Production simulation method and device considering new energy prediction deviation, equipment and medium

Abstract

The application discloses a production simulation method and device considering new energy prediction deviation, equipment and medium. The method determines the first term coefficient and the second term coefficient of the coal consumption cost of the thermal power generating unit in the target area and the coal consumption cost of the operation of the thermal power generating unit. The unit start-stop state variable and the unit output variable are created. Based on the first term coefficient and the second term coefficient of the coal consumption cost of the thermal power generating unit and the coal consumption cost of the operation of the thermal power generating unit, the unit start-stop state variable and the unit output variable, and considering the distribution of the new energy prediction deviation, the optimization model is constructed with the minimum unit coal consumption cost as the target. The constraint condition of the optimization model is determined, the optimization model is solved, and the unit output and the start-stop machine scheduling plan are obtained. The method can be used for the time sequence production simulation calculation and analysis in the area with high new energy installed capacity, can provide guidance value for the formulation of the unit start-stop, output and maintenance plan, can guarantee the balance between power supply and demand in the area, and can ensure the safety of the power utilization of the user.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power grid dispatching, and particularly relates to a production simulation method, device, equipment and medium considering new energy prediction deviation. BACKGROUND

[0002] The operation of a power system needs to keep supply and demand balance at all times, so the dispatching department planning office and the research institute will periodically calculate the power supply and demand balance. The traditional power time sequence production simulation software is based on deterministic unit commitment (UC), and this calculation model can take into account unit maintenance and start-stop conditions. However, with the increase in wind power and photovoltaic installed capacity, their randomness, intermittency and volatility have a huge impact on power planning and operation. In particular, the deviation between new energy predicted output and actual output is usually large, which brings certain difficulty to power balance calculation. Therefore, the production simulation method based on deterministic UC cannot meet the power balance calculation requirements under high proportion of new energy installed capacity in the future.

[0003] To solve this problem, the prior art mainly applies the idea of stochastic programming to improve the traditional time sequence production simulation method. This method relies on constructing different new energy output scenarios and their expectations, then performing weighted calculation on the output under all scenarios, and finally taking the weighted calculation result as input to carry out time sequence production simulation calculation. However, this method is limited by the problem of too long calculation time when there are too many scenarios, and it is also difficult to accurately generate new energy output and its corresponding expectation under different scenarios. SUMMARY

[0004] The purpose of the present application is to provide a production simulation method, device, equipment and medium considering new energy prediction deviation, to solve the problem of too long calculation time when there are too many scenarios in the prior art.

[0005] To achieve the above purpose, the present application adopts the following technical solutions:

[0006] In a first aspect of the present application, a production simulation method considering new energy prediction deviation is provided, comprising the following steps:

[0007] Determine the probability distribution of the new energy prediction deviation in the target area based on data fitting;

[0008] Determine the first-order coefficient and the second-order coefficient of the coal consumption cost of the thermal power unit start-stop in the target area and the coal consumption cost of the thermal power unit operation;

[0009] Create a unit start-stop state variable and a unit output variable;

[0010] Based on the probability distribution of the new energy prediction deviation of the target area, the first term coefficient and the second term coefficient of the coal consumption cost of the thermal power unit start-stop and the coal consumption cost of the thermal power unit operation, and the unit start-stop state variable and the unit output variable, an optimization model is constructed with the minimum unit coal consumption cost as the target, and the constraint conditions of the optimization model are determined.

[0011] The optimization model is solved based on the constraint conditions, and the unit output and the start-stop schedule plan are obtained.

[0012] As a further improvement of the present scheme, the constraint conditions include: unit output constraint and power balance constraint.

