Wind power plant day-ahead quotation method considering prediction error

Abstract

The invention provides a wind power plant day-ahead quotation method considering prediction errors, and relates to the technical field of new energy. The method comprises the following steps: inputting a probability of a prediction error scene of predicted output of a wind turbine generator in a target time period, a simulated bid winning electric quantity and a simulated node electricity price of the wind turbine generator in the target time period, the predicted output of the wind turbine generator in the target time period and a predicted error value of the wind turbine generator in the target time period into an upper-layer wind power plant quotation model; obtaining an intermediate report quotation of the wind power plant output by an upper-layer wind power plant quotation model; determining whether the current number of iterations in the step 1 is smaller than the standard number of iterations; and if not, determining the day-ahead quotation result of the wind power plant based on the intermediate quantity report quotation of all the wind turbine generators output by the upper-layer wind power plant quotation model, so as to reduce deviation punishment and maintain the system adjustment consumption capability.

Description

Technical Field

[0001] The present application relates to the field of new energy technology, and in particular to a method for day-ahead bidding of a wind farm taking into account prediction errors. Background Art

[0002] The purpose of new energy manufacturers participating in strategic bidding in the electricity spot market is mainly to reduce risks and increase profits. Domestic and foreign scholars have conducted many studies, which can be mainly divided into two categories. The first category is to jointly operate with other energy media such as energy storage. When faced with problems such as insufficient energy storage capacity and insufficient flexibility of external adjustable resources, the uncertainty risks of new energy cannot be fully transferred, resulting in deviation penalties for new energy manufacturers. The second category is to reduce the loss of revenue faced by new energy by bidding reasonably in different markets. The quotation form of its declaration strategy for new energy such as wind power is mainly to declare a quantity-price pair for each time period or to only report the bid output for each time period. Its quantity-price curve is relatively simple and the curve reflects the reliability of new energy output and the impact of deviation is relatively small. The quotation form is single, and the market means of quotation is not well used to alleviate the impact of uncertainty and reduce penalties.

[0003] Therefore, how to reduce deviation penalties while increasing electric field gains has become an urgent problem to be solved. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a method for day-ahead bidding of a wind farm taking into account prediction errors, so as to reduce deviation penalties and maintain the regulation and absorption capacity of the system.

[0005] In a first aspect, the present invention provides a method for day-ahead bidding of a wind farm taking into account prediction errors, the method comprising:

[0006] The wind farm includes a preset number of wind turbines. The day-ahead quotation of the wind farm is predicted in the following way:

[0007] Step 1: for each wind turbine in the wind farm, the probability of the prediction error scenario of the predicted output of the wind turbine in the target period, the simulated winning bid quantity and simulated node electricity price of the wind turbine in the target period, the predicted output of the wind turbine in the target period, and the prediction error value of the wind turbine in the target period are input into the upper-level wind farm quotation model to obtain the intermediate quotation of the wind farm output by the upper-level wind farm quotation model;

[0008] Step 2: Determine whether the current number of iterations in step 1 is less than the standard number of iterations;

[0009] Step 3: If yes, input the intermediate bid quantity quotation of the wind farm output by the upper-level wind farm bidding model into the lower-level day-ahead spot market clearing model, obtain the simulated winning bid quantity and simulated node electricity price output by the lower-level day-ahead spot market clearing model, update the simulated winning bid quantity and simulated node electricity price based on the fitness of the intermediate bid quantity quotation of the wind farm output by the upper-level wind farm bidding model, and return to execute step 1;

[0010] Step 4: If not, determine the day-ahead quotation result of the wind farm based on the intermediate quotation of all wind turbines output by the upper-level wind farm quotation model.

[0011] In an optional implementation, when step one is executed for the first time, the simulated winning electricity quantity and the simulated node electricity price are determined in the following manner: for each wind turbine unit in the wind farm, the initial bid price of the wind turbine unit is determined; with the goal of maximizing fitness, the initial bid price is updated; and the updated bid prices of all wind turbine units are input into the lower-level day-ahead spot market clearing model to obtain the simulated winning electricity quantity and the simulated node electricity price output by the lower-level day-ahead spot market clearing model.

