A method and system for regional renewable energy configuration based on power carbon emissions

Abstract

A regional renewable energy configuration method and system based on power carbon emission belong to the technical field of carbon emission, and the method comprises the following steps: S1, collecting regional historical power generation, load power and other data, and establishing a regional source-load power unbalanced prediction model; S2, taking the minimum total cost of regional power generation as the target, constructing a renewable energy investment model, and determining the renewable energy investment capacity; S3, based on power flow information, a regional node power carbon emission factor library is established; S4, taking the minimum regional carbon emission as the target, combining the regional power grid node power consumption and the power carbon emission factor, the location of the renewable energy project in the power grid node is determined. The present application can effectively reduce the overall operation cost of the regional power system, and consider the carbon emission characteristics of the power grid. By identifying the high carbon emission nodes in the regional power grid, the renewable energy can be configured, and the total amount of regional power carbon emission can be effectively reduced.

Description

Technical Field

[0001] The present invention relates to a low-carbon configuration of distributed power sources, and in particular to a method and system for configuring regional renewable energy based on electricity carbon emissions. Background Art

[0002] With increasing global energy demand and growing environmental awareness, consumption of renewable energy sources, such as solar and wind power, continues to grow. Efficient and economical deployment of renewable energy continues to improve regional renewable energy utilization, reduce carbon emissions from power generation, and assist in regional energy management, promoting grid structure optimization and green, low-carbon transformation.

[0003] Existing regional renewable energy configuration methods often carry out modeling with the goal of optimizing their own development, and fail to effectively combine the carbon emission characteristics of the regional power grid for development and layout, weakening the supporting role of distributed renewable energy development in optimizing the overall regional energy structure and green and low-carbon development, and failing to maximize carbon reduction benefits at the regional macro-carbon reduction level. Summary of the Invention

[0004] The purpose of the present invention is to address the deficiencies in the above-mentioned background technology and to provide a method and system for regional renewable energy configuration based on electricity carbon emissions.

[0005] To achieve the above objectives, the technical solution of the present invention is:

[0006] In a first aspect, the present invention proposes a method for configuring regional renewable energy based on electricity carbon emissions, comprising:

[0007] S1. Collect historical power generation and load data of the target area, establish a regional source-load imbalance prediction model, and determine the target area source-load imbalance prediction value;

[0008] S2. Determine the newly added renewable energy installed capacity based on the predicted value of source-load imbalance in the target area;

[0009] S3. Based on power flow information, establish a regional node power carbon emission factor database;

[0010] S4. Based on the newly added installed capacity of renewable energy, with the goal of minimizing regional carbon emissions, and combined with the regional grid node electricity consumption and electricity carbon emission factors, determine the grid node location of the renewable energy project.

[0011] In S1, the regional source-charge imbalance prediction model constructed in 1.1 is:

[0012] Q balance,t =Q source,t -Q load,t

[0013] Where Q balance,t , Q source,t , Q load,t are the unbalanced electricity, generated electricity, and load electricity in the target area during period t.

[0014] 1.2 Power generation Q source,t The predicted values ​​are:

[0015]

[0016] Where K is the number of regional power types, Q k,t is the predicted value of the power generation of the kth power source in the region during period t.

[0017] The values ​​of the Spearman correlation coefficient are:

[0018]

[0019] Where ρ is the Spearman correlation coefficient, R(x j )、R(y j ) are the positions of variables x and y respectively, are the average ranks of variables x and y respectively, and m is the number of samples.

[0020] The Stacking algorithm is used to calculate the power generation Q of different power types in the target area. k,t To make a prediction, the prediction steps are:

[0021] 1.2.1 Use the Spearman correlation coefficient method to analyze the correlation between factors affecting power generation and the power generation of different power sources. Rank the factors affecting power generation based on the Spearman correlation coefficient. Select the historical data of the top n factors with the highest correlation. Combine this with the historical power generation to form an initial data set for each power source type. Perform steps 1.2.2-1.2.4 separately for each power source type.

[0022] 1.2.2 Split the initial dataset into a meta-training set and a meta-test set on the meta-learner, and split the meta-training set into a base training set and a base test set on the base learner;

[0023] 1.2.3 Train and predict the base learners. Select the ResNet, VGG, and MobileNet algorithm models as base learners. Use K-fold cross-validation to train the models on the K-1 folds and validate them on the K-th fold. Train each base learner K times, for a total of 3K times, to generate all base models and prediction results.

[0024] 1.2.4 Based on the prediction data of the base learner, a prediction model is built on the meta-learner. Linear regression and logistic regression algorithms are used as meta-learners respectively. The training results of the two learners are averaged to obtain the final prediction result.

