An optimized scheduling method, device, terminal equipment and storage medium for a wind-solar-storage integrated system

Abstract

The present invention discloses an optimization scheduling method, device, terminal equipment and storage medium for a wind, solar and storage integrated system. First, the constructed initial scheduling model is solved, and the objective function value corresponding to the optimal solution of the initial scheduling model is used as a reference value. Then, based on the initial scheduling model, preference factors and uncertainty coefficients, a target scheduling model is constructed with the goal of minimizing the negative impact of power uncertainty on the system and the system power offset being less than the target power offset. Since the preference factors and uncertainty coefficients quantify the impact of power uncertainty on system risk or benefit, when the target scheduling model is solved to generate the final scheduling variable, the scheduling variable can ensure that the power offset remains within the power offset limited by the preference factors, and the power uncertainty meets the range of the system preference requirements, thereby effectively reducing the system power fluctuation and ensuring the stability and security of the power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of system optimization and scheduling, and in particular to an optimization and scheduling method, device, terminal equipment and storage medium for a wind, solar and storage integrated system. Background Art

[0002] An integrated wind, solar, and storage system is a novel energy system that integrates wind, solar, and energy storage technologies. Wind turbines convert wind energy into electricity, while solar panels convert solar energy into electricity. Energy storage devices (such as battery storage systems) provide a stable power supply when wind and solar energy are insufficient, ensuring continuous system operation. In an integrated wind, solar, and storage system, the coordinated scheduling of multiple devices is crucial. Wind and solar energy are naturally complementary, and energy storage devices play a role in "peak shaving" within the system.

[0003] Since the output power of wind turbines and solar panels often fluctuates, wind and solar power generation is volatile and intermittent, which may lead to unstable power supply to the system. In order to deal with these volatility and intermittency problems, it is often necessary to optimize the power generation output of various energy sources and the charging and discharging power of energy storage devices through multi-device coordinated scheduling to ensure stable power supply and efficient operation of the system. However, when performing multi-device coordinated scheduling of the system, the existing technology often analyzes historical wind and solar output data based on big data to predict future power generation trends and formulates scheduling strategies based on these prediction results. Although it can reflect the cyclical laws of power generation to a certain extent, it is difficult to accurately respond to sudden power fluctuations. The lack of consideration of power uncertainty leads to the inability to cope with voltage and frequency fluctuations when scheduling and controlling the system's energy, thereby affecting the stability and security of the power grid. Summary of the Invention

[0004] The embodiments of the present invention provide an optimization scheduling method, apparatus, terminal device and storage medium for an integrated wind, solar and storage system. The preference factors and uncertainty coefficients introduced enable the target scheduling variables ultimately generated to effectively reduce power fluctuations and the risk of instability in the system caused by excessive power deviations. This can effectively solve the problem in the prior art of being difficult to accurately respond to sudden power fluctuations, thereby affecting the stability and security of the power grid.

[0005] An embodiment of the present invention provides an optimization scheduling method for a wind-solar-storage integrated system, including:

[0006] Based on the power data of the wind, solar and storage integrated system in different scenarios and the average power values ​​in different scenarios, an initial scheduling model is constructed with the goal of minimizing the average absolute deviation of the system's total output power in all scenarios. The constraints of the initial scheduling model include: wind farm output constraints, photovoltaic power station output constraints, energy storage station charge and discharge power constraints, and energy storage station state of charge constraints.

[0007] Solving the initial scheduling model, and outputting the current objective function value corresponding to the initial scheduling model when the average absolute deviation of the total system output power in all scenarios is minimized;

[0008] Taking the objective function value as a reference value, and based on the initial scheduling model, the preference factor, and the uncertainty coefficient, a target scheduling model is constructed with the goal of minimizing the negative impact of power uncertainty on the system and ensuring that the system's power offset is less than the target power offset; wherein the preference factor is used to characterize the system's risk tolerance when facing power uncertainty; the uncertainty coefficient is used to characterize the degree of impact of power uncertainty on the system; and the target power offset is the power offset defined by the preference factor.

[0009] Solving the target scheduling model under wind farm output constraints, photovoltaic power station output constraints, energy storage power station charge and discharge power constraints, and energy storage power station state of charge constraints, and generating corresponding target scheduling variables when the negative impact of power uncertainty on the system is minimized and the system power offset is less than the target power offset; wherein the target scheduling variables include: target energy storage power station charging power and target energy storage power station discharging power;

[0010] According to the target scheduling variable, the output of the energy storage power station in the wind-solar-storage integrated system is controlled.

[0011] Preferably, the target scheduling model includes: a risk aversion model; the preference factors include:

[0012] a risk aversion factor for characterizing the degree of risk aversion of the system in the face of power uncertainty; the uncertainty coefficient comprising: a first uncertainty coefficient for characterizing the degree of influence of power uncertainty on system risk; the target power offset comprising: a maximum power offset defined by the risk aversion factor;

[0013] The construction of the risk aversion model includes:

[0014] Taking the objective function value as the reference value, and based on the initial scheduling model, the risk aversion factor and the first uncertainty coefficient, a risk aversion model is constructed with the goal of maximizing the risk impact of power uncertainty on the system and ensuring that the power offset does not exceed the maximum power offset.

[0015] Preferably, the target scheduling model includes: a risk preference model; the preference factors include:

[0016] a risk preference factor for characterizing the system's risk preference for balancing risk and benefit when faced with power uncertainty; the uncertainty coefficient comprising: a second uncertainty coefficient for characterizing the degree of impact of power uncertainty on system benefits; the target power offset comprising: a minimum power offset defined by the risk preference factor;

[0017] The construction of the risk preference model includes:

[0018] Taking the objective function value as a reference value, and based on the initial scheduling model, the risk preference factor and the second uncertainty coefficient, a risk preference model is constructed with the goal of minimizing the impact of power uncertainty on system benefits and ensuring that the power offset is less than the minimum power offset.

[0019] Preferably, the target scheduling variable includes: a first scheduling variable, or a second scheduling variable;

[0020] Solving the target scheduling model to generate a corresponding target scheduling variable when the negative impact of power uncertainty on the system is minimized and the power offset of the system is less than the target power offset includes:

[0021] When the preference factor is determined to be a risk aversion factor, the risk aversion model is solved under the wind farm output constraint, the photovoltaic power station output constraint, the energy storage power station charge and discharge power constraint, and the energy storage power station state of charge constraint, and a first scheduling variable is generated when the power uncertainty has the greatest impact on the system risk and the power offset does not exceed the maximum power offset; wherein the first scheduling variable includes: the charging power of the first energy storage power station and the discharging power of the first energy storage power station;

[0022] When the preference factor is determined to be a risk preference factor, the risk preference model is solved under the wind farm output constraint, the photovoltaic power station output constraint, the energy storage power station charging and discharging power constraint, and the energy storage power station state of charge constraint, and a second scheduling variable is generated when the impact of power uncertainty on system benefits is minimized and the power offset is less than the minimum power offset; wherein the second scheduling variable includes: the charging power of the second energy storage power station and the discharging power of the second energy storage power station.

