Power system reserve capacity determination method and device, electronic equipment and storage medium

Abstract

The invention discloses a method and device for determining the reserve capacity of a power system, electronic equipment and a storage medium, and belongs to the technical field of power systems, and the method comprises the steps: constructing a power shortage expectation function of the power system and an expectation generating capacity function of the reserve capacity in unit time; according to the power insufficiency expectation function and the expectation generating capacity function, by taking the minimum total cost obtained by subtracting the standby calling income from the sum of the standby configuration cost and the power insufficiency expectation cost in unit time as a target, constructing a target function corresponding to a standby capacity decision; and solving the target function to obtain the reserve capacity of the corresponding power system when the total cost obtained by subtracting the reserve calling income from the sum of the reserve configuration cost and the expected cost of insufficient power in unit time is minimum. The problem that in the prior art, the selection decision accuracy of the reserve capacity is not high can be solved.

Description

Technical Field

[0001] The present invention relates to the field of power systems, and in particular, to a method, device, electronic device, and storage medium for determining the reserve capacity of a power system. Background Art

[0002] With the continuous increase in the penetration rate of new energy (such as wind power and photovoltaic power) in the power system, the randomness, intermittency, and volatility of its output have brought severe challenges to the safe and economic operation of the power grid. The prediction of new energy output and load prediction are important bases for the safe operation of the power system. The large net load prediction error caused by the superposition of the prediction errors of the two is an important problem that plagues the safe and economic operation of the power system and is still difficult to change in the long term in the future. The probabilistic modeling of the new energy output prediction error and the load prediction error is an important basis for analyzing the impact of the prediction error and controlling the consequences of the prediction error.

[0003] Among them, the reserve capacity demand for power system operation depends on the net load reserve capacity demand and the accident reserve capacity demand. The accident reserve is generally determined according to the maximum single unit capacity of unified dispatching or the maximum DC power received capacity, which is a fixed value independent of the load power and new energy output. The net load reserve capacity demand depends on the load prediction error and the new energy output prediction error. The main work of the operation reserve decision lies in the net load reserve decision.

[0004] The strong uncertainty superposition of new energy output and load leads to an increasing uncertainty in the operation of the power system, which poses higher requirements for the reserve decision. At present, the deterministic scheduling method formulates the power generation plan based on the load prediction and new energy prediction, and the reserve capacity decision is completed manually, which results in the arbitrariness and blindness of the reserve capacity selection decision, with low accuracy. When the reserve capacity is too low, it is difficult to match the large fluctuations in new energy output and load power, and the system security is difficult to guarantee; on the contrary, it will cause waste of reserve resources and affect the economy of system operation. Summary of the Invention

[0005] The present invention provides a method, device, electronic device, and storage medium for determining the reserve capacity of a power system, which can solve the problem that the reserve capacity selection decision in the prior art is completed manually, resulting in low accuracy.

[0006] To solve the above technical problems, an embodiment of the present invention provides a method for determining the reserve capacity of a power system, including:

[0007] Obtain the reserve decision data of the power system; wherein, the reserve decision data includes: historical actual data of new energy output, historical prediction data of new energy output, historical actual data of load, and historical prediction data of load;

[0008] Calculate the corresponding net load prediction error data according to the standby decision data, and calculate the sample cumulative probability corresponding to the net load prediction error data;

[0009] Construct a normal distribution model corresponding to the net load prediction error data according to the sample cumulative probability, and construct an expected energy not supplied function of the power system and an expected power generation function of the standby capacity per unit time according to the probability density function and cumulative probability distribution function corresponding to the normal distribution model;

[0010] Construct an objective function corresponding to the standby capacity decision with the goal of minimizing the total cost per unit time according to the expected energy not supplied function and the expected power generation function; wherein, the total cost is obtained by adding the standby configuration cost and the expected energy not supplied cost and then subtracting the standby call revenue;

[0011] Solve the objective function according to the sample cumulative probability corresponding to the net load prediction error data, and obtain the standby capacity of the corresponding power system when the total cost per unit time is minimized.

[0012] As a preferred solution, the calculating the corresponding net load prediction error data according to the standby decision data includes:

[0013] Subtract the historical actual data of new energy output at the same time from the historical actual data of the load to obtain the corresponding historical actual data of the net load;

[0014] Subtract the historical predicted data of new energy output at the same time from the historical predicted data of the load to obtain the corresponding historical predicted data of the net load;

[0015] Subtract the historical predicted data of the net load at the same time from the historical actual data of the net load to obtain the corresponding net load prediction error data.