[0013] As a further improvement of the present scheme, the unit output constraint is as follows:

[0014]

[0015] p i (ω)=p i -α i ω

[0016] Wherein, ω is the probability distribution of the new energy output prediction deviation, p i (ω) is the unit output, p i is the output without considering the new energy prediction deviation, α i ω is the output increased or decreased for balancing the new energy prediction deviation, is the maximum output parameter of the unit i, is the minimum output parameter of the unit i; α i is the new energy prediction deviation bearing coefficient, 0≤α i ≤u i ; the new energy prediction deviation bearing coefficient satisfies u i is the unit start-stop state, u i =0, the unit is stopped, u i =1, the unit is started; 1-ε is the confidence interval.

[0017] As a further improvement of the present scheme, the power balance constraint is as follows:

[0018]

[0019] Wherein, p i is the unit output without considering the load prediction deviation, p w is the wind power prediction output, p vFor photovoltaic predicted power, D represents the total load of the target area; i is the set of all units, i is the specific unit element, w is the set of all wind farms, v is the set of all photovoltaic stations, and omega is the probability distribution of the total wind power and photovoltaic power prediction deviation of the target area.

[0020] As a further improvement of the present scheme, the target area historical power prediction deviation probability distribution is determined according to the probability fitting of the deviation between the new energy historical prediction power level curve and the actual power curve.

[0021] As a further improvement of the present scheme, in the step of constructing an optimization model with the target of minimizing the unit coal consumption cost, the optimization model is as follows:

[0022]

[0023] Wherein, C 0，i represents the start-stop coal consumption cost of the thermal power unit, C 1，i represents the first term coefficient of the coal consumption cost of the thermal power unit, C 2，i represents the second term coefficient of the coal consumption cost of the thermal power unit; p i (ω) is the unit output, u i is the unit start-stop state.

[0024] As a further improvement of the present scheme, the step of solving the optimization model based on the constraint condition specifically comprises:

[0025] The probability constraint condition in the optimization model is converted into an equivalent linear constraint, and the converted optimization model is substituted into the Cplex solver for solving;

[0026] The converted optimization model is specifically as follows:

[0027]

[0028]

[0029] Wherein, p i is the unit output without considering the load prediction deviation, alpha i is the new energy prediction deviation bearing coefficient, Phi -1 (1-epsilon) is the inverse function of the new energy prediction deviation probability distribution with a confidence interval of 1-epsilon, wherein, is the maximum output parameter of the unit i, is the minimum output parameter of the unit i, u i is the unit start-stop state.

[0030] The second aspect of the present application provides a production simulation device considering new energy prediction deviation, comprising:

[0031] The first determining module is configured to determine a probability distribution of the new energy prediction deviation of the target region in a data fitting manner;

[0032] The second determining module is configured to determine a linear term coefficient and a quadratic term coefficient of the start-stop coal consumption cost and the operation coal consumption cost of the thermal power unit in the target region;

[0033] The variable creating module is configured to create a unit start-stop state variable and a unit output variable;

[0034] The model constructing module is configured to construct an optimization model based on the probability distribution of the new energy prediction deviation of the target region, the linear term coefficient and the quadratic term coefficient of the start-stop coal consumption cost and the operation coal consumption cost of the thermal power unit, and the unit start-stop state variable and the unit output variable, and determine a constraint condition of the optimization model, wherein the optimization model aims to minimize the unit coal consumption cost.

[0035] The optimization solving module is configured to solve the optimization model based on the constraint condition, and obtain the unit output and the start-stop unit scheduling plan.

[0036] In a third aspect, the present application provides an electronic device comprising a processor and a memory, wherein the processor is configured to execute a computer program stored in the memory to implement the production simulation method considering new energy prediction deviation.

[0037] In a fourth aspect, the present application provides a computer readable storage medium, wherein the computer readable storage medium stores at least one instruction, and the at least one instruction is executed by a processor to implement the production simulation method considering new energy prediction deviation.