[0023] Preferably, the risk aversion model includes:

[0024] maxα r ;

[0025] maxF1≤(1+β r )F0;

[0026]

[0027] Among them, β r is the risk aversion factor, F0 is the objective function value corresponding to the initial scheduling model when the average absolute deviation of the total system output power in all scenarios is minimized, and P L,t,s is the sum of the output power of the wind-solar-storage integrated system in the sth scenario during the t-th period, P L,av,s is the average power within the scheduling period T in the sth scenario, is the probability of clustering the sth scene, Ω s is the cluster center set, α r is the first uncertainty coefficient;

[0028] P equip,t is the preset total demand power corresponding to the sum of the user's electricity power and the distributed photovoltaic power generation power in period t, The predicted total power demand between user electricity consumption and distributed photovoltaic power generation; is the power demand model, used to characterize the predicted total power demand The power demand model is used to solve the uncertainty coefficient α in the first r The corresponding predicted total power demand value is:

[0029] P Load,t is the electricity consumption of the user during period t, P dispv,t is the distributed photovoltaic power generation power in period t, P wt,t,s is the output of the wind farm in the sth scenario during the t period, P pv,t,s is the output of the photovoltaic power station in the t period under the sth scenario, P essdis,t,s is the charging power of the energy storage station in the sth scenario during the t period, P essch,t,s is the discharge power of the energy storage power station in the sth scenario during the t period;

[0030] The risk preference model includes:

[0031] minα c ;

[0032] minF2≤(1-β c )F0;

[0033]

[0034] Among them, β c is the risk preference factor, α c is the second uncertainty coefficient.

[0035] Preferably, the process of generating power data in different scenarios includes:

[0036] Obtain historical data on user power consumption, distributed photovoltaic power generation, wind farm output forecast deviation, and photovoltaic power station output forecast deviation under different scenarios;

[0037] Fitting each historical data of output forecast deviations of the wind farm and each historical data of output forecast deviations of the photovoltaic power station to generate a first probability density function for characterizing the degree of output deviation of the wind farm and a second probability density function for characterizing the degree of output deviation of the photovoltaic power station;

[0038] Constructing a joint distribution probability model for characterizing the correlation between the forecast deviations of the wind farm and the photovoltaic power station based on the first probability density function, the second probability density function, and the correlation coefficient; wherein the correlation coefficient is used to characterize the correlation between the forecast output deviations of the wind farm and the photovoltaic power station;

[0039] Solving the joint distribution probability model based on the first probability density function and the second probability density function to generate wind farm output forecast deviation data corresponding to different scenarios and photovoltaic power station output forecast deviation data corresponding to different scenarios;

[0040] Based on the wind farm output forecast deviation data, photovoltaic power station output forecast deviation data, user power consumption and distributed photovoltaic power generation power under different scenarios, power data corresponding to different scenarios are generated.

[0041] Preferably, generating wind farm output forecast deviation data corresponding to different scenarios and photovoltaic power station output forecast deviation data corresponding to different scenarios includes:

[0042] Randomly generate samples to be selected corresponding to the joint distribution probability model;

[0043] Sort the samples to be selected in ascending order to generate a corresponding column vector; wherein the first and last ends of the column vector are the preset endpoint values ​​corresponding to the first probability density function and the second probability density function respectively;

[0044] Dividing the column vector into a plurality of intervals, and constructing a spline polynomial corresponding to each interval based on a spline interpolation method;

[0045] Substituting each candidate sample into the corresponding spline polynomial to generate the predicted output deviation value of the wind farm and the predicted output deviation value of the photovoltaic power station;

[0046] The predicted output deviation values ​​of the wind farm and the predicted output deviation values ​​of the photovoltaic power station are clustered according to different scenarios to generate wind farm output prediction deviation data corresponding to different scenarios and photovoltaic power station output prediction deviation data corresponding to different scenarios.

[0047] Based on the above method embodiments, the present invention provides corresponding device embodiments.

[0048] An embodiment of the present invention provides an optimization scheduling device for a wind-solar-storage integrated system, comprising: an initial scheduling model construction module, a first solution module, a target scheduling model construction module, a second solution module, and an output control module;

[0049] The initial scheduling model construction module is used to construct an initial scheduling model with the goal of minimizing the average absolute deviation of the system's total output power in all scenarios based on the power data of the wind, solar and storage integrated system in different scenarios and the average power values ​​in different scenarios; wherein the constraints of the initial scheduling model include: wind farm output constraints, photovoltaic power station output constraints, energy storage station charge and discharge power constraints, and energy storage station state of charge constraints;

[0050] The first solving module is used to solve the initial scheduling model and output the current objective function value corresponding to the initial scheduling model when the average absolute deviation of the total system output power in all scenarios is minimized;

[0051] The target scheduling model construction module is used to construct a target scheduling model with the objective function value as a reference value and, based on the initial scheduling model, the preference factor and the uncertainty coefficient, with the goal of minimizing the negative impact of power uncertainty on the system and ensuring that the system's power offset is less than the target power offset; wherein the preference factor is used to characterize the system's risk tolerance when facing power uncertainty; the uncertainty coefficient is used to characterize the degree of impact of power uncertainty on the system; and the target power offset is the power offset defined by the preference factor;

[0052] The second solving module is configured to solve the target scheduling model under the constraints of wind farm output, photovoltaic power station output, energy storage power station charge and discharge power, and energy storage power station state of charge, and generate corresponding target scheduling variables when the negative impact of power uncertainty on the system is minimized and the system power offset is less than the target power offset; wherein the target scheduling variables include: target energy storage power station charging power and target energy storage power station discharging power;

[0053] The output control module is used to control the output of the energy storage power station in the wind-solar-storage integrated system according to the target scheduling variable.

[0054] Based on the above method embodiments, the present invention provides corresponding terminal device embodiments.

[0055] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the optimization scheduling method of the wind, solar and storage integrated system described in the above-mentioned embodiment of the invention.

[0056] Based on the above method embodiment, the present invention provides a corresponding storage medium embodiment.

[0057] Another embodiment of the present invention provides a storage medium, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the optimization scheduling method of the wind, solar and storage integrated system described in the above-mentioned embodiment of the invention.

[0058] The following beneficial effects are achieved by implementing the present invention:

[0059] The embodiment of the present invention provides an optimization scheduling method, device, terminal equipment and storage medium for a wind, solar and storage integrated system. The present invention first constructs an initial scheduling model based on the total power data and power average value in different scenarios, with the goal of minimizing the average absolute deviation of the system's total output power in all scenarios, and corresponding to key constraints such as wind farm output, photovoltaic power station output and energy storage power station charging and discharging power. After a preliminary solution to the initial scheduling model, the objective function value corresponding to the minimum average absolute deviation of the system's total output power in all scenarios is obtained; further, in order to introduce the consideration of power uncertainty and the negative impact of power uncertainty on the system in the solution process of each output in the initial scheduling model, the present invention further takes the objective function value corresponding to the optimal solution of the initial scheduling model as a benchmark value, and constructs an initial scheduling model based on the influence of power uncertainty on the system according to the initial scheduling model, preference factors and uncertainty coefficients. The target scheduling model takes the negative impact of power uncertainty on the system as the minimum and the power offset of the system as the target power offset. Schematically, since a preference factor for characterizing the risk tolerance of the system in the face of power uncertainty and an uncertainty coefficient for characterizing the impact of power uncertainty on the system are introduced when constructing the target scheduling model, the final scheduling variables or scheduling strategies can be automatically adjusted within a certain power uncertainty range when solving the target scheduling model; and the uncertainty coefficient quantifies the specific impact of power uncertainty on the system, so that when solving the target scheduling model, while minimizing the negative impact of power uncertainty on the system, risk control can be maximized while minimizing the negative impact on benefits, ensuring that the final scheduling variables, in which the offsets of each power are always kept within the power offset limited by the preference factor, and that the power uncertainty meets the range of the system preference requirements. Compared with the existing technology, the present invention can further optimize the initial scheduling model based on the introduced preference factors and uncertainty coefficients, so that based on the formed new target scheduling model, the target scheduling variables finally generated can effectively reduce power fluctuations and reduce the instability risk of the system caused by excessive power deviation. The final scheduling scheme can effectively cope with the fluctuations of voltage and frequency in the system, so that when the units of the energy storage power station are scheduled based on the target scheduling variables, the output of the energy storage power station can be reasonably scheduled to ensure the stability and security of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is a flow chart of an optimization scheduling method for a wind-solar-storage integrated system provided by one embodiment of the present invention.

