A Method and System for Predicting Accuracy and Power Deviation Constraint of a New Energy Power Plant

Abstract

The present invention discloses a method and system for predicting accuracy and power deviation constraint of a new energy power plant. The method includes: inputting the equipment parameters of the regional power grid including the new energy power plant into the SCUC model to output the unit output plan; establishing an optimization model for the power prediction accuracy requirement, inputting the unit output plan, the short-term power prediction accuracy requirement and the cost parameter, and outputting the optimal power prediction accuracy requirement; establishing an optimization model for the power deviation free-inspection range, inputting the optimal power prediction accuracy requirement and the power parameter, and outputting the power deviation free-inspection range to constrain the power deviation of the new energy power plant. The system includes: a data acquisition module, a power prediction accuracy optimization and determination module, and a power deviation range optimization and determination module. The present invention considers the linkage relationship between the power prediction accuracy requirement and the power deviation range of the new energy power plant, and while meeting the supply-demand balance and dispatching requirements, ensures the participation enthusiasm of the new energy power plant.

Description

Technical Field

[0001] The invention relates to a method for constraining power plant electricity deviation, and relates to the field of electric power technology, and in particular to a method and system for constraining prediction accuracy and electricity deviation of a new energy power plant. Background Art

[0002] A large number of new energy or new power generation entities containing new energy have emerged in the power system. When such power generation entities participate in power market transactions, they will introduce multiple types of new market entities to the market. Compared with traditional generators, the power generation capacity of new energy power plants is volatile and random because it depends on wind and solar power generation to varying degrees. In order to ensure the balance of power supply and demand, it is necessary to put forward requirements for the power prediction accuracy and power deviation range of new energy power plants; considering that the degree of deviation between the actual power generation of new energy power plants and the preset power generation will also be different under different power prediction accuracies, a too small deviation inspection-free range will lead to negative returns for new energy power plants in transactions, reducing the enthusiasm of new energy power plants to participate in transactions, and a too large deviation assessment range will be unfavorable to the improvement of the prediction technology and control technology of new energy power plants themselves. The existing power market technology lacks a description method for the joint relationship between the power prediction accuracy and deviation power of new energy power plants, and lacks a joint constraint method for the power prediction accuracy requirements and deviation assessment range of new energy power plants. Summary of the invention

[0003] In order to solve the problems existing in the background technology, the present invention provides a method and system for constraining prediction accuracy and electricity deviation of a new energy power plant.

[0004] The technical solution adopted by the present invention is:

[0005] 1. A method for constraining prediction accuracy and power deviation of a new energy power plant, comprising:

[0006] S1) Input the equipment parameters of the regional power grid including the renewable energy power plant into the security-constrained unit commitment mathematical model SCUC (Security-Constrained Unit Commitment), and the security-constrained unit commitment mathematical model SCUC outputs the unit output plan results of the generator sets of the regional power grid and the renewable energy power plant.

[0007] S2) Establish a power forecast accuracy requirement optimization model for new energy power plants, input the output plan results of the generator sets of the regional power grid and the new energy power plants, the short-term power forecast accuracy requirements of the regional power grid for the grid-connected entities, and the cost parameters of the new energy power plants into the power forecast accuracy requirement optimization model, and the power forecast accuracy requirement optimization model outputs the optimal power forecast accuracy requirements for the new energy power plants.

[0008] S3) Establish an optimization model for the inspection-free range of power deviation of new energy power plants. Input the optimal power prediction accuracy requirements and power parameters of new energy power plants into the optimization model for the inspection-free range of power deviation. The optimization model for the inspection-free range of power deviation outputs the inspection-free range of power deviation of new energy power plants. Constrain the power deviation of new energy power plants according to the inspection-free range of power deviation to achieve the constraint of power deviation of new energy power plants. The cost can be specifically measured by electricity.

[0009] In the step S1) described above, the equipment parameters of the regional power grid include the node parameters, branch parameters, output parameters and cost parameters of each generator set and new energy power plant, and the power demand parameters of the power load, etc.; the generator sets are the original thermal power units in the regional power grid; the new energy power plants are new power generation entities with volatility and randomness in the power output of new energy or power containing new energy.

[0010] The unit output plan results of the generator sets and new energy power plants in the regional power grid include whether each generator set and new energy power plant in the regional power grid outputs power in each of the 24 time periods of the next day.

[0011] The objective function of the SCUC model is to minimize the total power generation operation cost in 24 time periods of the next day. The constraint conditions include the power balance constraint of each node in the regional power grid, the line transmission capacity constraint, the system reserve capacity constraint, the generator set start-stop state constraint, the upper and lower limits of generator set output constraint, the generator set ramp rate constraint, the continuous start-stop time constraint of the generator set, and the output capacity constraint of the new energy power plant, etc.