[0025] 1.3 Regional electricity load forecasting is carried out using a multi-layer perceptron model based on empirical mode decomposition. The specific steps are:

[0026] 1.3.1 Construct a regional historical load and electricity data set, divide the regional historical load and electricity data set into a training set and a test set, and use the empirical mode decomposition algorithm to decompose the historical load and electricity data into n eigenmode components and 1 residual:

[0027]

[0028] Where Q load 、IMF i , Res are the historical load power, the i-th intrinsic mode component, and the residual respectively;

[0029] 1.3.2 The decomposed intrinsic mode components are reconstructed using the extreme point partitioning method to form high-frequency, medium-frequency, and low-frequency components. A multi-layer perceptron prediction model is established for each frequency band, namely HF-MLP, IF-MLP, and LF-MLP.

[0030] 1.3.3 Analyze the correlation between candidate features and high-frequency, medium-frequency, and low-frequency components using the Spearman correlation coefficient method, and select components with absolute values ​​of correlation coefficients greater than the set threshold as inputs to the HF-MLP, IF-MLP, and LF-MLP models;

[0031] 1.3.4 Use the training set to train and adjust the parameters of the HF-MLP, IF-MLP, and LF-MLP models. After the models are trained, the high-frequency, medium-frequency, and low-frequency components of the power load data are predicted on the test set, and the prediction results are superimposed to obtain the load power prediction value Q load,t .

[0032] In S2: Based on the target area source-charge imbalance power prediction value Q balance,t , considering the cost of new renewable energy power generation P invest,t , the cost of electricity generation from existing renewable energy sources P genera,t , the cost of electricity transferred from other regions P buy,t And the conversion into green certificate income P green,t , taking the total regional power generation cost P all,t Build a renewable energy investment model with the minimum goal:

[0033] minP all,t =min(P invest,t +Pgenera,t +P buy,t -P green,t )

[0034]

[0035] P buy,t =-Q balance,t ×W t

[0036]

[0037] In the formula, R is the type of newly added renewable energy power source; G r,t , U r,t , H r,t , λ r,t are the installed capacity, unit installed capacity cost, generating utilization hours, and unit power operation and maintenance cost coefficient of the rth type of newly added renewable energy power source in the t period; b is the discount rate; T r is the service life of the rth type of newly added renewable energy power source; V k,t is the degree of electricity cost of the kth type of inventory power source in the t period; W t is the electricity trading unit price between regions in the t period; S is the renewable energy type in the inventory power source; Q s,t is the power generation of the sth type of inventory renewable power source in the t period; δ is the quantitative coefficient of renewable energy power generation converted into green certificates, I t is the average trading price of green certificates in the t period.

[0038] In the S3, the node power carbon emission factor is obtained based on power flow, which is the average carbon emission factor of the target regional node a in the t period, and has:

[0039]

[0040] In the formula, c a is the average grid carbon emission factor of the regional node a, is the direct carbon emission of the power plant directly connected with the node a, is the indirect carbon emission of the carbon emission flow injected into the node a, Q a is the load power of the node a, Q out is the flow-out power flow of the node a; the target region has A nodes, and the power carbon emission factor library has {c1, c2,...., c A}.

[0041] In the S4, according to the newly added renewable energy installed capacity, the newly added renewable energy power source node is placed at the node x, and the regional power carbon emission model is established, and has:

[0042]

[0043] Where, Γ area,t is the total electricity carbon emissions of the region in period t, c a,t , Q a,t are the average grid carbon emission factor and load power of node a during period t, x is the node where the newly added renewable energy power source is located, and 1≤x≤A, c x,t is the average grid carbon emission factor of node x during period t;

[0044] The newly added renewable energy power sources are placed at Node 1, Node 2, ..., Node A respectively. The regional electricity carbon emissions under each scenario are calculated. The scenario with the lowest regional electricity carbon emissions is taken as the grid node location where the new renewable energy power source is located.

[0045] In a second aspect, the present invention proposes a regional renewable energy configuration system based on electricity carbon emissions, comprising:

[0046] Regional source-charge imbalance prediction model building module: used to collect historical power generation and load data of the target area and establish a regional source-charge imbalance prediction model;

[0047] Renewable energy investment model building module: used to build a renewable energy investment model with the goal of minimizing the total cost of regional power generation and determine the newly added renewable energy installed capacity;

[0048] Regional node power carbon emission factor library construction module: used to establish a regional node power carbon emission factor library based on power flow information;

[0049] Renewable energy configuration plan generation module: used to determine the grid node location of renewable energy projects with the goal of minimizing regional carbon emissions, combined with regional grid node power consumption and electricity carbon emission factors.

