Opportunity constraint day-ahead reserve optimization method and device for new energy-containing power system

Abstract

The invention discloses an opportunity constraint day-ahead reserve optimization method and device for a power system containing new energy, and relates to the field of power system dispatching operation, and the method comprises the steps: constructing an opportunity constraint day-ahead reserve optimization model according to the total output probability distribution of all new energy stations, and carrying out the optimization of the opportunity constraint day-ahead reserve through a risk cost item in a target function, assessing the risk that the new energy output exceeds the system standby configuration range; the peak regulation cost caused by new energy and load fluctuation is quantified through a start-stop cost item and a deep peak regulation cost item in the target function; solving of a new energy probability distribution inverse function is simplified through a discretization method; and solving the day-ahead reserve optimization model to obtain a reserve plan meeting a given confidence level. According to the method, the difference of output uncertainty of the new energy station at different moments is considered, it is guaranteed that the standby plan meets the confidence level given by the system, the reliability and economical efficiency of the standby plan are considered, new energy consumption is promoted, and safe and stable operation of a power system is guaranteed.

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 the opportunity-constrained day-ahead reserve of a power system containing new energy, an electronic device, a storage medium and a computer program product. Background Art

[0002] At present, global climate change and energy security issues are becoming increasingly prominent. Accelerating the development of new energy such as wind power and photovoltaic power generation is an important measure to deal with energy and environmental issues. my country's new energy installed capacity and power generation continue to grow. By the end of 2022, my country's non-fossil energy installed capacity will reach 1.27 billion kilowatts, and non-fossil energy power generation will reach 3.1 trillion kilowatt-hours. Among them, the installed capacity of wind power and photovoltaic power generation is 760 million kilowatts, accounting for 30% of the total installed capacity; wind power and photovoltaic power generation are 1.2 trillion kilowatt-hours, accounting for 14% of the total power generation.

[0003] However, compared with traditional thermal power units, the output of new energy sources such as wind power and photovoltaic power is affected by factors such as meteorological conditions and geographical location, and has characteristics such as volatility and uncertainty. Volatility is an inherent characteristic of new energy, which refers to the continuous change of output power over time, which is caused by the constant change of natural factors such as wind speed and light. Uncertainty refers to the deviation between the predicted output and the actual output of new energy, which is limited by the prediction accuracy and the influence of uncertain factors such as meteorology. Unlike load forecasting, the forecast deviation of new energy output is large and increases sharply as the time span increases.

[0004] In order to deal with load forecast errors, unit failures, output deviations of new energy stations, etc. in the system, the power system needs to reserve a certain amount of spare capacity to ensure the balance of power supply and demand and ensure the safe and stable operation of the power system. In traditional power systems, the spare capacity is generally configured according to the N-1 criterion or the load percentage criterion, that is, it is configured according to the system's maximum unit capacity or predicted load percentage. However, with the continuous increase in the penetration rate of new energy, the traditional method of configuring spare capacity can no longer meet the needs of coping with the uncertainty of new energy and promoting the consumption of new energy. Therefore, this technical problem needs to be solved urgently. 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 differences in output uncertainty 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, which takes into account both the reliability and economy of the reserve plan, 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 and solving 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 new energy output exceeding the system backup configuration range through the risk cost item of the new energy output 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 the 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 the output of thermal power units, ramp rate constraints on thermal power units, start and stop time constraints on thermal power units, 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 constraint in the constraint condition is transformed, and the inverse function of the probability distribution of the new energy source is required to be used after the transformation; 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 sub-intervals, The probability value at the midpoint of the sub-interval represents the value in 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 is the i-th value of , a is the precision of discretization, and α is the confidence level of system spare capacity configuration.

[0019] In a second aspect, a device for optimizing the day-ahead reserve of an opportunity-constrained power system containing new energy 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] According to 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, wherein the storage medium 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 when running.

[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 opportunity-constrained day-ahead reserve optimization methods for a new energy power system when running. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0029] Figure 1 It is a flow chart 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;

[0030] Figure 2 It is a flow chart of a method for optimizing the day-ahead reserve of an electric power system containing new energy sources with opportunity constraints according to an embodiment of the present invention;

[0031] Figure 3 The 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 is shown. DETAILED DESCRIPTION

[0032] 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 reserve configuration range is evaluated through the risk cost item of the new energy output exceeding the system reserve configuration range in the objective function; the peak-shaving cost caused by new energy and load fluctuations is considered 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 reserve plan that meets a given confidence level can be obtained to ensure that the system reserve 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.