[0016] As a preferred solution, the solving the objective function according to the sample cumulative probability corresponding to the net load prediction error data to obtain the standby capacity of the corresponding power system when the total cost per unit time is minimized includes:

[0017] Obtain a first sample cumulative probability when the net load prediction error is 0 and a second sample cumulative probability when the net load prediction error is the standby capacity to be solved according to the sample cumulative probability corresponding to the net load prediction error data;

[0018] Determine a first cumulative probability when the normal distribution random variable value of the net load prediction error is 0 according to the first sample cumulative probability, and determine a second cumulative probability when the normal distribution random variable value of the net load prediction error is the standby capacity to be solved according to the second sample cumulative probability;

[0019] Based on the first cumulative probability and the second cumulative probability, construct a standard normal distribution cumulative probability table corresponding to the standard normal distribution, and then, based on the standard normal distribution cumulative probability table, obtain the mean and standard deviation of the normal distribution model corresponding to the net load prediction error data;

[0020] Based on the mean and standard deviation, solve the objective function to obtain the reserve capacity of the power system corresponding to the minimum total cost per unit time.

[0021] As a preferred solution, the objective function is:

[0022] min J(R)=C R R+C EDNS P EDNS (R)-C INC P INC (R);

[0023] where J(R) is the total cost per unit time, C R is the unit cost of reserve capacity, R is the reserve capacity of the power system; C EDNS is the unit cost of the expected energy not supplied, P EDNS (R) is the expected energy not supplied; C INC is the unit revenue of the increased power generation when the reserve is called, P INC (R) is the expected power generation of the reserve capacity.

[0024] Based on the above embodiments, another embodiment of the present invention provides a device for determining the reserve capacity of a power system, including: a reserve decision data acquisition module, a net load prediction error data calculation module, an expected energy not supplied and expected power generation calculation module, an objective function construction module, and a reserve capacity solution module;

[0025] The reserve decision data acquisition module is used to acquire the reserve decision data of the power system; wherein, the reserve decision data includes: historical actual data of new energy output, historical predicted data of new energy output, historical actual data of load, and historical predicted data of load;

[0026] The net load prediction error data calculation module is used to calculate the corresponding net load prediction error data according to the reserve decision data, and calculate the sample cumulative probability corresponding to the net load prediction error data;

[0027] The expected energy not supplied and expected power generation calculation module is used to construct a normal distribution model corresponding to the net load prediction error data according to the sample cumulative probability, and construct an expected energy not supplied function of the power system and an expected power generation function of the reserve capacity per unit time according to the probability density function and cumulative probability distribution function corresponding to the normal distribution model;

[0028] The objective function construction module is used to construct an objective function corresponding to the reserve capacity decision with the goal of minimizing the total cost per unit time according to the power shortage expectation function and the expected power generation function; wherein, the total cost is obtained by adding the reserve configuration cost and the power shortage expectation cost and then subtracting the reserve call revenue;

[0029] The reserve capacity solving module is used to solve the objective function according to the sample cumulative probability corresponding to the net load prediction error data, and obtain the reserve capacity of the power system corresponding to the minimum total cost per unit time.

[0030] As a preferred solution, calculating the corresponding net load prediction error data according to the reserve decision data includes:

[0031] Subtracting the historical actual data of new energy output at the same time from the historical actual data of the load to obtain the corresponding historical actual data of the net load;

[0032] Subtracting the historical predicted data of new energy output at the same time from the historical predicted data of the load to obtain the corresponding historical predicted data of the net load;

[0033] Subtracting the historical predicted data of the net load at the same time from the historical actual data of the net load to obtain the corresponding net load prediction error data.

[0034] As a preferred solution, solving the objective function according to the sample cumulative probability corresponding to the net load prediction error data to obtain the reserve capacity of the power system corresponding to the minimum total cost per unit time includes:

[0035] According to the sample cumulative probability corresponding to the net load prediction error data, obtain the first sample cumulative probability when the net load prediction error is 0, and the second sample cumulative probability when the net load prediction error is the reserve capacity to be solved;

[0036] According to the first sample cumulative probability, determine the first cumulative probability when the normal distribution random variable value of the net load prediction error is 0, and according to the second sample cumulative probability, determine the second cumulative probability when the normal distribution random variable value of the net load prediction error is the reserve capacity to be solved;

[0037] According to the first cumulative probability and the second cumulative probability, construct a standard normal distribution cumulative probability table corresponding to the standard normal distribution, and then according to the standard normal distribution cumulative probability table, obtain the mean and standard deviation of the normal distribution model corresponding to the net load prediction error data;

[0038] According to the mean value and the standard deviation, solve the objective function to obtain the reserve capacity of the power system corresponding to the minimum total cost per unit time.

[0039] As a preferred solution, the objective function is:

[0040] min J(R) = C R R + C EDNS P EDNS (R) - C INC P INC (R);

[0041] where J(R) is the total cost per unit time, C R is the unit cost of reserve capacity, R is the reserve capacity of the power system; C EDNS is the unit cost of the expected power shortage, P EDNS (R) is the expected power shortage; C INC is the unit revenue of the increased power generation when the reserve is called, P INC (R) is the expected power generation of the reserve capacity.