[0050] In S1, the regional source-charge imbalance prediction model constructed in 1.1 is:

[0051] Q balance,t =Q source,t -Q load,t

[0052] Where Q balance,t , Q source,t , Q load,t are the unbalanced electricity, generated electricity, and load electricity in the target area during period t.

[0053] 1.2 Power generation Q source,t The predicted values ​​are:

[0054]

[0055] Where K is the number of regional power types, Q k,t is the predicted value of the power generation of the kth power source in the region during period t.

[0056] The values ​​of the Spearman correlation coefficient are:

[0057]

[0058] Where ρ is the Spearman correlation coefficient, R(x j )、R(y j ) are the positions of variables x and y respectively, are the average ranks of variables x and y respectively, and m is the number of samples.

[0059] The Stacking algorithm is used to calculate the power generation Q of different power types in the target area. k,t To make a prediction, the prediction steps are:

[0060] 1.2.1 Use the Spearman correlation coefficient method to analyze the correlation between factors affecting power generation and the power generation of different power sources. Rank the factors affecting power generation based on the Spearman correlation coefficient. Select the historical data of the top n factors with the highest correlation. Combine this with the historical power generation to form an initial data set for each power source type. Perform steps 1.2.2-1.2.4 separately for each power source type.

[0061] 1.2.2 Split the initial dataset into a meta-training set and a meta-test set on the meta-learner, and split the meta-training set into a base training set and a base test set on the base learner;

[0062] 1.2.3 Train and predict the base learners. Select the ResNet, VGG, and MobileNet algorithm models as base learners. Use K-fold cross-validation to train the models on the K-1 folds and validate them on the K-th fold. Train each base learner K times, for a total of 3K times, to generate all base models and prediction results.

[0063] 1.2.4 Based on the prediction data of the base learner, a prediction model is built on the meta-learner. Linear regression and logistic regression algorithms are used as meta-learners respectively. The training results of the two learners are averaged to obtain the final prediction result.

[0064] 1.3 Regional electricity load forecasting is carried out using a multi-layer perceptron model based on empirical mode decomposition. The specific steps are:

[0065] 1.3.1 Construct a regional historical load and electricity data set, divide the regional historical load and electricity data set into a training set and a test set, and use the empirical mode decomposition algorithm to decompose the historical load and electricity data into n eigenmode components and 1 residual:

[0066]

[0067] Where Q load 、IMF i , Res are the historical load power, the i-th intrinsic mode component, and the residual respectively;

[0068] 1.3.2 The decomposed intrinsic mode components are reconstructed using the extreme point partitioning method to form high-frequency, medium-frequency, and low-frequency components. A multi-layer perceptron prediction model is established for each frequency band, namely HF-MLP, IF-MLP, and LF-MLP.

[0069] 1.3.3 Analyze the correlation between candidate features and high-frequency, medium-frequency, and low-frequency components using the Spearman correlation coefficient method, and select components with absolute values ​​of correlation coefficients greater than the set threshold as inputs to the HF-MLP, IF-MLP, and LF-MLP models;

[0070] 1.3.4 Use the training set to train and adjust the parameters of the HF-MLP, IF-MLP, and LF-MLP models. After the models are trained, the high-frequency, medium-frequency, and low-frequency components of the power load data are predicted on the test set, and the prediction results are superimposed to obtain the load power prediction value Q load,t .

[0071] In S2: Consider the cost of generating electricity from new renewable energy sources P invest,t , the cost of electricity generation from existing renewable energy sources P genera,t , the cost of electricity transferred from other regions P buy,t And the conversion into green certificate income P green,t , taking the total regional power generation cost P all,t Build a renewable energy investment model with the minimum goal:

[0072] minP all,t =min(P invest,t +P genera,t +P buy,t -P green,t )

[0073]

[0074] P buy,t =-Q balance,t ×W t

[0075]

[0076] Where R is the type of new renewable energy power source; G r,t 、U r,t 、H r,t ,λ r,tare the installed capacity, unit installed capacity cost, power generation utilization hours, and unit power operation and maintenance cost coefficient of the rth type of newly added renewable energy power source in period t; b is the discount rate; T r Add the service life of renewable energy power source for category r; V k,t W is the electricity cost of the kth type of stock power source in the region during period t; t is the inter-regional electricity transaction price during period t; S is the type of renewable energy in the stock power supply; Q s,t is the power generation of the sth type of stock renewable power source in period t; δ is the quantitative coefficient of renewable energy power generation converted into green certificates, I t is the average transaction price of green certificates during period t.

[0077] In S3, the node power carbon emission factor is obtained based on the power flow and is the average carbon emission factor of node a in the target area during period t, which is:

[0078]

[0079] Where c a is the average grid carbon emission factor of regional node a, is the direct carbon emission of the power plant directly connected to node a, is the indirect carbon emission injected into the carbon emission flow of node a, Q a is the load power of node a, Q out is the power flow out of node a; the target area has A nodes, and the power carbon emission factor library has {c1, c2, ..., c A}.