[0034] The following describes the 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 It is a flow chart of a method for optimizing the opportunity-constrained day-ahead reserve of 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 new energy power system 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] Among them, 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.

[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 of the embodiment of the present invention takes into account the energy cost of power generation of 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 there are nonlinear terms in the objective function of the model in the embodiment of the present invention. 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 calculation and solution of the model.

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

[0044] It can be understood that the constraints of the model in the embodiment of the present invention are considered from two aspects: the unit and the system. In terms of the unit, the upper and lower limits of the output of the thermal power unit, the ramp rate constraint, the start and stop time constraint, and the spare capacity constraint that the unit can provide are considered. In terms of the system, the system power balance constraint and the probability constraint that the spare configuration meets the given confidence level of the system are considered.

[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] It is understandable that the probability constraint cannot be solved directly and needs to be transformed into a deterministic one. After the transformation, the inverse function of the probability distribution of the new energy source needs to be used. Since it is more complicated to calculate the inverse function of the probability distribution by analytical methods, 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 sites 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 new energy sources 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 position of units, minimum start-stop time, ramp rate, maximum output, minimum output, initial start-stop status of 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, quotation of each peak load gear, single start-stop fee, and quotation of spare capacity;

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

[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, including:

[0057] 2-1) The model takes into account the energy cost, start-up and shutdown cost, deep peak regulation cost, standby cost, and the risk cost that the output of new energy may exceed the system standby configuration range. The optimization goal of the model is to maximize social benefits, that is, to minimize the total operating cost of the system. 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 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 whether the i-th thermal power unit is 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 as follows:

[0067]

[0068] where n p,i is the number of peak load levels 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 load 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 It 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 It is 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 load regulation cost of thermal power units, and the linearization of the system risk cost. The specific process is as follows:

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

[0080] The process of piecewise linearization is: in [0,P max,i ]Select (n l +1) segment points Divide the interval into n l Considering the possibility that the unit may be shut down, the starting point of the interval is 0, rather than the lower limit of the unit output P. min,i . To 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 time period t is G,i,t Sum output square 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 bid for 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 load shifting gear, p G,iis the output of thermal power unit i. The max(.) function takes the larger of the two values. Although the max(.) function is also a nonlinear function, solvers such as Gurobi (a high-performance mathematical programming optimizer that can solve large-scale linear programming, mixed integer programming, quadratic programming, and other types of optimization problems) can write the max(.) function as a generalized constraint to facilitate program implementation. In addition, the max(.) function can also 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 Divide into n w Segment, n w <<n x , R is approximately expressed by the area of ​​the step-shaped region ov,t The size of R ov,t It can be approximately expressed as:

[0091]

[0092] in is 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. Its 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 constraint conditions of the opportunity-constrained day-ahead reserve optimization model, including system power balance constraints, upper and lower limits of thermal power unit output constraints, thermal power unit ramp rate constraints, thermal power unit start and stop time constraints, reserve capacity constraints, and probability constraints that reserve configuration meets the given confidence level of the system. Specifically 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 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 ) max,i (18)

[0105] p G,i,t-1 -p G,i,t ≤P ramp,d,i +(1-u i,t ) 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) The start and stop time constraints of thermal power units are as follows:

[0108]

[0109] on i,1 =(ui,1 -ss i )(1-ss i ) (twenty two)

[0110] off i,t =(ss i -u i,1 )ss i (twenty three)

[0111] on i,t -off i,t =u i,t -u i,t-1 ,2≤t≤T (24)

[0112] on i,t +off i,t ≤1,2≤t≤T (25)

[0113]

[0114] Where ss i is the initial start-stop state of the i-th thermal power unit, T on,ss,i ,T off,ss,i 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 and stop time of the i-th thermal power unit.

[0115] 4-5) Reserve capacity constraint, the detailed expression is as follows:

[0116] 0≤R u,i,t ≤u i,t P max,i -p G,i,t (30)

[0117] 0≤R d,i,t ≤p G,i,t -u i,t P min,i (31)

[0118]

[0119] 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 It is the reserve capacity provided by the i-th thermal power unit in time period t.