[0061] Figure 2 It is a structural flow chart of a method for operating and scheduling a wind-solar-storage integrated system provided by another embodiment of the present invention.

[0062] Figure 3 It is a structural diagram of an optimization scheduling device for a wind-solar-storage integrated system provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0063] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0064] like Figure 1 FIG. 1 is a flow chart of an optimization scheduling method for a wind-solar-storage integrated system provided by one embodiment of the present invention. The optimization scheduling method for the wind-solar-storage integrated system includes:

[0065] Step S1: Based on the power data of the wind-solar-storage integrated system in different scenarios and the average power values ​​in different scenarios, an initial scheduling model is constructed with the goal of minimizing the average absolute deviation of the system's total output power in all scenarios; wherein the constraints of the initial scheduling model include: wind farm output constraints, photovoltaic power station output constraints, energy storage station charge and discharge power constraints, and energy storage station state of charge constraints;

[0066] Step S2: Solve the initial scheduling model and output the current objective function value of the initial scheduling model when the average absolute deviation of the total system output power in all scenarios is minimized;

[0067] Step S3: Taking the objective function value as a reference value, and based on the initial scheduling model, the preference factor, and the uncertainty coefficient, constructing a target scheduling model with the goal of minimizing the negative impact of power uncertainty on the system and ensuring that the system's power offset is less than the target power offset; wherein the preference factor is used to characterize the system's risk tolerance when facing power uncertainty; the uncertainty coefficient is used to characterize the degree of impact of power uncertainty on the system; and the target power offset is the power offset defined by the preference factor.

[0068] Step S4: Solving the target scheduling model under the constraints of wind farm output, photovoltaic power station output, energy storage power station charge and discharge power, and energy storage power station state of charge. When the negative impact of power uncertainty on the system is minimized and the system power offset is less than the target power offset, generating corresponding target scheduling variables; wherein the target scheduling variables include: target energy storage power station charging power and target energy storage power station discharging power;

[0069] Step S5: Control the output of the energy storage power station in the wind-solar-storage integrated system according to the target scheduling variable.

[0070] Regarding step S1, in a preferred embodiment, the present invention can obtain power data and average power values ​​for the wind, solar, and storage integrated system in different scenarios, thereby constructing an initial scheduling model based on the acquired data. The primary purpose is to leverage the charging and discharging characteristics of energy storage to reduce the volatility of the total power delivered by the wind, solar, and storage integrated system to the larger power grid, thereby mitigating the impact of the randomness and volatility of wind and photovoltaic power generation on the grid. Therefore, the initial scheduling model established in this embodiment of the present invention optimizes the minimum volatility of the total power delivered by the wind, solar, and storage integrated system, i.e., the minimum average absolute deviation of the system's total power delivered across all scenarios.

[0071] In a preferred embodiment, the power data in different scenarios include: wind farm output forecast deviation data in different scenarios, photovoltaic power station output forecast deviation data in different scenarios, and user power consumption and distributed photovoltaic power generation power in different scenarios.

[0072] First, obtain the historical data of user power consumption, distributed photovoltaic power generation, wind farm output forecast deviation, and photovoltaic power station output forecast deviation under different scenarios;

[0073] Fitting each historical data of output forecast deviations of the wind farm and each historical data of output forecast deviations of the photovoltaic power station to generate a first probability density function for characterizing the degree of output deviation of the wind farm and a second probability density function for characterizing the degree of output deviation of the photovoltaic power station;

[0074] Constructing a joint distribution probability model for characterizing the correlation between the forecast deviations of the wind farm and the photovoltaic power station based on the first probability density function, the second probability density function, and the correlation coefficient; wherein the correlation coefficient is used to characterize the correlation between the forecast output deviations of the wind farm and the photovoltaic power station;

[0075] Solving the joint distribution probability model based on the first probability density function and the second probability density function to generate wind farm output forecast deviation data corresponding to different scenarios and photovoltaic power station output forecast deviation data corresponding to different scenarios;

[0076] Finally, based on the wind farm output forecast deviation data, photovoltaic power station output forecast deviation data, user power consumption and distributed photovoltaic power generation power under the above different scenarios, power data corresponding to different scenarios can be generated for building models.

[0077] In a preferred embodiment, when generating wind farm output forecast deviation data corresponding to different scenarios and photovoltaic power station output forecast deviation data corresponding to different scenarios, the following steps are specifically included:

[0078] Randomly generate samples to be selected corresponding to the joint distribution probability model;

[0079] Sort the samples to be selected in ascending order to generate a corresponding column vector; wherein the first and last ends of the column vector are the preset endpoint values ​​corresponding to the first probability density function and the second probability density function respectively;

[0080] Dividing the column vector into a plurality of intervals, and constructing a spline polynomial corresponding to each interval based on a spline interpolation method;

[0081] Substituting each candidate sample into the corresponding spline polynomial to generate the predicted output deviation value of the wind farm and the predicted output deviation value of the photovoltaic power station;

[0082] The predicted output deviation values ​​of the wind farm and the predicted output deviation values ​​of the photovoltaic power station are clustered according to different scenarios to generate wind farm output prediction deviation data corresponding to different scenarios and photovoltaic power station output prediction deviation data corresponding to different scenarios.

[0083] Specifically, the present invention constructs a corresponding joint distribution probability model based on the forecast deviations of wind farm and photovoltaic power station outputs. This model then generates output forecast deviation data for wind farms and photovoltaic power stations under different scenarios. This allows for more accurate capture of the randomness and volatility of wind and photovoltaic outputs. By accounting for the correlation between wind and photovoltaic outputs, the overall forecast accuracy is improved.

[0084] It is understandable that the embodiments of the present invention can ensure that the entire possible range of the joint distribution probability model is covered by randomly generating samples to be selected, thereby more comprehensively simulating the output deviations of wind farms and photovoltaic power stations under different scenarios, so that the generated deviation data can better reflect the diversity of actual conditions. By randomly generating and sorting samples to be selected, the continuous probability distribution can be discretized into a series of ordered sample points, which not only simplifies the calculation process but also reduces the computational complexity. At the same time, by using the spline interpolation method to construct a polynomial within the interval, the predicted value of any given point can be efficiently calculated, thereby improving computational efficiency.

[0085] Furthermore, constructing the spline polynomial for each interval based on the spline interpolation method can flexibly adapt to the data change characteristics of different intervals, that is, it can provide smooth and continuous prediction values, avoiding abrupt jumps between data points, thereby improving the accuracy and reliability of the prediction.

