Wind and light storage station regulation and control method considering electricity-green-carbon price prediction, equipment, medium and product

Abstract

The invention discloses a wind and light storage station regulation and control method and device considering electricity-green-carbon price prediction, a medium and a product, and relates to the technical field of new energy, and the method comprises the steps: inputting an obtained historical price data combination into a price prediction model, and outputting a predicted price; determining a deviation vector of the price data based on the historical price data combination and the predicted price, and generating an uncertainty set of the predicted price by adopting a multi-core-based single-class support vector machine algorithm; a neural network classifier is adopted to classify the uncertainty set of the predicted price to obtain multiple types of price scenes; solving the joint clearing model based on the predicted prices in various price scenes by adopting a mottled turnip optimization algorithm to obtain an operation strategy of the wind and light storage station; and regulating and controlling the wind and light storage station based on the operation strategy of the wind and light storage station. According to the invention, the prediction precision of the electricity price, the carbon sink price and the green certificate price is improved, and the robustness of regulation and control of the wind and light storage station is improved.

Description

Technical Field

[0001] The present application relates to the field of renewable energy power generation technology, and in particular to a method, device, medium and product for regulating a wind-solar-storage station taking into account electricity-green-carbon price forecasting. Background Art

[0002] With the acceleration of global energy transformation, the proportion of renewable energy electricity is increasing year by year. In order to cope with the uncertainty of renewable energy power generation, grid dispatching needs to be more flexible. In addition, various price factors such as electricity prices, carbon sink prices and green certificate prices affect the arrangement and implementation of renewable energy station dispatching plans. At present, electricity prices, carbon sink prices and green certificate prices all show significant time series characteristics, have complex long-term and short-term dependencies, and have large fluctuations and uncertainties, but traditional forecasting methods are difficult to fully capture these characteristics, thus affecting the forecast accuracy. At the same time, under price fluctuations and uncertainties, how to quickly and efficiently obtain the fluctuation range of price forecasts and improve the robustness of wind, solar and storage station dispatching plans within this range is a key issue that needs to be solved urgently. Summary of the invention

[0003] The purpose of this application is to provide a wind, solar and storage station control method, equipment, medium and product that takes into account the electricity-green-carbon price forecast, improve the prediction accuracy of electricity prices, carbon sink prices and green certificate prices, and enhance the robustness of wind, solar and storage station control.

[0004] To achieve the above objectives, this application provides the following solutions.

[0005] In the first aspect, the present application provides a method for obtaining a combination of historical price data; the combination of historical price data includes: historical carbon prices, historical green certificate prices, and historical electricity prices;

[0006] The historical price data combination is input into the price prediction model, and the predicted price is output; the predicted price includes: a carbon price prediction value, a green certificate price prediction value, and an electricity price prediction value; the price prediction model is obtained by training an LSTM neural network model based on a hybrid attention mechanism using a training data set; the training data set includes a sample price data combination and a corresponding predicted value of the sample price data combination;

[0007] Determining a deviation vector of price data based on the combination of the historical price data and the predicted price;

[0008] A multi-core based single-class support vector machine algorithm is used to generate the uncertainty set of the predicted price based on the deviation vector of the price data;

[0009] A neural network classifier is used to classify the uncertainty set of the predicted price to obtain multiple price scenarios; each price scenario includes multiple predicted prices;

[0010] The Kingfisher optimization algorithm is used to solve the joint clearing model based on the predicted prices under multiple price scenarios to obtain the operation strategy of the wind-solar-storage station; the joint clearing model includes a wind-solar-storage station power generation strategy model, a thermal power unit power generation strategy model, a first objective function and a first constraint condition; the wind-solar-storage station power generation strategy model includes a second objective function and a second constraint condition; the thermal power unit power generation strategy model includes a third objective function and a third constraint condition;

[0011] The wind and solar energy storage station is regulated based on its operation strategy.

[0012] In a second aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the above-described methods for controlling wind, solar and storage stations taking into account electricity-green-carbon price forecasts.

[0013] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-mentioned methods for controlling wind, solar and storage stations taking into account electricity-green-carbon price forecasts.

[0014] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned methods for controlling wind, solar and storage stations taking into account electricity-green-carbon price forecasts.

