Wind and light storage capacity distribution robust configuration method and system based on information gap decision

Abstract

The invention relates to the technical field of power grid side large-scale wind and light and energy storage configuration, in particular to a wind and light storage capacity distribution robust configuration method and system based on information gap decision, and the method comprises the steps: taking the lowest comprehensive cost as an objective function, and building a power grid side wind and light energy storage optimal configuration model considering inertia constraint; processing the data by using a multivariate time sequence density peak value clustering algorithm based on a principal component analysis method; uncertain factors in the optimal configuration model are considered, and a probability distribution fuzzy set based on a mixed norm is constructed; establishing a two-stage distribution robust optimization configuration model based on data driving, considering the uncertainty of wind and light output prediction, and converting the distribution robust optimization configuration model based on an information gap decision theory; and converting the distributed robust optimal configuration model into a mixed integer linear programming model by using a linearization technology, and solving the mixed integer linear programming model by using a column and number generation algorithm to obtain an optimal configuration and operation scheme of each device.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of large-scale wind and light and energy storage configuration on the grid side, and particularly relates to a wind and light and energy storage capacity distribution robust configuration method and system based on information gap decision. BACKGROUND

[0002] With the construction of new power systems, new energy such as wind power and photovoltaic power develops rapidly. Although large-scale new energy access will significantly reduce the dependence on fossil fuels and promote the green transformation of energy structure, it also brings many challenges, including the stability of the power system, the flexibility of the dispatching and the demand for energy storage. Energy storage technology can not only cooperate with thermal power units in the power grid to realize peak clipping and valley filling of the power system and improve the new energy consumption rate, but also provide virtual inertia to stabilize the grid frequency and improve the frequency stability of the grid. Therefore, reasonable optimization of the wind and light and energy storage capacity configuration on the grid side has great significance for fully tapping the wide space-time complementary effect of wind power and photovoltaic resources and ensuring the safe access of new energy to the grid.

[0003] In the prior art, the research on the optimal configuration of wind and light and energy storage on the grid side mainly focuses on improving the consumption rate of new energy in the grid or reducing the comprehensive operation cost of the multi-energy system by configuring energy storage, but there are few studies on solving the frequency stability problem of the grid after large-scale wind and light are connected to the grid and the optimal wind and light and energy storage configuration capacity of the grid under the condition of meeting the minimum inertia constraint.

[0004] Therefore, it is necessary to focus on the frequency stability problem of the grid after large-scale new energy is connected to the grid, consider the minimum inertia constraint of the system, and configure the capacity of wind power, photovoltaic power and energy storage. This planning problem is the current problem that needs to be solved.

[0005] The information disclosed in the background section is only intended to deepen the understanding of the overall background of the present application, and should not be regarded as admitting or implying in any form that the information constitutes prior art known to those skilled in the art. SUMMARY

[0006] The present application provides a wind and light and energy storage capacity distribution robust configuration method and system based on information gap decision, thereby effectively solving the problems in the background.

[0007] In order to achieve the above purpose, the technical scheme adopted by the present application is as follows: a wind and light and energy storage capacity distribution robust configuration method based on information gap decision, comprising the following steps:

[0008] An optimal configuration model of wind and light and energy storage on the grid side considering inertia constraint is established, taking the minimum comprehensive cost as the objective function, considering investment decision constraints and operation constraints including system minimum inertia constraints.

[0009] Considering the temporal nature of historical wind and solar power output data, a multivariate time-series density peak clustering algorithm based on principal component analysis is used to process the data;

[0010] Considering the uncertainties in the optimization configuration model, a fuzzy set of probability distribution based on the mixture norm is constructed;

[0011] A data-driven two-stage sub-Blu-rod optimization configuration model is established, taking into account the uncertainty of wind and solar power output prediction, and the sub-Blu-rod optimization configuration model is transformed based on the information gap decision theory.

[0012] The linearization technique is used to transform the sub-Bruker optimal configuration model into a mixed integer linear programming model, and the column and number generation algorithm is used to solve the mixed integer linear programming model to obtain the optimal configuration and operation scheme of each device.

[0013] Furthermore, considering the temporal nature of historical wind and solar power output data, a multivariate time-series density peak clustering algorithm based on principal component analysis is used to process the data, including:

[0014] Considering the correlation between wind power output and photovoltaic power output, as well as their temporal correlation, the historical data of wind and solar power output are treated as a multivariate time series for clustering.

[0015] Principal component analysis was used to reduce the dimensionality of the original multivariate time series, and then a clustering algorithm based on fast search and discovery of density peaks was used to cluster the data.

[0016] Furthermore, the process involves using principal component analysis to reduce the dimensionality of the original multivariate time series, followed by clustering the data using a clustering algorithm based on fast search and discovery of density peaks, including:

[0017] any point in the dataset Local density Defined as:

[0018] ;

[0019] ;

[0020] In the formula, The defined logical judgment function; For point and points The distance between them; Given the cutoff distance;

[0021] If point If it has the largest local density, then Defined as:

[0022] ;

[0023] point Minimum distance between other high-density objects Defined as:

[0024] ;

[0025] The clustering steps are as follows:

[0026] Calculate each data point ;

[0027] Calculate each data point ;

[0028] Drawing x-axis The decision graph is the y-axis; the point in the upper right corner of the decision graph is used as the cluster center, which has high density and is far away from other high-density points.