[0086] In a preferred embodiment, Figure 2The model construction process and solution process shown in the figure first generate a set of predicted output deviation value scenarios, use the K-means clustering algorithm to cluster the scenarios, and then construct a wind-solar-storage integrated system operation optimization model (i.e., the above-mentioned initial scheduling model) that considers multiple scenarios of wind-solar-storage new energy output correlation based on the scenario clustering data. After the initial scheduling model is preliminarily solved, the optimal function value of the preliminarily solved is used as the benchmark value of the IGDT model (i.e., the target scheduling model in the above-mentioned embodiment), thereby realizing the construction of the risk aversion model and the risk preference model, and based on different risk aversion factors and risk preference factors, obtaining the scheduling strategies under the two models under different uncertainty coefficients, such as: the optimal solution value of the objective function under different risk preference factors and different deviation factors; the uncertainty degree coefficient under different risk preference factors and different deviation factors; the decision variables under different risk preference factors and different deviation factors: the power generation power of the wind farm and the power generation power of the photovoltaic power station in the s-th scenario and the time period t; the discharge power and charging power of the energy storage power station in the s-th scenario and the time period t.

[0087] Specifically, when modeling the uncertainty of wind and solar power output, each predicted output deviation value of the wind farm and each predicted output deviation value of the photovoltaic power station can be clustered by scenario, and the wind farm output prediction deviation data corresponding to different scenarios and the photovoltaic power station output prediction deviation data corresponding to different scenarios can be generated, specifically:

[0088] Considering that the output of wind and solar energy is mainly related to meteorological conditions and there is a certain correlation between regional wind speed and sunlight, when first modeling the correlation between the deviation characteristics of wind and solar energy forecast output and actual output, the historical data of the forecast output deviation values ​​of wind farms and photovoltaic power stations on day D can be selected, and the probability density function of the forecast output deviation of wind farms and photovoltaic power stations in each period within 24 hours can be fitted based on the non-parametric kernel density estimation method. Among them, the kernel function selects the Gaussian kernel function, and the expression is as follows:

[0089]

[0090] Where: h is the bandwidth, x wt,t and x pv,t are the predicted output deviation values ​​of the wind farm and photovoltaic power station in time period t, X wt,d,t and X pv,d,t are the historical data values ​​of the predicted output deviation of the wind farm and photovoltaic power station at time t on day d, G(·) is the Gaussian kernel function. Taking the predicted output deviation of the wind farm as an example, the expression is as follows:

[0091]

[0092] Then, the first probability density function and the second probability density function are obtained by integration, which are and

[0093] The Frank-Copula function is selected, and the joint distribution function of the predicted output deviation of wind farms and photovoltaic power stations in each period is constructed based on the first probability density function, the second probability density function and the correlation coefficient:

[0094]

[0095] Where: u t and v t are the cumulative distribution functions of the predicted output deviations of wind farms and photovoltaic power stations at each time period t, θ t is the cumulative distribution function of the predicted output deviation of wind farms and photovoltaic power stations at each time period t )and The correlation coefficient in the Frank-Copula function can be obtained by the maximum likelihood estimation method.

[0096] Based on the constructed Frank-Copula function, for each period t, generate n random number samples that conform to the joint distribution of the established Frank-Copula function

[0097] will u t and v t The random number samples are organized into column vectors and sorted in ascending order. The interval endpoints 0 and 1 of the cumulative probability function are added to the beginning and end of the column vectors. The expression is as follows:

[0098]

[0099] Where: and are the nth random number samples after sorting.

[0100] From the above formula, we can see that and Each contains n+2 random numbers; then n+1 small intervals can be formed, and the endpoints of each interval are and The sample, that is The samples represented by each interval endpoint are the values ​​of the cumulative distribution function of the predicted output deviation of the wind farm and photovoltaic power station, which can be expressed by the cumulative distribution function and Obtain the predicted output deviation value x of the wind farm and photovoltaic power station wt,t and x pv,t .

[0101] Furthermore, for each interval, the cubic spline interpolation method is used to construct the cubic spline polynomial on the interval:

[0102]

[0103] Where: a i 、b i 、c i d i are the coefficients of the cubic spline polynomial, a i ′、b i ′、c i ′、d i ' Similarly, the equation system can be constructed and solved by cubic spline interpolation method. and They are respectively the endpoints of the n+1 cells constructed above.

[0104] After obtaining the coefficients of the cubic spline polynomials on the above intervals, the u extracted by the joint distribution of the established Frank-Copula function is t and v t Substituting the random number samples into the polynomial equation, the predicted output deviation value x of the wind farm and photovoltaic power station in each period can be obtained by reverse deduction. wt,t and x pv,t Scene collection.

[0105] According to the predicted output deviation value x of wind farm and photovoltaic power station wt,t and x pv,t The K-means clustering algorithm is used to cluster the scenarios. After clustering, multiple cluster centers and the number of scenarios belonging to each cluster center category are obtained. Based on this number of scenarios, the proportion of each category is calculated, which is used as the probability of the cluster center's occurrence. This allows for the reduction of wind farm and photovoltaic power station output deviations across multiple scenarios. The reduction results are represented by cluster centers and their probabilities, allowing for the generation of wind farm and photovoltaic power station output deviation data for different scenarios.

[0106] Schematically, the optimization objective of the initial scheduling model can be:

[0107]

[0108] Among them, F0 is the function value of the initial scheduling model, P L,t,s is the sum of the output power of the wind-solar-storage integrated system in the sth scenario during the t-th period, P L,av,s is the average power within the scheduling period T in the sth scenario, is the probability of clustering the sth scene, Ω s is the set of cluster center points.

[0109] The constraints of the initial dispatch model include: wind farm output constraints, photovoltaic power station output constraints, energy storage station charge and discharge power constraints, and energy storage station charge state constraints;

[0110] Then, the wind farm output constraints include:

[0111]

[0112] Among them, P wt,t,s is the output of the wind farm in the sth scenario during period t, is the preset output of the wind farm in the t period under the sth scenario, x wt,t,s is the wind farm output forecast deviation value in period t under the sth scenario;

[0113] The photovoltaic power station output constraints include:

[0114]

[0115] Among them, P pv,t,s is the output of the photovoltaic power station in the sth scenario during the t period, is the preset output of the photovoltaic power station corresponding to the t period under the sth scenario, x pv,t,s The predicted deviation value of the photovoltaic power station in the t period under the sth scenario;

[0116] The energy storage station charging and discharging power constraints include:

[0117]

[0118] Among them, μ essdis,t,s and μ essch,t,s are the Boolean variables of the discharge state and the charging state of the energy storage power station in the t period under the sth scenario; P essdis,min is the minimum power value of the energy storage station discharge, P essdis,max is the maximum power value of the energy storage station discharge, P essch,min The minimum power value for charging the energy storage station, P essch,max The maximum power value for charging the energy storage station;

[0119] The state of charge constraints of the energy storage power station include:

[0120]

[0121] Among them, E ess,t,s is the preset power of the energy storage station in period t under the sth scenario, η ess is the charge and discharge efficiency coefficient; SOC max and SOC minare the upper and lower limits of the state of charge of the energy storage power station; N battery The preset total capacity of the energy storage power station; and They are the reference values ​​of the state of charge of the energy storage power station at different times.

[0122] The initial scheduling model can be solved based on the above constraints to ensure that the energy storage equipment operates under safe and stable conditions and prevent equipment damage or safety accidents caused by overcharging, over-discharging or abnormal charge state.

[0123] The above constraints provide clear boundary conditions for system scheduling, allowing the scheduling strategy to be flexibly adjusted while meeting these conditions. This helps optimize the use of renewable energy, balance the supply and demand of the power grid, and improve the overall operational efficiency of the system.