[0015] According to the specific embodiments provided by the present application, the present application has the following technical effects: the present application discloses a method, equipment, medium and product for regulating wind-solar-storage station considering electricity-green-carbon price prediction, and establishes a price prediction model to predict carbon price, green certificate price and electricity price; wherein, the price prediction model is established based on the LSTM neural network model based on the hybrid attention mechanism, which can effectively capture the long-term and short-term dependencies between electricity price, carbon sink price and green certificate price in time series data, and improve the prediction accuracy of price prediction. The uncertainty set of predicted price is generated by a multi-core single-class support vector machine algorithm, which effectively handles outliers and noise data and improves the reliability of the uncertainty set. The uncertainty set of predicted price is classified by a neural network classifier to improve the accuracy and stability of price scenario classification; finally, the joint clearing model under multiple price scenarios established, including the wind-solar-storage station power generation strategy model and the thermal power unit power generation strategy model, can achieve the stability of benefits under price uncertainty and can effectively regulate the operation of wind-solar-storage station. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 A schematic flow chart of a method for controlling a wind-solar-storage station taking into account electricity-green-carbon price forecasts provided in an embodiment of the present application.

[0018] Figure 2 Another flowchart of a wind-solar-storage station control method taking into account electricity-green-carbon price prediction provided in one embodiment of the present application.

[0019] Figure 3 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0021] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0022] In an exemplary embodiment, Figure 1 and Figure 2 As shown, a wind-solar-storage station control method considering electricity-green-carbon price prediction is provided, including the following steps S1 to S7.

[0023] Step S1, obtaining a combination of historical price data; the combination of historical price data includes: historical carbon price, historical green certificate price and historical electricity price. The carbon price is the price in the carbon trading market, the green certificate price is the price in the green certificate trading market, and the electricity price is the day-ahead electricity price in the electricity spot market.

[0024] Step S2, input the historical price data combination into the price prediction model, and output the predicted price; the predicted price includes: carbon price prediction value, green certificate price prediction value and electricity price prediction value; the price prediction model is obtained by training the LSTM neural network model based on the hybrid attention mechanism using the training data set; the training data set includes the sample price data combination and the corresponding predicted value of the sample price data combination. Among them, the sample price data combination is the price data combination at the previous t moments, and the predicted value of the sample price data combination is the price data combination at the future predicted moment corresponding to the previous t moments, and both are historical data.

[0025] Specifically, historical price data combinations are obtained on a daily basis. Each data combination includes the electricity price for each hour of the day, the carbon price for the day, and the green certificate price for the day. That is, a set of data includes 26 data points. The data from each quarter for many years constitute a training data subset; the data from all quarters for many years constitute a training data set, that is, the training data set contains four training data subsets.

[0026] Step S3, determining a deviation vector of price data based on the combination of the historical price data and the predicted price.

[0027] Step S4, using a multi-core based single-class support vector machine algorithm to generate an uncertainty set of the predicted price based on the deviation vector of the price data.

[0028] Step S5, using a neural network classifier to classify the uncertainty set of the predicted price to obtain multiple categories of price scenarios; wherein each category of price scenarios includes multiple predicted prices.

[0029] Step S6, using the Pied Kingfisher optimization algorithm, based on the predicted prices under multiple price scenarios, solve the joint clearing model to obtain the operation strategy of the wind-solar-storage station; the joint clearing model includes a wind-solar-storage station power generation strategy model, a thermal power unit power generation strategy model, a first objective function and a first constraint condition; the wind-solar-storage station power generation strategy model includes a second objective function and a second constraint condition; the thermal power unit power generation strategy model includes a third objective function and a third constraint condition.

[0030] Step S7, regulating the wind-solar-storage station based on the operation strategy of the wind-solar-storage station.

[0031] As an optional implementation, in step S2, the LSTM neural network model based on the hybrid attention mechanism includes a convolutional neural network, a sequential convolutional attention module, and a long short-term memory recursive neural network connected in sequence.

[0032] Specifically, the LSTM neural network model structure based on the hybrid attention mechanism includes: convolutional neural network (CNN), sequential convolution attention module (SCAM) and long short term memory recursive neural network (LSTM). The input is a combination of historical price data at t moments before the prediction moment, and the output is the predicted value of electricity price, carbon price and green certificate price at the prediction moment. Various types of prices are typical chaotic time series with significant time dependence. As an excellent time feature extractor, LSTM can more comprehensively describe the changes in time series; in order to further improve the feature extraction ability of the model, CNN is introduced to extract features between data; considering a large number of different input features, a sequential convolution attention module SCAM is used to fuse feature mappings between different data.