[0012] The SCUC model can be solved using an optimization model solver to obtain the unit output plan and the output size in each time period of N generator sets and M new energy power plants in each of the 24 time periods of the next day, and record the unit combinations of the generator sets and new energy power plants as one of the input parameters of the subsequent model.

[0013] In the step S2) described above, the optimization model for the power prediction accuracy requirements of new energy power plants is specifically as follows:

[0014]

[0015] Among them, F PRE represents the total input power cost of the regional power grid in each time period of the next day; T represents the total number of operating time periods of the next day, which can take the value of 24, the length of each time period is 1 hour, N represents the number of generator sets in the regional power grid, and M represents the number of new energy power plants in the regional power grid; and respectively represent the piecewise preset cost parameter curves of the i-th generator set and the m-th new energy power plant in the regional power grid at time t; Denote the output of the \(i\)-th generating unit in the regional power grid at time \(t\). Denote the output of the \(m\)-th new energy power plant at time \(t\) under the current power prediction accuracy \(\gamma\). p

[0016] The output plan result of the generating units of the regional power grid input into the optimization model for power prediction accuracy requirements is the output of the \(i\)-th generating unit in the regional power grid at time \(t\). The input cost parameters of the new energy power plant include the piecewise preset cost parameter curve of the \(i\)-th generating unit in the regional power grid at time \(t\). And the piecewise preset cost parameter curve of the \(m\)-th new energy power plant at time \(t\).

[0017] Establish an optimization model for power prediction accuracy requirements of new energy power plants considering supply-demand balance and input power economy in combination with the SCED model. The supply-demand balance means that the power generation of the power generation entities in the regional power grid can meet the electricity consumption demands of all node loads in the power matching of each time period of the next day; the input power economy means the optimization of the total input power cost of the regional power grid for the next day.

[0018] The output of the \(m\)-th new energy power plant at time \(t\) under the current power prediction accuracy \(\gamma\). p Specifically as follows:

[0019]

[0020] Wherein, Denote the output of the \(m\)-th new energy power plant at time \(t\) when the current power prediction accuracy \(\gamma\) p Is 100%; Denote the output of the \(m\)-th new energy power plant at time \(t\); \(\xi\) is a random number.

[0021] The output plan result of the generating units of the new energy power plant input into the optimization model for power prediction accuracy requirements is the output of the \(m\)-th new energy power plant at time \(t\).

[0022] The optimization model for power prediction accuracy requirements is based on the feasible solution constraints of power prediction accuracy, specifically as follows:

[0023]

[0024] Wherein, And Are respectively the upper and lower limits of the value of the current power prediction accuracy \(\gamma\). p

[0025] ​​​The regional power grid that optimizes the input of the power prediction accuracy requirement requires the short-term power prediction accuracy of the grid-connected entity to be the current power prediction accuracy γ p lower limit of the value The power prediction accuracy requirement optimization model is based on the power prediction accuracy γ finally output after being solved by the optimization model solver p is the optimal power prediction accuracy The optimization model solver can select solvers such as Cplex and Gurobi

[0026] It also includes the constraints of the security-constrained economic dispatch mathematical model SCED (Security-Constrained Economic Dispatch)

[0027] The reported quantity parameters of the generating units are the same as those in the SCUC model calculation. At the same time, the generating units will call the reserve capacity according to the fluctuations of the power curves of both the supply and demand sides in the system to ensure the supply-demand balance of the system; the short-term power prediction accuracy requirements of the regional power grid for grid-connected entities include the short-term power prediction accuracy requirements of the regional power grid for all generating entities

[0028] In the step S3), the power deviation exemption range optimization model of the new energy power plant is specifically as follows

[0029]

[0030] Among them, F Diff represents the estimated power deviation cost parameter of the new energy power plant at the optimal power prediction accuracy ; M represents the number of new energy power plants in the regional power grid; T represents the total number of operating periods of the next day represents the power deviation cost of the mth new energy power plant at the optimal power prediction accuracy at the tth period represents the deviation power of the mth new energy power plant at the optimal power prediction accuracy at the tth period; C Diff represents the preset power deviation cost parameter; η represents the preset power deviation coefficient

[0031] The power parameters of the new energy power plant input into the power deviation exemption range optimization model include the preset power deviation cost parameter C Diff .