[0012] In an optional implementation manner, the day-ahead quotation result of the wind farm includes a low price quotation C1 corresponding to a reliable output interval [0, P1], an intermediate price quotation C2 for a relatively unreliable output interval [P1, P2], and a high price quotation C3 corresponding to an unreliable output interval [P2, P3]. The step of obtaining the intermediate quotation of the wind farm output by the upper-level wind farm quotation model specifically includes:

[0013] P1, P2, C1, C2, C3 are used as the positions of lemurs in the lemur optimization algorithm. The position of each lemur is iterated based on the fitness to obtain the global optimal position of the lemur as the quotation corresponding to each wind turbine in the wind farm; wherein the fitness is calculated in the following way:

[0014]

[0015] Where T is the time period, N w is the number of wind turbines, λ n,t is the simulated node electricity price of wind turbine n at time t, N M is the number of prediction error scenarios, π i is the probability of the i-th forecast error scenario, M is the deviation cost coefficient, is the prediction error value of wind turbine n at time t in prediction error scenario i, is the predicted output of wind turbine n at time t.

[0016] In an optional implementation, the lower-layer day-ahead spot market clearing model includes a unit combination model and an economic dispatch model, and the step of obtaining the simulated winning electricity quantity and the simulated node electricity price output by the lower-layer day-ahead spot market clearing model specifically includes: solving the optimal solution of the unit combination model by a planning solver to determine the start and stop states of the wind turbines and the thermal power units, wherein the formula of the unit combination model is:

[0017]

[0018] Among them, N is the number of units participating in the market, M is the number of units’ quotation segments, and C i,t,m is the price quoted by wind turbine i in the mth period of time t, P i,t,m is the force of wind turbine i in the mth segment of the tth period, N th is the total number of thermal power units, U i,t is the start-up and shutdown cost of thermal power unit i at time t;

[0019] The optimal solution of the economic dispatch model is solved by the planning solver to obtain the simulated winning bid power and simulated node electricity price of each wind turbine in the wind farm. The formula of the economic dispatch model is:

[0020]

[0021] In an optional embodiment, the probability of a prediction error scenario is determined in the following manner: a prediction error probability density distribution fitting curve is constructed based on the error between the historical predicted output and the actual output of the wind farm; a prediction error exceedance probability curve is determined by considering the prediction error probability density distribution fitting curve; a preset number of prediction error scenarios are extracted from the prediction error probability density distribution fitting curve through a Latin hypercube sampling algorithm; and the probability corresponding to each prediction error scenario is matched in the prediction error exceedance probability curve.

[0022] In an optional embodiment, before the step of constructing the prediction error probability density distribution fitting curve, the method further includes:

[0023] Each prediction error value is normalized as follows:

[0024]

[0025] Among them, ΔP * is the per-unit value of the prediction error, ΔP is the prediction error value, P N is the rated power of the wind farm.

[0026] In an optional embodiment, considering the prediction error probability density distribution fitting curve, the step of determining the prediction error exceedance probability curve specifically includes: integrating the prediction error probability density distribution fitting curve to obtain the cumulative distribution function of the prediction error; and obtaining the exceedance probability curve corresponding to each output interval by the following formula:

[0027]

[0028] Among them, P n is the reported output of the nth output interval, F(P n ) is P n The cumulative distribution function value at is the predicted output of wind turbine n at time t, For P n The prediction error of wind turbine n at time t in the event of

[0029] In a second aspect, the present invention provides a wind farm day-ahead quotation device taking into account prediction errors, the device comprising:

[0030] The wind farm includes a preset number of wind turbines, and the forecasting module is used to forecast the day-ahead quotation of the wind farm in the following ways:

[0031] Step 1: for each wind turbine in the wind farm, the probability of the prediction error scenario of the predicted output of the wind turbine in the target period, the simulated winning bid quantity and simulated node electricity price of the wind turbine in the target period, the predicted output of the wind turbine in the target period, and the prediction error value of the wind turbine in the target period are input into the upper-level wind farm quotation model to obtain the intermediate quotation of the wind farm output by the upper-level wind farm quotation model;

[0032] Step 2: Determine whether the current number of iterations in step 1 is less than the standard number of iterations;

[0033] Step 3: If yes, input the intermediate bid quantity quotation of the wind farm output by the upper-level wind farm bidding model into the lower-level day-ahead spot market clearing model, obtain the simulated winning bid quantity and simulated node electricity price output by the lower-level day-ahead spot market clearing model, update the simulated winning bid quantity and simulated node electricity price based on the fitness of the intermediate bid quantity quotation of the wind farm output by the upper-level wind farm bidding model, and return to execute step 1;

[0034] Step 4: If not, determine the day-ahead quotation result of the wind farm based on the intermediate quotation of all wind turbines output by the upper-level wind farm quotation model.