[0080] In S4, based on the newly added renewable energy installed capacity, the newly added renewable energy power source node is placed at node x, and a regional power carbon emission model is established, which is:

[0081]

[0082] Where, Γ area,t is the total electricity carbon emissions of the region in period t, c a,t , Q a,t are the average grid carbon emission factor and load power of node a during period t, x is the node where the newly added renewable energy power source is located, and 1≤x≤A, c x,t is the average grid carbon emission factor of node x during period t;

[0083] The newly added renewable energy power sources are placed at Node 1, Node 2, ..., Node A respectively. The regional electricity carbon emissions under each scenario are calculated. The scenario with the lowest regional electricity carbon emissions is taken as the grid node location where the new renewable energy power source is located.

[0084] Compared with the prior art, the present invention has the following beneficial effects:

[0085] The present invention provides a regional source-charge imbalance prediction model constructed in a regional renewable energy configuration method based on electricity carbon emissions. On the one hand, the Stacking algorithm is used to separately predict the power generation of different power types, and then the multi-layer perceptron (EMD-MLP) model based on empirical mode decomposition is used to carry out regional electricity load prediction. Finally, the results of the above two predictions are used to calculate the predicted value of the regional source-charge imbalance. The prediction results are more accurate and the prediction accuracy is higher. On the other hand, a renewable energy investment model, a regional node electricity carbon emission factor library, and a regional electricity carbon emission model are constructed. According to the investment capacity calculated by the renewable energy investment model, calculations are performed one by one in the regional electricity carbon emission model, and the scenario with the smallest regional electricity carbon emissions is selected as the grid node location where the new renewable energy power source is located. While reducing the overall operating cost of the regional power system, this design effectively reduces the total amount of regional electricity carbon emissions and helps the green and low-carbon transformation of the regional power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] Figure 1 It is a flow chart of the method of the present invention.

[0087] Figure 2 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0088] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0089] Example 1:

[0090] See also Figure 1 , a regional renewable energy configuration method based on electricity carbon emissions, comprising:

[0091] S1. Collect historical power generation and load data of the target area and establish a regional source-load imbalance prediction model;

[0092] 1.1 The regional source-charge imbalance prediction model constructed is:

[0093] Q balance,t =Q source,t -Q load,t

[0094] Where Q balance,t、 Q source,t、 Q load,t are the unbalanced electricity, generated electricity, and load electricity in the target area during period t.

[0095] 1.2 Power generation Q source,t The predicted values ​​are:

[0096]

[0097] Where K is the number of regional power types, Q k,t is the predicted value of the power generation of the kth power source in the region during period t.

[0098] The values ​​of the Spearman correlation coefficient are:

[0099]

[0100] Where ρ is the Spearman correlation coefficient, R(x j )、R(y j ) are the positions of variables x and y respectively, are the average ranks of variables x and y respectively, and m is the number of samples.

[0101] The Stacking algorithm is used to calculate the power generation Q of different power types in the target area. k,t To make a prediction, the prediction steps are:

[0102] 1.2.1 Use the Spearman correlation coefficient method to analyze the correlation between power generation influencing factors and the power generation of different types of power sources. Rank the power generation influencing factors based on the Spearman correlation coefficient. Select the historical data of the top two power generation influencing factors and combine them with the historical power generation to form the initial data set for different power source types. Perform steps 1.2.2-1.2.4 for each power source type.

[0103] Selecting the historical data of the top two power generation influencing factors with the highest relevance helps reduce the amount of calculation;

[0104] 1.2.2 Split the initial dataset into a meta-training set and a meta-test set on the meta-learner, and split the meta-training set into a base training set and a base test set on the base learner;

[0105] 1.2.3 Train and predict the base learners. Select the ResNet (Residual Network), VGG (Visual Geometry Group Convolutional Neural Network), and MobileNet (Lightweight Convolutional Neural Network) algorithm models as base learners. Use K-fold cross-validation to train the model on the K-1 fold and verify it on the K-th fold. Each base learner is trained K times, for a total of 3K times, to generate all base models and prediction results.

[0106] 1.2.4 Based on the prediction data of the base learner, a prediction model is built on the meta-learner. Linear regression and logistic regression algorithms are used as meta-learners respectively. The training results of the two learners are averaged to obtain the final prediction result.

[0107] After training on the meta-training set, the model is obtained and the meta-test set is used directly for prediction.