[0032] The deviation power of the mth new energy power plant at the optimal power prediction accuracy at the tth period is specifically as follows

[0033]

[0034] Among them, and respectively represent the preset maximum positive and negative deviation check power. In a specific embodiment, and take values of -5MW and 5MW respectively; Δt is the duration of a unit time period; is the preset deviation rate of the m-th new energy power plant at the optimal power prediction accuracy at time t, that is, the deviation rate of the actual output compared to the preset output; and are respectively the upper limit of the percentage of the positive deviation check range of the new energy power plant's electricity and the lower limit of the percentage of the negative deviation check range; and are respectively the percentages of the positive and negative deviation free check ranges of the new energy power plant's electricity, that is, the optimization variables of the electricity deviation free check range optimization model; represents the deviation electricity of the m-th new energy power plant at the optimal power prediction accuracy at time t; represents the output of the m-th new energy power plant at the optimal power prediction accuracy at time t; represents the output of the m-th new energy power plant at time t when the current power prediction accuracy γ p is 100%.

[0035] The electricity parameters of the new energy power plant input into the electricity deviation free check range optimization model also include the deviation electricity of the m-th new energy power plant at the optimal power prediction accuracy at time t at the optimal power prediction accuracy at time t and the output at time t when the current power prediction accuracy γ p is 100%

[0036] The electricity deviation free check range optimization model of the new energy power plant is solved a preset number of times based on an intelligent optimization algorithm, and the results are sorted according to the probabilities that appear in each solution. Finally, the percentage of the positive deviation free check range of the new energy power plant's electricity and the percentage of the negative deviation free check range are output as the electricity deviation free check range of the new energy power plant.

[0037] The electricity deviation free check range optimization model of the new energy power plant is based on the power generation cost recovery constraint of the new energy power plant and the feasible solution constraint of the percentage of the electricity deviation free check range, specifically as follows:

[0038]

[0039] in, Indicates the optimal power prediction accuracy of the mth renewable energy power plant λ is the marginal cost parameter of the node in period t, i.e., the clearing cost parameter; re Indicates the cost recovery factor of the renewable energy power plant; represents the electricity cost parameter of the mth renewable energy power plant; and They are respectively the upper limit of the percentage of the positive deviation check range and the lower limit of the percentage of the negative deviation check range of the electricity of the new energy power plant; and They are respectively the percentage of the positive and negative deviation of electricity quantity exempt from inspection range of new energy power plants.

[0040] 2. A prediction accuracy and power deviation constraint system for a new energy power plant, comprising:

[0041] The data acquisition module is used to obtain the relevant input parameters of the safety constraint unit combination mathematical model SCUC and solve the unit output plan results of the generating units of the regional power grid and the new energy power plant.

[0042] The power prediction accuracy optimization module is used to establish a power prediction accuracy optimization model for new energy power plants in combination with the safety-constrained economic dispatch mathematical model SCED.

[0043] The power prediction accuracy determination module is used to determine the relevant input parameters of the power prediction accuracy optimization model and solve the optimal power prediction accuracy requirements of the new energy power plant.

[0044] The power deviation inspection-free range optimization module is used to establish a power deviation inspection-free range optimization model based on the joint relationship between the power prediction accuracy and deviation power of the new energy power plant.

[0045] The module for determining the inspection-free range of power deviation is used to determine the relevant input parameters of the optimization model for the inspection-free range of power deviation, to solve and obtain the optimal inspection-free range percentage of the power deviation of the new energy power plant, and to constrain the power deviation of the new energy power plant.

[0046] An electronic device of the present invention comprises: a memory and a processor coupled to each other, wherein the memory stores program data, and the processor calls the program data to execute the above-mentioned method.

[0047] The beneficial effects of the present invention are:

[0048] The method and system of the present invention are based on the calculation results of the SCUC model, and in combination with the SCED model, an optimization model for the power prediction accuracy requirements considering the supply-demand balance and the economy of the input power is established. By solving, the optimal power prediction accuracy requirements for new energy power plants can be obtained; the simultaneous relationship between the power prediction accuracy and the deviation power of new energy power plants is described, and an optimization model for the exemption range of power deviation inspection is established. By solving, the positive and negative deviation assessment ranges for new energy power plants can be obtained.

[0049] The method and system of the present invention consider the linkage relationship between the power prediction accuracy requirements and the deviation assessment range of new energy power plants. While meeting the requirements of power supply-demand balance and economic dispatch, it can ensure the participation enthusiasm of new energy power plants, overcome the technical problem in the related art of lacking a combined determination method for the power prediction accuracy requirements and the deviation assessment range of new energy power plants, and can provide a reference for formulating the power prediction accuracy requirements and the deviation assessment range of new energy power plants. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 is a flowchart of the implementation of the method of the present invention shown according to an exemplary embodiment;

[0051] Figure 2 is a power output curve diagram of the new energy power plant of the present invention under different power prediction accuracy requirements shown according to an exemplary embodiment;

[0052] Figure 3 is a framework diagram of the optimization model for the power prediction accuracy requirements of the new energy power plant of the present invention shown according to an exemplary embodiment;

