Opportunity-constrained day-ahead reserve optimization method and device for power systems containing new energy

Abstract

The present invention discloses a method and device for optimizing day-ahead reserve for a power system containing new energy, and relates to the field of power system dispatching and operation. The method comprises: constructing a day-ahead reserve optimization model based on the probability distribution of the total output of all new energy stations, and evaluating the risk of the new energy output exceeding the system reserve configuration range through the risk cost item in the objective function; quantifying the peak regulation cost caused by new energy and load fluctuations through the start-stop cost item and the deep peak regulation cost item in the objective function; simplifying the solution of the inverse function of the new energy probability distribution through a discretization method; and obtaining a reserve plan that meets a given confidence level by solving the day-ahead reserve optimization model. This method takes into account the different uncertainties in the output of new energy stations at different times, ensures that the reserve plan meets the confidence level given by the system, and takes into account the reliability and economy of the reserve plan, which is conducive to promoting the absorption of new energy and ensuring the safe and stable operation of the power system.

Description

Technical Field

[0001] The present invention relates to the technical field of power system dispatching and operation, and in particular to a method and device for optimizing opportunity-constrained day-ahead reserve in a power system containing new energy, an electronic device, a storage medium, and a computer program product. Background Art

[0002] As global climate change and energy security become increasingly prominent, accelerating the development of new energy sources such as wind power and photovoltaic power generation is a key measure to address energy and environmental challenges. my country's installed capacity and power generation from new energy sources continue to grow. By the end of 2022, my country's non-fossil energy installed capacity reached 1.27 billion kilowatts, and non-fossil energy power generation reached 3.1 trillion kilowatt-hours. Of this, wind power and photovoltaic power generation accounted for 760 million kilowatts, or 30% of total installed capacity, and 1.2 trillion kilowatt-hours, or 14% of total power generation.

[0003] However, compared to traditional thermal power plants, the output of renewable energy sources like wind power and photovoltaics is affected by factors such as weather conditions and geographic location, resulting in volatility and uncertainty. Volatility is an inherent characteristic of renewable energy, referring to the continuous variation in output power over time due to changes in natural factors such as wind speed and sunlight. Uncertainty refers to the deviation between predicted and actual renewable energy output, influenced by limitations in forecast accuracy and uncertainties such as weather. Unlike load forecasting, the forecast deviation for renewable energy output is large and increases dramatically over time.

[0004] To address load forecast errors, unit failures, and output deviations from renewable energy stations, power systems need to reserve a certain amount of reserve capacity to ensure a balance between power supply and demand and secure and stable operation. In traditional power systems, reserve capacity is generally allocated according to the N-1 principle or load percentage principle, i.e., according to the system's maximum unit capacity or a percentage of the forecasted load. However, with the increasing penetration of renewable energy, traditional reserve capacity allocation methods are no longer sufficient to address the uncertainties of renewable energy and promote its uptake. Therefore, a solution to this technical problem is urgently needed. Summary of the Invention

[0005] The present invention aims to solve one of the technical problems in the related art at least to a certain extent.

[0006] To this end, one purpose of the present invention is to propose an opportunity-constrained day-ahead reserve optimization method that takes into account the uncertainty of renewable energy output. This method takes into account the different uncertainties in the output of renewable energy sites at different times in the reserve plan, and proposes a system day-ahead reserve plan modeling and solution method that takes into account the uncertainty of renewable energy output, taking into account the reliability and economy of the reserve plan, which is conducive to promoting the consumption of renewable energy, reducing the system reserve cost, and ensuring the safe and stable operation of the power system.

[0007] To achieve the above-mentioned objectives, in the first aspect, an embodiment of the present invention proposes an opportunity-constrained day-ahead reserve optimization method for a new energy power system, comprising the following steps: obtaining the probability distribution of the total output of all new energy stations and obtaining system parameters; constructing the objective function of the opportunity-constrained day-ahead reserve optimization model; linearizing the nonlinear terms in the objective function; constructing the constraint conditions of the opportunity-constrained day-ahead reserve optimization model; and transforming the probability constraints in the constraint conditions and discretizing the inverse function of the new energy probability distribution.

