Vehicle network integrated renewable energy planning method considering scene uncertainty

Abstract

The invention discloses a vehicle-network integrated renewable energy planning method considering scene uncertainty, and belongs to the technical field of renewable energy micro-grid planning, and the method comprises the steps: S1, building an uncertainty model based on the operation characteristics of an electric vehicle and the output intermittence of renewable energy; s2, presetting input parameters based on historical operation data of the electric vehicle and historical power generation data of renewable energy sources, generating an uncertain scene by using an uncertain model based on the input parameters, and processing the uncertain scene based on an Euclidean distance elimination method to obtain a target scene; and S3, on the basis of the target scene, constructing a target function by taking maximization of the carrying capacity of the micro-grid and minimization of the energy loss of the micro-grid as targets, and planning the vehicle-network integrated renewable energy source on the basis of the target function. The problem that in the prior art, it is difficult to have high comprehensiveness while low complexity is ensured is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of renewable energy microgrid planning, and in particular to a vehicle-grid integrated renewable energy planning method taking scenario uncertainty into consideration. Background Art

[0002] With the popularity of electric vehicles and the development of microgrids, incorporating electric vehicles into microgrids and optimizing their scheduling has become a hot topic. However, the uncertainty of electric vehicles will bring new challenges to the optimization and scheduling of cluster electric vehicle grid-connected microgrids. The existing technology mainly plans renewable energy, such as a hybrid distribution network planning method containing renewable energy generation with patent number CN110867892B. Figure 2 As shown, it includes an AC / DC hybrid distribution network, which includes a DC part and an AC part. Both the AC part and the DC part can be connected to renewable energy power generation devices and user loads. The DC part and the AC part are connected through power electronic conversion equipment, and the AC part is also connected to the external power grid. Although the above scheme takes into account the randomness of the output power of renewable energy power generation and the need for the distribution network to participate in external power market transactions, it does not take the uncertainty of electric vehicles into consideration. In addition, the above scheme combines and processes all uncertain parameters, so it is highly complex and difficult to plan renewable energy and electric vehicles while ensuring the maximization of the microgrid's carrying capacity and minimization of the microgrid's energy loss. Summary of the invention

[0003] In view of the problem that the existing technology is difficult to have high comprehensiveness while ensuring low complexity, the present invention provides a vehicle-grid integrated renewable energy planning method that considers scenario uncertainty, establishes an uncertainty model by deeply analyzing the operating characteristics of electric vehicles and the intermittent output of renewable energy, considers the impact of uncertain factors on planning, and then accurately identifies the most representative target scenario through the quantitative feature difference characteristics of the Euclidean distance elimination method, and ensures low complexity while initially ensuring comprehensiveness. Based on the most representative target scenario accurately identified, an objective function is constructed with the goal of maximizing the microgrid carrying capacity and minimizing the microgrid energy loss. While ensuring low complexity, the efficiency and stability of the microgrid are further considered, thereby further ensuring comprehensiveness. The present invention overcomes the problem that the existing technology is difficult to have high comprehensiveness while ensuring low complexity.

[0004] In order to solve the above technical problems, the present invention provides a vehicle-grid integrated renewable energy planning method considering scenario uncertainty, comprising the following steps: S1: Establish an uncertain model based on the operating characteristics of electric vehicles and the intermittent output of renewable energy; S2: Preset input parameters based on the historical operation data of electric vehicles and the historical power generation data of renewable energy, generate uncertain scenarios based on the input parameters using uncertain models, and process the uncertain scenarios to obtain target scenarios based on the Euclidean distance elimination method; S3: Based on the target scenario, an objective function is constructed with the goal of maximizing the microgrid carrying capacity and minimizing the microgrid energy loss, and the vehicle-grid integrated renewable energy is planned based on the objective function.