[0033] In the CNN layer: CNN is used for unified feature extraction, the input is the price data combination of the previous t moments, and the output is a high-dimensional feature map. The ReLU activation function is applied between each convolution layer and pooling layer to ignore irrelevant features and improve the convergence speed of the model. The pooling layer uses the maximum pooling method. The convolution process is shown in the following formula.

[0034]

[0035] Among them, I0 and O are the original input; I ai represents the feature output of the ai-th layer; f ReLU is the ReLU activation function; is the convolution operation; W ai is the weight vector of the convolution kernel of the ai-th layer; b ai is the bias vector of the ai-th layer; Q ai is the feature output of the ai-th pooling layer, and max represents the maximum pooling operation.

[0036] In the SCAM layer: For one-dimensional sequence prediction tasks that require more accurate numerical information, a hybrid attention model consisting of a channel attention model (CAM) and a temporal attention module (TAM) is proposed, namely the sequential convolutional attention module (SCAM). Among them, CAM assigns weights to different feature maps and automatically selects the most meaningful input features for each task; TAM concatenates the input features selected by CAM according to the time dimension, extracts the convolution kernel features, and uses the Sigmoid function to obtain the temporal attention matrix to achieve weight allocation in the time dimension. The input of the SCAM layer is a high-dimensional feature map, which captures the important features of the sequence from the two dimensions of channel and time, thereby avoiding the loss of key information, and finally outputs the features of the channel and time series.

[0037] In the LSTM layer: LSTM is a recurrent neural network used to process sequences with specific time dependencies. LSTM can effectively capture hidden time features. The LSTM unit includes three gate structures: input gate, forget gate, and output gate. These gate structures work together to solve the problems of gradient disappearance and gradient explosion during training. The calculation method is as follows.

[0038]

[0039] Among them, ρ represents the LSTM related operation function, w t , g t , o t Respectively represent the output features of the input gate, forget gate and output gate at the tth time period, W w , W g , W o is the weight index, b w 、b g 、b o is the bias term, x t 、h t-1 are the input of the tth period and the output of the t-1th period respectively.

[0040] As an optional implementation, in step S2, the training process of the price prediction model specifically includes the following steps.

[0041] Step S21, obtaining a training data set.

[0042] Step S22, input the training data set into the LSTM neural network model based on the hybrid attention mechanism, and output the predicted value of the sample price data combination.

[0043] Step S23, construct a loss function according to the predicted value of the sample price data combination and the true value of the sample price data combination, and iteratively optimize the model parameters of the LSTM neural network model based on the hybrid attention mechanism according to the loss function until the iteration round reaches the maximum value or the loss function reaches the minimum value, stop the iterative optimization, and obtain the price prediction model.

[0044] The training data set is input into the four-channel CNN-SCAM-LSTM network model to complete the training. After the training is completed, the historical price data combination before the prediction time t is input into the CNN-SCAM-LSTM network model, and the predicted price at the prediction time is output.

[0045] As an optional implementation, step S3 specifically includes the following steps.

[0046] Step S31, obtaining an uncertainty data set, the uncertainty data set includes multiple sets of data, each set of data is in the same format as the training data set, and the difference between the two is that they are data sets from different times. For example, if the training data set is data from 2022, then the uncertainty data set can be data from 2023.

[0047] Step S32, input multiple groups of data of the uncertainty data set into the CNN-SCAM-LSTM neural network model respectively, obtain the predicted price vector, and compare it with the true value (i.e. the corresponding historical price data combination) to obtain the deviation vector of the price data, and the calculation formula is as follows.

[0048]

[0049] in, is the deviation vector of the price data of the nth group i; is the predicted price of the price data of the nth group No. i; It is the nth group and the ith combination of historical price data.

[0050] As an optional implementation, in step S4, the expression of the uncertainty set of the predicted price is as follows.

[0051]

[0052] Among them, U(D) is the uncertainty set of the predicted price; v is the uncertainty vector of the predicted price; α i * is the optimized value of the i-th dual variable in α; ε m * is the optimized value of the mth kernel function coefficient; K m (v,v i ) is the uncertainty vector v of the m-th kernel function in predicting the price and the sample v i; p* is the optimized value of the bias term, where v i Represents the i-th deviation vector (i.e., the i-th sample) in the uncertainty vector of the predicted price.

[0053] Specifically, the deviation vector of price data is taken as input data and multiple kernel functions are adopted. A method for generating the uncertainty set of predicted prices by considering a one-class support vector machine (OC-SVM) based on multiple kernels (MKL) is proposed. The expression of OC-SVM based on MKL is as follows.