[0124] For step S2, the present invention can solve the initial scheduling model based on the wind farm output constraint, the photovoltaic power station output constraint, the energy storage power station charging and discharging power constraint, and the energy storage power station charge state constraint, and output the current objective function value corresponding to the initial scheduling model when the average absolute deviation corresponding to the total system output power in all scenarios is minimized; schematically, the objective function value is the function value corresponding to the optimal solution of the initial scheduling model, and then the optimal solution of the initial scheduling model at this time is the initial energy storage power station charging power and the initial energy storage power station discharging power corresponding to the initial scheduling model under the objective function value.

[0125] It can be understood that after obtaining the preliminary objective function value F0 and the current optimal solution (such as the initial charging power of the energy storage power station and the initial discharging power of the energy storage power station), in order to further consider the correlation between the output of the wind farm and the photovoltaic power station, the uncertainty of the load and distributed photovoltaics, the current optimal solution can be further optimized and adjusted, that is, the present invention can use the objective function value F0 corresponding to the optimal solution of the initial scheduling model as a benchmark value, and further construct a target scheduling model, that is, a risk aversion model and a risk preference model, so as to obtain a control strategy that effectively reduces the volatility of the power output of the wind, solar and storage integrated system.

[0126] For step S3, in a preferred embodiment, in order to further enable the system scheduling to take power uncertainty into consideration and optimize, the embodiment of the present invention can continue to use the initial scheduling model as the basic function after obtaining the optimal function value F0 of the initial scheduling model, and then combine the preference factor and the uncertainty coefficient to construct a target scheduling model with the goal of minimizing the negative impact of power uncertainty on the system and the system power offset being less than the target power offset.

[0127] Since there is uncertainty in the output of wind farms and photovoltaic power stations, and there is uncertainty in user load power demand, this uncertainty will directly affect the system's output power and scheduling strategy. Therefore, system scheduling needs to pay attention not only to the average value or expected value of power, but also to consider the volatility and uncertainty of power.

[0128] To achieve a trade-off between risk and benefit, the target scheduling model should focus on reducing the negative impact of power uncertainty on the system and ensuring the stability and safety of system operation. The target scheduling model can automatically adjust the final scheduling variables or scheduling strategies within a certain power uncertainty range. This minimizes the negative impact of power uncertainty on the system, maximizes risk control, and minimizes the negative impact on benefits. This ensures that the final scheduling variables, including each power offset, always remain within the power offset defined by the preference factor, and that power uncertainty meets the system's preference requirements.

[0129] To meet the risk preferences of different decision makers or cope with different market environments, when constructing the target scheduling model, the preference factor can be set to a risk aversion factor that represents the system's risk aversion when facing power uncertainty. This can limit the power offset and ensure that the system can maintain stable operation in the face of power uncertainty. Alternatively, the preference factor can be set to a risk preference factor that represents the system's risk preference for balancing risk and benefit when facing power uncertainty. This allows the target scheduling model to focus on the trade-off between risk and benefit, and to obtain greater system benefits by accepting a certain amount of power uncertainty.

[0130] Therefore, the present invention can set preference factors with different definitions so that when constructing a target scheduling model, a risk avoidance model can be constructed with the goal of maximizing the risk impact of power uncertainty on the system and the power offset not exceeding the maximum power offset; and a risk preference model can be constructed with the goal of minimizing the impact of power uncertainty on system benefits and the power offset being less than the minimum power offset.

[0131] It is understandable that by building a risk aversion model, it is possible to limit power offsets and ensure that the system can maintain stable operation in the face of power uncertainty, thereby reducing system failures and downtime, and improving system reliability and availability. By building a risk preference model, it is possible to accept a certain amount of power uncertainty, allowing the system to adopt a more proactive scheduling strategy in certain situations, thereby fully utilizing renewable energy and improving the system's power generation efficiency and economic benefits.

[0132] Illustratively, in a preferred embodiment, the target scheduling model may be a risk aversion model. The preference factor in the target scheduling model is a risk aversion factor used to characterize the system's risk aversion when faced with power uncertainty. The uncertainty coefficient is a first uncertainty coefficient used to characterize the impact of power uncertainty on system risk. The target power offset is the maximum power offset defined by the risk aversion factor.

[0133] Then, the construction of the target scheduling model includes the construction of a risk aversion model, specifically:

[0134] Taking the objective function value as the reference value, and based on the initial scheduling model, the risk aversion factor and the first uncertainty coefficient, a risk aversion model is constructed with the goal of maximizing the risk impact of power uncertainty on the system and ensuring that the power offset does not exceed the maximum power offset.

[0135] Illustratively, in another preferred embodiment, the target scheduling model may be a risk preference model; the preference factor in the target scheduling model is a risk preference factor used to characterize the degree of risk preference of the system in balancing risk and benefit when facing power uncertainty; the uncertainty coefficient is a second uncertainty coefficient used to characterize the degree of impact of power uncertainty on system benefit; the target power offset is the minimum power offset defined by the risk preference factor;

[0136] The construction of the target scheduling model includes the construction of the risk preference model, and the specific process is as follows:

[0137] Taking the objective function value as a reference value, and based on the initial scheduling model, the risk preference factor and the second uncertainty coefficient, a risk preference model is constructed with the goal of minimizing the impact of power uncertainty on system benefits and ensuring that the power offset is less than the minimum power offset.

[0138] Specifically, in a risk aversion model, the risk aversion factor can be defined as the maximum power deviation corresponding to a range of power uncertainty. This factor reflects the tolerance for power uncertainty and the risk aversion attitude. By adjusting the risk aversion factor, the model can find a balance between safety and cost-effectiveness.

[0139] The first uncertainty coefficient defines the impact of power uncertainty on system risk. The uncertainty coefficient quantifies the impact of power uncertainty on system risk, taking into account factors such as the magnitude and distribution of power uncertainty and the system's sensitivity to uncertainty. By introducing this coefficient, the model can more accurately assess the impact of power uncertainty on system risk.

[0140] Therefore, the risk aversion model can maximize the risk impact of power uncertainty on the system (quantified by the first uncertainty coefficient), while ensuring that the power offset does not exceed the maximum power offset (quantified by the risk aversion factor). Its constraints are the constraints of the original initial scheduling model, such as wind farm output constraints, photovoltaic power station output constraints, energy storage station charge and discharge power constraints, and state of charge constraints, as well as additional constraints defined by the risk aversion factor and the first uncertainty coefficient.

[0141] Schematically, the risk aversion model includes:

[0142] maxα r ;

[0143] maxF1≤(1+β r )F0;

[0144]

[0145] Among them, β r is the risk aversion factor, F0 is the objective function value corresponding to the initial scheduling model when the average absolute deviation of the total system output power in all scenarios is minimized, and P L,t,s is the sum of the output power of the wind-solar-storage integrated system in the sth scenario during the t-th period, P L,av,s is the average power within the scheduling period T in the sth scenario, is the probability of clustering the sth scene, Ω s is the cluster center set, α r is the first uncertainty coefficient;

[0146] P equip,t is the preset total demand power corresponding to the sum of the user's electricity power and the distributed photovoltaic power generation power in period t, The predicted total power demand between user electricity consumption and distributed photovoltaic power generation; is the power demand model, used to characterize the predicted total power demand The power demand model is used to solve the uncertainty coefficient α in the first r The corresponding predicted total power demand value is:

[0147] P Load,t is the electricity consumption of the user during period t, P dispv,t is the distributed photovoltaic power generation power in period t, P wt,t,s is the output of the wind farm in the sth scenario during the t period, P pv,t,s is the output of the photovoltaic power station in the t period under the sth scenario, P essdis,t,s is the charging power of the energy storage station in the sth scenario during the t period, Pessch,t,s is the discharge power of the energy storage power station in the sth scenario during the t period.