[0042] Based on the above embodiments, another embodiment of the present invention provides an electronic device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the method for determining the reserve capacity of the power system described in the above embodiments of the present invention.

[0043] Based on the above embodiments, another embodiment of the present invention provides a storage medium, which includes a stored computer program. When the computer program runs, it controls the device where the storage medium is located to execute the method for determining the reserve capacity of the power system described in the above embodiments of the present invention.

[0044] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0045] The present invention provides a method for determining the reserve capacity of a power system, which includes obtaining the reserve decision data of the power system; wherein, the reserve decision data includes: historical actual data of new energy output, historical forecast data of new energy output, historical actual data of load, and historical forecast data of load; according to the reserve decision data, calculating the corresponding net load forecast error data, and calculating the sample cumulative probability corresponding to the net load forecast error data; according to the sample cumulative probability, constructing a normal distribution model corresponding to the net load forecast error data, and according to the probability density function and cumulative probability distribution function corresponding to the normal distribution model, constructing an expected energy not supplied function of the power system and an expected power generation function of the reserve capacity per unit time; according to the expected energy not supplied function and the expected power generation function, with the goal of minimizing the total cost per unit time, constructing an objective function corresponding to the reserve capacity decision; wherein, the total cost is obtained by adding the reserve configuration cost and the expected energy not supplied cost and then subtracting the reserve call revenue; according to the sample cumulative probability corresponding to the net load forecast error data, solving the objective function to obtain the reserve capacity of the corresponding power system when the total cost per unit time is minimized.

[0046] Through the present invention, according to the reserve decision data of the power system, with the goal of minimizing the total cost obtained by adding the reserve configuration cost and the expected energy not supplied cost per unit time and then subtracting the reserve call revenue, an objective function corresponding to the reserve capacity decision is constructed, and then the objective function is solved to automatically obtain the reserve capacity of the power system. Compared with the manual determination method in the prior art, the present invention can realize the automatic generation of the reserve capacity of the power system and improve the decision-making accuracy of the reserve capacity of the power system. Description of the Drawings

[0047] Figure 1 is a schematic flowchart of a method for determining the reserve capacity of a power system provided by an embodiment of the present invention;

[0048] Figure 2 is a schematic structural diagram of a device for determining the reserve capacity of a power system provided by an embodiment of the present invention. Detailed Embodiments

[0049] To make the objectives, technical solutions, and advantages of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments of the present application belong to the scope of protection of the present application.

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this application belongs; the terms used herein are for the purpose of describing specific embodiments only and are not intended to limit this application; the terms "comprising" and "having" and any variations thereof in the specification and claims of this application and the above drawings are intended to cover non-exclusive inclusion.

[0051] In the description of the embodiments of this application, technical terms such as "first" and "second" are only used to distinguish different objects and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity, specific order or primary-secondary relationship of the indicated technical features. In the description of the embodiments of this application, the meaning of "a plurality" is two or more unless otherwise specifically defined.

[0052] Referring to "embodiments" herein means that the specific features, structures or characteristics described in connection with the embodiments can be included in at least one embodiment of this application. The phrase appearing in various positions in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0053] In the description of the embodiments of this application, the term "and / or" is only a description of the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " herein generally represents an "or" relationship between the associated objects before and after.

[0054] In the description of the embodiments of this application, the terms "a plurality" and "several" refer to two or more (including two). Similarly, "multiple groups" refers to two or more groups (including two groups), and "multiple pieces" refers to two or more pieces (including two pieces).

[0055] In the description of the embodiments of this application, unless otherwise clearly specified and limited, technical terms such as "installation", "connection", "connection", "fixation" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or integrated; it can also be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two components or the interaction relationship between two components. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of this application can be understood according to specific circumstances.

[0056] Embodiment 1

[0057] Please refer to Figure 1, To solve the problem that the standby capacity decision in the prior art is completed manually, resulting in low accuracy of the selection decision of the standby capacity, the schematic flowchart of a method for determining the standby capacity of a power system provided by an embodiment of the present invention includes the following specific steps:

[0058] S1. Obtain the standby decision data of the power system; wherein, the standby decision data includes: historical actual data of new energy output, historical predicted data of new energy output, historical actual data of load, and historical predicted data of load;

[0059] Specifically, first obtain the standby decision data required for the standby decision of a high-proportion new energy power system: (1) historical actual data and historical predicted data of new energy output; (2) historical actual data and historical predicted data of load; (3) unit cost of standby capacity; (4) unit revenue of increased power generation when standby is called; (5) unit cost of load power outage.

[0060] S2. According to the standby decision data, calculate the corresponding net load prediction error data, and calculate the sample cumulative probability corresponding to the net load prediction error data;

[0061] Preferably, the calculating the corresponding net load prediction error data according to the standby decision data includes: subtracting the historical actual data of new energy output at the same moment from the historical actual data of load to obtain the corresponding historical actual data of net load; subtracting the historical predicted data of new energy output at the same moment from the historical predicted data of load to obtain the corresponding historical predicted data of net load; subtracting the historical predicted data of net load at the same moment from the historical actual data of net load to obtain the corresponding net load prediction error data.