[0054]

[0055] Where M represents the number of basic kernel functions; H m is the coefficient vector of the support vector; ε m is the coefficient of each kernel function; p is the bias term; χ i is a slack variable that allows some points to deviate from the separating hyperplane; u o and μ o are regularization parameters used to control the degree of relaxation of the model; N is the number of samples; Represents the feature mapping function from the input space to the high-dimensional feature space.

[0056] The strong duality theory is further used to obtain its dual problem. Assuming that all kernel functions are positive semidefinite, the dual problem is also convex and can be solved using a general convex optimization solver.

[0057]

[0058] Among them, α, γ, λ are the dual variables corresponding to the constraints of the original problem, K m (v i ,v j ) is the mth kernel function in sample v i and sample v j Values ​​between; α i and α j are the i-th and j-th dual variables in α respectively.

[0059] The solutions of the original problem (Equation (5)) and the dual problem (Equation (6)) are and {α * ,γ * ,λ *}; Using the solution result, its uncertainty set is expressed as formula (4).

[0060] As an optional implementation, step S5 specifically includes the following steps.

[0061] Step S51, based on the predicted price uncertainty set in step S4, Monte Carlo sampling is used to obtain a sample set as input data for the neural network classifier.

[0062] Step S52, using a neural network classifier taking particle swarm optimization into consideration, classify the sample set to obtain multiple price scenarios. Since the wind and solar storage station needs to formulate an operation strategy (determine the output situation) based on price data, different price data will lead to different strategies. Due to the uncertainty and diversity of the predicted price, multiple price scenarios are used to characterize it, that is, one type of price scenario is one type of predicted price data.

[0063] The price scenario is the arithmetic mean of the price data samples. According to the classification results, multiple price scenarios can be obtained. First, the sample subclass of the predicted price data is calculated according to the predicted price at the prediction time and the deviation value of the sample subclass. Then, the arithmetic mean of the sample subclass of the predicted price data is taken to obtain the price scenario under the sample subclass, as shown in formula (7). The price scenario weight is defined as shown in formula (8).

[0064]

[0065] in, is the deviation value of the price data of the nth group in the xth price scenario; is the average predicted value of the i-th price data under the x-th price scenario, is the predicted price value of the i-th price data; N x is the number of data sets under the x-th price scenario; ξ x is the weight of the x-th price scenario.

[0066] Furthermore, the specific process of constructing a neural network classifier considering particle swarm optimization is as follows.

[0067] The neural network classifier consists of three parts: input layer, hidden layer and output layer. In the hidden layer of the neural network classifier, the fitness value is calculated using Fuzzy C-Means (FCM) clustering, and the connection weight coefficient is calculated using Least Squares Estimation (LSE). In the output layer of the neural network classifier, the output value of the classifier is calculated by fuzzy reasoning. Fuzzy reasoning is the process of mapping given input data to output data using membership functions, logical operators and If-Then rules.

[0068] In the premise part of fuzzy rules, FCM clustering is used to group the data. FCM clustering is used in data analysis to find the centroid and degree of belonging of each cluster. The FCM clustering algorithm consists of the following steps.

[0069] 1) Determine the number of clusters and initialize the membership matrix U r .

[0070]

[0071] Among them, u ci,cj Represents sample x cj The membership degree belongs to the ci-th cluster, CI is the total number of clusters, and CJ is the total number of samples.

[0072] 2) Calculate the centroid of each cluster.

[0073]

[0074] Among them, cv ci is the centroid of cluster ci, and FC represents the fuzzification coefficient.

[0075] 3) Update the membership matrix using the Euclidean distance function.

[0076]

[0077] Among them, ||x cj -v ci || is x cj to v ci The Euclidean distance of d ci,cj are the Euclidean distances from the sample to the cluster centroid; U r+1 is the membership matrix of the r+1th iteration.

[0078] 4) Check the termination condition. If the following conditions are met, the iteration is terminated; otherwise, continue to return to the second step and perform the iteration.

[0079] ||U r+1 -U r ||≤δ u (12)

[0080] Among them, δ u It is a preset threshold used to determine whether the algorithm has converged.

[0081] In the inference part of the fuzzy rules, the least squares method is used to estimate and calculate the connection weight coefficients. LSE is a global learning algorithm that can minimize the overall square error between the model output and the target output. The mean square error (MSE) is used as the objective function of LSE.