[0029] Determine the cluster centers;

[0030] The non-cluster center data points are then categorized, and the clustering process is complete.

[0031] Furthermore, the two-stage sub-Bruker optimization configuration model includes:

[0032] The compact form is shown below:

[0033] ;

[0034] ;

[0035] In the formula, This represents the decision variables for the first stage, including the configuration capacity of energy storage batteries and wind and photovoltaic generator sets; This represents the feasible region of the decision variables in the first stage. This indicates the average daily investment cost; This represents the expected operating cost corresponding to the worst-case probability distribution within the uncertainty set of the probability distribution under a given wind, solar, and energy storage planning scheme. Represents a random variable; Indicates the variables for decision-making; This represents the determined first-stage investment decision variables. Given a timeframe, the system operation decision variables include the power generation of thermal power units, the power generation of wind and photovoltaic power units, and the power generation of energy storage batteries, etc. , , , , , , , These represent the corresponding coefficient matrices.

[0036] Furthermore, considering the uncertainty of wind and solar power output prediction, the sub-bulb bar optimization configuration model is transformed based on information gap decision theory, including the following steps:

[0037] Under deterministic conditions, the objective function is to minimize the overall cost;

[0038] The objective function includes:

[0039] ;

[0040] ;

[0041] ;

[0042] ;

[0043] ;

[0044] In the formula: , , These represent the daily operating cost, daily wind and solar curtailment cost, and average daily investment cost of thermal power units, respectively. Indicates the number of typical daily scenes; This represents the number of time periods divided into each typical day, with each hour representing one time period, and each typical day having 24 time periods. This indicates the total number of conventional thermal power units; Indicates the first Taiwan's conventional thermal power units in a typical day Fuel costs below; Indicates the first Taiwan's conventional thermal power units in a typical day Down Output power during the time period; Indicates the first Taiwan's conventional thermal power units in a typical day The start-up and shutdown costs are as follows; Indicates the first Taiwan's conventional thermal power units in a typical day Cost of deep peak shaving state; This indicates the unit's operating status, with 1 indicating operation and 0 indicating shutdown. Indicates the achievement of typical days The probability of; This indicates the unit cost of wind and solar power curtailment. , They represent typical days. The scenery below Predicting output at any given moment; , They represent typical days. The scenery below Actual output at any given moment; The dailyization coefficient representing investment costs; Indicates the capacity of the energy storage configuration; Indicates the maximum power of the energy storage configuration; and These represent the maximum power output of the wind power and solar power configurations, respectively. , , and These represent the unit cost of energy storage configuration capacity, the unit cost of energy storage configuration power, and the unit cost of wind power and photovoltaic configuration power, respectively. Indicates the lifespan of the equipment;

[0045] After considering the uncertainty of wind and solar power output forecasting, the fluctuation between its actual and forecast values ​​can be described as follows:

[0046] ;

[0047] ;

[0048] In the formula, and These are the uncertain radii of wind and solar power output, respectively. Uncertain set of forces contributing to wind and solar forecasting;

[0049] Comprehensive Uncertainty Radius It is a weighted sum of two uncertain radii, namely:

[0050] ;

[0051] The split-bar model based on the information gap decision theory is shown below:

[0052] ;

[0053] In the formula, This refers to the objective function value obtained in the deterministic model, which is the minimum total cost of the system in the deterministic model. This is the robustness coefficient, i.e., the cost deviation factor; Equality constraint; Inequality constraints;

[0054] Since the greater the deviation between the actual wind and solar power output and the predicted output, the higher the robustness and cost required by the system, the above model is transformed into the following form:

[0055] .

[0056] Furthermore, the step of using linearization techniques to transform the sub-bar optimization configuration model into a mixed-integer linear programming model, and using column and number generation algorithms to solve the mixed-integer linear programming model, includes:

[0057] The split-bar optimization allocation model based on information gap decision theory contains a nonlinear part. Different linearization methods are used to linearize it, transforming the model into a mixed-integer linear programming model.

[0058] The linearized model contains a min-max-min problem, which can be decomposed into a main problem and subproblems:

[0059] The main problem is to obtain the optimal solution that satisfies the given finite probability distribution of adverse conditions. This provides a lower bound for the split-bar optimal allocation model based on information gap decision theory, as follows:

[0060] ;

[0061] In the formula: This represents the current iteration number; For the first Typical day in the next iteration The second phase of decision-making;

[0062] The sub-problem is the given first-stage variables. In the case of finding the worst probability distribution, we can provide the main problem with further iterative calculations, and the subproblem provides an upper bound for the following formula;

[0063] When a first-stage variable is given This leads to the following subproblem:

[0064] ;

[0065] The meaning of subproblems is given by the main problem. Based on this, we find the worst-case scenario probability distribution within the confidence interval to provide iterative calculations for the main problem and obtain the lower bound of the model.

[0066] Since the constraints of the inner and outer min-max optimization problems are independent, they are decomposed into two steps, as shown in the following equation:

[0067] ;

[0068] ;

[0069] The mixed-integer linear programming model was solved by using Yalmip to call the Gurobi solver and employing the column and constraint generation C&CG algorithm to obtain the optimal configuration scheme for each device.