[0008] The opportunity-constrained day-ahead reserve optimization method for a power system containing new energy in an embodiment of the present invention evaluates the risk of the output of new energy exceeding the system backup configuration range through the risk cost item of the output of new energy exceeding the system backup configuration range in the objective function; considers the peak-shaving cost caused by new energy and load fluctuations through the start-stop cost item and the deep peak-shaving cost item in the objective function; simplifies the solution of the inverse function of the probability distribution of new energy through a discretization method; and obtains a backup plan that meets a given confidence level by constructing and solving a chance-constrained day-ahead reserve optimization model, ensuring that the system backup configuration covers the prediction error of new energy with a certain confidence level; it is conducive to promoting the consumption of new energy and improving the safety and economy of the power system containing new energy.

[0009] In addition, the opportunity-constrained day-ahead reserve optimization method for a power system containing new energy sources according to the above embodiment of the present invention may also have the following additional technical features:

[0010] Furthermore, in one embodiment of the present invention, the probability distribution of the total output of all new energy stations is obtained as a known quantity; the system parameters include one or more of the technical parameters of the thermal power units, the economic parameters of the thermal power units, the new energy station parameters, and the system operation parameters.

[0011] Furthermore, in one embodiment of the present invention, the objective function of the opportunity-constrained day-ahead reserve optimization model includes the energy cost of thermal power generation, start-up and shutdown costs, deep peak regulation costs, reserve costs, and risk costs of new energy output exceeding the system reserve configuration range.

[0012] Furthermore, in one embodiment of the present invention, the linearization of the nonlinear terms of the objective function includes the linearization of three costs: the energy cost of thermal power generation, the deep peak regulation cost, and the risk cost of the system backup plan, using a piecewise linearization method.

[0013] Furthermore, in one embodiment of the present invention, the constraints of the opportunity-constrained day-ahead reserve optimization model include system power balance constraints, upper and lower limit constraints on thermal power unit output, thermal power unit ramp rate constraints, thermal power unit start and stop time constraints, reserve capacity constraints, and probability constraints that the reserve configuration meets a given confidence level of the system.

[0014] Furthermore, in one embodiment of the present invention, the probability constraints in the constraint conditions are transformed, and after the transformation, an inverse function of the probability distribution of the new energy source is required; and the discretization solution of the inverse function of the probability distribution of the new energy source is performed, including:

[0015] The probability distribution of the total output of new energy stations F pnet,t Discretize and define the total installed capacity of all new energy stations as x amax , the discretization accuracy is a, and the total capacity interval of the new energy station is [0,x amax ] is divided into n x subintervals, The probability value at the midpoint of the sub-interval represents the value of the entire interval, so the probability distribution function of the total output of the new energy station has a total of n x A value;

[0016] Calculate the inverse function of the probability distribution of the total output of renewable energy, the probability distribution of the total output of renewable energy stations F pnet,t Discretized, F pnet,t -1 The calculation method of (α) is as follows:

[0017] F pnet,t -1 (α)=i·a,F pnet,t,i <α≤F pnet,t,i+1

[0018] Among them F pnet,t,i F pnet,t The i-th value of , a is the discretization accuracy, and α is the confidence level of the system spare capacity configuration.

[0019] In a second aspect, a device for optimizing the day-ahead reserve of a power system with new energy sources is provided, comprising:

[0020] An acquisition unit is used to obtain the probability distribution of the total output of all new energy stations and obtain system parameters;

[0021] The first construction unit is used to construct the objective function of the opportunity-constrained day-ahead reserve optimization model;

[0022] Linearization unit, used to linearize the nonlinear terms in the objective function;

[0023] The second construction unit is for the user to construct the constraint conditions of the opportunity-constrained day-ahead reserve optimization model; and

[0024] The transformation and solution unit is used to transform the probability constraints in the constraint conditions and to discretize and solve the inverse function of the probability distribution of new energy.

[0025] In a third aspect, an electronic device is provided, comprising a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute any of the above-mentioned opportunity-constrained day-ahead reserve optimization methods for a new energy power system.

[0026] In a fourth aspect, a storage medium is provided, which stores a computer program, wherein the computer program is configured to execute any of the above-mentioned opportunity-constrained day-ahead reserve optimization methods for a new energy power system during runtime.