[0035] In a third aspect, the present invention provides an electronic device, comprising: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, when the electronic device is running, the processor and the memory communicate through the bus, and the processor executes the machine-readable instructions to perform the steps of any of the above-mentioned embodiments for day-ahead bidding of a wind farm taking into account prediction errors.

[0036] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the day-ahead quotation of a wind farm taking into account the prediction error are performed as in any of the aforementioned embodiments.

[0037] The present application provides a wind farm day-ahead quotation method that takes prediction errors into consideration, proposes a wind power quotation method that takes prediction errors into consideration, and solves the model by establishing a quotation model for upper-level wind farms to participate in the day-ahead market and a lower-level day-ahead market clearing model that aims to maximize their own interests, thereby obtaining a day-ahead tiered quotation for the wind farm, which can reduce the wind farm deviation penalty and maintain the system regulation and absorption capacity, thereby improving the comprehensive benefits of the wind farm. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 A schematic diagram of a step curve quotation for a wind farm station during a certain period of time provided in an embodiment of the present application;

[0040] Figure 2 A flow chart of a method for day-ahead bidding of a wind farm taking into account prediction errors provided in an embodiment of the present application;

[0041] Figure 3 A 11-15h wind power forecast error probability density distribution fitting diagram provided in this application;

[0042] Figure 4 A 10-time period quotation chart for station No. 1 is provided in an embodiment of the present application. DETAILED DESCRIPTION

[0043] First, the applicable scenario of the present application is described. The technical solution of the present application is applicable to the prediction of the day-ahead quotation of a wind farm.

[0044] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0045] Embodiment 1

[0046] Figure 1 A schematic diagram of a step curve quotation for a wind farm station during a certain period of time provided for this application. Figure 2 A flow chart of a method for day-ahead bidding of a wind farm taking into account prediction errors provided in an embodiment of the present application.

[0047] A wind farm includes a preset number of wind turbines and thermal power units. Figure 2 As shown, in a specific embodiment, a step curve quotation method is provided, which can be a quotation curve in the form of three steps. The day-ahead quotation result of the wind farm includes a low price quotation C1 corresponding to the reliable output interval [0, P1], an intermediate price quotation C2 for the less reliable output interval [P1, P2], and a high price quotation C3 corresponding to the unreliable output interval [P2, P3].

[0048] Among them, P3 is the upper limit of the output of the new energy station in each period determined by the predicted power. If the point with a horizontal coordinate of 0 in the exceedance probability curve of the prediction error corresponds to P3, the probability size corresponding to the output range can be clearly known.

[0049] for Figure 1 The corresponding exceedance probability of the medium power output interval [0, P1] is the green dotted line, which means that the probability of wind power output being at least greater than P1 is high, that is, the output interval [0, P1] is reliable. Therefore, the wind farm needs to bid as low as possible to win the bid for this output interval. Similarly, the subsequent less reliable and unreliable output intervals can be delineated.

[0050] Figure 1 The reported output in the first section is the reliable output range, and the probability and reported output can be calculated by the following formula:

[0051]

[0052] Among them, P is the output of the wind turbine, P1 is the first section of wind power reporting, F(P1) is the cumulative distribution function value at P1, is the predicted output of wind turbine n at time t, is the prediction error of the wind turbine at time t when event P1 occurs.

[0053] Figure 1 The third output range is unreliable, that is, the power range with a high probability of error. This output range should be reported at a high price and try not to win the bid. The subsequent division process is similar to this, and multiple output ranges can be divided based on this. The price reported for each output range can be determined by a simulated clearing method.

[0054] Different from the case of bidding a low price, using a step curve to bid in the market may make the winning bid of wind power less than or equal to the day-ahead forecast output, so the deviation output and forecast error are not always equal. The deviation output can be obtained based on the winning bid output, forecast output and forecast error, as shown in the following formula:

[0055]

[0056] in, is the estimated deviation penalty output of wind turbine n at time t, P n,t is the winning bid output of wind turbine n at time t, is the prediction error of wind turbine n obtained from the exceedance probability at the tth moment.

[0057] In this embodiment, a method for day-ahead bidding of a wind farm taking into account prediction errors is provided. Figure 2 As shown, the method includes:

[0058] The day-ahead bid volume of the wind farm is predicted by:

[0059] S1. For each wind turbine in the wind farm, the probability of the prediction error scenario of the predicted output of the wind turbine in the target period, the simulated winning electricity volume and simulated node electricity price of the wind turbine in the target period, the predicted output of the wind turbine in the target period, and the prediction error value of the wind turbine in the target period are input into the upper-level wind farm quotation model to obtain the intermediate quotation of the wind farm output by the upper-level wind farm quotation model.