[0005] After adopting the above technical solution, the present invention has the following advantages: By deeply analyzing the operating characteristics of electric vehicles and the intermittent output of renewable energy, an uncertain model is established, the impact of uncertain factors on planning is considered, and based on the most representative target scenarios accurately identified, the objective function is constructed with the goal of maximizing the microgrid carrying capacity and minimizing the microgrid energy loss. The efficiency and stability of the microgrid are further considered, so it has a high degree of comprehensiveness. Through the quantitative feature difference characteristics of the Euclidean distance elimination method, the feature differences between different scenes are quantified. Based on the quantified feature differences, redundant scenes with high similarity are eliminated, thereby retaining the most representative target scene, and constructing the objective function based on the accurately identified target scene, thereby significantly reducing the complexity; An uncertain model is established by deeply analyzing the operating characteristics of electric vehicles and the intermittent output of renewable energy, taking into account the impact of uncertain factors on planning, and then through the quantitative feature difference characteristics of the Euclidean distance elimination method, the feature differences between different scenarios are quantified, and the most representative target scenarios are accurately identified, which ensures low complexity while initially ensuring comprehensiveness, and then based on the most representative target scenarios accurately identified, an objective function is constructed with the goal of maximizing the carrying capacity of the microgrid and minimizing the energy loss of the microgrid. While ensuring low complexity, the efficiency and stability of the microgrid are further considered, thereby further ensuring comprehensiveness. The present invention overcomes the problem that the prior art is difficult to have high comprehensiveness while ensuring low complexity.

[0006] Preferably, the S1 comprises: S11: Based on the state of charge in the operating characteristics, a distribution function of the electric vehicle is constructed using binary parameters existing in the electric vehicle; S12: Based on the characteristic parameters characterizing the intermittent nature of the output, a distribution function of a first renewable resource in the renewable resources and a distribution function of a second renewable resource in the renewable resources are respectively constructed.

[0007] The distribution function constructed by binary parameters can more accurately reflect the changing laws and trends of the state of charge of electric vehicles, which helps to more accurately obtain the charging demand and discharging behavior of electric vehicles. The distribution function constructed according to the characteristic parameters can more accurately obtain the output status and changing trends of renewable energy, which helps to more accurately analyze the uncertainty and volatility of renewable energy output, thereby facilitating the subsequent generation of uncertain scenarios based on the charging demand and discharging behavior of electric vehicles and the uncertainty and volatility of renewable energy output, taking into account the influence of various uncertain factors, preliminarily ensuring the comprehensiveness of the plan, and at the same time ensuring the reliability of uncertain scenarios.

[0008] Preferably, in S11, the distribution function of the electric vehicle is f is the distribution function of electric vehicles, X represents the binary parameters existing in electric vehicles, N is the mean value of the state of charge, M 2 is the standard deviation of the state of charge, EV is the minimum value of the state of charge, is the maximum value of the state of charge, EV i is the initial state of charge of electric vehicle i.

[0009] Preferably, in S12, the distribution function of the first renewable resource is C is the distribution function of the first renewable resource, v t is the wind speed at time t, is the size parameter at time t, σ t is the shape parameter at time t.

[0010] Preferably, in S12, the distribution function of the second renewable resource is D is the distribution function of the second renewable resource, φ is the first shape coefficient, θ is the second shape coefficient, Q is the normalized value of solar irradiance, and B is the beta function.

[0011] Preferably, S2 includes: S21: Based on the input parameters, use the uncertain model to randomly assign values ​​to each uncertain variable in the uncertain model; S22: Combining each of the randomly assigned uncertain variables to generate uncertain scenarios, and determining whether the number of uncertain scenarios meets a preset condition. If so, executing S23; if not, executing S21; S23: Based on the feature vectors of the uncertain scenarios, calculate the Euclidean distance between the uncertain scenarios, preset the initial probability of the uncertain scenarios based on the historical operation data and the historical power generation data, and obtain the target scenario based on the Euclidean distance between the uncertain scenarios and the initial probability of the uncertain scenarios.