[0027] In a fifth aspect, a computer program product is provided, comprising a computer program, wherein the computer program is configured to execute any of the above-mentioned methods for optimizing the opportunity-constrained day-ahead reserve of a power system containing new energy sources during operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0029] Figure 1 Flowchart of a method for optimizing the day-ahead reserve with opportunity constraints in a power system containing new energy sources according to an embodiment of the present invention;

[0030] Figure 2 Flowchart of a method for optimizing the day-ahead reserve with opportunity constraints in a power system containing new energy sources according to one embodiment of the present invention;

[0031] Figure 3 The structure diagram of the opportunity-constrained day-ahead reserve optimization device for a new energy power system provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0032] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and intended to be used to explain the present invention, but should not be understood as limiting the present invention.

[0033] One purpose of the present invention is to propose an opportunity-constrained day-ahead reserve optimization method for a power system containing new energy. The risk of new energy output exceeding the system's reserve configuration range is assessed by using the risk cost item in the objective function that the new energy output exceeds the system's reserve configuration range; the peak-shaving costs caused by new energy and load fluctuations are taken into account by using the start-stop cost item and the deep peak-shaving cost item in the objective function; the solution of the inverse function of the new energy probability distribution is simplified by a discretization method; and a reserve plan that meets a given confidence level can be obtained by constructing and solving an opportunity-constrained day-ahead reserve optimization model, ensuring that the system's reserve configuration covers the prediction error of new energy with a certain confidence level. This method is conducive to promoting the consumption of new energy and improving the safety and economy of the power system containing new energy.

[0034] The following describes an opportunity-constrained day-ahead reserve optimization method for a power system containing new energy sources according to an embodiment of the present invention with reference to the accompanying drawings.

[0035] Figure 1 The present invention is a flowchart of an opportunity-constrained day-ahead reserve optimization method for a power system containing new energy sources according to an embodiment of the present invention.

[0036] like Figure 1 As shown, the opportunity-constrained day-ahead reserve optimization method for the power system containing new energy includes the following steps:

[0037] In step S101, the probability distribution of the total output of all new energy stations is obtained, and the system parameters are obtained.

[0038] In one embodiment of the present invention, the probability distribution of the total output of all new energy stations is obtained as a known quantity; the system parameters include one or more of the technical parameters of the thermal power units, the economic parameters of the thermal power units, the parameters of the new energy stations, and the system operation parameters.

[0039] In step S102, the objective function of the opportunity-constrained day-ahead reserve optimization model is constructed.

[0040] It can be understood that the objective function of the model in the embodiment of the present invention takes into account the energy cost of power generation by thermal power units, the peak-shaving cost caused by new energy and load fluctuations, the backup cost caused by the uncertainty of new energy output, and the risk cost that the new energy output may exceed the system backup configuration range.

[0041] In step S103 , the nonlinear terms in the objective function are linearized.

[0042] It is understandable that the objective function of the model in this embodiment of the present invention contains nonlinear terms. The energy cost of the thermal power unit is a quadratic function, the deep peak regulation cost is a piecewise function, and the risk cost contains an integral term. Linearizing the nonlinear terms facilitates the computational solution of the model.

[0043] In step S104, the constraint conditions of the opportunity-constrained day-ahead reserve optimization model are constructed.

[0044] It is understood that the constraints of the model in this embodiment of the present invention are considered from both the unit and system perspectives. For the unit, these consider the upper and lower output limits of the thermal power units, the ramp rate constraints, the start and stop time constraints, and the reserve capacity constraints that the units can provide. For the system, these consider the system power balance constraints and the probability constraints that the reserve configuration meets a given confidence level for the system.

[0045] In step S105 , the probability constraints in the constraint conditions are transformed and the inverse function of the probability distribution of new energy is discretized and solved.

[0046] Understandably, probabilistic constraints cannot be solved directly and require a deterministic transformation. This transformation requires the use of the inverse function of the new energy probability distribution. Because analytical calculation of the inverse function of the probability distribution is complex, the probability distribution is discretized for calculation.

[0047] In summary, the method of the embodiment of the present invention takes into account the different uncertainties in the output of new energy stations at different times in the backup plan, takes into account the reliability and economy of the backup plan, is conducive to promoting the consumption of new energy, reducing the system backup cost, and ensuring the safe and stable operation of the power system.