[0053] Figure 4 is a relationship diagram between the power prediction accuracy of the new energy power plant of the present invention and the total input power cost of the regional power grid shown according to an exemplary embodiment;

[0054] Figure 5 is a schematic diagram of a deviation power inspection mode shown according to an exemplary embodiment;

[0055] Figure 6 is a diagram of the daily preset power generation curve and the actual power generation curve of the new energy power plant of the present invention shown according to an exemplary embodiment;

[0056] Figure 7 is a probability ranking diagram of the percentage of the exemption range of power deviation of the new energy power plant obtained by repeatedly solving the optimization model for the exemption range of power deviation shown according to an exemplary embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0057] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0058] Exemplary embodiments will be described in detail herein, and examples thereof are shown in the accompanying drawings. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0059] The terms used in the present application are for the purpose of describing specific embodiments only and are not intended to limit the present application. The singular forms "a", "said", and "the" used in the present application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0060] As Figure 1 shown, the new energy power plant prediction accuracy and power deviation constraint method of the present invention includes:

[0061] S1) Input the equipment parameters of the regional power grid including the new energy power plant into the security-constrained unit commitment mathematical model SCUC, and the security-constrained unit commitment mathematical model SCUC outputs the unit output plan results of the generating units in the regional power grid and the new energy power plant.

[0062] In step S1), the equipment parameters of the regional power grid include the node parameters, branch parameters, output parameters and cost parameters of each generating unit and the new energy power plant, and the power demand parameters of the power load, etc.; the generating units are the original thermal power units in the regional power grid; the new energy power plant is a new type of power generation entity with volatility and randomness in the power output of new energy or including new energy.

[0063] The unit output plan results of the generating units in the regional power grid and the new energy power plant include the unit output plan of whether each generating unit and the new energy power plant in the regional power grid output power in each of the 24 time periods of the next day.

[0064] The objective function of the SCUC model is to minimize the total power generation operation cost in 24 time periods of the next day, and the constraint conditions include the power balance constraint of each node in the regional power grid, the line transmission capacity constraint, the system reserve capacity constraint, the generating unit start-stop state constraint, the generating unit output upper and lower limit constraint, the generating unit ramp rate constraint, the generating unit continuous start-stop time constraint, the output capacity constraint of the new energy power plant, etc.

[0065] The SCUC model can be solved using an optimization model solver to obtain the unit output plan for each of the N generating units and M new energy power plants in the 24 time periods of the next day, indicating whether each unit generates power or not, as well as the output magnitude for each time period. Record the unit combinations of the generating units and new energy power plants, which serves as one of the input parameters for the subsequent model.

[0066] S2) Establish an optimization model for the power prediction accuracy requirements of new energy power plants. Input the unit output plan results of the generating units and new energy power plants in the regional power grid, the short-term power prediction accuracy requirements of the regional power grid for grid-connected entities, and the cost parameters of the new energy power plants into the power prediction accuracy requirements optimization model. The power prediction accuracy requirements optimization model outputs the optimal power prediction accuracy requirements for the new energy power plants.

[0067] In step S2), the optimization model for the power prediction accuracy requirements of new energy power plants is specifically as follows:

[0068]

[0069] Among them, F PRE represents the total input power cost of the regional power grid in each time period of the next day; T represents the total number of operating time periods in the next day, which can take the value of 24, and the length of each time period is 1 hour. N represents the number of generating units in the regional power grid, and M represents the number of new energy power plants in the regional power grid; and respectively represent the piecewise preset cost parameter curves of the i-th generating unit and the m-th new energy power plant in the t-th time period in the regional power grid; represents the output of the i-th generating unit in the regional power grid in the t-th time period; represents the output of the m-th new energy power plant in the t-th time period under the current power prediction accuracy γ p .

[0070] The unit output plan result of the generating units in the regional power grid input into the power prediction accuracy requirements optimization model is the output of the i-th generating unit in the regional power grid in the t-th time period The cost parameters of the new energy power plants input include the piecewise preset cost parameter curve of the i-th generating unit in the regional power grid in the t-th time period and the piecewise preset cost parameter curve of the m-th new energy power plant in the t-th time period

[0071] Combine the SCED model to establish an optimization model for the power prediction accuracy requirements of new energy power plants considering supply-demand balance and input power economy. The supply-demand balance means that the power generation of the power generation entities in the regional power grid can meet the electricity consumption demands of all node loads in the power matching of each time period of the next day; the input power economy means the optimization of the total input power cost of the regional power grid for the next day.

[0072] Output of the m-th new energy power plant at time t under the current power prediction accuracy γ p is as follows: Specifically:

[0073]

[0074] Among them, represents the output of the m-th new energy power plant at time t when the current power prediction accuracy γ p is 100%; represents the output of the m-th new energy power plant at time t; ξ is a random number.

