load forecast error; GC W,m is the mth order semi-invariant of the system wind power generation prediction error; GC PV,m is the mth order semi-invariant of the system photovoltaic power generation prediction error; GC i,m is the mth-order semi-invariant of thermal power unit i, and NG is the number of thermal power units.

[0128] By integrating multiple sources of uncertainty, such as load forecast error, wind power and photovoltaic forecast error, and the forced outage rate of thermal power units, a discrete probability distribution including a two-state model of thermal power units is constructed. The forced outage rate of thermal power units is incorporated into the backup demand calculation. This solves the problem in existing technologies where backup capacity assessment ignores the outage risk of traditional units, making the load loss probability calculation closer to actual operating scenarios and significantly improving the accuracy of system reliability assessment.

[0129] In S4, the semi-invariants of each order of the total uncertain net demand are normalized to obtain the first normalized value, the second normalized value, the third normalized value, the fourth normalized value, and the fifth normalized value. The probability distribution function of the total uncertain net demand is further generated based on the Edgeworth series expansion.

[0130] The normalized expression is:

[0131]

[0132] Among them, g1 is the first normalized value; g2 is the second normalized value; g3 is the third normalized value; g4 is the fourth normalized value; GC S,2 is the second-order semi-invariant of the total uncertainty net demand; GC S,3 is the third-order semi-invariant of the total uncertainty net demand; GC S,4 is the fourth-order semi-invariant of the total uncertainty net demand;

[0133] Fifth normalized value is the normalized value of the available reserve capacity R(r), which is obtained based on the frequency regulation, spinning reserve and non-spinning reserve capacity reported by the market. The fifth normalized value The expression is:

[0134]

[0135] Among them, GC S,1 is the first-order semi-invariant of the total uncertainty net demand, GC S,2 is the second-order semi-invariant of the total uncertain net demand, and r is the available spare capacity parameter.

[0136] like Figure 3 As shown, it is the probability distribution function of the total uncertain net demand The curve diagram of is expressed as follows:

[0137]

[0138] in, is the fifth normalized value, is the cumulative distribution function of the standard normal distribution, g3 is the third normalized value; g4 is the fourth normalized value;

[0139] is the second-order form of the Edgeworth series expansion, expressed as:

[0140]

[0141] is the third-order form of the Edgeworth series expansion, expressed as:

[0142]

[0143] is the fifth-order form of the Edgeworth series expansion, expressed as:

[0144]

[0145] The semi-invariant method is used to transform complex multi-dimensional probability convolution operations into semi-invariant linear superposition, and the Edgeworth series is combined to perform high-order corrections to the standard normal distribution. The probability distribution function of the total uncertainty net demand can be quickly constructed using only the first four moments. Compared with traditional methods, this method greatly reduces the computational complexity and shortens the computational time from high-dimensional operations of complex convolution to polynomial calculations of linear superposition and series expansion, meeting the needs of dynamic generation of ORDC at the hourly level or even shorter time slots in the day-ahead market.

[0146] In S5, the operating reserve demand curve ORDC has the available reserve capacity R on the horizontal axis and the scarcity price on the vertical axis, which is expressed as:

[0147]

[0148] in, is the scarcity price added at time t under the ORDC mechanism, λ represents the reserve market clearing price, R is the available reserve capacity; X is the minimum available reserve capacity, LOLP t (R) is the probability of power load loss when the available reserve capacity is R at time t;

[0149] The probability of power load loss when the available reserve capacity is R at time t is given by the total uncertain net demand The expression is:

[0150]

[0151] By traversing available reserve capacity to generate a set of scarcity additional price points, a dedicated ORDC curve is independently generated at each time t, avoiding the price lag and deviation caused by fixed time range curves. This allows the scarcity price to reflect short-term changes in load characteristics, renewable energy output, and unit availability in real time, accurately incentivizing investment in reserve capacity on the power generation side and demand regulation on the user side. Overall, this embodiment, through multi-source uncertainty fusion, efficient probabilistic modeling, and time-based dynamic calculation, not only improves the operational safety of the power system, but also provides the power market with more accurate price signals, effectively promoting the optimal allocation of market resources and efficient operation.

[0152] In practical application, the steps to obtain the scarcity price from the operating reserve demand curve ORDC are as follows: set the initial available reserve capacity value R = a0, the interval value ΔR, and the capacity length N R , assign N=N R ,calculate Let R=R+ΔR;N=N-1, if N>0, then switch to calculation Otherwise stop.