[0060] S2. Determine whether the current number of iterations in step 1 is less than the standard number of iterations.

[0061] S3. If yes, then the intermediate bid quantity quotation of the wind farm output by the upper-level wind farm quotation model is input into the lower-level day-ahead spot market clearing model, and the simulated winning electricity quantity and simulated node electricity price output by the lower-level day-ahead spot market clearing model are obtained, and the simulated winning electricity quantity and simulated node electricity price are updated based on the fitness of the intermediate bid quantity quotation of the wind farm output by the upper-level wind farm quotation model, and the process returns to step 1.

[0062] S4. If not, then determining the day-ahead quotation result of the wind farm based on the intermediate quotation of all wind turbines output by the upper-level wind farm quotation model.

[0063] The present application provides a wind farm day-ahead quotation method that takes prediction errors into consideration, proposes a wind power quotation method that takes prediction errors into consideration, and solves the model by establishing a quotation model for upper-level wind farms to participate in the day-ahead market and a lower-level day-ahead market clearing model that aims to maximize their own interests, thereby obtaining a day-ahead tiered quotation for the wind farm, which can reduce the wind farm deviation penalty and maintain the system regulation and absorption capacity, thereby improving the comprehensive benefits of the wind farm.

[0064] Embodiment 2

[0065] The wind farm submits the bid to the operator, and then obtains the winning bid volume and price, and updates the bid accordingly. In this embodiment, the wind farm forecast error is analyzed and a two-layer model of wind farm bids considering the forecast error is established. The upper layer incorporates the forecast error into the target, and the lower layer clears the market by minimizing the purchase cost of electricity. The upper and lower models interact cyclically to obtain the wind power bid.

[0066] In a specific embodiment, the upper-level wind farm quotation model may be constructed through the following steps.

[0067] First, determine the wind farm prediction error scenario. Here, the actual value and predicted value of wind power in a certain wind farm in March can be selected for analysis. Before the step of constructing the prediction error probability density distribution fitting curve, each prediction error value is normalized in the following way:

[0068]

[0069] Among them, ΔP * is the per-unit value of the prediction error, ΔP is the prediction error value, P N is the rated power of the wind farm.

[0070] Figure 3 This application provides a 11-15h wind power forecast error probability density distribution fitting graph. The forecast error probability density distribution fitting curve can be constructed based on the error between the historical forecast output and the actual output of the wind farm. For example, Figure 3 As shown in FIG. 1 , the prediction error probability density distribution fitting curve is obtained by performing non-parametric estimation of the wind farm 11-15h prediction error using normal distribution as the kernel function and partial probability density fitting.

[0071] Based on the prediction error probability density distribution fitting curve, a prediction error exceedance probability curve is determined. A preset number of prediction error scenarios are extracted from the prediction error probability density distribution fitting curve by a Latin hypercube sampling algorithm. In the prediction error exceedance probability curve, the probability corresponding to each prediction error scenario can be matched.

[0072] According to the time series characteristics of wind power prediction error, the prediction error data of this area can be classified according to the time series, namely [1h, 5h], [6h, 7h], [8h, 11h], [12h, 15h], [16h, 21h], [22h, 24h], so that the estimated prediction error probability distribution is more reasonable.

[0073] According to the fitted prediction error probability density distribution, the Latin hypercube sampling method is used to extract the scenarios, and the probability distance is used to quickly reduce the scenarios. Finally, the five prediction error scenarios of the wind farm and their corresponding probabilities π are obtained. i , i = 1, 2, ..., 5. According to the prediction error of each time period in the five scenarios and the probability of the corresponding scenarios, the output range can be divided.

[0074] Since wind power and other new energy sources have no fuel cost, that is, the marginal cost is 0. Therefore, the short-term cost of wind power is not considered in this article. Here, we can study the quotation problem from the perspective of the day-ahead, focusing on the benefits related to the day-ahead market and the penalties based on the day-ahead winning electricity volume. Therefore, the target formula for the day-ahead quotation of wind power considering the forecast error field can be: ; ; in, For the time period, is the number of wind turbines, For wind turbines In the The simulated node electricity price at time, is the number of prediction error scenarios, For the The probability of a prediction error scenario, is the deviation cost coefficient, For the prediction error scenario Stroke generator set n exist t The prediction error value at time For wind turbines n exist t Time prediction output.

[0077] The second term in the above formula refers to the deviation cost, which is the penalty coefficient multiplied by the output deviation. The value may be negative, that is, the winning output is smaller than the expected actual output. In this case, the station can sell extra electricity in real-time transactions to obtain additional revenue. Since the above target is mainly used to solve the quantity and price declared on the day before, the deviation cost in the above situation can be regarded as 0 in the solution process and is not considered in the model target.