[0012] Taking into account the uncertainty of the status of electric vehicles and renewable resources, each randomly assigned uncertain variable is combined to generate an uncertain scenario. By judging whether the number of uncertain scenarios meets the preset conditions, it is ensured that a sufficient number of uncertain scenarios are generated to cover various uncertain situations that may be encountered. The Euclidean distance can measure the similarities and differences between scenarios, and the initial probability can reflect the possibility of each scenario occurring in actual operation. Obtaining the target scenario through the Euclidean distance and the initial probability can make the obtained target scenario more representative, which helps to improve the reliability of the target scenario, while reducing the computational complexity and optimizing resource utilization.

[0013] Preferably, in S23, the expression for calculating the Euclidean distance between uncertain scenes based on the feature vectors of the uncertain scenes is: Where n is the number of features in the feature vector of the uncertain scene, X[j] is the jth feature in the feature vector of the xth uncertain scene, and Y[j] is the jth feature in the feature vector of the yth uncertain scene.

[0014] Preferably, in S23, acquiring the target scene based on the Euclidean distance between the uncertain scenes includes: The uncertain scenes whose Euclidean distance is less than or equal to the preset distance are eliminated, and the initial probability of the uncertain scenes whose Euclidean distance to the eliminated uncertain scenes is greater than and closest to the preset distance is added to the initial probability of the eliminated uncertain scenes, and then the initial probability of the uncertain scenes whose Euclidean distance to the eliminated uncertain scenes is greater than and closest to the preset distance is updated, and the uncertain scene whose initial probability is greater than the preset probability is the target scene.

[0015] Taking into account that there are uncertain scenes with high similarity in the uncertain scenes, the uncertain scenes with high similarity are processed by calculating the Euclidean distance, thereby reducing the complexity and amount of subsequent calculations. Since the initial probability represents the possibility of each scene occurring in actual operation, the initial probability of the uncertain scenes retained in the uncertain scenes with high similarity is updated to ensure that while eliminating redundant scenes, the probability values ​​of the retained uncertain scenes can more accurately reflect their probability of occurrence in actual operation, and then determine the target scene by judging whether the initial probability is greater than the preset probability, thereby improving the reliability of the determined target scene.

[0016] Preferably, S3 includes: S31: Set constraints on electric vehicles and renewable energy based on the feature vector of the target scenario, and construct an objective function based on the constraints to maximize the microgrid carrying capacity and minimize the microgrid energy loss; S32: On the premise of satisfying the constraints, randomly generate the value of the independent variable in the objective function, obtain the value of the objective function based on the value of the independent variable, and judge whether the value of the objective function is greater than the preset maximum value. If it is greater, plan the vehicle-grid integrated renewable energy based on the value of the independent variable and the constraints. If it is less, execute S32. In order to ensure the feasibility and effectiveness of the subsequent planned scheme, the constraints of electric vehicles and renewable energy are set based on the characteristic vector of the target scenario. In order to find the optimal planning scheme, the value of the independent variable in the objective function is randomly generated on the premise of satisfying the constraints. The feasibility of the planning scheme is improved, and the objective function is constructed with the goal of maximizing the microgrid carrying capacity and minimizing the microgrid energy loss, which helps to improve the energy utilization efficiency and economic benefits of the microgrid.

[0017] Preferably, the objective function is Where F is the objective function value, NB WT is the wind power node set, is the wind power rated capacity of the i-th wind power node, NB PV is the photovoltaic node set, is the PV rated capacity of the jth PV node, C is the target scenario, π c is the initial probability of the cth target scenario, T is the energy loss of the microgrid within the preset time range, NL is the branch set, is the square of the current of branch ij in the branch set at time t under the cth target scenario, R ij is the equivalent resistance of branch ij.