[0070] The present invention also includes a wind-solar-storage capacity allocation system based on information gap decision-making, using the method described above, the system comprising:

[0071] The system modeling unit is used to establish an optimal configuration model for grid-side wind, solar and energy storage that takes into account inertia constraints, with the objective function of minimizing overall cost and considering investment decision constraints and operational constraints including minimum system inertia constraints.

[0072] The temporal clustering unit is used to take into account the temporal nature of historical wind and solar power output data. The data is processed using a multivariate temporal density peak clustering algorithm based on principal component analysis.

[0073] A fuzzy set modeling unit is used to consider the uncertainties in the optimization configuration model and construct a probability distribution fuzzy set based on the mixing norm.

[0074] The optimized configuration model modeling unit is used to establish a data-driven two-stage sub-Blu-rod optimized configuration model, taking into account the uncertainty of wind and solar power output prediction, and transforming the sub-Blu-rod optimized configuration model based on information gap decision theory;

[0075] The linear solution unit is used to transform the sub-bar optimization configuration model into a mixed integer linear programming model using linearization techniques, and to solve the mixed integer linear programming model using column and number generation algorithms to obtain the optimal configuration and operation scheme of each device.

[0076] The present invention also includes a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as described above.

[0077] The present invention also includes a storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described above.

[0078] The beneficial effects of this invention are as follows: Considering the temporal nature of historical wind and solar power output data, a multivariate time-series density peak clustering algorithm based on principal component analysis is used to process the data; considering uncertainties in the model, a probability distribution fuzzy set based on the mixture norm is constructed to ensure the system's robustness to uncertainties; a data-driven two-stage sub-Bruker optimal configuration model is established; a sub-Bruker optimal configuration model based on information gap decision theory is established; linearization techniques are used to transform the model into a mixed-integer linear programming model; and the column and constraint generation C&CG algorithm is used to solve the mixed-integer linear programming model to obtain the optimal configuration and operation scheme for each device. This invention can balance economic efficiency and load fluctuations, flexibly configure grid-side wind, solar, and energy storage capacity, introduce energy storage devices to absorb larger-scale wind and solar power resources, and avoid problems such as grid frequency instability caused by large-scale wind and solar energy grid connection by considering inertia constraints. Attached Figure Description

[0079] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0080] Figure 1 This is a flowchart of the method in Example 1;

[0081] Figure 2 This is a schematic diagram of the system structure in Example 1;

[0082] Figure 3 This is a schematic diagram of the power generation system in Example 2;

[0083] Figure 4 This is a flowchart of the solution process for the split-bar optimal configuration model based on information gap decision theory in Example 2;

[0084] Figure 5 This is a schematic diagram of the structure of the computer device of the present invention. Detailed Implementation

[0085] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0086] Example 1:

[0087] like Figure 1 As shown: A wind-solar-storage capacity allocation method based on information gap decision-making includes the following steps:

[0088] With the goal of minimizing overall cost, and considering investment decision constraints and operational constraints including minimum system inertia constraints, a grid-side wind, solar and energy storage optimization configuration model is established.

[0089] Considering the temporal nature of historical wind and solar power output data, a multivariate time-series density peak clustering algorithm based on principal component analysis is used to process the data;

[0090] Considering the uncertainties in the optimization configuration model, a fuzzy set of probability distribution based on the mixture norm is constructed;

[0091] A data-driven two-stage sub-Bluer bar optimization configuration model is established, taking into account the uncertainty of wind and solar power output prediction, and the sub-Bluer bar optimization configuration model is transformed based on information gap decision theory.

[0092] The linearization technique is used to transform the sub-integer bar optimization configuration model into a mixed integer linear programming model, and the column and number generation algorithm is used to solve the mixed integer linear programming model to obtain the optimal configuration and operation scheme of each device.

[0093] Considering the temporal nature of historical wind and solar power output data, a multivariate time-series density peak clustering algorithm based on principal component analysis is used to process the data. To ensure the system's robustness against uncertainties, a fuzzy set of probability distributions based on the mixture norm is constructed, taking into account uncertainties in the model. A data-driven two-stage sub-Bruker optimal configuration model is established. A sub-Bruker optimal configuration model based on information gap decision theory is also established. Linearization techniques are used to transform the model into a mixed-integer linear programming model. The column and constraint generation C&CG algorithm is used to solve the mixed-integer linear programming model, obtaining the optimal configuration and operation scheme for each device. This approach balances economic efficiency and load fluctuations, flexibly configures grid-side wind, solar, and energy storage capacity, introduces energy storage devices to absorb larger-scale wind and solar power resources, and considers inertia constraints to avoid grid frequency instability issues caused by large-scale wind and solar energy grid connection.

[0094] In this embodiment, considering the temporal nature of historical wind and solar power output data, a multivariate temporal density peak clustering algorithm based on principal component analysis is used to process the data, including:

[0095] Considering the correlation between wind power output and photovoltaic power output, as well as their temporal correlation, the historical data of wind and solar power output are treated as a multivariate time series for clustering.

[0096] Principal component analysis was used to reduce the dimensionality of the original multivariate time series, and then a clustering algorithm based on fast search and discovery of density peaks was used to cluster the data.