[0062] Specifically, after obtaining the standby decision data of the power system, calculate the corresponding net load prediction error data through the following steps:

[0063] (1) Subtract the actual value of new energy output at the same time from the historical actual value of load to obtain the actual value of net load;

[0064] (2) Subtract the predicted value of new energy output at the same time from the historical predicted value of load to obtain the predicted value of net load;

[0065] (3) Subtract the historical predicted value at the same time from the historical actual value of net load to obtain the net load prediction error value.

[0066] After calculating the corresponding net load prediction error data, calculate the sample cumulative probability distribution of the net load prediction error data:

[0067] Sort the net load prediction error data in ascending order. Assume that the net load prediction errors sorted from small to large are x1, x2…xN , where \(N\) represents the number of samples. The probability corresponding to each sample is Therefore, \(x_1, x_2, \cdots, x\) N The corresponding sample cumulative probabilities are respectively Taking the net load prediction error data as the abscissa and the sample cumulative probability as the ordinate, connect each sample point in sequence to form a sample cumulative probability curve, and denote \(\gamma(x)\) as the sample cumulative probability function.

[0068] S3. Construct a normal distribution model corresponding to the net load prediction error data according to the sample cumulative probability, and construct an expected energy not supplied function of the power system and an expected power generation function per unit time of the reserve capacity according to the probability density function and the cumulative probability distribution function corresponding to the normal distribution model;

[0069] Specifically, the expected energy not supplied function of the power system and the expected power generation function per unit time of the reserve capacity are constructed through the following steps:

[0070] (1) Establish a random model of the net load prediction error using the normal distribution. The normal distribution is often used to describe the new energy output prediction error distribution and the load prediction error distribution. Since the addition and subtraction operations of normal distribution random numbers are still normal distributions, the normal distribution can be used to describe the net load prediction error. The probability density function of the normal distribution can be described as:

[0071]

[0072] where \(x\) represents the random variable, which refers to the net load prediction error in the present invention; \(\mu\) represents the mean of the normal distribution; \(\sigma\) represents the standard deviation of the normal distribution, and \(\sigma>0\).

[0073] (2) Construct the expected energy not supplied function of the high-proportion new energy power system. If the net load prediction error of the system exceeds the system reserve capacity, the excess load will be powered off. The calculation formula for the expected energy not supplied of the system is:

[0074]

[0075] where \(R\) represents the decision reserve capacity; \(P\) EDNS (R) represents the expected power loss, which is a function of the decision reserve; \(\varphi(\cdot)\) represents the probability density function of the standard normal distribution; \(\varPhi(\cdot)\) represents the cumulative probability distribution function of the standard normal distribution.

[0076] The standard normal distribution refers to a normal distribution with a mean of zero and a standard deviation of 1. The calculation formula for its probability density function is:

[0077]

[0078] (3) Construct the expected power generation function of the decision-making reserve capacity per unit time. The expected power generation power of the decision-making reserve capacity consists of two parts: when the net load forecasting error x is greater than zero and less than the decision-making reserve capacity R, the power generation power of the reserve capacity is x; when the net load forecasting error x is greater than the decision-making reserve capacity R, the power generation power of the reserve capacity is R. Therefore, the calculation formula for the expected power generation of the decision-making reserve capacity per unit time is as follows:

[0079]

[0080] In the formula, P INC (R) represents the expected power generation of the decision-making reserve capacity per unit time and is a function of the decision-making reserve capacity.

[0081] S4. According to the power shortage expectation function and the expected power generation function, with the goal of minimizing the total cost per unit time, construct the objective function corresponding to the reserve capacity decision; where the total cost is obtained by adding the reserve configuration cost and the power shortage expectation cost and then subtracting the reserve call income;

[0082] Preferably, the objective function is:

[0083] min J(R) = C R R + C EDNS P EDNS (R) - C INC P INC (R);

[0084] Among them, J(R) is the total cost per unit time, C R is the unit cost of the reserve capacity, and R is the reserve capacity of the power system; C EDNS is the unit cost of the power shortage expectation, and P EDNS (R) is the power shortage expectation; C INC is the unit income of the increased power generation during reserve call, and P INC (R) is the expected power generation of the reserve capacity.

[0085] Specifically, the present invention uses the minimum total cost obtained by deducting the reserve call income from the sum of the reserve configuration cost and the power shortage expectation cost per unit time as the decision-making goal. After constructing the power shortage expectation function of the power system and the expected power generation function of the reserve capacity per unit time, according to the power shortage expectation and the expected power generation, the above objective function corresponding to the reserve capacity decision is determined.

[0086] S5. Solve the objective function according to the sample cumulative probability corresponding to the net load forecasting error data to obtain the reserve capacity of the corresponding power system when the total cost per unit time is the smallest.