[0120] 4-6) Probability constraint, the detailed expression is as follows:

[0121]

[0122] Where α u ,αd Configure the confidence level for the system spare capacity, Pr{.} represents the probability of the event occurring.

[0123] 5) Transform the probability constraints in the constraint conditions and solve the discretization of the inverse function of the probability distribution of new energy, including:

[0124] 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:

[0125]

[0126] F pnet,t (p net,t -R u,t )≤1-α u (37)

[0127] R u,t ≥p net,t -F pnet,t -1 (1-α u ) (38)

[0128]

[0129] F pnet,t (p net,t +R d,t )≥α d (40)

[0130] R d,t ≥F pnet,t -1 (α d )-p net,t (41)

[0131] 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 .

[0132] 5-2) Discretize and solve the inverse function of the probability distribution of new energy, including:

[0133] 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 sub-intervals, The probability value at the midpoint of the sub-interval represents the value in the entire interval, so the total output probability distribution function of the new energy station has n x A value.

[0134] 5-2-2) Calculate the inverse function of the probability distribution of total output of new energy. pnet,t After discretization, F pnet,t Total n x values, and the length of each subinterval is Calculate F pnet,t -1 (α), the confidence level α of the system reserve capacity configuration is known, and by comparing F pnet,t The value of and the size of α give the total output of new energy corresponding to α, p net,ac,t .

[0135] F pnet,t -1 (α)=i·a,F pnet,t,i <α≤F pnet,t,i+1 (42)

[0136] 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 renewable energy proposed in an embodiment of the present invention, the risk of renewable energy output exceeding the system's reserve configuration range is evaluated through the risk cost item of renewable energy output exceeding the system's reserve configuration range in the objective function; the peak-shaving cost caused by renewable energy and load fluctuations is considered 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 renewable energy is simplified through the discretization method; a reserve plan that meets a given confidence level can be obtained by constructing and solving a chance-constrained day-ahead reserve optimization model, ensuring that the system's reserve configuration covers the prediction error of renewable energy with a certain confidence level; it is beneficial to promote the consumption of renewable energy and to improve the safety and economy of the power system containing renewable energy.

[0138] It should be noted that the size of the sequence number of each step in the above embodiment does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present application. In practical applications, all the above possible implementation methods can be combined in any way to form possible embodiments of the present application, which will not be described one by one here.

[0139] Based on the opportunity-constrained day-ahead reserve optimization method for a power system containing renewable energy provided in 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 renewable energy.

[0140] Figure 3 1 is a structural diagram of an 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 the 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 acquire the probability distribution of the total output of all new energy stations and acquire system parameters;

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

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

[0144] A second construction unit 340, in which a user constructs constraint conditions of an 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 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 the output of thermal power units, climbing rate constraints on thermal power units, start and stop time constraints on thermal power units, reserve capacity constraints, and probability constraints that the reserve configuration meets a given confidence level of the system.

[0150] A possible implementation method is provided in the embodiment of the present application, wherein the conversion and solution unit 350 is further configured to:

[0151] Transform the probability constraints in the constraint conditions. After transformation, the inverse function of the probability distribution of new energy sources needs to be used; discretize and 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 sub-intervals, The probability value at the midpoint of the sub-interval represents the value in 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 is the i-th value of , a is the precision of discretization, and α is the confidence level of system spare capacity configuration.

[0156] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0157] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means 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 may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0158] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary 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 opportunity constraints containing new energy, 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 the constraints of the opportunity-constrained day-ahead reserve optimization model; and The transformation of probability constraints in constraint conditions and the discretization solution of inverse function of probability distribution of new energy are carried out.

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 risk costs of new 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 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.

5. The method according to claim 1, characterized in that 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.

6. The method according to claim 1, characterized in that Transform the probability constraints in the constraint conditions. After transformation, the inverse function of the probability distribution of new energy sources needs to be used; discretize and solve the inverse function of the probability distribution of new energy sources, 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 sub-intervals, The probability value at the midpoint of the sub-interval represents the value in 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 is the i-th value of , a is the precision of discretization, and α is the confidence level of system spare capacity configuration.

7. 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; The second construction unit is for the user to construct 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.

8. An electronic device, characterized in that: It comprises a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the opportunity-constrained day-ahead reserve optimization method for a new energy power system according to any one of claims 1 to 6.

9. 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 new energy power system according to any one of claims 1 to 6 when running.

10. 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 as described in any one of claims 1 to 6 when running.

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