[0097] Principal component analysis was used to reduce the dimensionality of the original multivariate time series data. Then, a clustering algorithm based on fast search and discovery of density peaks was used to cluster the data, including:

[0098] any point in the dataset Local density Defined as:

[0099] ;

[0100] ;

[0101] In the formula, The defined logical judgment function; For point and points The distance between them; Given the cutoff distance;

[0102] If point If it has the largest local density, then Defined as:

[0103] ;

[0104] point Minimum distance between other high-density objects Defined as:

[0105] ;

[0106] The clustering steps are as follows:

[0107] Calculate each data point ;

[0108] Calculate each data point ;

[0109] Drawing x-axis The decision graph is the y-axis; the point in the upper right corner of the decision graph is used as the cluster center, which has high density and is far away from other high-density points.

[0110] Determine the cluster centers;

[0111] The non-cluster center data points are then categorized, and the clustering process is complete.

[0112] The two-stage bibliophile optimization configuration model includes:

[0113] The compact form is shown below:

[0114] ;

[0115] ;

[0116] In the formula, This represents the decision variables for the first stage, including the configuration capacity of energy storage batteries and wind and photovoltaic generator sets; This represents the feasible region of the decision variables in the first stage. This indicates the average daily investment cost; This represents the expected operating cost corresponding to the worst-case probability distribution within the uncertainty set of the probability distribution under a given wind, solar, and energy storage planning scheme. Represents a random variable; Indicates the variables for decision-making; This represents the determined first-stage investment decision variables. Given a timeframe, the system operation decision variables include the power generation of thermal power units, the power generation of wind and photovoltaic power units, and the power generation of energy storage batteries, etc. , , , , , , , These represent the corresponding coefficient matrices.

[0117] In this embodiment, considering the uncertainty of wind and solar power output prediction, the split-bar optimal configuration model is transformed based on the information gap decision theory, including the following steps:

[0118] Under deterministic conditions, the objective function is to minimize the overall cost;

[0119] The objective function includes:

[0120] ;

[0121] ;

[0122] ;

[0123] ;

[0124] ;

[0125] In the formula: , , These represent the daily operating cost, daily wind and solar curtailment cost, and average daily investment cost of thermal power units, respectively. Indicates the number of typical daily scenes; This represents the number of time periods divided into each typical day, with each hour representing one time period, and each typical day having 24 time periods. This indicates the total number of conventional thermal power units; Indicates the first Taiwan's conventional thermal power units in a typical day Fuel costs below; Indicates the first Taiwan's conventional thermal power units in a typical day Down Output power during the time period; Indicates the first Taiwan's conventional thermal power units in a typical day The start-up and shutdown costs are as follows; Indicates the first Taiwan's conventional thermal power units in a typical day Cost of deep peak shaving state; This indicates the unit's operating status, with 1 indicating operation and 0 indicating shutdown. Indicates the achievement of typical days The probability of; This indicates the unit cost of wind and solar power curtailment. , They represent typical days. The scenery below Predicting output at any given moment; , They represent typical days. The scenery below Actual output at any given moment; The dailyization coefficient representing investment costs; Indicates the capacity of the energy storage configuration; Indicates the maximum power of the energy storage configuration; and These represent the maximum power output of the wind power and solar power configurations, respectively. , , and These represent the unit cost of energy storage configuration capacity, the unit cost of energy storage configuration power, and the unit cost of wind power and photovoltaic configuration power, respectively. Indicates the lifespan of the equipment;

[0126] After considering the uncertainty of wind and solar power output forecasting, the fluctuation between its actual and forecast values ​​can be described as follows:

[0127] ;

[0128] ;

[0129] In the formula, and These are the uncertain radii of wind and solar power output, respectively. Uncertain set of forces contributing to wind and solar forecasting;

[0130] Comprehensive Uncertainty Radius It is a weighted sum of two uncertain radii, namely:

[0131] ;

[0132] The split-bar model based on the information gap decision theory is shown below:

[0133] ;

[0134] In the formula, This refers to the objective function value obtained in the deterministic model, which is the minimum total cost of the system in the deterministic model. This is the robustness coefficient, i.e., the cost deviation factor; Equality constraint; Inequality constraints;

[0135] Since the greater the deviation between the actual wind and solar power output and the predicted output, the higher the robustness and cost required by the system, the above model is transformed into the following form:

[0136] .

[0137] The sub-Bruker optimal allocation model is transformed into a mixed-integer linear programming model using linearization techniques, and the mixed-integer linear programming model is solved using column and number generation algorithms, including:

[0138] The split-bar optimization allocation model based on information gap decision theory contains a nonlinear part. Different linearization methods are used to linearize it, transforming the model into a mixed-integer linear programming model.

[0139] The linearized model contains a min-max-min problem, which can be decomposed into a main problem and subproblems:

[0140] The main problem is to obtain the optimal solution that satisfies the given finite probability distribution of adverse conditions. This provides a lower bound for the split-bar optimal allocation model based on information gap decision theory, as follows:

[0141] ;

[0142] In the formula: This represents the current iteration number; For the first Typical day in the next iteration The second phase of decision-making;

[0143] The sub-problem is the given first-stage variables. In the case of finding the worst probability distribution, we can provide the main problem with further iterative calculations, and the subproblem provides an upper bound for the following formula;

[0144] When a first-stage variable is given This leads to the following subproblem:

[0145] ;

[0146] The meaning of subproblems is given by the main problem. Based on this, we find the worst-case scenario probability distribution within the confidence interval to provide iterative calculations for the main problem and obtain the lower bound of the model.