[0078] In order to make the quotation curve conform to the form of a ladder, there should also be constraints on the quotation quantity, namely:

[0079] 0≤C1≤C2≤…≤C n ≤C max ;

[0080]

[0081] The objective function of the upper model of the two-layer wind power quotation model established in this paper needs to be solved after the lower model is solved to simulate the winning bid volume and node electricity price before the target value can be obtained. The constraints in the upper model mainly play a role in the objectives and constraints of the lower model. Before solving the lower clearing model, the upper wind power quotation needs to be obtained.

[0082] Furthermore, the lower-level day-ahead spot market clearing model can be constructed in the following way:

[0083] After receiving the quantity and price curves of wind power and other power generation, the power market operator will conduct day-ahead market clearing to find the winning bid volume and clearing price of all parties participating in the market. In the day-ahead market clearing process, the operator constructs an economic dispatch model with the lowest power purchase cost as the objective function, as shown in the following formula:

[0084]

[0085] In the process of market clearing, it is also necessary to consider the constraints of market operation, including:

[0086] (1) Relationship between the winning bid output and the segmented winning bid output:

[0087]

[0088] Among them, P i,t is the winning bid output of unit i in period t.

[0089] (2) Node power balance:

[0090]

[0091] Among them, Ω Nbus is the set of other nodes connected to the node Nbus, P ij,t is the branch power flow at time t, Ω i is the set of nodes connected to node i, D Nbus,t is the load on node Nbus at time t.

[0092] (3) Node phase angle constraint:

[0093] -π≤θ i,t ≤π;

[0094] Among them, θ i,t is the phase angle of node i at time t.

[0095] (4) DC power flow constraints:

[0096] P ij,t =Bf i,j (θ i,t -θ j,t );

[0097] Among them, Bf i,j is the admittance between node i and node j.

[0098] (5) Upper and lower limit constraints of power flow:

[0099]

[0100] in, is the maximum power allowed to flow through branch ij.

[0101] (6) Output constraints of thermal power units:

[0102]

[0103] Among them, u i,t is the start / stop state of thermal power unit i at time t, which is a 0-1 variable, 0 means shutdown, 1 means startup, is the minimum output of thermal power unit i, is the maximum output of thermal power unit i.

[0104] (7) Output constraints of new energy units:

[0105]

[0106] (8) Thermal power unit climbing constraints:

[0107]

[0108] in, is the output of thermal power unit i at time t, is the maximum increase in the output of thermal power unit i in adjacent time periods, It is the maximum decrease in output of thermal power unit i in adjacent time periods.

[0109] (9) Start-up and shutdown time constraints of thermal power units:

[0110]

[0111] Among them, T s is the minimum continuous downtime, T o The minimum continuous power-on time.

[0112] (10) Thermal power start-stop logic constraints:

[0113] y i,t +z i,t ≤1;

[0114] y i,t -z i,t =u i,t -u i,t-1 ;

[0115] Among them, y i,t is the startup logic variable of the thermal power unit, z i,t It is the shutdown logic variable of the thermal power unit.

[0116] (11) Number of thermal power starts and stops:

[0117]

[0118] in, is the maximum number of startup times of thermal power unit i, is the maximum shutdown times of thermal power unit i.

[0119] Considering that the day-ahead clearing model contains a large number of integer variables, it is difficult to find the dual multiplier of the node power balance constraint as the node marginal electricity price. In this regard, it can be handled according to the mode of solving the node electricity price in the current actual power market, that is, first solve the unit combination model, fix the start and stop state of the generator, and then solve the economic dispatch model to obtain the electricity price and generator output. The optimization objective of the unit combination model is as follows:

[0120]

[0121] Among them, N is the number of units participating in the market, M is the number of units’ quotation segments, and C i,t,m is the price quoted by wind turbine i in the mth period of time t, P i,t,m is the force of wind turbine i in the mth segment of the tth period, N th is the total number of thermal power units, U i,t is the start-up and shutdown cost of thermal power unit i at time t.

[0122] Embodiment 3

[0123] When solving the two-layer model simultaneously, a large number of decision variables are multiplied, resulting in many nonlinear terms, which makes it difficult to solve. In order to solve the two-layer model efficiently, this paper uses the Lemur algorithm in the upper layer to first solve the wind farm's bid quotation, and then uses a commercial mathematical programming solver in the lower layer to calculate the winning bid quantity and electricity price.