[0082]

[0083] Among them, f ci (x cj ) is x cjThe output value of the next cluster ci; is the kth power of the cjth output; KL is a constant; A k is the connection weight coefficient; X is the input data matrix; Y is the target output matrix.

[0084] The resulting value of the connection weight coefficient is determined by the following formula.

[0085] A k =(X T X) -1 X T Y k (14)

[0086] The particle swarm optimization (PSO) algorithm is further used to optimize the parameters of the neural network classifier. The parameters optimized by PSO are related to three factors: the number of fuzzy rules used in fuzzy c-means clustering, the value of the fuzzification coefficient, and the polynomial type of the connection weight. The process of using particle swarm to optimize the parameters of the neural network classifier is as follows.

[0087] 1) Randomly generate a particle swarm Ja, particle position pa, and particle velocity va.

[0088] Ja(g)=[va1(g),va2(g),…,va b (g)] T (15)

[0089] Among them, b represents the number of parameters that need to be optimized, that is, the number of particles; g represents the number of times the current iteration of PSO is executed.

[0090] 2) Adjust the inertia weight ra.

[0091]

[0092] Among them, max andra min Respectively represent the maximum and minimum values ​​of the inertia weight; g max represents the maximum number of iterations; ra(g) is the inertia weight at the g-th iteration.

[0093] 3) Update particles: by using pa bt (g) and qa bt (g) value to adjust the particle's velocity va.

[0094] va(g+1)=ra(g)va(g)+c1e1[pa bt (g)-pa(g)]+c2e2[qa bt (g)-pa(g)](17)

[0095] Where va(g) and va(g+1) are the particle velocities at the g-th iteration and the g+1-th iteration, respectively; c1 and c2 are acceleration constants, e1 and e2 are random values ​​between 0 and 1; pa bt (g) is the optimal position of the particle at the g-th iteration; qa bt (g) is the global optimal position at the g-th iteration; pa(g) is the attractor of the particle at the g-th iteration, which indicates the ability of the particle to attract other particles to approach.

[0096] 4) Update the position of each particle using the updated velocity, evaluate the updated particles using the objective function, and compare their performance in pa bt (g) and qa bt (g) performance.

[0097] pa(g+1)=pa(g)+va(g+1) (18)

[0098] Among them, pa(g+1) is the attractor of the particle at the g+1th iteration.

[0099] 5) If the termination condition is not met, repeat steps 2 to 4.

[0100] The above process is explained as follows. The particle structure is the parameter that needs to be optimized; the parameters are determined by the particles, and the selectable parameters are as follows.

[0101]

[0102] Among them, z1 is the number of fuzzy rules; z2 is the value of FC fuzzification coefficient; z3 is the polynomial type of connection weight; Constant represents a constant; Linear represents a linear expression.

[0103] According to the particle swarm optimization results, the neural network classifier is constructed using the selected parameters.

[0104] As an optional implementation, in step S6, a joint clearing model is established based on the power generation strategies of the wind-solar-storage station and the thermal power unit, and the corresponding objective function is the first objective function, and the expression of the first objective function is as follows.

[0105]

[0106] in, The quotation for the kth section of the wind and solar storage station at time t; is the output active power of the wind-solar storage station at the kth section at the tth time; The price for the kth section of the thermal power unit at time t; It is the output active power of the kth section of the thermal power unit at the tth time.

[0107] In the joint clearing model, in addition to considering the power generation strategy constraints of wind, solar and storage stations and thermal power units, it is required to meet the power load demand. Therefore, the power balance constraint is considered. The corresponding first constraint condition is as follows.

[0108]

[0109] Among them, p g,t is the actual output of the thermal power unit at time t; p w,t is the output active power of the wind turbine at the tth moment; p pv,t is the output active power of the photovoltaic unit at the tth moment; is the discharge power of the electrochemical energy storage system; Charging power for electrochemical energy storage system; p l,t For electrical load demand.

[0110] Among them, when establishing the power generation strategy model of the wind, solar and storage station, based on the predicted prices under multiple price scenarios, the power generation strategy model of the wind, solar and storage station takes maximizing its own profits as the goal and formulates a power generation strategy. The corresponding objective function is the second objective function, and the expression of the second objective function is as follows.

[0111]

[0112] Among them, ξ x is the weight of the x-th price scenario; is the electricity price at time t under the x-th price scenario; is the price of green certificates under the xth price scenario; a w,t is the green certificate allocation coefficient of the wind turbine; a pv,t Green certificate allocation coefficient for photovoltaic units.