[0108] 1.3 Regional electricity load forecasting is carried out using a multi-layer perceptron model based on empirical mode decomposition. The specific steps are:

[0109] 1.3.1 Construct a regional historical load and electricity data set, divide the regional historical load and electricity data set into a training set and a test set, and use the empirical mode decomposition algorithm to decompose the historical load and electricity data into n eigenmode components and 1 residual:

[0110]

[0111] Where Q load 、IMF i , Res are the historical load power, the i-th intrinsic mode component, and the residual respectively;

[0112] 1.3.2 The decomposed intrinsic mode components are reconstructed using the extreme point partitioning method to form high-frequency, medium-frequency, and low-frequency components. A multi-layer perceptron prediction model is established for each frequency band, namely HF-MLP, IF-MLP, and LF-MLP.

[0113] 1.3.3 The Spearman correlation coefficient method was used to analyze the correlation between the candidate features and the high-frequency, medium-frequency, and low-frequency components, and the components with an absolute value of the correlation coefficient greater than 0.6 were selected as the inputs of the HF-MLP, IF-MLP, and LF-MLP models.

[0114] Candidate features are factors that can affect load power, such as temperature and humidity. A correlation coefficient greater than 0.6 indicates a strong correlation.

[0115] 1.3.4 Use the training set to train and adjust the parameters of the HF-MLP, IF-MLP, and LF-MLP models. After the models are trained, the high-frequency, medium-frequency, and low-frequency components of the power load data are predicted on the test set, and the prediction results are superimposed to obtain the load power prediction value Q load,t .

[0116] The predicted value of regional source-charge imbalance is:

[0117] Q balance,t =Q source,t -Q load,t

[0118] Where Q balanc,et , Q source,t , Q load,t They are respectively the unbalanced electricity, generated electricity and load electricity of the region in period t.

[0119] Predicting the high-frequency, medium-frequency, and low-frequency components of electricity load data separately on the test set can improve the prediction accuracy of the model.

[0120] S2. Build a renewable energy investment model to determine the newly added renewable energy installed capacity, with the goal of minimizing the total cost of regional power generation;

[0121] Considering the cost of new renewable energy power generation P invest,t , the cost of electricity generation from existing renewable energy sources P genera,t , the cost of electricity transferred from other regions P buy,t And the conversion into green certificate income P green,t , taking the total regional power generation cost P all,t Build a renewable energy investment model with the minimum goal:

[0122] minP all,t =min(P invest,t +P genera,t +P buy,t -P green,t )

[0123]

[0124] P buy,t =-Q balance,t ×W t

[0125]

[0126] Where R is the type of new renewable energy power source; G r,t 、U r,t 、H r,t ,λ r,t are the installed capacity, unit installed capacity cost, power generation utilization hours, and unit power operation and maintenance cost coefficient of the rth type of newly added renewable energy power source in period t; b is the discount rate; T r Add the service life of renewable energy power source for category r; V k,t W is the electricity cost of the kth type of stock power source in the region during period t; t is the inter-regional electricity transaction price during period t; S is the type of renewable energy in the stock power supply; Q s,t is the power generation of the sth type of stock renewable power source in period t; δ is the quantitative coefficient of renewable energy power generation converted into green certificates, I t is the average transaction price of green certificates during period t.

[0127] The four interrelated parameters of newly added renewable energy power generation are installed capacity, unit installed capacity cost, generation utilization hours, and unit power operation and maintenance cost coefficient. In practice, once the installed capacity is determined, the other parameters can be estimated. Here, the only unknown variable is the installed capacity. Several renewable energy construction plans can be calculated separately, and then selected based on the results.

[0128] S3. Based on power flow information, establish a regional node power carbon emission factor database;

[0129] The node electricity carbon emission factor is obtained based on the power flow and is the average carbon emission factor of node a in the target area during period t, which is:

[0130]

[0131] Where c a is the average grid carbon emission factor of regional node a, is the direct carbon emission of the power plant directly connected to node a, is the indirect carbon emission injected into the carbon emission flow of node a, Q a is the load power of node a, Q out is the power flow out of node a; the target area has A nodes, and the power carbon emission factor library has {c1, c2, ..., c A}.

[0132] S4. With the goal of minimizing regional carbon emissions, determine the grid node locations for renewable energy projects based on regional grid node electricity consumption and electricity carbon emission factors;

[0133] According to the newly added renewable energy installed capacity, the newly added renewable energy power source node is placed at node x, and the regional power carbon emission model is established, which is:

[0134]

[0135] Where, Γ area,t is the total electricity carbon emissions of the region in period t, c a,t , Q a,t are the average grid carbon emission factor and load power of node a during period t, x is the node where the newly added renewable energy power source is located, and 1≤x≤A, c x,t is the average grid carbon emission factor of node x during period t.