[0048] The following will be combined Figure 2 The opportunity-constrained day-ahead reserve optimization method for power systems containing renewable energy is described in detail as follows:

[0049] 1) Obtain the probability distribution of the total output of all new energy stations and obtain system parameters, including:

[0050] 1-1) Probability distribution of total output of all renewable energy stations: obtained as a known quantity;

[0051] 1-2) Obtain system parameters, including:

[0052] 1-2-1) Technical parameters of thermal power units: number of thermal power units, peak load shifting gear of the units, minimum start-up and shutdown time, ramp rate, maximum output, minimum output, initial start-up and shutdown status of the thermal power units, units started, and shutdown time;

[0053] 1-2-2) Economic parameters of thermal power units: power generation cost coefficient of thermal power units, price quotes for each peak-shaving gear, single start-up and shutdown costs, and reserve capacity quotes;

[0054] 1-2-3) New energy station parameters: number of new energy stations, installed capacity of each station, and penalty fees per unit capacity when new energy output exceeds reserve capacity;

[0055] 1-2-3) System operating parameters: system load, system spare capacity configuration confidence level.

[0056] 2) Construct the objective function of the opportunity-constrained day-ahead reserve optimization model, specifically including:

[0057] The 2-1) model considers the energy costs of thermal power generation, startup and shutdown costs, deep peak-shaving costs, backup costs, and the risk cost of renewable energy output exceeding the system's backup configuration. The model's optimization goal is to maximize social benefits, that is, to minimize the system's total operating costs. The detailed expression of the objective function is as follows:

[0058] minC=C energy +C onoff +C peak +C reserve +C risk (1)

[0059] Where C is the total cost of system operation, C energy is the total power generation cost of thermal power units, C reserve is the system standby cost, C peak is the total deep peak regulation cost of thermal power units, C onoff is the total start-up and shutdown cost of thermal power units, C risk The risk cost of renewable energy output exceeding the reserve range.

[0060] 2-2) The energy cost of power generation by thermal power units is expressed as follows:

[0061]

[0062] Where T is the number of system optimization periods, n G is the number of thermal power units, a i ,b i ,c i is the power generation cost coefficient of the i-th thermal power unit, p G,i,t is the output of the i-th thermal power unit in time period t.

[0063] 2-3) The start-up and shutdown costs of thermal power generation units are expressed as follows:

[0064]

[0065] where γ on,i ,γ off,i is the cost of starting and stopping the i-th thermal power unit once, i,t Is the i-th thermal power unit turned on in time period t, off i,t Is the i-th thermal power unit shut down in time period t.

[0066] 2-4) The total deep peak regulation cost of thermal power generation is expressed as follows:

[0067]

[0068] where n p,i is the number of peak-shaving gears of the i-th thermal power unit, p G,i,t,j is the capacity of the i-th thermal power unit at the j-th peak-shaving gear in time period t, γ peak,i,j The quotation for the i-th thermal power unit in the j-th peak-shaving gear.

[0069] 2-5) System standby cost, the detailed expression is as follows:

[0070]

[0071] where γ u,i is the reserve capacity quotation for the i-th thermal power unit, γ d,i is the reserve capacity quotation for the i-th thermal power unit, R u,i,t is the upper reserve capacity provided by the i-th thermal power unit in time period t, R d,i,t is the reserve capacity provided by the i-th thermal power unit in time period t.

[0072] 2-6) System backup configuration risk cost, the detailed expression is as follows:

[0073]

[0074] where γ ov ,γ un The penalty fee for the unit capacity of renewable energy output exceeding the upper reserve and lower reserve, R ov,t In order to overestimate the output of renewable energy in period t, exceeding the expected capacity of upper reserve, R un,t The output of renewable energy is underestimated in period t, which exceeds the expected capacity of the reserve. ov,t , R un,t The detailed expression is as follows:

[0075]

[0076] where p net,t is the expected total output of renewable energy in period t, f pnet,t is the probability density function of the actual total output of renewable energy in period t, P net,max It is the maximum value of the actual total output of new energy.