[0148] It is understandable that the present invention can describe the randomness of distributed photovoltaic output and the randomness of user load, that is, the fractional uncertainty model is used to describe the total power demand P equip,t uncertainty.

[0149] By solving the risk aversion model, we can obtain a set of optimal scheduling variables (i.e., target scheduling variables) that both meet the system's scheduling requirements and account for the impact of power uncertainty on system risk. These optimal scheduling variables can serve as a reference for actual scheduling strategies, thereby improving system safety and economic efficiency.

[0150] Furthermore, the risk preference model includes:

[0151] minα c ;

[0152] minF2≤(1-β c )F0;

[0153]

[0154]

[0155] Among them, β c is the risk preference factor, α c is the second uncertainty coefficient.

[0156] Schematically, the core objective of the risk preference model is to minimize the impact of power uncertainty on system revenue while ensuring minimal power offset. This means that during the solution process, the risk preference model comprehensively considers the balance between system revenue and power fluctuations to generate an optimal set of scheduling variables. These optimal scheduling variables maximize the system's economic benefits while meeting system stability and reliability requirements.

[0157] It can be understood that the risk aversion model represents the total power demand P equip,t Within the uncertainty range, any decision-making scheme can make the objective function F2 no higher than the maximum acceptable deviation value, and solve P equip,t The maximum uncertainty range is obtained and the optimal control strategy is solved.

[0158] The risk preference model represents the total power demand P equip,t Within the uncertainty range, there is at least one decision solution that can make the objective function F3 reach a smaller offset value. Solve P equip,t Minimize the uncertainty range and solve the optimal control strategy.

[0159] Regarding step S4, in a preferred embodiment, the embodiment of the present invention can solve the target scheduling model under the constraints of wind farm output, photovoltaic power station output, energy storage power station charge and discharge power, and energy storage power station state of charge, so as to generate corresponding target scheduling variables when the negative impact of power uncertainty on the system is minimized and the system power offset is less than the target power offset; wherein the target scheduling variables corresponding to the optimal solution include target energy storage power station charging power and target energy storage power station discharging power that can be used to control the energy storage power station. In principle, it can also include target wind farm output and target photovoltaic power station output that can be used to control system equipment.

[0160] In this embodiment of the present invention, the initially solved function value can be used as a benchmark value, thereby providing a clear reference standard for solving the target scheduling model. This can also ensure consistency during the solution process, ensuring that the generated scheduling solution fluctuates within an acceptable range compared to the initial solution.

[0161] Furthermore, by using the initially solved function value as a baseline, the target scheduling model ensures that the final scheduling plan is implemented with minimal impact of power uncertainty on the system risk, thus preventing system crashes or failures caused by power fluctuations. Furthermore, a stable scheduling plan improves system reliability and security, ensuring the continuity and stability of power supply.

[0162] In a preferred embodiment, the target scheduling variable includes: a first scheduling variable, or a second scheduling variable;

[0163] Solving the target scheduling model to generate a corresponding target scheduling variable when the negative impact of power uncertainty on the system is minimized and the power offset of the system is less than the target power offset includes:

[0164] When the preference factor is determined to be a risk aversion factor, the risk aversion model is solved under the wind farm output constraint, the photovoltaic power station output constraint, the energy storage power station charge and discharge power constraint, and the energy storage power station state of charge constraint, and a first scheduling variable is generated when the power uncertainty has the greatest impact on the system risk and the power offset does not exceed the maximum power offset; wherein the first scheduling variable includes: the charging power of the first energy storage power station and the discharging power of the first energy storage power station;

[0165] When the preference factor is determined to be a risk preference factor, the risk preference model is solved under the wind farm output constraint, the photovoltaic power station output constraint, the energy storage power station charging and discharging power constraint, and the energy storage power station state of charge constraint, and a second scheduling variable is generated when the impact of power uncertainty on system benefits is minimized and the power offset is less than the minimum power offset; wherein the second scheduling variable includes: the charging power of the second energy storage power station and the discharging power of the second energy storage power station.

[0166] It's clear that both the risk aversion and risk preference models consider the system's physical constraints and power uncertainty, and both achieve the optimal solution for maximum benefit through optimization algorithms. This allows for more flexible scheduling solutions in practice, adapting to varying operating environments and changing demands.

[0167] Specifically, the present invention can construct a risk aversion model and a risk preference model based on the IGDT method. Specifically, the optimal solution value in the initial scheduling model can be used as the IGDT model reference value, represented by F0.

[0168] Since the risk aversion model and the risk preference model are two-layer optimization problems of max-max and min-min respectively, the embodiment of the present invention can define the outer optimization problem as the main problem and the inner optimization problem as the sub-problem.

[0169] For the risk aversion model, based on a set risk aversion factor β r , the solution process is:

[0170] 1) Set α r The initial value of 0 is used to optimize the subproblem, α r = 0, the optimal solution of the subproblem is the IGDT model reference value F0; 2) Let Δα r =0.1, Δα r is α r The change amount of each process setting is set to ε = 0.0001, which is the end of the solution; 3) Let α r =α r +Δα r , solve the sub-problem; 4) If the sub-problem has a solution, go back to step 3); if the sub-problem has no solution, first let Δα r =0.1Δα r , judge Δα r Is it equal to ε? If so, jump directly to step 5), otherwise return to step 3); 5) Output α r The optimal solutions to the sub-problems are the target wind farm output, target photovoltaic power station output, target energy storage station charging power, and target energy storage station discharging power in the target scheduling variables.

[0171] Furthermore, for the risk preference model, a risk preference factor β is set c , the solution process is:

[0172] 1) Set α c The initial value of is 1, and the subproblem is optimized and solved; 2) Let Δα c =0.1, Δα c is α c The change amount of each process setting is set to ε = 0.0001, which is the end of the solution; 3) Let α c =α c -Δα c , solve the sub-problem; 4) If the sub-problem has a solution, go back to step 3); if the sub-problem has no solution, first let Δα c =0.1Δα c , judge Δα c Is it equal to ε? If so, jump directly to step 5), otherwise return to step 3); 5) Output α c The optimal solution to the sub-problem is the wind farm output, photovoltaic power station output, energy storage station charging power, and energy storage station discharging power in the updated dispatch variables.

[0173] In the embodiment of the present invention, the results of the wind-solar-storage integrated system operation scheduling control strategy under different risk preference factors or different risk aversion factors can be obtained. The control strategy results may include:

[0174] 1) The optimal solution of the function under different risk preference factors or different deviation factors; 2) The uncertainty coefficient α under different risk preference factors and different deviation factors r With α c ; 3) Decision variables under different risk preference factors or different deviation factors, that is, the optimal dispatch variable value, such as: the output P of the wind farm in the t period under the sth scenario wt,t,s , the output P of the photovoltaic power station in the t period under the sth scenario pv,t,s , the charging power P of the energy storage station in the sth scenario during the t period essdis,t,s , the discharge power P of the energy storage station in the sth scenario during the t period essch,t,s .

[0175] In a preferred embodiment, all uncertain scenarios can be integrated to calculate the total power generation of the wind farm and photovoltaic power station in time period t by weighted sum of multiple scenarios, as shown in the following formula:

[0176]

[0177] The total discharge power and total charging power of the energy storage station in time period t can also be calculated by the weighted sum of multiple scenarios, as shown in the following formula:

[0178]

[0179] This embodiment of the present invention can output control strategy solutions for different risk preference factors and different deviation factors. By constructing a risk aversion model and a risk preference model, the present invention provides operational decision-makers with two decision-making solutions based on different risk preferences, enabling them to select the most appropriate scheduling strategy based on different operating environments and demand changes. Furthermore, the model also considers the uncertainty of total power demand, enabling the system to better cope with various uncertain scenarios and improve adaptability.