[0136] The newly added renewable energy power sources are placed at Node 1, Node 2, ..., Node A respectively. The regional electricity carbon emissions under each scenario are calculated. The scenario with the lowest regional electricity carbon emissions is taken as the grid node location where the new renewable energy power source is located.

[0137] Example 2:

[0138] See also Figure 2 , a regional renewable energy configuration system based on electricity carbon emissions, comprising:

[0139] Regional source-charge imbalance prediction model building module: used to collect historical power generation and load data of the target area and establish a regional source-charge imbalance prediction model;

[0140] 1.1 The regional source-charge imbalance prediction model constructed is:

[0141] Q balance,t =Q source,t -Q load,t

[0142] Where Q balance,t , Q source,t , Q load,t are the unbalanced electricity, generated electricity, and load electricity in the target area during period t.

[0143] 1.2 Power generation Q source,t The predicted values ​​are:

[0144]

[0145] Where K is the number of regional power types, Q k,t is the predicted value of the power generation of the kth power source in the region during period t.

[0146] The values ​​of the Spearman correlation coefficient are:

[0147]

[0148] Where ρ is the Spearman correlation coefficient, R(x j )、R(y j ) are the positions of variables x and y respectively, are the average ranks of variables x and y respectively, and m is the number of samples.

[0149] The Stacking algorithm is used to calculate the power generation Q of different power types in the target area. k,t To make a prediction, the prediction steps are:

[0150] 1.2.1 Use the Spearman correlation coefficient method to analyze the correlation between factors affecting power generation and the power generation of different power sources. Rank the factors affecting power generation based on the Spearman correlation coefficient. Select the historical data of the top n factors with the highest correlation. Combine this with the historical power generation to form an initial data set for each power source type. Perform steps 1.2.2-1.2.4 separately for each power source type.

[0151] 1.2.2 Split the initial dataset into a meta-training set and a meta-test set on the meta-learner, and split the meta-training set into a base training set and a base test set on the base learner;

[0152] 1.2.3 Train and predict the base learners. Select the ResNet, VGG, and MobileNet algorithm models as base learners. Use K-fold cross-validation to train the models on the K-1 folds and validate them on the K-th fold. Train each base learner K times, for a total of 3K times, to generate all base models and prediction results.

[0153] 1.2.4 Based on the prediction data of the base learner, a prediction model is built on the meta-learner. Linear regression and logistic regression algorithms are used as meta-learners respectively. The training results of the two learners are averaged to obtain the final prediction result.

[0154] 1.3 Regional electricity load forecasting is carried out using a multi-layer perceptron model based on empirical mode decomposition. The specific steps are:

[0155] 1.3.1 Construct a regional historical load and electricity data set, divide the regional historical load and electricity data set into a training set and a test set, and use the empirical mode decomposition algorithm to decompose the historical load and electricity data into n eigenmode components and 1 residual:

[0156]

[0157] Where Q load 、IMF i , Res are the historical load power, the i-th intrinsic mode component, and the residual respectively;

[0158] 1.3.2 The decomposed intrinsic mode components are reconstructed using the extreme point partitioning method to form high-frequency, medium-frequency, and low-frequency components. A multi-layer perceptron prediction model is established for each frequency band, namely HF-MLP, IF-MLP, and LF-MLP.

[0159] 1.3.3 Analyze the correlation between candidate features and high-frequency, medium-frequency, and low-frequency components using the Spearman correlation coefficient method, and select components with absolute values ​​of correlation coefficients greater than the set threshold as inputs to the HF-MLP, IF-MLP, and LF-MLP models;

[0160] 1.3.4 Use the training set to train and adjust the parameters of the HF-MLP, IF-MLP, and LF-MLP models. After the models are trained, the high-frequency, medium-frequency, and low-frequency components of the power load data are predicted on the test set, and the prediction results are superimposed to obtain the load power prediction value Q load,t .

[0161] Renewable energy investment capacity model construction module: used to build a renewable energy investment model with the goal of minimizing the total cost of regional power generation and determine the newly added renewable energy installed capacity;

[0162] Considering the cost of generating electricity from new renewable energy sources P invest,t , the cost of electricity generation from existing renewable energy sources P genera,t , the cost of electricity transferred from other regions P buy,t And the conversion into green certificate income P green,t , taking the total regional power generation cost P all,t Build a renewable energy investment model with the minimum goal:

[0163] minP all,t =min(P invest,t +P genera,t +P buy,t -P green,t )

[0164]

[0165] P buy,t =-Q balance,t ×W t

[0166]

[0167] Where R is the type of new renewable energy power source; G r,t 、U r,t 、H r,t ,λ r,t are the installed capacity, unit installed capacity cost, power generation utilization hours, and unit power operation and maintenance cost coefficient of the rth type of newly added renewable energy power source in period t; b is the discount rate; T r Add the service life of renewable energy power source for category r; V k,t W is the electricity cost of the kth type of stock power source in the region during period t; t is the inter-regional electricity transaction price during period t; S is the type of renewable energy in the stock power supply; Q s,t is the power generation of the sth type of stock renewable power source in period t; δ is the quantitative coefficient of renewable energy power generation converted into green certificates, I t is the average transaction price of green certificates during period t.