[0077] 3) Linearize the nonlinear terms in the objective function, including:

[0078] The linearization of the nonlinear terms of the objective function includes the linearization of the energy cost of thermal power units, the linearization of the deep peak-shaving cost of thermal power units, and the linearization of the system risk cost. The specific process is as follows:

[0079] 3-1) Linearization of thermal power unit energy cost. The energy cost curve of thermal power generation is a quadratic curve. The piecewise linearization method is used to approximate the thermal power energy cost curve.

[0080] The process of piecewise linearization is: max,i ]Select (n l +1) segment point Divide the interval into n l Considering the possibility of the unit shutting down, the starting point of the interval is 0, rather than the lower limit of the unit output P. min,i . Analyze the energy cost of unit i in period t, add (n l +1) gw i,t,k variable, n l gz i,t,k variables, and satisfy the constraints:

[0081]

[0082] Then the output p of the thermal power unit in period t is G,i,t Sum output squared p G,sq,i,t The linear expression of is as follows:

[0083]

[0084] The energy cost of unit i in period t is expressed as a i p G,sq,i,t +b i p G,i,t +c i , has been converted into a linear expression.

[0085] 3-2) Linearization of the deep peak-shaving cost of thermal power units. Due to the different quotations for different peak-shaving gears, the deep peak-shaving cost of thermal power units is a piecewise function. p Peak-shaving gears, the price of the j-th peak-shaving gear is γ peak,i,j The peak load regulation cost expression of thermal power unit i is:

[0086]

[0087] where p G,i,j is the output upper limit of the j-th peak-shaving gear, p G,iis the output of thermal power unit i. The max(.) function takes the larger of two values. Although max(.) is also nonlinear, solvers such as Gurobi (a high-performance mathematical programming optimizer capable of solving large-scale linear programming, mixed integer programming, quadratic programming, and other types of optimization problems) can express max(.) as a generalized constraint, facilitating program implementation. Furthermore, the max(.) function can be linearized using the Big M method.

[0088] 3-3) Linearization of system risk cost. The system risk cost expressed by formula (7) and (8) is an integral formula, which is difficult to solve. ov,t Perform piecewise linearization:

[0089]

[0090] It can be seen that R ov,t It is by F pnet,t (p), F pnet,t =0 and p=p net,t -R u,t The area of ​​the enclosed region. pnet,t Divided into n w segment, n w <<n x , R is approximately represented by the area of ​​the stepped region ov,t Size. ov,t It can be approximately expressed as:

[0091]

[0092] in The interval of total output of new energy [0,P net,max ] is divided into n w The midpoint of each section after the segment, Δp is the length of the output interval of each section, n' is (p net,t -R u,t ) is a positive real number, n0 is (p net,t -R u,t ) is a positive integer. The basic principle is the same as 3-2).

[0093] R un,t The principle of linearization is the same as R ov,t , as shown in formula (15):

[0094]

[0095] in

[0096] 4) Construct the constraints of the opportunity-constrained day-ahead reserve optimization model, including system power balance constraints, upper and lower limits of thermal power unit output, thermal power unit ramp rate constraints, thermal power unit start and stop time constraints, reserve capacity constraints, and probability constraints that reserve configuration meets a given confidence level of the system. Specifically, they include:

[0097] 4-1) System power balance constraint, the detailed expression is as follows:

[0098]

[0099] Where p load,t is the load of the system in time period t.

[0100] 4-2) The upper and lower limits of thermal power unit output are as follows:

[0101] u i,t P min,i ≤p G,i,t ≤u i,t P max,i (17)

[0102] Where u i,t is the operating status of the i-th thermal power unit in period t, P max,i is the maximum output of the i-th thermal power unit, P min,i is the minimum output of the i-th thermal power unit.

[0103] 4-3) Thermal power unit ramp rate constraint, the detailed expression is as follows:

[0104] p G,i,t -p G,i,t-1 ≤P ramp,u,i +(1-u i,t-1 )P max,i (18)

[0105] p G,i,t-1 -p G,i,t ≤P ramp,d,i +(1-u i,t )P max,i (19)

[0106] Where P ramp,u,i is the upward climbing rate of the i-th thermal power unit, P ramp,d,i is the downward climbing rate of the i-th thermal power unit.