[0087] Preferably, solving the objective function according to the sample cumulative probability corresponding to the net load prediction error data to obtain the reserve capacity of the power system when the total cost per unit time is minimized includes: obtaining a first sample cumulative probability when the net load prediction error is 0 and a second sample cumulative probability when the net load prediction error is the reserve capacity to be solved according to the sample cumulative probability corresponding to the net load prediction error data; determining a first cumulative probability when the value of the normal distribution random variable of the net load prediction error is 0 according to the first sample cumulative probability, and determining a second cumulative probability when the value of the normal distribution random variable of the net load prediction error is the reserve capacity to be solved according to the second sample cumulative probability; constructing a standard normal distribution cumulative probability table corresponding to the standard normal distribution according to the first cumulative probability and the second cumulative probability, and then obtaining the mean and standard deviation of the normal distribution model corresponding to the net load prediction error data according to the standard normal distribution cumulative probability table; solving the objective function according to the mean and standard deviation to obtain the reserve capacity of the power system when the total cost per unit time is minimized.

[0088] Specifically, the specific steps for solving the objective function according to the sample cumulative probability corresponding to the net load prediction error data and the cumulative probability table of the standard normal distribution are as follows:

[0089] (1) Establish the necessary optimality conditions for solving the optimization problem in Equation (5):

[0090] The optimal solution of the reserve probability decision problem in Equation (5) needs to satisfy the necessary optimality conditions in Equation (6):

[0091]

[0092] Since:

[0093]

[0094]

[0095] Substituting Equation (7) and Equation (8) into Equation (6), we can get:

[0096]

[0097] After arranging Equation (9), we can get:

[0098]

[0099] (2) Determine the key positions of the cumulative probability curve that affect the decision-making accuracy in the probabilistic reserve decision.

[0100] As can be seen from Equation (10), the factors affecting the probabilistic reserve decision result include the cumulative probability at the point where the net load prediction error is 0, and the cumulative probability and probability density at the point where the net load prediction error is R.

[0101] Since the normal distribution has only two parameters, the mean and the standard deviation, it has only two degrees of freedom, and the parameters of the normal distribution can be determined based on two points on the cumulative probability distribution curve.

[0102] For the actual power system, since C EDNS >> C INC , C EDNS >> C R , so is close to 1, and is a small positive number.

[0103] (3) Parameter estimation of the normal distribution model of net load prediction error based on the approximation of the cumulative probability of key position samples.

[0104] To improve the reserve decision accuracy as much as possible, the cumulative probabilities of the normal distribution probability model of net load prediction error at 0 and R are taken to be equal to the sample cumulative probabilities at the corresponding positions, that is:

[0105]

[0106]

[0107] where γ(0) and γ(R) are the sample cumulative probabilities of the normal distribution probability model of net load prediction error at 0 and R respectively. The sample cumulative probability refers to the cumulative probability calculated based on the sample data (x1, x2... xN), that is, the calculation result in S2, and the cumulative probability without a prefix refers to the cumulative probability of the normal distribution model.

[0108] From Equations (10), (11) and (12), we can get:

[0109]

[0110] Since R is a decision variable and needs to be determined after the reserve decision, it is still an unknown quantity during the parameter estimation of the normal distribution model. Considering that is a small positive number, the initial value of the decision variable R can be calculated according to Equation (14).

[0111]

[0112] where the superscript 0 represents the initial value.

[0113] The right-hand side of Equation (14) can be obtained from the original parameters and sample data, so R can be obtained by linear interpolation of the sample cumulative probability curve (0) .

[0114] Comparing Equation (13) and Equation (14), it can be seen that:

[0115] γ(R)≥γ(R (0) ) (15)

[0116] γ(R (0) ) is the sample cumulative probability of the normal distribution probability model of the net load prediction error at R (0) . Since the sample cumulative probability curve is a monotonically increasing curve, thus:

[0117] R≥R (0) (16)

[0118] Equation (16) shows that R (0) is the lower bound of the probabilistic reserve decision.

[0119] Using R (0) to replace R in Equation (12), we get:

[0120]

[0121] From Equation (11), we can obtain:

[0122]

[0123] From Equation (17), we can obtain:

[0124]

[0125] By simultaneously solving Equation (18) and Equation (19), the mean and standard deviation of the normal distribution model are respectively:

[0126]

[0127] The solutions of Equation (20) and Equation (21) require obtaining the quantiles from the cumulative probability with the help of the cumulative probability curve of the standard normal distribution.

[0128] (4) Establish the cumulative probability table of the standard normal distribution model.