[0168] Regional node power carbon emission factor library construction module: used to establish a regional node power carbon emission factor library based on power flow information;

[0169] The node electricity carbon emission factor is obtained based on the power flow and is the average carbon emission factor of node a in the target area during period t, which is:

[0170]

[0171] Where c a is the average grid carbon emission factor of regional node a, is the direct carbon emission of the power plant directly connected to node a, is the indirect carbon emission injected into the carbon emission flow of node a, Q a is the load power of node a, Q out is the power flow out of node a; the target area has A nodes, and the power carbon emission factor library has {c1, c2, ..., c A}.

[0172] Renewable energy configuration plan generation module: used to determine the grid node location of renewable energy projects with the goal of minimizing regional carbon emissions, combined with regional grid node power consumption and electricity carbon emission factors.

[0173] According to the newly added renewable energy installed capacity, the newly added renewable energy power source node is placed at node x, and the regional power carbon emission model is established, which is:

[0174]

[0175] Where, Γ area,t is the total electricity carbon emissions of the region in period t, c a,t , Q a,t are the average grid carbon emission factor and load power of node a during period t, x is the node where the newly added renewable energy power source is located, and 1≤x≤A, c x,t is the average grid carbon emission factor of node x during period t;

[0176] The newly added renewable energy power sources are placed at Node 1, Node 2, ..., Node A respectively. The regional electricity carbon emissions under each scenario are calculated. The scenario with the lowest regional electricity carbon emissions is taken as the grid node location where the new renewable energy power source is located.

Claims

1. A method for regional renewable energy configuration based on electricity carbon emissions, characterized in that: include: S1. Collect historical power generation and load data of the target area, establish a regional source-charge imbalance prediction model, and determine the target area source-charge imbalance prediction value. The values ​​of the Spearman correlation coefficient are: Where ρ is the Spearman correlation coefficient, R(x j )、R(y j ) are the positions of variables x and y respectively, are the average ranks of variables x and y, respectively, and m is the number of samples; S2. Determine the newly added renewable energy installed capacity based on the target area source-load imbalance power forecast value Q balance,t , considering the cost of new renewable energy power generation P invest,t , the cost of electricity generation from existing renewable energy sources P genera,t , the cost of electricity transferred from other regions P buy,t And the conversion into green certificate income P green,t , taking the total regional power generation cost P all,t Build a renewable energy investment model with the minimum goal: minP all,t =min(P invest,t +P genera,t +P buy,t -P green,t ) Where R is the type of new renewable energy power source; G r,t 、U r,t 、H r,t ,λ r,t are the installed capacity, unit installed capacity cost, power generation utilization hours, and unit power operation and maintenance cost coefficient of the rth type of newly added renewable energy power source in period t; b is the discount rate; T r Add the service life of renewable energy power source to category r; Q k,t V is the predicted value of the k-th power generation in the region during period t; k,t W is the electricity cost of the kth type of stock power source in the region during period t; t is the inter-regional electricity transaction price during period t; S is the type of renewable energy in the stock power supply; Q s,t is the power generation of the sth type of stock renewable power source in period t; δ is the quantitative coefficient of renewable energy power generation converted into green certificates, I t is the average transaction price of green certificates during period t; S3. Based on power flow information, establish a regional node power carbon emission factor database; S4. Based on the newly added installed capacity of renewable energy, with the goal of minimizing regional carbon emissions, and combined with the regional grid node electricity consumption and electricity carbon emission factors, determine the grid node location of the renewable energy project.

2. The method for regional renewable energy configuration based on electricity carbon emissions according to claim 1, characterized in that: In S1, the regional source-charge imbalance prediction model constructed in 1.1 is: Q balance,t =Q source,t -Q load,t Where Q balance,t , Q source,t , Q load,t are the unbalanced electricity, generated electricity, and load electricity in the target area during period t.

3. The method for regional renewable energy configuration based on electricity carbon emissions according to claim 2, characterized in that: In S1, 1.2 generated electricity Q source,t The predicted values ​​are: Where K is the number of regional power types, Q k,t is the predicted value of the power generation of the kth power source in the region during period t.