[0075] The unit output plan result of the new energy power plant input to the power prediction accuracy requirement optimization model is the output of the m-th new energy power plant at time t

[0076] The power prediction accuracy requirement optimization model is based on the power prediction accuracy feasible solution constraint, specifically as follows:

[0077]

[0078] Among them, and are the upper and lower limits of the value of the current power prediction accuracy γ p respectively.

[0079] The short-term power prediction accuracy requirement of the regional power grid for grid-connected entities input to the power prediction accuracy requirement optimization model is the lower limit of the value of the current power prediction accuracy γ p respectively. The power prediction accuracy γ finally output by the power prediction accuracy requirement optimization model based on the optimization model solver is p the optimal power prediction accuracy The optimization model solver can select solvers such as Cplex and Gurobi.

[0080] It also includes the constraints of the security-constrained economic dispatch mathematical model SCED.

[0081] The reporting parameters of the generator set are the same as those in the SCUC model calculation. At the same time, the generator set will call the reserve capacity according to the fluctuations of the power curves of both supply and demand sides in the system to ensure the supply-demand balance of the system; the short-term power prediction accuracy requirement of the regional power grid for grid-connected entities includes the short-term power prediction accuracy requirement of the regional power grid for all power generation entities.

[0082] S3) Establish an optimization model for the electricity deviation exemption range of a new energy power plant, input the optimal power prediction accuracy requirement and electricity parameters of the new energy power plant into the optimization model for the electricity deviation exemption range, the optimization model for the electricity deviation exemption range outputs the electricity deviation exemption range of the new energy power plant, and constrain the electricity deviation of the new energy power plant according to the electricity deviation exemption range to achieve the electricity deviation constraint of the new energy power plant.

[0083] In step S3), the optimization model for the electricity deviation exemption range of the new energy power plant is specifically as follows:

[0084]

[0085] Among them, F Diff represents the estimated electricity deviation cost parameter of the new energy power plant at the optimal power prediction accuracy ; M represents the number of new energy power plants in the regional power grid; T represents the total number of operation periods of the next day; represents the electricity deviation cost of the m-th new energy power plant at the t-th period at the optimal power prediction accuracy ; represents the deviation electricity of the m-th new energy power plant at the t-th period at the optimal power prediction accuracy ; C Diff represents the preset electricity deviation cost parameter; η represents the preset electricity deviation coefficient.

[0086] The electricity parameters of the new energy power plant input into the optimization model for the electricity deviation exemption range include the preset electricity deviation cost parameter C Diff .

[0087] The deviation electricity of the m-th new energy power plant at the t-th period at the optimal power prediction accuracy is specifically as follows:

[0088]

[0089] Among them, and respectively represent the preset maximum positive and negative deviation inspection electricity. In a specific embodiment, and take the values of -5MW and 5MW respectively; Δt is the duration of a unit period; is the preset deviation rate of the m-th new energy power plant at the t-th period at the optimal power prediction accuracy , that is, the deviation rate of the actual output compared to the preset output; and are respectively the upper limit of the positive deviation inspection range percentage and the lower limit of the negative deviation inspection range percentage of the electricity of the new energy power plant; and are the positive and negative deviation inspection-free range percentages of the power generation of the new energy power plant, respectively, which are the optimization variables of the power deviation inspection-free range optimization model; represents the deviation power of the m-th new energy power plant at time t under the optimal power prediction accuracy ; represents the output of the m-th new energy power plant at time t under the optimal power prediction accuracy ; represents the output of the m-th new energy power plant at time t when the current power prediction accuracy γ p is 100%.

[0090] The power parameters of the new energy power plant input into the power deviation inspection-free range optimization model also include the deviation power of the m-th new energy power plant at time t under the optimal power prediction accuracy ; the output at time t under the optimal power prediction accuracy ; and the output at time t when the current power prediction accuracy γ p is 100%.

[0091] The power deviation inspection-free range optimization model of the new energy power plant is solved a preset number of times based on an intelligent optimization algorithm, and the results are sorted according to the probabilities that appear in each solution. Finally, the positive deviation inspection-free range percentage of the power generation of the new energy power plant with the highest probability and the negative deviation inspection-free range percentage are output as the power deviation inspection-free range of the new energy power plant.

[0092] The power deviation inspection-free range optimization model of the new energy power plant is based on the power generation cost recovery constraint of the new energy power plant and the feasible solution constraint of the power deviation inspection-free range percentage, as follows:

[0093]

[0094] where represents the nodal marginal cost parameter of the m-th new energy power plant at time t under the optimal power prediction accuracy , that is, the clearing cost parameter; λ re represents the cost recovery coefficient of the new energy power plant; represents the cost parameter per unit power of the m-th new energy power plant; and are the upper limit of the positive deviation inspection range percentage and the lower limit of the negative deviation inspection range percentage of the power generation of the new energy power plant, respectively; and They are the percentages of the positive and negative deviation inspection exemption ranges of the power generation of the new energy power plant respectively.