[0180] For step S5, in a preferred embodiment, the present invention can control the output of the energy storage power station in the wind-solar-storage integrated system based on the solved target scheduling variable, thereby ensuring that the system's output power matches the total power demand, thereby maintaining stable operation of the system and reducing system fluctuations and failures caused by power mismatch.

[0181] Moreover, according to different operating environments and demand changes, different risk preference factors and control schemes under different deviation factors can be selected to meet various complex operating requirements.

[0182] Therefore, the present invention takes into account the correlation between the output of wind farms and photovoltaic power stations, and constructs an operation scheduling optimization model (i.e., an initial scheduling model) of a wind-solar-storage integrated system that takes into account the correlation between wind and solar output based on the Frank-Copula method. The optimal solution of the initial scheduling model is used as the benchmark value of the IGDT method, thereby constructing a risk aversion model and a risk preference model that take into account the correlation between wind and solar output and are based on the IGDT method. This allows the optimized scheduling strategy finally solved to comprehensively consider the correlation between the output of wind farms and photovoltaic power stations, the uncertainty of load and distributed photovoltaics, and obtain a control strategy that effectively reduces the volatility of the power output of the wind-solar-storage integrated system.

[0183] like Figure 3 As shown, based on the above-mentioned embodiments of the optimization scheduling method of the various wind-solar-storage integrated systems, the present invention provides corresponding device embodiments;

[0184] An embodiment of the present invention provides an optimization scheduling device for a wind-solar-storage integrated system, comprising: an initial scheduling model construction module, a first solution module, a target scheduling model construction module, a second solution module, and an output control module;

[0185] The initial scheduling model construction module is used to construct an initial scheduling model with the goal of minimizing the average absolute deviation of the system's total output power in all scenarios based on the power data of the wind, solar and storage integrated system in different scenarios and the average power values ​​in different scenarios; wherein the constraints of the initial scheduling model include: wind farm output constraints, photovoltaic power station output constraints, energy storage station charge and discharge power constraints, and energy storage station state of charge constraints;

[0186] The first solving module is used to solve the initial scheduling model and output the current objective function value corresponding to the initial scheduling model when the average absolute deviation of the total system output power in all scenarios is minimized;

[0187] The target scheduling model construction module is used to construct a target scheduling model with the objective function value as a reference value and, based on the initial scheduling model, the preference factor and the uncertainty coefficient, with the goal of minimizing the negative impact of power uncertainty on the system and ensuring that the system's power offset is less than the target power offset; wherein the preference factor is used to characterize the system's risk tolerance when facing power uncertainty; the uncertainty coefficient is used to characterize the degree of impact of power uncertainty on the system; and the target power offset is the power offset defined by the preference factor;

[0188] The second solving module is configured to solve the target scheduling model under the constraints of wind farm output, photovoltaic power station output, energy storage power station charge and discharge power, and energy storage power station state of charge, and generate corresponding target scheduling variables when the negative impact of power uncertainty on the system is minimized and the system power offset is less than the target power offset; wherein the target scheduling variables include: target energy storage power station charging power and target energy storage power station discharging power;

[0189] The output control module is used to control the output of the energy storage power station in the wind-solar-storage integrated system according to the target scheduling variable.

[0190] It should be noted that the device embodiments described above are merely illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, and may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement the present invention without paying any creative effort.

[0191] Those skilled in the art can clearly understand that, for the sake of convenience and brevity, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0192] Based on the above-mentioned embodiments of the optimization scheduling method of the various wind-solar-storage integrated systems, the present invention provides corresponding embodiments of terminal equipment items.

[0193] An embodiment of the present invention provides a terminal device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements an optimization scheduling method for a wind, solar and storage integrated system as described in any method embodiment of the present invention.

[0194] The terminal device may be a computing terminal device such as a desktop computer, a notebook computer, a palmtop computer, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0195] Based on the above-mentioned embodiments of the optimization scheduling method of the various wind-solar-storage integrated systems, the present invention provides corresponding embodiments of storage media items.

[0196] An embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute an optimization scheduling method for a wind, solar and storage integrated system described in any method embodiment of the present invention.

[0197] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. An optimization scheduling method for a wind-solar-storage integrated system, characterized in that: include: Based on the power data of the wind, solar and storage integrated system in different scenarios and the average power values ​​in different scenarios, an initial scheduling model is constructed with the goal of minimizing the average absolute deviation of the system's total output power in all scenarios. The constraints of the initial scheduling model include: wind farm output constraints, photovoltaic power station output constraints, energy storage station charge and discharge power constraints, and energy storage station state of charge constraints. Solving the initial scheduling model, and outputting the current objective function value corresponding to the initial scheduling model when the average absolute deviation of the total system output power in all scenarios is minimized; Taking the objective function value as a reference value, and based on the initial scheduling model, the preference factor, and the uncertainty coefficient, a target scheduling model is constructed with the goal of minimizing the negative impact of power uncertainty on the system and ensuring that the system's power offset is less than the target power offset; wherein the preference factor is used to characterize the system's risk tolerance when facing power uncertainty; the uncertainty coefficient is used to characterize the degree of impact of power uncertainty on the system; and the target power offset is the power offset defined by the preference factor. Solving the target scheduling model under wind farm output constraints, photovoltaic power station output constraints, energy storage power station charge and discharge power constraints, and energy storage power station state of charge constraints, and generating corresponding target scheduling variables when the negative impact of power uncertainty on the system is minimized and the system power offset is less than the target power offset; wherein the target scheduling variables include: target energy storage power station charging power and target energy storage power station discharging power; Controlling the output of the energy storage power station in the wind-solar-storage integrated system according to the target scheduling variable; The target scheduling model includes: a risk aversion model and a risk preference model; the preference factor includes: a risk aversion factor for characterizing the risk aversion degree of the system when facing power uncertainty, and a risk preference factor for characterizing the risk preference degree of the system in balancing risk and benefit when facing power uncertainty; the uncertainty coefficient includes: a first uncertainty coefficient for characterizing the impact of power uncertainty on system risk, and a second uncertainty coefficient for characterizing the impact of power uncertainty on system benefit; the target power offset includes: a maximum power offset defined by the risk aversion factor, and a minimum power offset defined by the risk preference factor; The construction of the risk aversion model includes: Taking the objective function value as a reference value, and based on the initial scheduling model, the risk aversion factor, and the first uncertainty coefficient, constructing a risk aversion model with the goal of maximizing the risk impact of power uncertainty on the system and ensuring that the power offset does not exceed the maximum power offset; The construction of the risk preference model includes: Taking the objective function value as a reference value, and based on the initial scheduling model, the risk preference factor and the second uncertainty coefficient, a risk preference model is constructed with the goal of minimizing the impact of power uncertainty on system benefits and ensuring that the power offset is less than the minimum power offset.