[0107] 4-4) Thermal power unit start and stop time constraints, the detailed expression is as follows:

[0108] When ss i =1 and

[0109] When ssi =0 and

[0110] on i,1 =(u i,1 -ss i )(1-ss i ) (twenty two)

[0111] off i,t =(ss i -u i,1 )ss i (twenty three)

[0112] on i,t -off i,t =u i,t -u i,t-1 ,2≤t≤T (24)

[0113] on i,t +off i,t ≤1,2≤t≤T (25)

[0114]

[0115] Where ss i is the initial start-stop state of the i-th thermal power unit, T on,ss,i ,T off,ss,i T is the startup and shutdown time of the i-th thermal power unit at the initial moment. on,i ,T off,i is the minimum start-stop time of the i-th thermal power unit.

[0116] 4-5) Spare capacity constraint, the detailed expression is as follows:

[0117] 0≤R u,i,t ≤u i,t P max,i -p G,i,t (30)

[0118] 0≤R d,i,t ≤p G,i,t -u i,t P min,i (31)

[0119]

[0120] Where R u,i,t is the upper reserve capacity provided by the i-th thermal power unit in time period t, R d,i,t is the reserve capacity provided by the i-th thermal power unit in time period t.

[0121] 4-6) Probability constraint, the detailed expression is as follows:

[0122]

[0123] Where α u ,α d Configure the confidence level for the system spare capacity, Pr{.} represents the probability of the event occurring.

[0124] 5) Transform the probability constraints in the constraints and discretize the inverse function of the probability distribution of new energy, including:

[0125] 5-1) Deterministic transformation of probabilistic constraints. Constraints (34) and (35) in 4-6) cannot be solved directly and need to be transformed into deterministic linear constraints. The derivation is as follows:

[0126]

[0127] F pnet,t (p net,t -R u,t )≤1-α u (37)

[0128] R u,t ≥p net,t -F pnet,t -1 (1-α u ) (38)

[0129]

[0130] F pnet,t (p net,t +R d,t )≥α d (40)

[0131] R d,t ≥F pnet,t -1 (α d )-p net,t (41)

[0132] Among them F pnet,t is the probability distribution function of the actual total output of renewable energy in period t, F pnet,t -1 F pnet,t The inverse function of .

[0133] 5-2) Discretize and solve the inverse function of the probability distribution of new energy, including:

[0134] 5-2-1) Probability distribution of total output of new energy stations F pnet,t Discretize. Define the total installed capacity of all new energy stations as x amax, the discretization accuracy is a, and the total capacity interval of the new energy station is [0,x amax ] is divided into n x subintervals, The probability value at the midpoint of the sub-interval represents the value of the entire interval, so the total output probability distribution function of the new energy station has a total of n x A value.

[0135] 5-2-2) Calculate the inverse function of the probability distribution of total output of new energy. pnet,t After discretization, F pnet,t There are n x values, and the length of each subinterval is Calculate F pnet,t -1 (α), the confidence level α of the system spare capacity configuration is known, by comparing F pnet,t The value of and the size of α, the corresponding new energy total output p net,ac,t .

[0136] F pnet,t -1 (α)=i·a,F pnet,t,i <α≤F pnet,t,i+1 (42) 6) Use a commercial solver (such as Gurobi) to solve the above mixed integer linear programming problem and obtain the system's day-ahead backup plan.

[0137] According to the opportunity-constrained day-ahead reserve optimization method for a power system containing new energy, proposed in an embodiment of the present invention, the risk of the output of new energy exceeding the system backup configuration range is evaluated through the risk cost item of the output of new energy exceeding the system backup configuration range in the objective function; the peak-shaving cost caused by new energy and load fluctuations is taken into account through the start-stop cost item and the deep peak-shaving cost item in the objective function; the solution of the inverse function of the probability distribution of new energy is simplified through the discretization method; by constructing and solving the opportunity-constrained day-ahead reserve optimization model, a backup plan that meets a given confidence level can be obtained, ensuring that the system backup configuration covers the prediction error of new energy with a certain confidence level; it is conducive to promoting the consumption of new energy and improving the safety and economy of the power system containing new energy.

[0138] It should be noted that the order of execution of the steps in the above embodiments does not necessarily imply a specific order of execution. The order of execution of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. In practical applications, all possible implementation methods described above can be combined in any manner to form possible embodiments of the present application, and will not be described in detail here.