[0018] Beneficial effects of this program: By deeply analyzing the operating characteristics of electric vehicles and the intermittent output of renewable energy, an uncertain model is established, the impact of uncertain factors on planning is considered, and based on the most representative target scenarios accurately identified, the objective function is constructed with the goal of maximizing the microgrid carrying capacity and minimizing the microgrid energy loss. The efficiency and stability of the microgrid are further considered, so it has a high degree of comprehensiveness. Through the quantitative feature difference characteristics of the Euclidean distance elimination method, the feature differences between different scenes are quantified. According to the quantified feature differences, redundant scenes with high similarity are eliminated, thereby retaining the most representative target scenes, improving the reliability of the acquired target scenes, and constructing the objective function based on the accurately identified target scenes, thereby significantly reducing the complexity; An uncertain model is established by deeply analyzing the operating characteristics of electric vehicles and the intermittent output of renewable energy, taking into account the impact of uncertain factors on planning, and then through the quantitative feature difference characteristics of the Euclidean distance elimination method, the feature differences between different scenarios are quantified, and the most representative target scenarios are accurately identified, which ensures low complexity while initially ensuring comprehensiveness, and then based on the most representative target scenarios accurately identified, an objective function is constructed with the goal of maximizing the carrying capacity of the microgrid and minimizing the energy loss of the microgrid. While ensuring low complexity, the efficiency and stability of the microgrid are further considered, thereby further ensuring comprehensiveness. The present invention overcomes the problem that the prior art is difficult to have high comprehensiveness while ensuring low complexity. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Other features, objects and advantages of the present invention will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings. The drawings are only for the purpose of illustrating preferred embodiments and are not to be considered as limiting the present invention. Also, the same reference symbols are used throughout the drawings to represent the same parts.

[0020] Figure 1 A flow chart of a vehicle-grid integrated renewable energy planning method considering scenario uncertainty according to the present invention; Figure 2 A structural diagram of a hybrid distribution network for renewable energy generation in a comparative document in the background technology; Figure 3 This is a diagram of the scheduling situation of the planning scheme in an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific implementation method described herein is only an optimal embodiment of the present invention, which is only used to explain the present invention and does not limit the scope of protection of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0022] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the operations (or steps) as sequential processes, many of the operations (or steps) therein can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the drawings; the process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0023] Embodiment 1: like Figure 1 As shown in FIG, a vehicle-grid integrated renewable energy planning method considering scenario uncertainty includes the following steps: S1: Establish an uncertain model based on the operating characteristics of electric vehicles and the intermittent output of renewable energy.

[0024] The S1 includes: S11: Based on the state of charge in the operating characteristics, a distribution function of the electric vehicle is constructed using binary parameters existing in the electric vehicle; S12: Based on the characteristic parameters characterizing the intermittent nature of the output, a distribution function of a first renewable resource in the renewable resources and a distribution function of a second renewable resource in the renewable resources are respectively constructed.

[0025] In S11, the distribution function of the electric vehicle is: f is the distribution function of electric vehicles, X represents the binary parameters existing in electric vehicles, N is the mean value of the state of charge, M 2 is the standard deviation of the state of charge, EV is the minimum value of the state of charge, is the maximum value of the state of charge, EV i is the initial state of charge of electric vehicle i.

[0026] In S12, the distribution function of the first renewable resource is C is the distribution function of the first renewable resource, v t is the wind speed at time t, is the size parameter at time t, σ t is the shape parameter at time t.

[0027] In S12, the distribution function of the second renewable resource is D is the distribution function of the second renewable resource, φ is the first shape coefficient, θ is the second shape coefficient, Q is the normalized value of solar irradiance, and B is the beta function.

[0028] In this embodiment, the first renewable resource is wind energy and the second renewable resource is solar energy. The size parameter at time t in the distribution function of the first renewable resource is is the variance of wind speed at time t, GF is Euler's second integral, and the shape parameter at time t is the mean wind speed at time t. The second shape coefficient in the distribution function of the second renewable resource ω Q is the variance of the normalized value of solar radiation intensity at time t, k Qis the mean of the normalized solar radiation intensity at time t, the first shape coefficient By deeply analyzing the operating characteristics of electric vehicles and the intermittent output of renewable energy, an uncertain model is established, which fully considers the impact of uncertain factors on planning, preliminarily ensures the comprehensiveness of the planning, and guarantees the reliability of the uncertain scenarios generated subsequently.