[0124] For the upper model, the lemur algorithm can be considered for multi-objective optimization. P1, P2, C1, C2, C3 are used as the positions of lemurs in the lemur optimization algorithm. The position of each lemur is iterated based on fitness to obtain the global optimal position of the lemur as the quotation corresponding to each wind turbine in the wind farm;

[0125] The fitness is calculated in the following way:

[0126]

[0127] Where T is the time period, N w is the number of wind turbines, λ n,t is the simulated node electricity price of wind turbine n at time t, N M is the number of prediction error scenarios, π i is the probability of the i-th forecast error scenario, M is the deviation cost coefficient, is the prediction error value of wind turbine n at time t in prediction error scenario i, is the predicted output of wind turbine n at time t.

[0128] Specifically, the search phase can utilize the jumping and dancing behaviors of lemurs, determine the behaviors of lemurs based on the risk rate, and explore the search space to assign the best position to each solution, which is related to the fitness function of the solution. In each iteration, the global optimum and the best nearest lemur can be selected based on the fitness, and the position solution can be updated based on the two options.

[0129] After each iteration, for the lemur positions whose size relationship does not meet the range constraint, each decision variable needs to be modified. For example, for P1, P2, and C2, random values ​​can be taken within their valid ranges. 3， If it exceeds the specified upper and lower limits, the value of the position can be set to the corresponding upper and lower limits.

[0130] The lower-level clearing model can be solved using a planning solver, and the unit combination problem is solved first and then the economic dispatch problem, and finally the node electricity price and winning electricity volume of each power company are obtained.

[0131] The Lemur algorithm is used to obtain the quantity-price pairs declared by wind turbines in each period, which are substituted into the market clearing model. Then, the simulated winning bid quantity and node electricity price are obtained by solving the lower model. The results are then sent back to the upper wind power quotation model to obtain the profit and deviation of the wind farm.

[0132] Specifically, the step of obtaining the simulated winning electricity quantity and the simulated node electricity price output by the lower-layer day-ahead spot market clearing model specifically includes:

[0133] The optimal solution of the unit combination model is solved by a planning solver to determine the start and stop status of the wind turbine unit and the thermal power unit. The optimal solution of the economic dispatch model is solved by a planning solver to obtain the simulated winning bid power and simulated node electricity price of each wind turbine unit in the wind farm.

[0134] In a specific embodiment, the pseudo code for solving the double-layer model is shown in Table 1.

[0135] Table 1 Pseudocode for solving the two-layer model

[0136]

[0137] Before the actual solution step, the initial position needs to be generated. Randomly select a combination of bids and quotations within the space specified by the electricity and price as the initial state. Then the Lemur algorithm is updated. The population updates its position using jumping and dancing behaviors based on the Lemur algorithm and the corresponding fitness, with the goal of maximizing fitness. Then the lower model is solved based on the quotation. First, the objective function of the unit combination model is solved using the solver with constraints (1)-(11) as constraints. Then, the unit combination state is fixed, and the objective function of the economic dispatch model is solved using the solver with constraints (1)-(8) as constraints to obtain the electricity price and the winning bid electricity. Finally, the fitness of the population is calculated based on the objective function of the upper model.

[0138] Perform a preset number of upper and lower layer iterations, and update the population position based on the Lemur algorithm according to the latest fitness until the maximum number of iterations is reached, and finally obtain the day-ahead quotation result of the wind farm. Figure 4 This is a 10-period quotation chart for Station 1 provided in an embodiment of the present application. Figure 4 As shown, the final day-ahead quotation result is a step-by-step one, where the predicted output of station 1 is 48.95MW in the 10th period, and the predicted output is used as the upper limit of the quotation. According to the forecast error scenario analysis, the station has a 100% probability of generating at least 12.15MW of electricity, a 65% probability of generating electricity in the range of 12.15MW to 33.6MW, and a 45% probability of generating at least 33.6MW of electricity. After solving the model, it can be seen that in the 10th period, the station reported 40 yuan / MWh in the power range of [0, 12.15], 410 yuan / MWh in the power range of [33.6, 48.95], and reported a medium-high price for the remaining output of [12.15, 33.6].

[0139] Exemplarily, when step 1 is performed for the first time, the simulated winning bid amount and the simulated node electricity price may be determined in the following manner:

[0140] For each wind turbine in the wind farm, determine the initial bid price of the wind turbine. Update the initial bid price with the goal of maximizing fitness. Input the updated bid prices of all wind turbines into the lower-level day-ahead spot market clearing model to obtain the simulated winning bid quantity and simulated node electricity price output by the lower-level day-ahead spot market clearing model.