[0038] Compared with the prior art, the present application has the following advantages:

[0039] The production simulation method provided by the application determines the coal consumption cost of starting and stopping of a thermal power unit in a target area, a linear term coefficient and a quadratic term coefficient of the coal consumption cost of running of the thermal power unit; creates a unit start-stop state variable and a unit output variable; constructs an optimization model based on the coal consumption cost of starting and stopping of the thermal power unit, the linear term coefficient and the quadratic term coefficient of the coal consumption cost of running of the thermal power unit, and the unit start-stop state variable and the unit output variable, with the minimum coal consumption cost of the unit as the target; determines the constraint condition of the optimization model; and solves the optimization model based on the constraint condition to obtain the unit output and the start-stop machine scheduling plan. The scheme can ensure power balance on the basis of considering the prediction deviation of wind power and photovoltaic power, and minimize the coal consumption cost of the whole system, thereby guiding the planning, operation and investment construction of traditional adjustable units such as thermal power and hydropower, and stably ensuring power safety to a certain extent. The production simulation device considering the prediction deviation of new energy, the electronic equipment and the computer readable storage medium provided by the application also solve the problems proposed in the background part. BRIEF DESCRIPTION OF DRAWINGS

[0040] The accompanying drawings, which form a part of this application, are included to provide a further understanding of the application, and are incorporated in and constitute a part of this application. The embodiments of the application illustrated in the drawings, and their description thereto, are presented to provide the practitioner with an enabling description of the application, and are not intended to limit the scope of the application undesirably. In the drawings:

[0041] Figure 1 The flow chart of the production simulation method considering the prediction deviation of new energy according to an embodiment of the application;

[0042] Figure 2 The flow chart of the production simulation method considering the prediction deviation of new energy according to another embodiment of the application;

[0043] Figure 3 The structural block diagram of the production simulation device considering the prediction deviation of new energy according to an embodiment of the application;

[0044] Figure 4 The structural block diagram of the electronic equipment according to an embodiment of the application. DETAILED DESCRIPTION

[0045] The application will be described in detail below with reference to the drawings and in conjunction with the embodiments. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0046] The following detailed description is exemplary and is intended to provide further detailed description of the application. Unless otherwise specified, all technical terms used in the present application have the same meanings as those generally understood by those skilled in the art to which the present application belongs. The terms used in the present application are only for the purpose of describing the specific embodiments and are not intended to limit the exemplary embodiments according to the present application.

[0047] Embodiment 1

[0048] The scheme provides a production simulation method considering new energy prediction deviation, which can perform power and electricity balance calculation on the basis of considering new energy prediction deviation, guide the compilation of unit start-stop, output and maintenance plan, ensure regional power supply and demand balance and user electricity safety.

[0049] As shown in Figure 1 , a production simulation method considering new energy prediction deviation comprises the following steps:

[0050] S1, based on data fitting, the probability distribution of new energy prediction deviation in the target region is determined.

[0051] Specifically, based on the data-driven mode, the new energy prediction deviation in the past long time scale is selected, and the expectation and standard deviation are obtained by fitting.

[0052] S2, the first order coefficient and the second order coefficient of the coal consumption cost of the thermal power unit start-stop and the coal consumption cost of the thermal power unit operation in the target region are determined.

[0053] In the scheme, the coal consumption cost of the thermal power unit start-stop is C 0，i , the first order coefficient of the coal consumption cost of the thermal power unit operation is C 1，i , and the second order coefficient of the coal consumption cost of the thermal power unit operation is C 2，i .

[0054] S3, create unit start-stop state variable and unit output variable.

[0055] In the scheme, the unit start-stop state variable and the unit output variable are u i and p i (ω) respectively; when u i =0, the unit is stopped, and when u i =1, the unit is started.

[0056] S4, based on the first order coefficient and the second order coefficient of the coal consumption cost of the thermal power unit start-stop and the coal consumption cost of the thermal power unit operation, and the unit start-stop state variable and the unit output variable, an optimization model is constructed with the minimum unit coal consumption cost as the target, and the constraint conditions of the optimization model are determined.