[0113] The second constraint corresponding to the wind-solar-storage station power generation constraint is as follows.

[0114]

[0115]

[0116] in, is the predicted output of the photovoltaic unit at time t; is the installed capacity of the PV unit at time t; is the predicted output of the wind turbine at time t; is the installed capacity of the wind turbine at the tth moment; p ess,min is the minimum charging and discharging power of the electrochemical energy storage system; p ess,max The maximum charge and discharge power of the electrochemical energy storage system; SoC ess,tSoC is the state of charge of the electrochemical energy storage system; ess,min SoC is the lower limit of the state of charge of the electrochemical energy storage system; ess,max is the upper limit of the electrochemical energy storage system and state of charge; η ess,in is the charging efficiency of the electrochemical energy storage system; η ess,out is the discharge efficiency of the electrochemical energy storage system; is the rated energy of the electrochemical energy storage system.

[0117] When establishing a thermal power generation strategy model, based on the predicted prices under multiple price scenarios, and taking the thermal power unit cost and carbon emission factor as the benchmark, a thermal power generation strategy is formulated. The corresponding objective function is the third objective function, and the expression of the third objective function is as follows.

[0118]

[0119] in, is the carbon sink price under the x-th price scenario; zi is the initial carbon quota in the scheduling period; b g,t is the total carbon emission factor of the thermal power unit at time t; b coal,t is the carbon emission factor of coal consumption of thermal power unit at time t; b loss,t is the loss carbon emission factor of the thermal power unit at the tth moment; b oil,t is the carbon emission factor of the thermal power unit at the time t; It is the lower limit output of thermal power units in the normal peak load regulation stage; It is the upper limit output of thermal power units in the normal peak load regulation stage; It is the lower limit output of thermal power units in the speed reduction and peak regulation stage; It is the lower limit output of thermal power units during the oil-loading peak-shaving stage.

[0120] Among them, the deep peak regulation model of thermal power units is as follows.

[0121]

[0122] Among them, C g,t is the total cost of the thermal power unit at time t; C coal,t is the coal consumption cost of the thermal power unit at the tth moment; C loss,t is the loss cost of the thermal power unit at the tth moment; C oil,t is the oil input cost of the thermal power unit at the tth moment; p g,t is the actual output of the thermal power unit at moment t.

[0123] Introducing binary variable linearization piecewise constraints, the total cost C of the thermal power unit g,t It is expressed as follows.

[0124]

[0125] in, and are binary variables representing the oil injection peak-shaving stage and speed reduction peak-shaving stage respectively.

[0126] The unit's consumption characteristics and energy cost characteristics can be expressed as piecewise functions divided by the peak-shaving stage. The unit's carbon emissions and coal consumption have a strong positive correlation and can also be divided according to the unit's peak-shaving stage. The specific expression is shown in formula (26).

[0127] The deep peak regulation of thermal power units needs to meet the ramp constraint and technical output constraint, which are taken as the third constraint condition. The third constraint condition is as follows.

[0128]

[0129] in, The minimum technical output of thermal power units; The maximum technical output of thermal power units; It is the upward climbing power limit of the thermal power unit; It is the downward ramp power limit of thermal power units.

[0130] As an optional implementation, in step S6, the Pied Kingfisher optimization algorithm is used to solve the joint clearing model that considers the above wind-solar-storage station and the power generation strategy of the thermal power unit. Finally, after the solution, the operation strategy of the wind-solar-storage station (i.e., the output of the wind-solar-storage station at each moment) can be obtained.

[0131] The Pied Kingfisher Optimization Algorithm combines three foraging operations to simulate the foraging strategy of Pied Kingfisher, including the perching and hovering stage, the diving stage, and the symbiotic stage. It can effectively solve various optimization problems in various search spaces and achieve the calculation accuracy requirements with a small calculation cost. The three PKO strategies are shown below, and the algorithm optimization process is as follows.

[0132] First, the population is initialized and a set of initial solutions are randomly generated from the search space as the first trial to start the search process. Second, a random number is generated and the Pied Kingfisher will choose between the perching and circling phases, i.e., Equation (32) and the diving phase, i.e., Equation (33), to update the position of the model solution. Further, the Pied Kingfisher enters the symbiotic phase, i.e., Equation (34). After the position is updated, the updated position X is evaluated. n The target fitness value of (t+1) is better than the current best position X best (t), the best position is replaced; finally, iterate continuously until the maximum number of iterations is reached, and the best position result, i.e. the optimal solution, is output.