[0147] Since the constraints of the inner and outer min-max optimization problems are independent, they are decomposed into two steps, as shown in the following equation:

[0148] ;

[0149] ;

[0150] The mixed-integer linear programming model was solved by using Yalmip to call the Gurobi solver and employing the column and constraint generation C&CG algorithm to obtain the optimal configuration scheme for each device.

[0151] like Figure 2 As shown, this embodiment also includes a wind-solar-storage capacity allocation system based on information gap decision-making. Using the method described above, the system includes:

[0152] The system modeling unit is used to establish an optimal configuration model for grid-side wind, solar and energy storage that takes into account inertia constraints, with the objective function of minimizing overall cost and considering investment decision constraints and operational constraints including minimum system inertia constraints.

[0153] The temporal clustering unit is used to take into account the temporal nature of historical wind and solar power output data. The data is processed using a multivariate temporal density peak clustering algorithm based on principal component analysis.

[0154] Fuzzy set modeling unit is used to consider uncertainties in the optimization configuration model and construct a probability distribution fuzzy set based on the mixing norm;

[0155] The optimized configuration modeling unit is used to establish a data-driven two-stage sub-Blu-rod optimized configuration model, taking into account the uncertainty of wind and solar power output prediction, and transforming the sub-Blu-rod optimized configuration model based on information gap decision theory.

[0156] The linear solution unit is used to transform the sub-bar optimization configuration model into a mixed-integer linear programming model using linearization techniques, and to solve the mixed-integer linear programming model using column and number generation algorithms to obtain the optimal configuration and operation scheme of each device.

[0157] Example 2:

[0158] like Figure 3 As shown, embodiments of the present invention include wind farms to be planned, photovoltaic power stations to be planned, and energy regulation includes thermal power plants and energy storage devices to be planned.

[0159] First, an objective function for minimizing overall cost is established, along with investment decision constraints and operational constraints including a minimum system inertia constraint. Finally, a grid-side wind-solar-storage energy optimization configuration model considering inertia constraints is established, including:

[0160] 1) Considering the inertia support capacity that energy storage equipment can provide, on the basis of meeting the operational requirements of power system inertia constraints, the grid-side wind, solar and energy storage joint planning is carried out with the goal of minimizing the sum of daily investment cost and operating cost. The daily investment cost includes the daily investment cost of wind power, photovoltaic and energy storage conversion, and the operating cost includes the fuel cost, start-up and shutdown cost and penalty cost of wind and solar curtailment of thermal power units.

[0161] ;

[0162] ;

[0163] ;

[0164] ;

[0165] ;

[0166] In the formula: , , These represent the daily operating cost, daily wind and solar curtailment cost, and average daily investment cost of thermal power units, respectively. Indicates the number of typical daily scenes; This represents the number of time periods divided into each typical day, with each hour representing one time period, and each typical day having 24 time periods. This indicates the total number of conventional thermal power units; Indicates the first Taiwan's conventional thermal power units in a typical day Fuel costs below; Indicates the first Taiwan's conventional thermal power units in a typical day Down Output power during the time period; Indicates the first Taiwan's conventional thermal power units in a typical day The start-up and shutdown costs are as follows; Indicates the first Taiwan's conventional thermal power units in a typical day Cost of deep peak shaving state; Indicates the unit's operating status (1 indicates running, 0 indicates stopped); Indicates the achievement of typical days The probability of; This indicates the unit cost of wind and solar power curtailment. , They represent typical days. The scenery below Predicting output at any given moment; , They represent typical days. The scenery below Actual output at any given moment; The dailyization coefficient representing investment costs; Indicates the capacity of the energy storage configuration; Indicates the maximum power of the energy storage configuration; and These represent the maximum power output of the wind power and solar power configurations, respectively. , , and These represent the unit cost of energy storage configuration capacity, the unit cost of energy storage configuration power, and the unit cost of wind power and photovoltaic configuration power, respectively. This indicates the lifespan of the equipment.

[0167] 2) The constraints of the grid-side wind, solar and energy storage optimization configuration model considering inertia constraints mainly include investment decision constraints for wind power, photovoltaic and energy storage, as well as operation constraints including inertia constraints under a given investment decision.

[0168] The investment decision-making constraints are the upper limit constraints on the installation of wind turbines, photovoltaic units, energy storage battery capacity, and energy storage battery power.

[0169] The operational constraints include:

[0170] System minimum inertia constraint:

[0171] To ensure system frequency stability, the system inertia at all times should be greater than the minimum system inertia requirement, as expressed below:

[0172] ;

[0173] ;

[0174] In the formula: and They represent typical days. Down The current total equivalent inertia and minimum inertia requirement of the time-period system; , They represent the first Rated power and inertial time constant of the synchronous thermal power unit; , These represent the rated power and virtual inertial time constant of the wind farm, respectively. , These represent the rated power and virtual inertial time constant of the photovoltaic power station, respectively. , These represent the rated power and virtual inertial time constant of the energy storage device, respectively. This indicates the operating status of a conventional thermal power unit; 1 indicates operation, and 0 indicates shutdown.

[0175] Node power balance constraints:

[0176] ;

[0177] In addition, operational constraints also include upper and lower limits for thermal power unit output, thermal power unit ramp-up constraints, minimum start-up and shutdown time constraints for thermal power units, upper and lower limits for energy storage output, and energy storage... Constraints include upper and lower limits for wind and solar power output, DC power flow constraints, and phase angle constraints.