[0057] In the scheme, the optimization model is as follows:

[0058]

[0059] Wherein, C 0，i represents the coal consumption cost of the thermal power unit start-stop, C 1，i represents the first order coefficient of the coal consumption cost of the thermal power unit operation, and C 2，iThe quadratic coefficient representing the coal consumption cost of the thermal power unit operation; is the square of the unit output p i (ω) is the unit output, u i is the start-stop state of the unit.

[0060] In the scheme, the constraint conditions include: unit output constraint and power balance constraint.

[0061] Specifically, the unit output constraint is as follows:

[0062]

[0063] p i (ω) = p i - α i ω

[0064] The unit output p i (ω) is determined by two parts: one part is the output p i without considering the prediction deviation of new energy; the other part is the output α i ω that is increased or decreased to balance the prediction deviation of new energy.

[0065] Wherein, ω is the probability distribution of the prediction deviation of new energy, p i (ω) is the unit output, p i is the output without considering the prediction deviation of new energy, α i ω is the output that is increased or decreased to balance the prediction deviation of new energy, is the maximum output parameter of the unit i, is the minimum output parameter of the unit i; α i is the bearing coefficient of the prediction deviation of new energy, 0≤α i ≤ u i ; the bearing coefficient of the prediction deviation of new energy satisfies u i is the start-stop state of the unit, u i = 0, the unit is stopped, u i = 1, the unit is started; 1-ε is the confidence interval.

[0066] Specifically, the power balance constraint is as follows:

[0067]

[0068] Wherein, p i is the output of the unit without considering the prediction deviation of load, p w is the predicted output of wind power, p v is the predicted output of photovoltaic, and the bearing coefficient of the prediction deviation of new energy satisfies D represents the total load of the region; i∈I is a set of all units, i is a specific unit element, w∈W is a set of all wind farms, v∈V is a set of all photovoltaic stations, ω is a probability distribution of the total wind power output and the total photovoltaic power output prediction deviation of the target region, which is determined according to the historical prediction output level curve and the actual output curve of new energy. The expectation and standard deviation of ω are obtained by fitting in step S1.

[0069] S5, solving the optimization model based on the constraint condition to obtain the unit output and start-stop scheduling plan.

[0070] Specifically, the method for solving the optimization model based on the constraint condition is:

[0071] The probability constraint condition in the optimization model is converted into an equivalent linear constraint. After conversion, the model is substituted into the Cplex solver for solving.

[0072] The converted optimization model is as follows:

[0073]

[0074]

[0075] Where, p i is the unit output without considering the load prediction deviation, Φ -1 (1-ε) is the inverse function of the new energy prediction deviation probability distribution with a confidence interval of 1-ε.

[0076] As shown in Figure 2 , as one of the possible implementations of the present scheme, in another embodiment, a production simulation method considering new energy prediction deviation is also provided, including the following steps:

[0077] Step 1, obtaining the future new energy output prediction curve of the target region, and the historical prediction output level curve and the actual output curve of the target region new energy; according to the historical prediction output level curve and the actual output curve of the new energy, the probability distribution ω of the total wind power output and the total photovoltaic power output prediction deviation of the target region is calculated.

[0078] Specifically, the new energy of the target region can include wind power generation and photovoltaic power generation.

[0079] In the present scheme, the future new energy output prediction curve of the target region and the probability distribution ω of the total wind power output and the total photovoltaic power output prediction deviation of the target region are taken as input data.

[0080] Step 2, setting the unit output p i (ω) is determined by two parts, one part is the output p i without considering the new energy prediction deviation; the other part is the output α increased or decreased by balancing the new energy prediction deviationi ω; then:

[0081] p i (ω) = p i -α i ω

[0082] wherein, α i is the bearing coefficient of the unit i to bear the new energy prediction deviation, and ω is the probability distribution of the total wind power output and the total photovoltaic power output prediction deviation of the target area.