[0133] X n (t+1)=Xn (t)+α pko L pko ×(X m (t)-X n (t)),m≠n,m,n∈{1,2,...,N pko}(32)

[0134] X n (t+1)=X n (t)+DS*o pko *α pko *(b pko -X best (t)),n∈{1,2,...,N pko}(33)

[0135]

[0136] Among them, X n (t) and X n (t+1) is the position of the nth individual at the tth iteration and the t+1th iteration, X m (t) is the position of the mth individual at the tth iteration; α pko is a random parameter; N pko is the population size; L pko are dynamic parameters related to perching and hovering behaviors; o pko and DS are the control parameters representing hunting ability, and pko and X best (t) related position parameters; X r1 (t) and X r2 (t) is two individuals randomly selected from the population; PE is the predation efficiency parameter; rand is a random number between 0 and 1.

[0137] The beneficial effects of this application are as follows.

[0138] 1) A neural network model integrating CNN-SCAM-LSTM was established, which has excellent time feature extraction capabilities, can comprehensively describe the changes in time series, effectively capture the long-term and short-term dependencies between electricity prices, carbon sink prices and green certificate prices in time series data, and improve the prediction accuracy of price prediction tasks.

[0139] (2) By utilizing multiple kernel functions to adapt to the distribution and characteristics of different prediction data, a prediction price uncertainty set based on a multi-kernel single-class support vector machine is proposed to effectively handle outliers and noise data and improve the reliability of the uncertainty set.

[0140] (3) For the neural network classifier for data set classification, the particle swarm optimization algorithm is used to optimize the parameters of the neural network, reduce the neural network training time, improve efficiency, and significantly improve the accuracy and stability of price scenario classification.

[0141] (4) A power generation strategy model for wind, solar and storage stations under various price scenarios is established, which can achieve benefit stability under price uncertainty, avoid excessive losses, and have excellent robustness. At the same time, the Kingfisher optimization algorithm is used to solve the joint clearing model, which can effectively solve various optimization problems in various search spaces and achieve computational accuracy requirements at a relatively low computational cost.

[0142] In an exemplary embodiment, a computer device is provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to implement a wind, solar, and storage station control method considering electricity, green, and carbon price forecasts.

[0143] In an exemplary embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, a wind-solar-storage station control method considering electricity-green-carbon price forecast is implemented.

[0144] In an exemplary embodiment, a computer program product is provided, including a computer program, which, when executed by a processor, implements a wind-solar-storage station control method considering electricity-green-carbon price prediction.

[0145] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a wind-solar storage station control method considering the electricity-green-carbon price forecast is implemented.

[0146] Those skilled in the art will understand that Figure 3The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0147] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0148] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0149] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0150] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0151] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A wind-solar-storage station control method considering electricity-green-carbon price forecast, characterized in that: The wind-solar-storage station control method considering electricity-green-carbon price prediction includes: Obtaining a combination of historical price data; the combination of historical price data includes: historical carbon prices, historical green certificate prices, and historical electricity prices; The historical price data combination is input into the price prediction model, and the predicted price is output; the predicted price includes: a carbon price prediction value, a green certificate price prediction value, and an electricity price prediction value; the price prediction model is obtained by training an LSTM neural network model based on a hybrid attention mechanism using a training data set; the training data set includes a sample price data combination and a corresponding predicted value of the sample price data combination; Determining a deviation vector of price data based on the combination of the historical price data and the predicted price; A multi-core based single-class support vector machine algorithm is used to generate the uncertainty set of the predicted price based on the deviation vector of the price data; A neural network classifier is used to classify the uncertainty set of the predicted price to obtain multiple price scenarios; each price scenario includes multiple predicted prices; The Kingfisher optimization algorithm is used to solve the joint clearing model based on the predicted prices under multiple price scenarios to obtain the operation strategy of the wind-solar-storage station; the joint clearing model includes a wind-solar-storage station power generation strategy model, a thermal power unit power generation strategy model, a first objective function and a first constraint condition; the wind-solar-storage station power generation strategy model includes a second objective function and a second constraint condition; the thermal power unit power generation strategy model includes a third objective function and a third constraint condition; The wind and solar energy storage station is regulated based on its operation strategy.

2. The method for controlling wind-solar-storage stations considering electricity-green-carbon price forecast according to claim 1 is characterized in that: The LSTM neural network model based on the hybrid attention mechanism includes a sequentially connected convolutional neural network, a sequential convolutional attention module, and a long short-term memory recursive neural network.