[0153] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described module can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0154] The electronic device of the present invention includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) or loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for device operation can also be stored. The CPU, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0155] Multiple components in the device are connected to the I / O interface, including: input units, such as a keyboard, mouse, etc.; output units, such as various types of displays, speakers, etc.; storage units, such as magnetic disks, optical disks, etc.; and communication units, such as network cards, modems, wireless communication transceivers, etc. The communication unit allows the device to exchange information / data with other devices via computer networks such as the Internet and / or various telecommunications networks. The processing unit performs the various methods and processes described above, such as the method of the present invention. For example, in some embodiments, the method of the present invention can be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program can be loaded and / or installed onto the device via ROM and / or the communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of the method of the present invention described above can be performed. Alternatively, in other embodiments, the CPU can be configured to perform the method of the present invention by any other suitable means (e.g., by means of firmware).

[0156] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0157] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. Such program code can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0158] In the context of the present invention, machine-readable medium can be a tangible medium that can contain or store a program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0159] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and such modifications or substitutions are intended to be within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. A fast calculation method for a dynamic operation standby demand curve, characterized in that: The specific steps include: S1. Collect the discrete probabilities of random variables of power load loss probability calculation parameters, including the discrete probabilities of the load forecast error random variable, the discrete probabilities of the wind power generation forecast error random variable, the discrete probabilities of the photovoltaic power generation forecast error random variable, and the discrete probabilities of the available power generation capacity of the thermal power generation unit; S2. Calculating the moments of the power load failure probability calculation variables based on the discrete probability distribution data of the power load failure probability calculation parameter random variables, and converting the moments into corresponding semi-invariants using a semi-invariant conversion formula; S3. Obtain the semi-invariant of the total uncertainty net demand based on the semi-invariant of the load forecast error, the semi-invariant of the wind power generation forecast error, the semi-invariant of the photovoltaic power generation forecast error, and the semi-invariant of the available power generation capacity of the thermal power units; S4. Normalizing the semi-invariant of the total uncertain net demand, and generating a probability distribution function of the total uncertain net demand based on an Edgeworth series expansion method; S5. Calculate the probability of power load loss based on the probability distribution function of the total uncertain net demand; combine the relationship between available reserve capacity and minimum reserve capacity, and generate a scarcity price premium based on the probability of power load loss, the value of load loss, and the reserve market clearing price; traverse different values of available reserve capacity to generate a corresponding set of scarcity price premium points, and fit them into a dynamic operating reserve demand curve.

2. A fast calculation method for a dynamic operation reserve demand curve according to claim 1, characterized in that: The discrete probability of the load forecast error random variable in S1 is expressed as: in, is the kth power value of the load forecast error random variable, is the probability corresponding to the kth power value of the load forecast error random variable, NL is the total number of load forecast error values, and The discrete probability of the wind power generation prediction error random variable is expressed as: in, is the kth power value of the wind power generation prediction error random variable, is the probability corresponding to the kth power value of the wind power generation prediction error random variable, NW is the total number of wind power generation prediction error values, and The discrete probability of the photovoltaic power generation prediction error random variable is expressed as: in, is the kth power value of the photovoltaic power generation prediction error random variable, is the probability corresponding to the kth power value of the photovoltaic power generation forecast error random variable, NPV is the total number of load forecast error values, and The discrete probability of the available generating capacity of a thermal power generator is expressed as: in: is the available generating capacity of thermal power generating unit i, i = 1, ..., NG; NG is the total number of thermal power generating units; C i is the installed capacity of thermal power generating unit i, in MW; is the probability of availability of thermal power generating unit i; The unavailability probability of thermal power generation unit i; EFOR i is the forced outage rate of thermal power generating unit i.

3. The method for rapidly calculating a dynamic operation reserve demand curve according to claim 1, characterized in that: In S2, the moments of the power load failure probability calculation variables include moments of load forecast error, moments of wind power generation forecast error, moments of photovoltaic power generation forecast error, and moments of available power generation capacity of thermal power generating units; The expressions of the load forecast error moments are as follows: Among them, GM L,m is the mth moment of system load forecast error; is the kth power value of the load forecast error, is the probability corresponding to the kth power value of the load forecast error, NL is the total number of load forecast error values, and The expressions of the moments of wind power generation prediction error are as follows: Among them, GM W,m is the mth moment of the system wind power generation prediction error; is the kth power value of wind power generation prediction error, is the probability corresponding to the kth power value of wind power generation prediction error, NW is the total number of wind power generation prediction error values, and The expressions of the moments of photovoltaic power generation prediction error are as follows: Among them, GM PV,m is the mth order moment of the system photovoltaic power generation prediction error; is the kth power value of photovoltaic power generation prediction error, is the probability corresponding to the kth power value of the load forecast error, NPV is the total number of load forecast error values, and The expressions of the moments of the available power generation capacity of the thermal power generating unit i based on the two-state model are as follows: Among them: GM i,m is the mth-order moment of thermal power generator set i; is the probability of available capacity of generator set i, EFOR i is the front-row outage rate of generator set i; C i is the installed capacity of generator set i.