[0029] As an implementation method, an uncertain model can also be established by collecting historical operation data of electric vehicles and historical power generation data of renewable energy. Specifically, typical days are extracted by collecting historical operation data of electric vehicles and historical power generation data of renewable energy, and fuzzy sets are constructed through the data of typical days. The fuzzy sets need to include all possible distributions of uncertain parameters such as wind speed and radiation intensity. The fuzzy sets are the uncertain models. Through historical data, the actual operation of electric vehicles and the actual power generation of renewable energy can be more comprehensively reflected, thereby improving the accuracy and reliability of the uncertain model.

[0030] S2: Preset input parameters based on the historical operation data of electric vehicles and the historical power generation data of renewable energy. Based on the input parameters, use the uncertain model to generate uncertain scenarios, and process the uncertain scenarios based on the Euclidean distance elimination method to obtain the target scenarios.

[0031] The S2 includes: S21: Based on the input parameters, use the uncertain model to randomly assign values ​​to each uncertain variable in the uncertain model; S22: Combining each of the randomly assigned uncertain variables to generate uncertain scenarios, and determining whether the number of uncertain scenarios meets a preset condition. If so, executing S23; if not, executing S21; S23: Based on the feature vectors of the uncertain scenarios, calculate the Euclidean distance between the uncertain scenarios, preset the initial probability of the uncertain scenarios based on the historical operation data and the historical power generation data, and obtain the target scenario based on the Euclidean distance between the uncertain scenarios and the initial probability of the uncertain scenarios.

[0032] In S23, the expression for calculating the Euclidean distance between uncertain scenes based on the feature vector of the uncertain scene is: Where n is the number of features in the feature vector of the uncertain scene, X[j] is the jth feature in the feature vector of the xth uncertain scene, and Y[j] is the jth feature in the feature vector of the yth uncertain scene.

[0033] In S23, acquiring the target scene based on the Euclidean distance between the uncertain scenes includes: The uncertain scenes whose Euclidean distance is less than or equal to the preset distance are eliminated, and the initial probability of the uncertain scenes whose Euclidean distance to the eliminated uncertain scenes is greater than and closest to the preset distance is added to the initial probability of the eliminated uncertain scenes, and then the initial probability of the uncertain scenes whose Euclidean distance to the eliminated uncertain scenes is greater than and closest to the preset distance is updated, and the uncertain scene whose initial probability is greater than the preset probability is the target scene.

[0034] In this embodiment, the preset input parameters based on the historical operation data of electric vehicles and the historical power generation data of renewable energy are specifically: the historical operation data of electric vehicles and the historical power generation data of renewable energy are processed to obtain the determined independent variable values ​​in the uncertain scenario model, i.e., the input parameters, and then the uncertain variables in the uncertain scenario model are randomly assigned according to the determined independent variable values. The features in the feature vector of the uncertain scenario include wind speed, radiation intensity, power generation output power, electric vehicle charging and discharging data, etc. The uncertain scenarios with higher similarity are processed by calculating the Euclidean distance, thereby reducing the complexity and amount of subsequent calculations.

[0035] The initial probability of the uncertain scene preset based on the historical operation data and the historical power generation data is specifically as follows: Assuming that there are two uncertain scenes, representing the impact of different weather conditions on renewable energy power generation (such as wind power and photovoltaic power) and electric vehicle charging demand, scene 1 is sunny and high wind speed; scene 2 is cloudy and low wind speed. According to the data records of the past year, it is found that scene 1 appeared for 200 days and scene 2 appeared for 165 days. According to the above historical data statistics, the initial probability of the uncertain scene is preset in the following manner: the initial probability of scene 1 = the number of days when scene 1 appears / total number of days, the initial probability of scene 2 = the number of days when scene 2 appears / total number of days. The initial probability represents the possibility of each scene appearing in actual operation. By updating the initial probability of the uncertain scenes retained in the uncertain scenes with higher similarity, it is ensured that while eliminating redundant scenes, the probability value of the retained uncertain scenes can more accurately reflect the probability of their occurrence in actual operation, and then determine the target scene by judging whether the initial probability is greater than the preset probability, thereby improving the reliability of the determined target scene.

[0036] As an implementation method, the average Euclidean distance between each scene and all other scenes may also be calculated, and the scene with the smallest average distance and a higher initial probability may be selected as the target scene.