[0178] Next, considering the temporal nature of historical wind and solar power output data, a multivariate temporal density peak clustering algorithm based on principal component analysis is used to process the data, including:

[0179] Considering the correlation between wind power output and photovoltaic power output, as well as their temporal correlation, the historical data of wind and solar power output are treated as a multivariate time series for clustering, in order to preserve the temporal continuity of the data and the correlation between different categories of data to the greatest extent.

[0180] First, principal component analysis (PCA) is used to reduce the dimensionality of the original multivariate time series. Then, a clustering algorithm based on fast search and discovery of density peaks is used to cluster the data. The core idea of ​​this clustering algorithm is to identify cluster centers that have a higher density relative to their surrounding points and are significantly distant from other cluster centers.

[0181] any point in the dataset The local density is defined as:

[0182] ;

[0183] ;

[0184] In the formula, The defined logical judgment function; For point and points The distance between them; The given cutoff distance.

[0185] If point If it has the largest local density, then Defined as:

[0186] ;

[0187] point Minimum distance between other high-density objects Defined as:

[0188] ;

[0189] The specific steps are as follows:

[0190] (1) Calculate the data points ;

[0191] (2) Calculate the value of each data point ;

[0192] (3) Drawing x-axis The decision graph is plotted along the y-axis. The point in the upper right corner of the decision graph should be the cluster center; this point should have high density and be far away from other high-density points.

[0193] (4) Determine the cluster centers;

[0194] (5) Classify the non-cluster center data points, and the clustering ends.

[0195] By using the multivariate temporal density peak clustering algorithm based on principal component analysis to cluster the raw data of wind and solar power output, the resulting typical scenarios can not only reflect the correlation between multiple categories of data, but also preserve the temporal continuity within a single category of data.

[0196] like Figure 4 As shown, next, considering the uncertainties in the model, a fuzzy set of probability distributions based on the mixture norm is constructed, including:

[0197] Assume there is A number of historical scenarios were obtained using a multivariate temporal density peak clustering method based on principal component analysis. Let there be discrete typical days, and assume that the initial probability distribution of all uncertain typical days is as follows: Then the first one The initial probability of a typical day is .

[0198] Considering the randomness of wind and solar power output, based on Norm and The norm establishes confidence sets for different confidence levels, as shown in the following formula:

[0199] ;

[0200] In the formula: A fuzzy set representing the mixed probability distribution of typical sunrise power; Indicates the first The actual probability of a typical day; and They represent Norm and The probability deviation limit for typical scenarios under norm constraints. Using Hoeffding's inequality... and Configure:

[0201] ;

[0202] In the formula: , These represent the confidence levels that the two probability distribution inequalities hold.

[0203] From the above formula, it can be seen that when the scale of historical data is large... When it increases, The values ​​all decrease, as Increase to hour, and The value will approach 0 infinitely.

[0204] Next, based on the established grid-side wind-solar-storage energy optimization configuration model considering inertia constraints and the hybrid probability distribution fuzzy set, a data-driven two-stage sub-Bruker optimization configuration model is established, including:

[0205] The first stage is the investment decision-making problem, optimizing the configuration capacity of energy storage batteries and wind and solar power generators; the second stage is the operation optimization problem, aiming to minimize the coal consumption cost, start-up and shutdown cost, and wind and solar curtailment cost of thermal power in the power grid. Therefore, the compact form of the data-driven two-stage partial Blower capacity optimization configuration model is shown below:

[0206] ;

[0207] ;

[0208] In the formula, This represents the decision variables for the first stage, including the configuration capacity of energy storage batteries and wind and photovoltaic generator sets; This represents the feasible region of the decision variables in the first stage. This indicates the average daily investment cost; This represents the expected operating cost corresponding to the worst-case probability distribution within the uncertainty set of the probability distribution under a given wind, solar, and energy storage planning scheme. Represents a random variable; Indicates the variables for decision-making; This represents the determined first-stage investment decision variables. Given a timeframe, the system operation decision variables include the power generation of thermal power units, the power generation of wind and photovoltaic power units, and the power generation of energy storage batteries, etc. , , , , , , , These represent the corresponding coefficient matrices.

[0209] Next, a split-bar optimal allocation model based on information gap decision theory is established, including:

[0210] The objective function under deterministic conditions is shown above:

[0211] ;

[0212] After considering the uncertainty of wind and solar power output forecasting, the fluctuation between its actual and forecast values ​​can be described as follows:

[0213] ;

[0214] ;

[0215] In the formula, and These are the uncertain radii of wind and solar power output, respectively. For wind and solar power forecasting, the output of the uncertain set is given.

[0216] Comprehensive Uncertainty Radius It is a weighted sum of two uncertain radii, namely:

[0217] ;

[0218] The split-bar model based on the information gap decision theory is shown below:

[0219] ;

[0220] In the formula, This refers to the objective function value obtained in the deterministic model, which is the minimum total cost of the system in the deterministic model. This is the robustness coefficient, i.e., the cost deviation factor; Equality constraint; This is an inequality constraint.

[0221] Since the greater the deviation between the actual wind and solar power output and the predicted output, the higher the robustness and cost required by the system, the above model can be transformed into the following form:

[0222] .