[0139] Based on the opportunity-constrained day-ahead reserve optimization method for a power system containing new energy provided in each of the above embodiments, and based on the same inventive concept, an embodiment of the present application also provides an opportunity-constrained day-ahead reserve optimization device for a power system containing new energy.

[0140] Figure 3 This is a structural diagram of the opportunity-constrained day-ahead reserve optimization device for a new energy power system provided in an embodiment of the present application. Figure 3 As shown, the opportunity-constrained day-ahead reserve optimization device for a new energy power system may specifically include an acquisition unit 310 , a first construction unit 320 , a linearization unit 330 , a second construction unit 340 , and a conversion and solution unit 350 .

[0141] An acquisition unit 310 is used to obtain the probability distribution of the total output of all new energy stations and obtain system parameters;

[0142] The first construction unit 320 is used to construct the objective function of the opportunity-constrained day-ahead reserve optimization model;

[0143] A linearization unit 330, configured to linearize nonlinear terms in the objective function;

[0144] The second construction unit 340 is used for the user to construct the constraint conditions of the opportunity-constrained day-ahead reserve optimization model;

[0145] The transformation and solution unit 350 is used to transform the probability constraints in the constraint conditions and to discretize and solve the inverse function of the probability distribution of the new energy.

[0146] A possible implementation method is provided in an embodiment of the present application, wherein the probability distribution of the total output of all new energy stations is obtained as a known quantity; the system parameters include one or more of the technical parameters of the thermal power units, the economic parameters of the thermal power units, the new energy station parameters, and the system operation parameters.

[0147] A possible implementation method is provided in an embodiment of the present application, wherein the objective function of the opportunity-constrained day-ahead reserve optimization model includes the energy cost of thermal power generation, start-up and shutdown costs, deep peak regulation costs, reserve costs, and risk costs of new energy output exceeding the system reserve configuration range.

[0148] A possible implementation method is provided in an embodiment of the present application, wherein the linearization of the nonlinear terms of the objective function includes the linearization of three costs: the energy cost of power generation by thermal power units, the deep peak regulation cost, and the risk cost of the system backup plan, using a piecewise linearization method.

[0149] A possible implementation method is provided in an embodiment of the present application, wherein the constraint conditions of the opportunity-constrained day-ahead reserve optimization model include system power balance constraints, upper and lower limit constraints on thermal power unit output, thermal power unit ramp rate constraints, thermal power unit start and stop time constraints, reserve capacity constraints, and probability constraints that the reserve configuration meets a given confidence level of the system.

[0150] In an embodiment of the present application, a possible implementation is provided, wherein the conversion and solution unit 350 is further configured to:

[0151] Transform the probability constraints in the constraints. After transformation, the inverse function of the probability distribution of new energy sources must be used. Discretely solve the inverse function of the probability distribution of new energy sources, including:

[0152] The probability distribution of the total output of new energy stations F pnet,t Discretize and define the total installed capacity of all new energy stations as x amax , the discretization accuracy is a, and the total capacity interval of the new energy station is [0,x amax ] is divided into n x subintervals, The probability value at the midpoint of the sub-interval represents the value of the entire interval, so the probability distribution function of the total output of the new energy station has a total of n x A value;

[0153] Calculate the inverse function of the probability distribution of the total output of renewable energy, the probability distribution of the total output of renewable energy stations F pnet,t Discretized, F pnet,t -1 The calculation method of (α) is as follows:

[0154] F pnet,t -1 (α)=i·a,F pnet,t,i <α≤F pnet,t,i+1

[0155] Among them F pnet,t,i F pnet,t The i-th value of , a is the discretization accuracy, and α is the confidence level of the system spare capacity configuration.