[0083] The unit output composition in the scheme is different from the case where the prediction deviation is not considered, and part of it is used to balance the new energy prediction deviation.

[0084] Step 3, obtaining the maximum output parameter of the unit i in the target area the minimum output parameter the maximum output parameter the minimum output parameter are all known unit inherent parameters, which are input data and construct the unit output constraint as:

[0085]

[0086] The above formula can be rewritten as:

[0087]

[0088] wherein, 0≤α i ≤u i , u i is the unit start-stop state, u i =0, the unit is stopped, u i =1, the unit is started; 1-ε is the confidence interval, which represents the probability of the left probability constraint being established; since the present application considers the new energy prediction deviation as an uncertain factor, the form of probability constraint is adopted to ensure that the unit output does not exceed the limit within the confidence interval of 1-ε, is the probability that the constraint in [] satisfies under the probability ω distribution.

[0089] Step 4, constructing the power balance constraint:

[0090]

[0091] wherein, p w is the wind power prediction output as input in step 1, p v is the photovoltaic prediction output as input in step 1, and at the same time, the bearing coefficient also needs to satisfy That is, all units jointly adjust the output to bear the new energy prediction deviation and ensure power balance, and when the sum of the coefficients is 1, the sum of all units for balancing the additional increase / decrease power of the new energy prediction deviation can smooth the regional new energy prediction deviation ω; D represents the total load of the region; i∈I is the set of all units, i is a specific unit element, w∈W is the set of all wind farms, v∈V is the set of all photovoltaic stations, and ω is the probability distribution of the target regional total wind power output and total photovoltaic power output prediction deviation.

[0092] Step 5, set the unit operation target as the expectation of minimizing the unit coal consumption cost, obtain an optimization model, and the optimization model is as follows:

[0093]

[0094] wherein, C 0，i represents the start-stop coal consumption cost of the thermal power unit, C 1，i represents the first term coefficient of the coal consumption cost of the thermal power unit, C 2，i represents the second term coefficient of the coal consumption cost of the thermal power unit. is the square of the unit output.

[0095] Step 6, calculate and solve the optimization model to obtain the unit output and start-stop schedule.

[0096] Specifically, the probability constraint condition in the optimization model is converted into an equivalent linear constraint, after conversion, the model is continued to be substituted into the Cplex solver to obtain the unit output and start-stop schedule.

[0097] The converted optimization model is as follows:

[0098]

[0099]

[0100] wherein, p i is the unit output without considering the load prediction deviation, Φ -1 (1-ε) is the inverse function of the new energy prediction deviation probability distribution with a confidence interval of 1-ε.

[0101] Embodiment 2

[0102] As shown in the embodiment, based on the same inventive concept as the above embodiment, the scheme also provides a production simulation device considering new energy prediction deviation, comprising: Figure 3 A first determination module is configured to determine the probability distribution of the target regional new energy prediction deviation based on a data fitting manner.

[0103]

[0104] ​The second determining module is configured to determine a linear term coefficient and a quadratic term coefficient of a coal consumption cost of a thermal power unit start-stop and a coal consumption cost of a thermal power unit operation in the target region;

[0105] The variable creating module is configured to create a unit start-stop state variable and a unit output variable;

[0106] The model constructing module is configured to construct an optimization model based on a probability distribution of a new energy prediction deviation in the target region, the linear term coefficient and the quadratic term coefficient of the coal consumption cost of the thermal power unit start-stop and the coal consumption cost of the thermal power unit operation, and the unit start-stop state variable and the unit output variable, and determine a constraint condition of the optimization model.

[0107] The optimization solving module is configured to solve the optimization model based on the constraint condition to obtain a unit output and a start-stop machine scheduling plan.

[0108] Embodiment 3

[0109] The application further provides an electronic device 100 for implementing the production simulation method considering the new energy prediction deviation in the above embodiment; the electronic device 100 comprises a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and capable of running on the at least one processor 102, and at least one communication bus 104. The memory 101 can be used for storing the computer program 103, the processor 102 can realize the steps of the production simulation method considering the new energy prediction deviation in Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101.