[0095] As Figure 2 shown, it is the power output curve of the new energy power plant under different power prediction accuracies. The lower limit of the short-term power prediction accuracy requirement of the regional power grid for the grid-connected entity is 90%. In a specific embodiment, takes a value of 92%, takes a value of 99%, then the power prediction accuracy γ of the new energy power plant p can have a value range of 92% - 99%. Under the goal of minimizing the total input power cost of the regional power grid, the finally solved optimal power prediction accuracy requirement is 94%, as Figure 4 shown.

[0096] As Figure 3 shown, it is the framework diagram of the power prediction accuracy requirement optimization model. The optimization variables of the SCED model are the intra-day output curves of the generator sets and the new energy power plant in the regional power grid, including the output of the generator sets in 24 time periods and the output of the new energy power plant in 24 time periods. The objective function is to minimize the input power cost of the regional power grid; on the basis of the SCED model, an optimization variable of the power prediction accuracy of the new energy power plant is added. At the same time, according to the preset power generation of the new energy power plant, a relationship model between the power prediction accuracy of the new energy power plant and the intra-day output curve is established. The intra-day output curve is the actual power generation of the new energy power plant. Furthermore, an optimization model for the power prediction accuracy requirement of the new energy power plant is established. The power prediction accuracy should meet the short-term power prediction accuracy requirement of the regional power grid for the grid-connected entity. The objective function is to minimize the input power cost of the regional power grid from the generator sets and the new energy power plant. The constraint conditions include the original constraint conditions of the SCED model, and at the same time, the upper and lower limit constraints of the power prediction accuracy requirement of the new energy power plant are added.

[0097] As Figure 5 shown, it is a schematic diagram of the deviation inspection mode. In a specific embodiment, the execution deviation variable of the new energy power plant is jointly determined by the deviation power and the percentages of the positive and negative deviation inspection ranges; the positive and negative deviation inspection ranges are respectively and are the power deviation inspection exemption ranges, that is, when the deviation power does not exceed the deviation inspection range, the new energy power plant will not be punished. When the deviation power exceeds the deviation inspection range, a certain deviation inspection cost parameter needs to be output for compensation. and The absolute values of can be different; in a specific embodiment, the maximum positive and negative deviation inspection powers and are set, that is, when the positive and negative deviation inspection powers of a single new energy power plant exceed and When and Perform a deviation check.

[0098] like Figure 6 As shown, the day-ahead preset power generation curve and the actual power generation curve of the new energy power plant; in a specific embodiment, the preset power deviation cost parameter is 307.8 1 / MW·h, and the power deviation coefficient is 1.

[0099] like Figure 7 As shown, in a specific embodiment, the probability ranking relationship of the positive and negative deviation inspection-free range percentages of the new energy power plant is obtained by repeatedly solving the power deviation inspection-free range optimization model. Preferably, the positive deviation and negative deviation inspection-free range percentages of the new energy power plant are 4% and -4%, respectively, that is, when the deviation rate between the actual power generation of the new energy power plant and the preset power generation is between -4% and 4%, the deviation check is exempted, and when the positive deviation exceeds 4% or the negative deviation exceeds -4%, a deviation check is performed.

[0100] The prediction accuracy and power deviation constraint system of the new energy power plant of the present invention is specifically as follows:

[0101] The data acquisition module is used to obtain the relevant input parameters of the safety constraint unit combination mathematical model SCUC and solve the unit output plan results of the generating units of the regional power grid and the new energy power plant.

[0102] The power prediction accuracy optimization module is used to establish a power prediction accuracy optimization model for new energy power plants in combination with the safety-constrained economic dispatch mathematical model SCED.

[0103] The power prediction accuracy determination module is used to determine the relevant input parameters of the power prediction accuracy optimization model and solve the optimal power prediction accuracy requirements of the new energy power plant.

[0104] The power deviation inspection-free range optimization module is used to establish a power deviation inspection-free range optimization model based on the joint relationship between the power prediction accuracy and deviation power of the new energy power plant.

[0105] The module for determining the inspection-free range of power deviation is used to determine the relevant input parameters of the optimization model for the inspection-free range of power deviation, to solve and obtain the optimal inspection-free range percentage of the power deviation of the new energy power plant, and to constrain the power deviation of the new energy power plant.