[0141] Embodiment 4

[0142] Based on the same inventive concept, an embodiment of the present application further provides a wind farm day-ahead quotation device taking into account prediction errors, wherein the wind farm includes a preset number of wind turbines, and the device includes:

[0143] The prediction module is used to predict the day-ahead quotation of the wind farm in the following ways:

[0144] Step 1: for each wind turbine in the wind farm, the probability of the prediction error scenario of the predicted output of the wind turbine in the target period, the simulated winning bid quantity and simulated node electricity price of the wind turbine in the target period, the predicted output of the wind turbine in the target period, and the prediction error value of the wind turbine in the target period are input into the upper-level wind farm quotation model to obtain the intermediate quotation of the wind farm output by the upper-level wind farm quotation model;

[0145] Step 2: Determine whether the current number of iterations in step 1 is less than the standard number of iterations;

[0146] Step 3: If yes, input the intermediate bid quantity quotation of the wind farm output by the upper-level wind farm bidding model into the lower-level day-ahead spot market clearing model, obtain the simulated winning bid quantity and simulated node electricity price output by the lower-level day-ahead spot market clearing model, update the simulated winning bid quantity and simulated node electricity price based on the fitness of the intermediate bid quantity quotation of the wind farm output by the upper-level wind farm bidding model, and return to execute step 1;

[0147] Step 4: If not, determine the day-ahead quotation result of the wind farm based on the intermediate quotation of all wind turbines output by the upper-level wind farm quotation model.

[0148] In one embodiment of the present application, an electronic device includes a processor, a memory, and a bus.

[0149] The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via a bus. When the machine-readable instructions are executed by the processor, a step of a day-ahead quotation for a wind farm considering prediction errors in the above method embodiment can be executed. The specific implementation method can be found in the method embodiment, which will not be described in detail here.

[0150] An embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of a wind farm day-ahead quotation taking into account prediction errors in the above method embodiment can be executed. The specific implementation method can be found in the method embodiment, which will not be repeated here.

[0151] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0152] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0153] In addition, the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0154] Furthermore, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0155] It should be noted that if the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can essentially be embodied in the form of a software product, or the part that contributes to the prior art or the part of the technical solution. The computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM) random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0156] In this document, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0157] The above description is only an embodiment of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A wind farm day-ahead quotation method considering prediction error, characterized in that: The method comprises: The wind farm includes a preset number of wind turbines, and the day-ahead quotation quantity of the wind farm is predicted by: Step 1: for each wind turbine in the wind farm, the probability of the prediction error scenario of the predicted output of the wind turbine in the target period, the simulated winning bid quantity and simulated node electricity price of the wind turbine in the target period, the predicted output of the wind turbine in the target period, and the prediction error value of the wind turbine in the target period are input into the upper-level wind farm quotation model to obtain the intermediate quotation of the wind farm output by the upper-level wind farm quotation model; Step 2: Determine whether the current number of iterations in step 1 is less than the standard number of iterations; Step 3: If yes, input the intermediate bid quantity quotation of the wind farm output by the upper-level wind farm bidding model into the lower-level day-ahead spot market clearing model, obtain the simulated winning bid quantity and simulated node electricity price output by the lower-level day-ahead spot market clearing model, update the simulated winning bid quantity and simulated node electricity price based on the fitness of the intermediate bid quantity quotation of the wind farm output by the upper-level wind farm bidding model, and return to execute step 1; Step 4: If not, determine the day-ahead quotation result of the wind farm based on the intermediate quotation of all wind turbines output by the upper-level wind farm quotation model.

2. The method according to claim 1, characterized in that When step 1 is executed for the first time, the simulated winning electricity quantity and simulated node electricity price are determined in the following way: For each wind turbine in the wind farm, determine the initial quotation of the wind turbine; With the goal of maximizing fitness, the initial quotation is updated; The updated bid quantities and quotations of all wind turbines are input into the lower-level day-ahead spot market clearing model to obtain the simulated winning bid quantity and simulated node electricity price output by the lower-level day-ahead spot market clearing model.