[0156] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0157] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0158] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for optimizing the day-ahead reserve of a power system with new energy sources, characterized in that: The following steps are involved: Obtain the probability distribution of the total output of all new energy stations and obtain system parameters; Construct the objective function of the opportunity-constrained day-ahead reserve optimization model; Linearize the nonlinear terms in the objective function; Constructing constraints for the opportunity-constrained day-ahead reserve optimization model; and Carry out the transformation of probability constraints in the constraints and the discretization solution of the inverse function of the probability distribution of new energy; Among them, the probability constraints in the constraint conditions are transformed, and after the transformation, the inverse function of the probability distribution of new energy needs to be used; the discretization solution of the inverse function of the probability distribution of new energy is performed, including: The probability distribution of the total output of new energy stations F pnet,t Discretize and define the total installed capacity of all new energy stations as x amax , the discretization accuracy is a, and the total capacity interval of the new energy station is [0,x amax ] is divided into n x subintervals, The probability value at the midpoint of the sub-interval represents the value of the entire interval, so the probability distribution function of the total output of the new energy station has a total of n x A value; Calculate the inverse function of the probability distribution of the total output of renewable energy, the probability distribution of the total output of renewable energy stations F pnet,t Discretized, F pnet,t -1 The calculation method of (α) is as follows: F pnet,t -1 (a)=i·a,F pnet,t,i <α≤F pnet,t,i+1 Among them F pnet,t,i F pnet,t The i-th value of , a is the discretization accuracy, and α is the confidence level of the system spare capacity configuration.

2. The method according to claim 1, characterized in that The probability distribution of the total output of all new energy stations is obtained as a known quantity; the system parameters include one or more of the technical parameters of the thermal power units, the economic parameters of the thermal power units, the parameters of the new energy stations, and the system operation parameters.

3. The method according to claim 1, characterized in that The objective function of the opportunity-constrained day-ahead reserve optimization model includes the energy cost of thermal power generation, start-up and shutdown costs, deep peak regulation costs, reserve costs, and the risk cost of renewable energy output exceeding the system reserve configuration range.

4. The method according to claim 3, characterized in that The linearization of the nonlinear terms of the objective function includes the linearization of three costs: the energy cost of thermal power generation, the deep peak regulation cost, and the risk cost of the system backup plan, using a piecewise linearization method.

5. The method according to claim 1, characterized in that The constraints of the opportunity-constrained day-ahead reserve optimization model include system power balance constraints, upper and lower output limits of thermal power units, ramp rate constraints of thermal power units, start and stop time constraints of thermal power units, reserve capacity constraints, and probability constraints that reserve configuration meets a given confidence level of the system.

6. An opportunity-constrained day-ahead reserve optimization device for a new energy power system, characterized in that: include: An acquisition unit is used to obtain the probability distribution of the total output of all new energy stations and obtain system parameters; The first construction unit is used to construct the objective function of the opportunity-constrained day-ahead reserve optimization model; Linearization unit, used to linearize the nonlinear terms in the objective function; In the second construction unit, the user constructs the constraint conditions of the opportunity-constrained day-ahead reserve optimization model; as well as The transformation and solution unit is used to transform the probability constraints in the constraint conditions and to discretize and solve the inverse function of the probability distribution of new energy; Among them, the probability constraints in the constraint conditions are transformed, and after the transformation, the inverse function of the probability distribution of new energy needs to be used; the discretization solution of the inverse function of the probability distribution of new energy is performed, including: The probability distribution of the total output of new energy stations F pnet,t Discretize and define the total installed capacity of all new energy stations as x amax , the discretization accuracy is a, and the total capacity interval of the new energy station is [0,x amax ] is divided into n x subintervals, The probability value at the midpoint of the sub-interval represents the value of the entire interval, so the probability distribution function of the total output of the new energy station has a total of n x A value; Calculate the inverse function of the probability distribution of the total output of renewable energy, the probability distribution of the total output of renewable energy stations F pnet,t Discretized, F pnet,t -1 The calculation method of (α) is as follows: F pnet,t -1 (a)=i·a,F pnet,t,i <α≤F pnet,t,i+1 Among them F pnet,t,i F pnet,t The i-th value of , a is the discretization accuracy, and α is the confidence level of the system spare capacity configuration.

7. An electronic device, characterized in that: The method comprises a processor and a memory, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the opportunity-constrained day-ahead reserve optimization method for a power system containing new energy sources as described in any one of claims 1 to 5.

8. A storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to execute the opportunity-constrained day-ahead reserve optimization method for a power system containing new energy sources according to any one of claims 1 to 5 when running.

9. A computer program product comprising a computer program, characterized in that The computer program is configured to execute the opportunity-constrained day-ahead reserve optimization method for a power system containing new energy sources according to any one of claims 1 to 5 when running.

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