3. The method for controlling wind-solar-storage stations considering electricity-green-carbon price forecast according to claim 2 is characterized in that: The training process of the price prediction model specifically includes: Get the training dataset; Input the training data set into the LSTM neural network model based on the hybrid attention mechanism, and output the predicted value of the sample price data combination; A loss function is constructed according to the predicted value of the sample price data combination and the true value of the sample price data combination, and the model parameters of the LSTM neural network model based on the hybrid attention mechanism are iteratively optimized according to the loss function until the iteration round reaches the maximum value or the loss function reaches the minimum value, the iterative optimization is stopped, and the price prediction model is obtained.

4. The method for controlling wind-solar-storage stations considering electricity-green-carbon price forecast according to claim 1 is characterized in that: The uncertainty set of the predicted price is expressed as: Among them, U(D) is the uncertainty set of the predicted price; v is the uncertainty vector of the predicted price; α i * is the optimized value of the i-th dual variable in α; ε m * is the optimized value of the mth kernel function coefficient; K m (v,v i ) is the uncertainty vector v of the m-th kernel function in predicting the price and the sample v i ; p* is the optimized value of the bias term.

5. The method for controlling wind-solar-storage stations considering electricity-green-carbon price forecast according to claim 1 is characterized in that: The expression of the first objective function is: in, The quotation for the kth section of the wind and solar storage station at time t; is the output active power of the wind-solar storage station at the kth section at the tth time; The price for the kth section of the thermal power unit at time t; is the output active power of the kth section of the thermal power unit at time t; The first constraint is: Among them, p g,t is the actual output of the thermal power unit at time t; p w,t is the output active power of the wind turbine at the tth moment; p pv,t is the output active power of the photovoltaic unit at the tth moment; is the discharge power of the electrochemical energy storage system; Charging power for electrochemical energy storage system; p l,t For electrical load demand.

6. The method for controlling wind-solar-storage stations considering electricity-green-carbon price forecast according to claim 5 is characterized in that: The expression of the second objective function is: Among them, ξ x is the weight of the x-th price scenario; is the electricity price at time t under the x-th price scenario; is the price of green certificates under the xth price scenario; a w,t is the green certificate allocation coefficient of the wind turbine; a pv,t Green certificate allocation coefficient for PV units; The second constraint is: in, is the predicted output of the photovoltaic unit at time t; is the installed capacity of the PV unit at time t; is the predicted output of the wind turbine at time t; is the installed capacity of the wind turbine at the tth moment; p ess,min is the minimum charging and discharging power of the electrochemical energy storage system; p ess,max The maximum charge and discharge power of the electrochemical energy storage system; SoC ess,t SoC is the state of charge of the electrochemical energy storage system; ess,min SoC is the lower limit of the state of charge of the electrochemical energy storage system; ess,max is the upper limit of the electrochemical energy storage system and state of charge; η ess,in is the charging efficiency of the electrochemical energy storage system; η ess,out is the discharge efficiency of the electrochemical energy storage system; is the rated energy of the electrochemical energy storage system.

7. The method for controlling wind-solar-storage stations considering electricity-green-carbon price forecast according to claim 6 is characterized in that: The expression of the third objective function is: in, is the carbon sink price under the x-th price scenario; zi is the initial carbon quota in the scheduling period; b g,t is the total carbon emission factor of the thermal power unit at time t; b coal,t is the carbon emission factor of coal consumption of thermal power unit at time t; b loss,t is the loss carbon emission factor of the thermal power unit at the tth moment; b oil,t is the carbon emission factor of the thermal power unit at the time t; It is the lower limit output of thermal power units in the normal peak load regulation stage; It is the upper limit output of thermal power units in the normal peak load regulation stage; It is the lower limit output of thermal power units in the speed reduction and peak regulation stage; It is the lower limit output of the thermal power unit during the oil-loading peak-shaving stage; The third constraint is: in, The minimum technical output of thermal power units; The maximum technical output of thermal power units; It is the upward climbing power limit of the thermal power unit; It is the downward ramp power limit of thermal power units.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the wind-solar-storage station control method considering electricity-green-carbon price prediction as described in any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the wind, solar and storage station control method considering electricity, green and carbon price prediction described in any one of claims 1 to 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the wind, solar and storage station control method considering electricity, green and carbon price prediction described in any one of claims 1 to 7 is implemented.

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