[0223] Finally, the C&CG algorithm is used to solve the problem and obtain the optimal configuration scheme for each device, including:

[0224] The bibliometric optimization configuration model based on information gap decision theory contains nonlinear components, such as the fuel cost of thermal power units and the upper and lower limits of energy storage output. Different linearization methods are used to linearize these components, transforming the model into a mixed integer linear programming model, which is easier to solve.

[0225] The linearized model contains min-max-min problems that are difficult to solve directly, so it is decomposed into two parts: the main problem and the subproblems.

[0226] The main problem is to obtain the optimal solution that satisfies the given finite probability distribution of adverse conditions. This provides a lower bound for the split-Bruker model based on information gap decision theory, as follows:

[0227] ;

[0228] In the formula: This represents the current iteration number; For the first Typical day in the next iteration The second phase of decision-making.

[0229] The sub-problem is the given first-stage variables. In the case of finding the worst probability distribution, we can provide the main problem with further iterative calculations, and the subproblem provides an upper bound for the following formula;

[0230] When a first-stage variable is given This leads to the following subproblem:

[0231] ;

[0232] The meaning of subproblems is given by the main problem. Based on this, we find the worst-case scenario probability distribution within the confidence interval to provide iterative calculations for the main problem and obtain the lower bound of the model.

[0233] Since the constraints of the inner and outer min-max optimization problems are independent, they can be decomposed into two steps, as shown in the following equation:

[0234] ;

[0235] ;

[0236] The mixed-integer linear programming model was solved by using the Gurobi solver via Yalmip and the Column and Constraint Generation (C&CG) algorithm to obtain the optimal configuration scheme for each device.

[0237] Please see Figure 5 The diagram shows a structural schematic of a computer device provided in an embodiment of this application. An embodiment of this application provides a computer device 400, including a processor 410 and a memory 420. The memory 420 stores a computer program executable by the processor 410. When the computer program is executed by the processor 410, it performs the method described above.

[0238] This application embodiment also provides a storage medium 430, on which a computer program is stored, and the computer program is executed by a processor 410 to perform the above method.

[0239] The storage medium 430 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0240] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. "A plurality of" means two or more, unless otherwise explicitly specified.

[0241] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0242] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0243] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0244] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0245] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0246] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0247] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A wind-solar-storage capacity allocation method based on information gap decision-making, characterized in that, Includes the following steps: With the goal of minimizing overall cost, and considering investment decision constraints and operational constraints including minimum system inertia constraints, an optimal configuration model for grid-side wind, solar and energy storage is established. Considering the temporal nature of historical wind and solar power output data, a multivariate time-series density peak clustering algorithm based on principal component analysis is used to process the data; Considering the uncertainties in the optimization configuration model, a fuzzy set of probability distribution based on the mixture norm is constructed; A data-driven two-stage sub-Blu-rod optimization configuration model is established, taking into account the uncertainty of wind and solar power output prediction, and the sub-Blu-rod optimization configuration model is transformed based on the information gap decision theory. The linearization technique is used to transform the sub-Bruker optimal configuration model into a mixed integer linear programming model, and the column and number generation algorithm is used to solve the mixed integer linear programming model to obtain the optimal configuration and operation scheme of each device.

2. The wind-solar-storage capacity allocation method based on information gap decision-making according to claim 1, characterized in that, Considering the temporal nature of historical wind and solar power output data, a multivariate time-series density peak clustering algorithm based on principal component analysis is used to process the data, including: Considering the correlation between wind power output and photovoltaic power output, as well as their temporal correlation, the historical data of wind and solar power output are treated as a multivariate time series for clustering. Principal component analysis was used to reduce the dimensionality of the original multivariate time series, and then a clustering algorithm based on fast search and discovery of density peaks was used to cluster the data.

3. The wind-solar-storage capacity allocation method based on information gap decision-making according to claim 2, characterized in that, The process involves using principal component analysis to reduce the dimensionality of the original multivariate time series, followed by clustering the data using a clustering algorithm based on fast search and discovery of density peaks, including: any point in the dataset Local density Defined as: ； ； In the formula, The defined logical judgment function; For point and points The distance between them; Given the cutoff distance; If point If it has the largest local density, then Defined as: ； point Minimum distance between other high-density objects Defined as: ； The clustering steps are as follows: Calculate each data point ; Calculate each data point ; Drawing x-axis The decision graph is y-axis; the point in the upper right corner of the decision graph is used as the cluster center, which has high density and is far away from other high-density points; Determine the cluster centers; The non-cluster center data points are then categorized, and the clustering process is complete.

4. The wind-solar-storage capacity allocation method based on information gap decision-making according to claim 1, characterized in that, The two-stage sub-Brussels bar optimization configuration model includes: The compact form is shown below: ； ； In the formula, This represents the decision variables for the first stage, including the configuration capacity of energy storage batteries and wind and photovoltaic generator sets; This represents the feasible region of the decision variables in the first stage. This indicates the average daily investment cost; This represents the expected operating cost corresponding to the worst-case probability distribution within the uncertainty set of the probability distribution under a given wind, solar, and energy storage planning scheme. Represents a random variable; Indicates the variables for decision-making; This represents the determined first-stage investment decision variables. Given a time, the set of system operation decision variables includes the power generation of thermal power units, the power generation of wind and photovoltaic power units, the power generation of energy storage batteries, etc. , , , , , , , These represent the corresponding coefficient matrices.