3. The method according to claim 1, characterized in that The wind farm's day-ahead quotation results include a low price quotation C1 corresponding to a reliable output interval [0, P1], an intermediate price quotation C2 for a relatively unreliable output interval [P1, P2], and a high price quotation C3 corresponding to an unreliable output interval [P2, P3]. The step of obtaining the intermediate quotation of the wind farm output by the upper-level wind farm quotation model specifically includes: Take P1, P2, C1, C2, C3 as the positions of lemurs in the lemur optimization algorithm, iterate the position of each lemur based on fitness, so as to obtain the global optimal position of the lemur, which is used as the quotation corresponding to each wind turbine in the wind farm; The fitness is calculated in the following way: 0≤C1≤C2≤…≤C n ≤C max ; Where T is the time period, N w is the number of wind turbines, λ n,t is the simulated node electricity price of wind turbine n at time t, N M is the number of prediction error scenarios, π i is the probability of the i-th forecast error scenario, M is the deviation cost coefficient, is the prediction error value of wind turbine n at time t in prediction error scenario i, is the predicted output of wind turbine n at time t.

4. The method according to claim 1, characterized in that: The lower-layer day-ahead spot market clearing model includes a unit commitment model and an economic dispatch model. The step of obtaining the simulated winning bid quantity and the simulated node electricity price output by the lower-layer day-ahead spot market clearing model specifically includes: The optimal solution of the unit combination model is solved by a planning solver to determine the start and stop states of the wind turbines and thermal power units, wherein the formula of the unit combination model is: Among them, N is the number of units participating in the market, M is the number of units’ quotation segments, and C i,t,m is the price quoted by wind turbine i in the mth period of time t, P i,t,m is the force of wind turbine i in the mth segment of the tth period, N th is the total number of thermal power units, U i,t is the start-up and shutdown cost of thermal power unit i at time t; The optimal solution of the economic dispatch model is solved by a planning solver to obtain the simulated winning bid power and simulated node electricity price of each wind turbine in the wind farm, wherein the economic dispatch model formula is:

5. The method according to claim 1, characterized in that: The probability of the forecast error scenario is determined by: Based on the error between the historical predicted output and the actual output of the wind farm, a fitting curve of the probability density distribution of the prediction error is constructed; Determining a prediction error exceedance probability curve based on the prediction error probability density distribution fitting curve; Extracting a preset number of prediction error scenarios from the prediction error probability density distribution fitting curve by using a Latin hypercube sampling algorithm; In the prediction error exceedance probability curve, the probability corresponding to each prediction error scenario is matched.

6. The method according to claim 5, characterized in that Before the step of constructing the prediction error probability density distribution fitting curve, the method further includes: Each prediction error value is normalized as follows: Among them, ΔP * is the per-unit value of the prediction error, ΔP is the prediction error value, P N is the rated power of the wind farm.

7. The method according to claim 5, characterized in that The step of determining a prediction error exceedance probability curve based on the prediction error probability density distribution fitting curve specifically includes: Integrating the prediction error probability density distribution fitting curve to obtain a cumulative distribution function of the prediction error; The exceedance probability curve corresponding to each output range is obtained by the following formula: Among them, P n is the reported output of the nth output interval, F(P n ) is P n The cumulative distribution function value at is the predicted output of wind turbine n at time t, For P n The prediction error of wind turbine n at time t in the event of 8. A wind farm day-ahead quotation device taking into account prediction error, characterized in that: The device comprises: The wind farm includes a preset number of wind turbines, and a prediction module, which is used to predict the day-ahead quotation of the wind farm in the following manner: Step 1: for each wind turbine in the wind farm, the probability of the prediction error scenario of the predicted output of the wind turbine in the target period, the simulated winning bid quantity and simulated node electricity price of the wind turbine in the target period, the predicted output of the wind turbine in the target period, and the prediction error value of the wind turbine in the target period are input into the upper-level wind farm quotation model to obtain the intermediate quotation of the wind farm output by the upper-level wind farm quotation model; Step 2: Determine whether the current number of iterations in step 1 is less than the standard number of iterations; Step 3: If yes, input the intermediate bid quantity quotation of the wind farm output by the upper-level wind farm bidding model into the lower-level day-ahead spot market clearing model, obtain the simulated winning bid quantity and simulated node electricity price output by the lower-level day-ahead spot market clearing model, update the simulated winning bid quantity and simulated node electricity price based on the fitness of the intermediate bid quantity quotation of the wind farm output by the upper-level wind farm bidding model, and return to execute step 1; Step 4: If not, determine the day-ahead quotation result of the wind farm based on the intermediate quotation of all wind turbines output by the upper-level wind farm quotation model.

9. An electronic device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the memory via the bus, and the processor executes the machine-readable instructions to perform the steps of day-ahead quotation of a wind farm taking into account prediction errors as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the day-ahead quotation of a wind farm considering prediction errors as claimed in any one of claims 1 to 7 are performed.

Images