[0110] The memory 101 can mainly comprise a program storage area and a data storage area, wherein the program storage area can store an operating system, application programs (such as a sound playing function, an image playing function, etc.) required by at least one function, etc.; the data storage area can store data (such as audio data) created according to the use of the electronic device 100, etc. In addition, the memory 101 can comprise a non-volatile memory, for example, a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0111] The at least one processor 102 can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic component, a discrete hardware component, or the like. The processor 102 can be a microprocessor or the processor 102 can also be any conventional processor and the like, and the processor 102 is a control center of the electronic device 100, and connects various parts of the entire electronic device 100 through various interfaces and lines.

[0112] The memory 101 in the electronic device 100 stores a plurality of instructions to implement a production simulation method considering new energy prediction deviation, and the processor 102 can execute the plurality of instructions to implement:

[0113] Determine the probability distribution of the new energy prediction deviation in the target region based on a data fitting method;

[0114] Determine the first-order coefficient and the second-order coefficient of the coal consumption cost of the thermal power unit start-stop and the coal consumption cost of the thermal power unit operation in the target region;

[0115] Create a unit start-stop state variable and a unit output variable;

[0116] Based on the probability distribution of the new energy prediction deviation in the target region, the first-order coefficient and the second-order coefficient of the coal consumption cost of the thermal power unit start-stop and the coal consumption cost of the thermal power unit operation, and the unit start-stop state variable and the unit output variable, an optimization model is constructed with the minimum unit coal consumption cost as the target, and the constraint conditions of the optimization model are determined;

[0117] Solve the optimization model based on the constraint conditions to obtain the unit output and start-stop machine scheduling plan.

[0118] Embodiment 4

[0119] The modules / units integrated in the electronic device 100, if implemented in the form of software function units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on such understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of each method embodiment described above can be implemented. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate forms. The computer-readable medium can include any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, and read-only memory (ROM).

[0120] Those skilled in the art will appreciate that embodiments of the application can be supplied as a method, a system, or a computer program product. Therefore, the application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0121] The application is described with reference to the flowcharts and / or block diagrams of the methods, apparatus (systems), and computer program products according to embodiments of the application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that performs the functions specified in one or more flows and / or blocks.

[0122] These computer program instructions can also be stored in a computer-readable memory that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction apparatus, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that performs the functions specified in one or more flows and / or blocks.

[0123] These computer program instructions can also be loaded into a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 Figure 1

[0124] In this description, references to "one embodiment", "an example", "certain examples" etc. mean that the particular feature, structure, material or characteristic being described is included in at least one embodiment or example of the application. The appearances of an expression like "in one embodiment", "in an example", "in certain examples" or the like in

[0125] Finally, it should be noted that the above-mentioned embodiments are merely given as an illustration of the technical solution of the present application, and are not intended to limit the present application. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that the specific embodiments described above are only used to explain the present application and are not intended to limit the present application. Any modification or equivalent replacement of the specific embodiments of the present application, which does not depart from the spirit and scope of the present application, should be covered by the claims of the present application.​​