[0106] In summary, the embodiment of the present invention proposes a method and system for constraining prediction accuracy and power deviation of a new energy power plant. First, based on the calculation results of the SCUC model, a power prediction accuracy requirement optimization model considering the supply and demand balance and the economic efficiency of the input power is established in combination with the SCED model. The optimal power prediction accuracy requirement of the new energy power plant can be obtained by solving it; the joint relationship between the power prediction accuracy and the deviation power of the new energy power plant is described, and an optimization model for the power deviation inspection-free range is established. The positive and negative deviation inspection-free range of the new energy power plant can be obtained by solving it. The method and system provided by the present invention take into account the linkage relationship between the power prediction accuracy requirement and the deviation inspection range of the new energy power plant, and can ensure the participation enthusiasm of the new energy power plant while meeting the requirements of power supply and demand balance and economic dispatch, overcome the technical problem of the lack of a joint determination method for the power prediction accuracy requirement and the deviation inspection range of the new energy power plant in the related technology, and can provide a reference for the formulation of the power prediction accuracy requirement and the deviation inspection range of the new energy power plant.

[0107] Those skilled in the art will readily appreciate other embodiments of the present application after considering the description and practicing the contents disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The description and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the claims.

[0108] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for constraining prediction accuracy and power deviation of a new energy power plant, characterized in that: include: S1) inputting the equipment parameters of the regional power grid including the renewable energy power plant into the safety constraint unit combination mathematical model SCUC, and the safety constraint unit combination mathematical model SCUC outputs the unit output plan results of the generator sets of the regional power grid and the renewable energy power plant; S2) Establishing a power forecast accuracy requirement optimization model for a new energy power plant, inputting the output plan results of the generator sets of the regional power grid and the new energy power plant, the short-term power forecast accuracy requirements of the regional power grid for the grid-connected entities, and the cost parameters of the new energy power plant into the power forecast accuracy requirement optimization model, and the power forecast accuracy requirement optimization model outputs the optimal power forecast accuracy requirements of the new energy power plant; S3) Establishing an optimization model for the inspection-free range of electric quantity deviation of a new energy power plant, inputting the optimal power prediction accuracy requirement and electric quantity parameters of the new energy power plant into the optimization model for the electric quantity deviation inspection-free range, and outputting the inspection-free range of electric quantity deviation of the new energy power plant by the optimization model for the electric quantity deviation inspection-free range, constraining the electric quantity deviation of the new energy power plant according to the inspection-free range of electric quantity deviation, and realizing the electric quantity deviation constraint of the new energy power plant; The cost is measured in terms of electricity.

2. The prediction accuracy and power deviation constraint method of a new energy power plant according to claim 1 is characterized by: In the step S1), the equipment parameters of the regional power grid include node parameters, branch parameters, output parameters and cost parameters of each generator set and new energy power plant, and power demand parameters of the power load; The unit output plan results of the generator sets and renewable energy power plants in the regional power grid include the unit output plans of each generator set and renewable energy power plant in the regional power grid and whether they will output power in each of the 24 time periods of the next day.

3. The prediction accuracy and power deviation constraint method of a new energy power plant according to claim 1 is characterized by: In the step S2), the power prediction accuracy of the new energy power plant requires the optimization model to be as follows: Among them, F PRE It represents the total input electricity cost of the regional power grid in each period of the next day; T represents the total number of operating periods of the next day, N represents the number of generating units in the regional power grid, and M represents the number of renewable energy power plants in the regional power grid; and They represent the segmented preset cost parameter curves of the i-th generator set and the m-th renewable energy power plant in the regional power grid in period t respectively; represents the output of the i-th generator set in the regional power grid during period t; Indicates the current power prediction accuracy γ of the mth renewable energy power plant p The output in period t; The power prediction accuracy requires that the unit output plan of the generator set of the regional power grid input by the optimization model is the output of the i-th generator set in the regional power grid during the t period. The input cost parameters of the renewable energy power plant include the segmented preset cost parameter curve of the i-th generator set in the regional power grid during the t period. And the segmented preset cost parameter curve of the mth renewable energy power plant in period t 4. The prediction accuracy and power deviation constraint method of a new energy power plant according to claim 3 is characterized by: The current power prediction accuracy of the m-th renewable energy power plant is γ p The output at time t The details are as follows: in, Indicates the current power prediction accuracy γ of the mth renewable energy power plant p The output at time t when is 100%, represents the output of the mth renewable energy power plant in period t; ξ is a random number; The power prediction accuracy requires that the output plan of the unit of the new energy power plant input by the optimization model is the output of the mth new energy power plant in the t period.