4. The method for regional renewable energy configuration based on electricity carbon emissions according to claim 3, characterized in that: In S1, the Stacking algorithm is used to calculate the power generation Q of different power types in the target area. k,t To make a prediction, the prediction steps are: 1.2.1 Use the Spearman correlation coefficient method to analyze the correlation between factors affecting power generation and the power generation of different power sources. Rank the factors affecting power generation based on the Spearman correlation coefficient. Select the historical data of the top n factors with the highest correlation. Combine this with the historical power generation to form an initial data set for each power source type. Perform steps 1.2.2-1.2.4 separately for each power source type. 1.2.2 Split the initial dataset into a meta-training set and a meta-test set on the meta-learner, and split the meta-training set into a base training set and a base test set on the base learner; 1.2.3 Train and predict the base learners. Select the ResNet, VGG, and MobileNet algorithm models as base learners. Use K-fold cross-validation to train the models on the K-1 folds and validate them on the K-th fold. Train each base learner K times, for a total of 3K times, to generate all base models and prediction results. 1.2.4 Based on the prediction data of the base learner, a prediction model is built on the meta-learner. Linear regression and logistic regression algorithms are used as meta-learners respectively. The training results of the two learners are averaged to obtain the final prediction result.

5. The method for configuring regional renewable energy based on electricity carbon emissions according to claim 4, characterized in that: In S1, 1.3 performs regional electricity load forecasting by using a multi-layer perceptron model based on empirical mode decomposition. The specific steps are: 1.3.1 Construct a regional historical load and electricity data set, divide the regional historical load and electricity data set into a training set and a test set, and use the empirical mode decomposition algorithm to decompose the historical load and electricity data into n eigenmode components and 1 residual: Where Q load 、IMF i , Res are the historical load power, the i-th intrinsic mode component, and the residual respectively; 1.3.2 The decomposed intrinsic mode components are reconstructed using the extreme point partitioning method to form high-frequency, medium-frequency, and low-frequency components. A multi-layer perceptron prediction model is established for each frequency band, namely HF-MLP, IF-MLP, and LF-MLP. 1.3.3 Analyze the correlation between candidate features and high-frequency, medium-frequency, and low-frequency components using the Spearman correlation coefficient method, and select components with absolute values ​​of correlation coefficients greater than the set threshold as inputs to the HF-MLP, IF-MLP, and LF-MLP models; 1.3.4 Use the training set to train and adjust the parameters of the HF-MLP, IF-MLP, and LF-MLP models. After the models are trained, the high-frequency, medium-frequency, and low-frequency components of the power load data are predicted on the test set, and the prediction results are superimposed to obtain the load power prediction value Q load,t .

6. The method for configuring regional renewable energy based on electricity carbon emissions according to claim 5, characterized in that: In S3, the node power carbon emission factor is obtained based on the power flow and is the average carbon emission factor of node a in the target area during period t, which is: Where c a is the average grid carbon emission factor of regional node a, is the direct carbon emission of the power plant directly connected to node a, is the indirect carbon emission injected into the carbon emission flow of node a, Q a is the load power of node a, Q out is the power flow out of node a; the target area has A nodes, and the power carbon emission factor library has {c1, c2, ..., c A }.

7. The method for configuring regional renewable energy based on electricity carbon emissions according to claim 6, characterized in that: In said S4: According to the newly added renewable energy installed capacity, the newly added renewable energy power source node is placed at node x, and the regional power carbon emission model is established, which is: Where, Γ area,t is the total electricity carbon emissions of the region in period t, c a,t , Q a,t are the average grid carbon emission factor and load power of node a during period t, x is the node where the newly added renewable energy power source is located, and 1≤x≤A, c x,t is the average grid carbon emission factor of node x during period t; The newly added renewable energy power sources are placed at Node 1, Node 2, ..., Node A respectively. The regional electricity carbon emissions under each scenario are calculated. The scenario with the lowest regional electricity carbon emissions is taken as the grid node location where the new renewable energy power source is located.

8. A regional renewable energy configuration system based on electricity carbon emissions, characterized in that: The renewable energy configuration system is used to execute a regional renewable energy configuration method based on electricity carbon emissions as described in any one of claims 1 to 7: The renewable energy configuration system includes: Regional source-charge imbalance prediction model building module: used to collect historical power generation and load data of the target area, establish a regional source-charge imbalance prediction model, and determine the predicted value of the target area's source-charge imbalance; Renewable energy investment model building module: used to determine the newly added renewable energy installed capacity based on the predicted value of the source-load imbalance in the target area; Regional node power carbon emission factor library construction module: used to establish a regional node power carbon emission factor library based on power flow information; Renewable energy configuration plan generation module: used to determine the grid node location of renewable energy projects based on the newly added renewable energy installed capacity, with the goal of minimizing regional carbon emissions, combined with regional grid node power consumption and electricity carbon emission factors.

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