Claims

1. A production simulation method considering new energy prediction bias, characterized in that, Includes the following steps: Based on data fitting, the probability distribution of the prediction deviation of new energy sources in the target area is determined; Determine the primary and secondary coefficients of the start-up and shutdown coal consumption costs and the operating coal consumption costs of thermal power units within the target area; Create unit start / stop status variables and unit output variables; Based on the probability distribution of the prediction deviation of new energy in the target area, the first-order and second-order coefficients of the start-up and shutdown coal consumption cost of the thermal power unit, the operating coal consumption cost of the thermal power unit, as well as the unit start-up and shutdown state variables and the unit output variables, are used to construct an optimization model with the goal of minimizing the unit coal consumption cost, and the constraints of the optimization model are determined. The optimization model is solved based on the constraints to obtain the output and start-up / shutdown plans for each unit. The constraints include: unit output constraints and power balance constraints; The unit output constraints are as follows: in, It is the probability distribution of the prediction deviation of new energy power output. To contribute to the unit, To disregard power output under the condition of new energy forecasting bias, This is to balance the increased or decreased output caused by the forecasting deviation of new energy sources. For the unit Maximum output parameters For the unit The minimum output parameter; It is the coefficient for bearing the prediction deviation of new energy sources. The new energy prediction deviation bearing coefficient meets the requirements. ; This indicates the unit's start / stop status. At that time, the unit was shut down. When the unit starts, 1-ε is the confidence interval; The step of solving the optimization model based on the constraints specifically includes: The probabilistic constraints in the optimization model are transformed into equivalent linear constraints, and the transformed optimization model is then substituted into the Cplex solver for solution. The transformed optimization model is as follows: in, To disregard unit output under load forecasting deviations, It is the new energy forecast deviation bearing coefficient, Φ -1 (1-ε) is the inverse function of the probability distribution of new energy prediction deviation under the confidence interval of 1-ε, where, For the unit Maximum output parameters For the unit The minimum output parameter, This indicates the unit's start-up and shutdown status.

2. The production simulation method considering new energy prediction bias according to claim 1, characterized in that, The power balance constraints are as follows: in, For the unit's output without considering load forecast deviations, Contribute to wind power forecasting, For the photovoltaic power output forecast, D represents the total regional load; i∈I is the set of all generating units, where i is a specific generating unit element; w∈W is the set of all wind farms; and v∈V is the set of all photovoltaic power stations. The probability distribution of the prediction deviation of the total wind power output and total photovoltaic power output in the target area.

3. The production simulation method considering new energy prediction bias according to claim 1, characterized in that, The probability distribution of historical power output prediction deviation in the target area is determined by probability fitting of the deviation between the historical predicted power output curve and the actual power output curve of new energy.

4. The production simulation method considering new energy prediction bias according to claim 1, characterized in that, In the step of constructing an optimization model with the objective of minimizing unit coal consumption cost, the optimization model is as follows: in, This indicates the coal consumption cost for starting and stopping thermal power units. This represents the coefficient of the first-order term of the coal consumption cost of thermal power units. The coefficient of the quadratic term representing the coal consumption cost of thermal power unit operation; To contribute to the unit, This indicates the unit's start-up and shutdown status.

5. A production simulation apparatus that considers new energy prediction bias, used to implement the production simulation method considering new energy prediction bias according to any one of claims 1 to 4, characterized in that, include: The first determining module is used to determine the probability distribution of the new energy prediction deviation in the target area based on data fitting. The second determining module is used to determine the first-order and second-order coefficients of the coal consumption cost for starting and stopping thermal power units and the coal consumption cost for operating thermal power units within the target area. The variable creation module is used to create unit start-up and shutdown status variables and unit output variables; The model building module is used to construct an optimization model based on the probability distribution of the prediction deviation of new energy in the target area, the first-term coefficient and the second-term coefficient of the start-up and shutdown coal consumption cost of the thermal power unit, the first-term coefficient and the second-term coefficient of the operating coal consumption cost of the thermal power unit, as well as the start-up and shutdown state variables and the output variables of the unit, with the goal of minimizing the coal consumption cost of the unit, and to determine the constraints of the optimization model. The optimization solution module is used to solve the optimization model based on the constraints to obtain the output and start-up / shutdown plans for each unit.

6. An electronic device, characterized in that, It includes a processor and a memory, the processor being used to execute a computer program stored in the memory to implement the production simulation method considering new energy prediction bias as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction, which, when executed by a processor, implements the production simulation method considering new energy prediction bias as described in any one of claims 1 to 4.

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