5. The prediction accuracy and power deviation constraint method of a new energy power plant according to claim 3 is characterized by: The power prediction accuracy requires that the optimization model can solve the constraints based on the power prediction accuracy, as follows: in, and are the current power prediction accuracy γ p The upper and lower limits of the value of ; The power prediction accuracy requirement of the regional power grid for the optimization model input is the current power prediction accuracy γ p The lower limit of The power prediction accuracy requires that the optimization model is based on the power prediction accuracy of the final output after the optimization model solver solves the problem. p The optimal power prediction accuracy 6. The prediction accuracy and power deviation constraint method of a new energy power plant according to claim 1 is characterized by: In the step S3), the optimization model of the power deviation inspection-free range of the new energy power plant is as follows: Among them, F Diff Indicates the optimal power prediction accuracy of new energy power plants The estimated power deviation cost parameter at the time of the power grid; M represents the number of renewable energy power plants in the regional power grid; T represents the total number of operating hours the next day; Indicates the optimal power prediction accuracy of the mth renewable energy power plant The power deviation cost in period t; Indicates the optimal power prediction accuracy of the mth renewable energy power plant The deviation power in time period t; C Diff represents the preset power deviation cost parameter; η represents the preset power deviation coefficient; The power deviation free inspection range optimization model inputs the power parameters of the new energy power plant including the preset power deviation cost parameter C Diff .

7. The prediction accuracy and power deviation constraint method of a new energy power plant according to claim 6 is characterized by: The optimal power prediction accuracy of the m-th renewable energy power plant The deviation power in time period t The details are as follows: in, and They represent the preset maximum positive and negative deviation inspection power respectively; Δt is the duration of the unit time period; is the optimal power prediction accuracy of the mth renewable energy power plant The preset deviation rate in time period t; and They are respectively the upper limit of the percentage of the positive deviation check range and the lower limit of the percentage of the negative deviation check range of the electricity of the new energy power plant; and They are respectively the percentage of the positive and negative deviations of the electricity quantity of the new energy power plant that are exempt from inspection; Indicates the optimal power prediction accuracy of the mth renewable energy power plant The deviation power in time period t; Indicates the optimal power prediction accuracy of the mth renewable energy power plant The output in period t; Indicates the current power prediction accuracy γ of the mth renewable energy power plant p The output in period t when it is 100%; The power deviation free inspection range optimization model inputs the power parameters of the new energy power plant and also includes the optimal power prediction accuracy of the mth new energy power plant. The deviation power in time period t The optimal power prediction accuracy Output at time t As well as the current power prediction accuracy γ p The output at time t when the power is 100% The power deviation inspection-free range optimization model performs a preset number of solutions based on an intelligent optimization algorithm, sorts the results according to the probability of occurrence in each solution, and finally outputs the percentage of the power positive deviation inspection-free range of the new energy power plant with the highest probability of occurrence. And negative deviation exemption range percentage As a new energy power plant, the electricity deviation is exempt from inspection.

8. The prediction accuracy and power deviation constraint method of a new energy power plant according to claim 6 is characterized by: The optimization model for the power deviation inspection-free range of the new energy power plant is based on the power generation cost recovery constraint of the new energy power plant and the feasible solution constraint of the power deviation inspection-free range percentage, which is as follows: in, Indicates the optimal power prediction accuracy of the mth renewable energy power plant λ is the marginal cost parameter of the node in period t, i.e., the clearing cost parameter; re Indicates the cost recovery factor of the renewable energy power plant; represents the electricity cost parameter of the mth renewable energy power plant; and They are respectively the upper limit of the percentage of the positive deviation check range and the lower limit of the percentage of the negative deviation check range of the electricity of the new energy power plant; and They are respectively the percentage of the positive and negative deviation of electricity quantity exempt from inspection range of new energy power plants.

9. A prediction accuracy and power deviation constraint system for a new energy power plant applicable to the method according to any one of claims 1 to 8, characterized in that: It includes: The data acquisition module is used to obtain the relevant input parameters of the safety constraint unit combination mathematical model SCUC and solve the unit output plan results of the generating units of the regional power grid and the new energy power plant; The power prediction accuracy optimization module is used to establish a power prediction accuracy optimization model for new energy power plants in combination with the safety-constrained economic dispatch mathematical model SCED; The power prediction accuracy determination module is used to determine the relevant input parameters of the power prediction accuracy optimization model and solve the optimal power prediction accuracy requirements of the new energy power plant; The module for optimizing the range of power deviation without checking is used to establish an optimization model for the range of power deviation without checking based on the joint relationship between the power prediction accuracy and the power deviation of the new energy power plant; The module for determining the inspection-free range of power deviation is used to determine the relevant input parameters of the optimization model for the inspection-free range of power deviation, to solve and obtain the optimal inspection-free range percentage of the power deviation of the new energy power plant, and to constrain the power deviation of the new energy power plant.

10. An electronic device, characterized in that: include: A memory and a processor coupled to each other, wherein the memory stores program data, and the processor calls the program data to execute the method according to any one of claims 1 to 8.

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