[0129] The cumulative probability table of the standard normal distribution can be obtained by numerical integration. The cumulative probability of the standard normal distribution has the following properties:

[0130] Φ(0)=0.5 (22)

[0131] Φ(-z)=1 - Φ(z) (23)

[0132] Since Φ(5) ≈ 0.9999997, in practice, it is only necessary to establish a cumulative probability table of the standard normal distribution between [0 - 5]. When it is negative, the formula (23) can be used for calculation. When it exceeds 5, the cumulative probability can be approximately considered as 1, which can meet the accuracy requirements in engineering. When establishing the cumulative probability table of the standard normal distribution by numerical integration, it is recommended to take a relatively small integration step (such as 0.0001) to ensure the accuracy of numerical integration. When using the table lookup method, due to the existence of the integration data interval, the linear interpolation method can be used to obtain the cumulative probability value corresponding to the specified value when looking up the corresponding interval of the value. The integration formula for the cumulative probability between [0 - 5] is:

[0133]

[0134] The calculation formula for the numerical integration of the cumulative probability of the standard normal distribution is:

[0135]

[0136] where Δz is the integration step and k is the number of integration steps.

[0137] According to the above formulas (20) and (21), the mean μ and standard deviation σ of the prediction error normal distribution model are calculated. According to formula (25), a z - Φ(z) look - up table for the interval [0 - 5] can be established. Therefore, Φ(z) can be obtained from z or z can be obtained from Φ(z) by the table lookup method (and linear interpolation).

[0138] (5) Probability reserve decision - making based on the prediction error normal distribution model of the net load.

[0139] After the mean μ and standard deviation σ of the prediction error normal distribution model are determined, the reserve decision - making variable R can be obtained according to the optimality condition of formula (10).

[0140] Since the right - hand side of formula (10) contains the decision - making variable R, it is difficult to directly solve it numerically. Here, an iterative method of formula (26) is constructed for solution:

[0141]

[0142] In the formula, the superscript i represents the number of iterations.

[0143] The solution of formula (26) also needs to rely on the cumulative probability table of the standard normal distribution to obtain the quantile from the cumulative probability.

[0144] If the deviation between the results of two adjacent iterations is less than the set allowable error, the iteration converges. The convergence criterion is taken as:

[0145] |R (i+1) -R (i) |≤ε (27)

[0146] where ε is the allowable error.

[0147] Let i = 0, 1, 2…, and continuously iterate Equation (26) until the convergence criterion of Equation (27) is satisfied, and take R (i+1) as the spare capacity decision result.

[0148] It can be seen that the present invention provides a method for determining the spare capacity of a power system. The present invention comprehensively considers three factors: spare cost, expected power generation revenue of the spare, and expected loss of power outage, gives the optimality conditions of the probabilistic spare decision and the iterative solution algorithm, and realizes the probabilistic spare decision with the optimal expected comprehensive revenue. According to the key factors affecting the optimality of the spare decision, the parameters of the normal distribution model of the net load prediction error are determined, and the accuracy of the probabilistic spare decision result is improved by approximating the cumulative probability at the key positions. By selecting key points for cumulative probability approximation, the present invention can effectively improve the fitting accuracy of the cumulative probability in the small probability interval of the tail.

[0149] Embodiment 2

[0150] Please refer to Figure 2 , which is a schematic structural diagram of a device for determining the spare capacity of a power system provided by an embodiment of the present invention. The device includes: a spare decision data acquisition module, a net load prediction error data calculation module, a power shortage expectation and expected power generation calculation module, an objective function construction module, and a spare capacity solution module;

[0151] The spare decision data acquisition module is used to acquire the spare decision data of the power system; wherein, the spare decision data includes: historical actual data of new energy output, historical predicted data of new energy output, historical actual data of load, and historical predicted data of load;

[0152] The net load prediction error data calculation module is used to calculate the corresponding net load prediction error data according to the spare decision data, and calculate the sample cumulative probability corresponding to the net load prediction error data;

[0153] The power shortage expectation and expected power generation calculation module is used to construct a normal distribution model corresponding to the net load prediction error data according to the sample cumulative probability, and construct a power shortage expectation function of the power system and an expected power generation function per unit time of the spare capacity according to the probability density function and cumulative probability distribution function corresponding to the normal distribution model;

[0154] The objective function construction module is used to construct an objective function corresponding to the spare capacity decision with the minimum total cost per unit time as the goal according to the power shortage expectation function and the expected power generation function; wherein, the total cost is obtained by adding the spare configuration cost and the power shortage expectation cost and then subtracting the spare call revenue;

[0155] The standby capacity solving module is configured to solve the objective function according to the sample cumulative probability corresponding to the net load prediction error data, so as to obtain the standby capacity of the power system corresponding to the minimum total cost per unit time.

[0156] Preferably, calculating the corresponding net load prediction error data according to the standby decision data includes: subtracting the historical actual data of new energy output at the same time from the historical actual data of the load to obtain the corresponding historical actual data of the net load; subtracting the historical predicted data of new energy output at the same time from the historical predicted data of the load to obtain the corresponding historical predicted data of the net load; subtracting the historical predicted data of the net load at the same time from the historical actual data of the net load to obtain the corresponding net load prediction error data.