4. A fast calculation method for a dynamic operation reserve demand curve according to claim 1, characterized in that: In said S2, the load forecast error random variable, the wind power generation forecast error random variable, the photovoltaic power generation forecast error random variable and the semi-invariant of the available power generation capacity of the thermal power generation unit are calculated. The semi-invariant expression of the load forecast error random variable is: Among them, GC L,m is the mth order semi-invariant of the system load forecast error; GM L,m is the mth moment of system load forecast error; is the binomial formula; The semi-invariant expression of the wind power generation prediction error random variable is: Among them, GC W，m is the mth order semi-invariant of the system wind power generation prediction error; GM W，m is the mth moment of the system wind power generation prediction error; is the binomial formula; The semi-invariant expression of the photovoltaic power generation prediction error random variable is: Among them, GC PV,m is the mth order semi-invariant of the system photovoltaic power generation prediction error; GM PV,m is the mth order moment of the system photovoltaic power generation prediction error; is the binomial formula.

5. The method for quickly calculating a dynamic operation reserve demand curve according to claim 1, characterized in that: In S3, the semi-invariant of the total uncertainty net demand is: Among them: GC s,m is the mth order semi-invariant of the total uncertainty net demand; GC L,m is the mth order semi-invariant of the system load forecast error; GC W,m is the mth order semi-invariant of the system wind power generation prediction error; GC PV,m is the mth order semi-invariant of the system photovoltaic power generation prediction error; GC i,m is the mth-order semi-invariant of thermal power unit i, and NG is the number of thermal power units.

6. A fast calculation method for a dynamic operation reserve demand curve according to claim 1, characterized in that: In S4, normalizing the semi-invariants of each order of the total uncertain net demand to obtain a first normalized value, a second normalized value, a third normalized value, a fourth normalized value, and a fifth normalized value, and further generating a probability distribution function of the total uncertain net demand based on an Edgeworth series expansion. The expression of the normalization process is: Among them, g1 is the first normalized value; g2 is the second normalized value; g3 is the third normalized value; g4 is the fourth normalized value; GC S,2 is the second-order semi-invariant of the total uncertainty net demand; GC S,3 is the third-order semi-invariant of the total uncertainty net demand; GC S,4 is the fourth-order semi-invariant of the total uncertainty net demand; The fifth normalized value is the normalized value of the available reserve capacity R(r), which is obtained based on the frequency regulation, spinning reserve and non-spinning reserve capacity reported by the market. The fifth normalized value The expression is: Among them, GC S,1 is the first-order semi-invariant of the total uncertainty net demand, GC S,2 is the second-order semi-invariant of the total uncertain net demand, and r is the available spare capacity parameter.

7. A fast calculation method for a dynamic operation reserve demand curve according to claim 6, characterized in that: The probability distribution function of the total uncertainty net demand The expression is: in, is the fifth normalized value, is the cumulative distribution function of the standard normal distribution, g3 is the third normalized value; g4 is the fourth normalized value; is the second-order form of the Edgeworth series expansion, expressed as: is the third-order form of the Edgeworth series expansion, expressed as: is the fifth-order form of the Edgeworth series expansion, expressed as:

8. A fast calculation method for a dynamic operation reserve demand curve according to claim 1, characterized in that: In S5, the operating reserve demand curve ORDC has the available reserve capacity R as its abscissa and the scarcity price as its ordinate, which can be expressed as follows: in, is the scarcity price added at time t under the ORDC mechanism, λ represents the reserve market clearing price, R is the available reserve capacity; X is the minimum available reserve capacity, LOLP t (R) is the probability of power load loss when the available reserve capacity is R at time t; The probability of power load loss when the available reserve capacity at time t is R is given by the total uncertainty net demand The expression is:

9. An electronic device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the program, the method according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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