5. The wind-solar-storage capacity allocation method based on information gap decision-making according to claim 4, characterized in that, The process of considering the uncertainty in wind and solar power output prediction, and transforming the sub-bulb bar optimization configuration model based on information gap decision theory, includes the following steps: Under deterministic conditions, the objective function is to minimize the overall cost; The objective function includes: ； ； ； ； ； In the formula: , , These represent the daily operating cost, daily wind and solar curtailment cost, and average daily investment cost of thermal power units, respectively. Indicates the number of typical daily scenes; This represents the number of time periods divided into each typical day, with each hour representing one time period, and each typical day having 24 time periods. This indicates the total number of conventional thermal power units; Indicates the first Taiwan's conventional thermal power units in a typical day Fuel costs below; Indicates the first Taiwan's conventional thermal power units in a typical day Down Output power during the time period; Indicates the first Taiwan's conventional thermal power units in a typical day The start-up and shutdown costs are as follows; Indicates the first Taiwan's conventional thermal power units in a typical day Cost of deep peak shaving state; This indicates the unit's operating status, with 1 indicating operation and 0 indicating shutdown. Indicates the achievement of typical days The probability of; This indicates the unit cost of wind and solar power curtailment. , They represent typical days. The scenery below Predicting output at any given moment; , They represent typical days. The scenery below Actual output at any given moment; The dailyization coefficient representing investment costs; Indicates the capacity of the energy storage configuration; Indicates the maximum power of the energy storage configuration; and These represent the maximum power output of the wind power and solar power configurations, respectively. , , and These represent the unit cost of energy storage configuration capacity, the unit cost of energy storage configuration power, and the unit cost of wind power and photovoltaic configuration power, respectively. Indicates the lifespan of the equipment; After considering the uncertainty of wind and solar power output forecasting, the fluctuation between its actual and forecast values ​​can be described as follows: ； ； In the formula, and These are the uncertain radii of wind and solar power output, respectively. Uncertain set of forces contributing to wind and solar forecasting; Comprehensive Uncertainty Radius It is a weighted sum of two uncertain radii, namely: ； The split-bar model based on the information gap decision theory is shown below: ； In the formula, This refers to the objective function value obtained in the deterministic model, which is the minimum total cost of the system in the deterministic model. This is the robustness coefficient, i.e., the cost deviation factor; For equality constraints; Inequality constraints; Since the greater the deviation between the actual wind and solar power output and the predicted output, the higher the robustness and cost required by the system, the above model is transformed into the following form: 。 6. The wind-solar-storage capacity-sharing bar configuration method based on information gap decision-making according to claim 5, characterized in that, The process of transforming the sub-Bruker optimal configuration model into a mixed-integer linear programming model using linearization techniques, and solving the mixed-integer linear programming model using column and number generation algorithms, includes: The split-bar optimization allocation model based on information gap decision theory contains a nonlinear part. Different linearization methods are used to linearize it, transforming the model into a mixed-integer linear programming model. The linearized model contains a min-max-min problem, which can be decomposed into a main problem and subproblems: The main problem is to obtain the optimal solution that satisfies the given finite probability distribution of adverse conditions. This provides a lower bound for the split-bar optimal allocation model based on information gap decision theory, as follows: ； In the formula: This represents the current iteration number; For the first Typical day in the next iteration The second phase of decision-making; The sub-problem is the given first-stage variables. In the case of finding the worst probability distribution, we can provide the main problem with further iterative calculations, and the subproblem provides an upper bound for the following formula; When a first-stage variable is given This leads to the following subproblem: ； The meaning of subproblems is given by the main problem. Based on this, we find the worst-case scenario probability distribution within the confidence interval to provide iterative calculations for the main problem and obtain the lower bound of the model. Since the constraints of the inner and outer min-max optimization problems are independent, they are decomposed into two steps, as shown in the following equation: ； ； The mixed-integer linear programming model was solved by using Yalmip to call the Gurobi solver and employing the column and constraint generation C&CG algorithm to obtain the optimal configuration scheme for each device.

7. A wind-solar-storage capacity allocation system based on information gap decision-making, characterized in that, Using the method of any one of claims 1 to 6, the system comprises: The system modeling unit is used to establish an optimal configuration model for grid-side wind, solar and energy storage that takes into account inertia constraints, with the objective function of minimizing overall cost and considering investment decision constraints and operational constraints including minimum system inertia constraints. The temporal clustering unit is used to take into account the temporal nature of historical wind and solar power output data. The data is processed using a multivariate temporal density peak clustering algorithm based on principal component analysis. A fuzzy set modeling unit is used to consider the uncertainties in the optimization configuration model and construct a probability distribution fuzzy set based on the mixing norm. The optimized configuration model modeling unit is used to establish a data-driven two-stage sub-Blu-rod optimized configuration model, taking into account the uncertainty of wind and solar power output prediction, and transforming the sub-Blu-rod optimized configuration model based on information gap decision theory; The linear solution unit is used to transform the sub-bar optimization configuration model into a mixed integer linear programming model using linearization techniques, and to solve the mixed integer linear programming model using column and number generation algorithms to obtain the optimal configuration and operation scheme of each device.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1-6.

9. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-6.

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