2. The optimization scheduling method of the wind-solar-storage integrated system according to claim 1, characterized in that: The target scheduling variable includes: a first scheduling variable or a second scheduling variable; Solving the target scheduling model to generate a corresponding target scheduling variable when the negative impact of power uncertainty on the system is minimized and the power offset of the system is less than the target power offset includes: When the preference factor is determined to be a risk aversion factor, the risk aversion model is solved under the wind farm output constraint, the photovoltaic power station output constraint, the energy storage power station charge and discharge power constraint, and the energy storage power station state of charge constraint, and a first scheduling variable is generated when the power uncertainty has the greatest impact on the system risk and the power offset does not exceed the maximum power offset; wherein the first scheduling variable includes: the charging power of the first energy storage power station and the discharging power of the first energy storage power station; When the preference factor is determined to be a risk preference factor, the risk preference model is solved under the wind farm output constraint, the photovoltaic power station output constraint, the energy storage power station charging and discharging power constraint, and the energy storage power station state of charge constraint, and a second scheduling variable is generated when the impact of power uncertainty on system benefits is minimized and the power offset is less than the minimum power offset; wherein the second scheduling variable includes: the charging power of the second energy storage power station and the discharging power of the second energy storage power station.

3. The optimization scheduling method of the wind-solar-storage integrated system according to claim 2, characterized in that: The risk aversion model includes: ； ； ； ； ； ； in, is the risk aversion factor, is the objective function value corresponding to the initial scheduling model when the average absolute deviation of the total system output power in all scenarios is minimized, is the objective function value corresponding to the risk aversion model, T is the scheduling period, For the wind, solar and storage integrated system In this scenario The sum of the output power of the time period, For the Scheduling cycle in each scenario The average power within is the probability of clustering the sth scene, is the set of cluster centers, is the first uncertainty coefficient; for The preset total demand power corresponding to the sum of the user's electricity power and the distributed photovoltaic power generation power in the time period, The predicted total power demand between user electricity consumption and distributed photovoltaic power generation; is the power demand model, used to characterize the predicted total power demand The power demand model is used to solve the uncertainty coefficient of the first degree of uncertainty The corresponding predicted total power demand value is: for The power consumption of users during the time period, for Distributed photovoltaic power generation power in each period, For wind farms In this scenario Output during the period, For photovoltaic power station In this scenario Output during the period, For energy storage power station In this scenario Charging power during the time period, For energy storage power station In this scenario Discharge power during the time period; The risk preference model includes: ； ； ； ； ； ； in, is the risk preference factor, is the second uncertainty coefficient, is the objective function value corresponding to the risk preference model.

4. The optimization scheduling method for a wind-solar-storage integrated system according to claim 1, characterized in that: The process of generating power data in different scenarios includes: Obtain historical data on user power consumption, distributed photovoltaic power generation, wind farm output forecast deviation, and photovoltaic power station output forecast deviation under different scenarios; Fitting each historical data of output forecast deviations of the wind farm and each historical data of output forecast deviations of the photovoltaic power station to generate a first probability density function for characterizing the degree of output deviation of the wind farm and a second probability density function for characterizing the degree of output deviation of the photovoltaic power station; Constructing a joint distribution probability model for characterizing the correlation between the forecast deviations of the wind farm and the photovoltaic power station based on the first probability density function, the second probability density function, and the correlation coefficient; wherein the correlation coefficient is used to characterize the correlation between the forecast output deviations of the wind farm and the photovoltaic power station; Solving the joint distribution probability model based on the first probability density function and the second probability density function to generate wind farm output forecast deviation data corresponding to different scenarios and photovoltaic power station output forecast deviation data corresponding to different scenarios; Based on the wind farm output forecast deviation data, photovoltaic power station output forecast deviation data, user power consumption and distributed photovoltaic power generation power under different scenarios, power data corresponding to different scenarios are generated.

5. The optimization scheduling method of the wind-solar-storage integrated system according to claim 4, characterized in that: The generating of wind farm output prediction deviation data corresponding to different scenarios and photovoltaic power station output prediction deviation data corresponding to different scenarios includes: Randomly generate samples to be selected corresponding to the joint distribution probability model; Sort the samples to be selected in ascending order to generate a corresponding column vector; wherein the first and last ends of the column vector are the preset endpoint values ​​corresponding to the first probability density function and the second probability density function respectively; Dividing the column vector into a plurality of intervals, and constructing a spline polynomial corresponding to each interval based on a spline interpolation method; Substituting each candidate sample into the corresponding spline polynomial to generate the predicted output deviation value of the wind farm and the predicted output deviation value of the photovoltaic power station; The predicted output deviation values ​​of the wind farm and the predicted output deviation values ​​of the photovoltaic power station are clustered according to different scenarios to generate wind farm output prediction deviation data corresponding to different scenarios and photovoltaic power station output prediction deviation data corresponding to different scenarios.

6. An optimization and scheduling device for a wind, solar and storage integrated system, characterized in that: include: An initial scheduling model construction module, a first solving module, a target scheduling model construction module, a second solving module, and an output control module; The initial scheduling model construction module is used to construct an initial scheduling model with the goal of minimizing the average absolute deviation of the system's total output power in all scenarios based on the power data of the wind, solar and storage integrated system in different scenarios and the average power values ​​in different scenarios; wherein the constraints of the initial scheduling model include: wind farm output constraints, photovoltaic power station output constraints, energy storage station charge and discharge power constraints, and energy storage station state of charge constraints; The first solving module is used to solve the initial scheduling model and output the current objective function value corresponding to the initial scheduling model when the average absolute deviation of the total system output power in all scenarios is minimized; The target scheduling model construction module is used to construct a target scheduling model with the objective function value as a reference value and, based on the initial scheduling model, the preference factor and the uncertainty coefficient, with the goal of minimizing the negative impact of power uncertainty on the system and ensuring that the system's power offset is less than the target power offset; wherein the preference factor is used to characterize the system's risk tolerance when facing power uncertainty; the uncertainty coefficient is used to characterize the degree of impact of power uncertainty on the system; and the target power offset is the power offset defined by the preference factor; The target scheduling model includes: a risk aversion model and a risk preference model; the preference factor includes: a risk aversion factor for characterizing the risk aversion degree of the system when facing power uncertainty, and a risk preference factor for characterizing the risk preference degree of the system in balancing risk and benefit when facing power uncertainty; the uncertainty coefficient includes: a first uncertainty coefficient for characterizing the impact of power uncertainty on system risk, and a second uncertainty coefficient for characterizing the impact of power uncertainty on system benefit; the target power offset includes: a maximum power offset defined by the risk aversion factor, and a minimum power offset defined by the risk preference factor; The construction of the risk aversion model includes: Taking the objective function value as a reference value, and based on the initial scheduling model, the risk aversion factor, and the first uncertainty coefficient, constructing a risk aversion model with the goal of maximizing the risk impact of power uncertainty on the system and ensuring that the power offset does not exceed the maximum power offset; The construction of the risk preference model includes: Taking the objective function value as a reference value, and based on the initial scheduling model, the risk preference factor, and the second uncertainty coefficient, constructing a risk preference model with the goal of minimizing the impact of power uncertainty on system benefits and ensuring that the power offset is less than the minimum power offset; The second solving module is configured to solve the target scheduling model under the constraints of wind farm output, photovoltaic power station output, energy storage power station charge and discharge power, and energy storage power station state of charge, and generate corresponding target scheduling variables when the negative impact of power uncertainty on the system is minimized and the system power offset is less than the target power offset; wherein the target scheduling variables include: target energy storage power station charging power and target energy storage power station discharging power; The output control module is used to control the output of the energy storage power station in the wind-solar-storage integrated system according to the target scheduling variable.

7. A terminal device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements the optimization scheduling method of the wind-solar-storage integrated system as described in any one of claims 1 to 5.

8. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein, when the computer program is running, the device where the storage medium is located is controlled to execute the optimization scheduling method for the wind-solar-storage integrated system according to any one of claims 1 to 5.

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