[0157] Preferably, solving the objective function according to the sample cumulative probability corresponding to the net load prediction error data to obtain the standby capacity of the power system corresponding to the minimum total cost per unit time includes:

[0158] According to the sample cumulative probability corresponding to the net load prediction error data, obtaining a first sample cumulative probability when the net load prediction error is 0 and a second sample cumulative probability when the net load prediction error is the standby capacity to be solved; according to the first sample cumulative probability, determining a first cumulative probability when the normal distribution random variable value of the net load prediction error is 0, and according to the second sample cumulative probability, determining a second cumulative probability when the normal distribution random variable value of the net load prediction error is the standby capacity to be solved; constructing a standard normal distribution cumulative probability table according to the first cumulative probability and the second cumulative probability, and then obtaining the mean and standard deviation of the normal distribution model corresponding to the net load prediction error data according to the standard normal distribution cumulative probability table; solving the objective function according to the mean and standard deviation to obtain the standby capacity of the power system corresponding to the minimum total cost per unit time.

[0159] Preferably, the objective function is:

[0160] min J(R)=C R R+C EDNS P EDNS (R)-C INC P INC (R);

[0161] wherein, J(R) is the total cost per unit time, C R is the unit cost of the standby capacity, R is the standby capacity of the power system; C EDNS is the unit cost of the expected power shortage, P EDNS (R) is the expected power shortage; C INCP is the unit revenue of the increased power generation during standby call INC (R) is the expected power generation of the standby capacity.

[0162] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the attached drawings of the device embodiments provided by the present invention, the connection relationship between modules indicates that they have a communication connection, which can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement without creative efforts.

[0163] Those skilled in the art can clearly understand that for the sake of convenience and brevity, the specific working process of the device described above can refer to the corresponding process in the foregoing method embodiments, and will not be described herein again.

[0164] Embodiment III

[0165] Correspondingly, an embodiment of the present invention provides an electronic device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the method for determining the standby capacity of the power system described in the foregoing embodiments of the present invention is implemented.

[0166] The electronic device may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The device may include, but is not limited to, a processor and a memory.

[0167] The so-called processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the device, and connects various parts of the entire device through various interfaces and lines.

[0168] Embodiment IV

[0169] Correspondingly, an embodiment of the present invention provides a storage medium, which includes a stored computer program. When the computer program runs, it controls the device where the storage medium is located to execute the power system reserve capacity determination method described in the above embodiments of the present invention.

[0170] The memory can be used to store the computer program. The processor realizes various functions of the device by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include high-speed random access memory, and can also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one magnetic disk storage device, flash device, or other volatile solid-state storage devices.

[0171] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of the above various method embodiments. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0172] The above is the preferred embodiment of the present invention. It should be pointed out that for those of ordinary skill in the art of this technology, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements are also regarded as the protection scope of the present invention.

Claims

1. A method for determining the reserve capacity of a power system, characterized in that, Including: Obtain the reserve decision data of the power system; wherein, the reserve decision data includes: historical actual data of new energy output, historical predicted data of new energy output, historical actual data of load, and historical predicted data of load; According to the reserve decision data, calculate the corresponding net load prediction error data, and calculate the sample cumulative probability corresponding to the net load prediction error data; According to the sample cumulative probability, construct the normal distribution model corresponding to the net load prediction error data, and according to the probability density function and cumulative probability distribution function corresponding to the normal distribution model, construct the expected energy not supplied function of the power system and the expected power generation amount function per unit time of the reserve capacity; According to the expected energy not supplied function and the expected power generation amount function, with the goal of minimizing the total cost per unit time, construct the objective function corresponding to the reserve capacity decision; wherein, the total cost is obtained by adding the reserve configuration cost and the expected energy not supplied cost and then subtracting the reserve call revenue; According to the sample cumulative probability corresponding to the net load prediction error data, solve the objective function to obtain the reserve capacity of the corresponding power system when the total cost per unit time is minimized.

2. The method for determining the reserve capacity of the power system according to claim 1, wherein The calculating the corresponding net load prediction error data according to the reserve decision data includes: Subtract the historical actual data of new energy output at the same time from the historical actual data of the load to obtain the corresponding historical actual data of the net load; Subtract the historical predicted data of new energy output at the same time from the historical predicted data of the load to obtain the corresponding historical predicted data of the net load; Subtract the historical predicted data of the net load at the same time from the historical actual data of the net load to obtain the corresponding net load prediction error data.

3. The method for determining the reserve capacity of the power system according to claim 1, characterized in that, The solving the objective function according to the sample cumulative probability corresponding to the net load prediction error data to obtain the reserve capacity of the corresponding power system when the total cost per unit time is minimized includes: According to the sample cumulative probability corresponding to the net load prediction error data, obtain the first sample cumulative probability when the net load prediction error is 0, and the second sample cumulative probability when the net load prediction error is the reserve capacity to be solved; According to the first sample cumulative probability, determine the first cumulative probability when the normal distribution random variable value of the net load prediction error is 0, and according to the second sample cumulative probability, determine the second cumulative probability when the normal distribution random variable value of the net load prediction error is the reserve capacity to be solved; According to the first cumulative probability and the second cumulative probability, construct the standard normal distribution cumulative probability table corresponding to the standard normal distribution, and then according to the standard normal distribution cumulative probability table, obtain the mean and standard deviation of the normal distribution model corresponding to the net load prediction error data; According to the mean and the standard deviation, solve the objective function to obtain the reserve capacity of the corresponding power system when the total cost per unit time is minimized.

4. The method for determining the reserve capacity of the power system according to claim 1, wherein The objective function is: min J(R) = C R R + C EDNS P EDNS (R) - C INC P INC (R); Among them, J(R) is the total cost per unit time, C R is the unit cost of reserve capacity, and R is the reserve capacity of the power system; C EDNS is the unit cost of the expected energy not supplied, P EDNS (R) is the expected energy not supplied; C INC is the unit revenue of the increased power generation when reserve is called, P INC (R) is the expected power generation of the reserve capacity.

5. A device for determining the reserve capacity of a power system, characterized in that, Including: A reserve decision data acquisition module, a net load prediction error data calculation module, an expected energy not supplied and expected power generation amount calculation module, an objective function construction module, and a reserve capacity solution module; The spare decision data acquisition module is used to acquire the spare decision data of the power system; wherein, the spare decision data includes: historical actual data of new energy output, historical predicted data of new energy output, historical actual data of load, and historical predicted data of load; The net load prediction error data calculation module is used to calculate the corresponding net load prediction error data according to the spare decision data, and calculate the sample cumulative probability corresponding to the net load prediction error data; The expected energy not supplied and expected power generation calculation module is used to construct a normal distribution model corresponding to the net load prediction error data according to the sample cumulative probability, and construct an expected energy not supplied function of the power system and an expected power generation function of the spare capacity per unit time according to the probability density function and cumulative probability distribution function corresponding to the normal distribution model; The objective function construction module is used to construct an objective function corresponding to the spare capacity decision with the minimum total cost per unit time as the objective according to the expected energy not supplied function and the expected power generation function; wherein, the total cost is obtained by adding the spare configuration cost and the expected energy not supplied cost and then subtracting the spare call revenue; The spare capacity solving module is used to solve the objective function according to the sample cumulative probability corresponding to the net load prediction error data, and obtain the spare capacity of the corresponding power system when the total cost per unit time is the minimum.

6. The power system reserve capacity determination device according to claim 5, wherein, The calculating the corresponding net load prediction error data according to the spare decision data includes: Subtracting the historical actual data of new energy output at the same moment from the historical actual data of load to obtain the corresponding historical actual net load data; Subtracting the historical predicted data of new energy output at the same moment from the historical predicted data of load to obtain the corresponding historical predicted net load data; Subtracting the historical predicted net load data at the same moment from the historical actual net load data to obtain the corresponding net load prediction error data.

7. The power system reserve capacity determination device according to claim 5, characterized in that The solving the objective function according to the sample cumulative probability corresponding to the net load prediction error data to obtain the spare capacity of the corresponding power system when the total cost per unit time is the minimum includes: Obtaining a first sample cumulative probability when the net load prediction error is 0 and a second sample cumulative probability when the net load prediction error is the spare capacity to be solved according to the sample cumulative probability corresponding to the net load prediction error data; Determining a first cumulative probability when the normal distribution random variable value of the net load prediction error is 0 according to the first sample cumulative probability, and determining a second cumulative probability when the normal distribution random variable value of the net load prediction error is the spare capacity to be solved according to the second sample cumulative probability; Constructing a standard normal distribution cumulative probability table corresponding to the standard normal distribution according to the first cumulative probability and the second cumulative probability, and then obtaining the mean and standard deviation of the normal distribution model corresponding to the net load prediction error data according to the standard normal distribution cumulative probability table; Solving the objective function according to the mean and standard deviation to obtain the spare capacity of the corresponding power system when the total cost per unit time is the minimum.

8. The power system spare capacity determination device according to claim 5, characterized in that, The objective function is: min J(R)=C R R + C EDNS P EDNS (R) - C INC P INC (R); Among them, J(R) is the total cost per unit time, and C R is the unit cost of reserve capacity, and R is the reserve capacity of the power system; C EDNS is the unit cost of expected power shortage, and P EDNS (R) is the expected power shortage; C INC is the unit revenue of the increased power generation when the reserve is called, and P INC (R) is the expected power generation of the reserve capacity.

9. An electronic device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the power system reserve capacity determination method according to any one of claims 1 to 4.

10. A storage medium, characterized in that, The storage medium includes a stored computer program. When the computer program runs, it controls the device where the storage medium is located to execute the power system reserve capacity determination method according to any one of claims 1 to 4.

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