[0037] S3: Based on the target scenario, an objective function is constructed with the goal of maximizing the microgrid carrying capacity and minimizing the microgrid energy loss, and the vehicle-grid integrated renewable energy is planned based on the objective function. The S3 includes: S31: Set constraints on electric vehicles and renewable energy based on the feature vector of the target scenario, and construct an objective function based on the constraints to maximize the microgrid carrying capacity and minimize the microgrid energy loss; S32: On the premise of satisfying the constraints, randomly generate the value of the independent variable in the objective function, obtain the value of the objective function based on the value of the independent variable, and judge whether the value of the objective function is greater than the preset maximum value. If it is greater, plan the vehicle-grid integrated renewable energy based on the value of the independent variable and the constraints. If it is less, execute S32.

[0038] The objective function is Where F is the objective function value, NB WT is the wind power node set, is the wind power rated capacity of the i-th wind power node, NB PV is the photovoltaic node set, is the PV rated capacity of the jth PV node, C is the target scenario, π c is the initial probability of the cth target scenario, T is the energy loss of the microgrid within the preset time range, NL is the branch set, is the square of the current of branch ij in the branch set at time t under the cth target scenario, R ij is the equivalent resistance of branch ij.

[0039] The constraints include node voltage and branch current constraints: In the formula, V, is the minimum and maximum node voltage, V j，t，c is the voltage of node j in the cth target scenario at time t, I ij，t，c is the current of branch ij in the cth target scenario at time t, is the maximum value of branch current; power exchange constraint with the public grid: In the formula, φ j It is a binary variable. When its value is 1, it means that the node is connected to the upper power grid. is the power of node j in the cth target scenario at time t, is the maximum power of the power grid; wind power and photovoltaic power should provide power between the lower and upper limits according to their rated capacity: In the formula, is the transmission power of the jth PV node at time t in scenario c, is the solar power available per unit area of ​​the jth photovoltaic node at time t under scenario c, is the transmission power of the jth wind power node at time t in scenario c, is the wind energy provided by the j-th wind power node to each unit at time t in scenario c, is the wind power rated capacity of the jth wind power node, is the photovoltaic rated capacity of the jth photovoltaic node. The following formula ensures that the rated capacity of renewable energy is not negative: The ratio of the energy utilized to the maximum available energy from renewable sources must be greater than or equal to γ: Where T is the energy loss of the microgrid within the time range t; the charging and discharging power constraint of the electric vehicle is: In the formula, is the minimum charging and discharging power of n electric vehicles at j wind power nodes, is the maximum charging and discharging power of electric vehicles at j wind power nodes, is the active power demand of n electric vehicles, It is a binary variable. When it is 1, it means that there is an electric car at the node. The energy storage constraint of the electric vehicle battery is: In the formula, Minimum and maximum limits for charging electric vehicle batteries; the energy stored in electric vehicle batteries is determined by the energy stored at the previous moment and the previous charge and discharge power: Based on the values ​​of independent variables and constraints, the vehicle-grid integrated renewable energy is planned, including the planning of the access location and capacity of photovoltaic and wind turbines, the output of photovoltaic and wind turbines, the charging and discharging of electric vehicles, and the operation of microgrids. On the premise of meeting the constraints, the values ​​of independent variables in the objective function are randomly generated. The feasibility of the planning scheme is improved. At the same time, the objective function is constructed with the goal of maximizing the microgrid carrying capacity and minimizing the microgrid energy loss, which helps to improve the energy utilization efficiency and economic benefits of the microgrid.

[0040] Based on the values ​​of independent variables and constraints, the vehicle-grid integrated renewable energy is planned, and then the planning scheme is output. The planning scheme is tested on the improved IEEE33 node distribution system. The candidate installation locations of wind power and photovoltaic systems are nodes 6, 14, and 31, the electric vehicle assembly points are nodes 18, 21, and 31, and the number of electric vehicles is 300. The electric vehicle V2G mode is used for renewable planning. The carrying capacity, energy loss, voltage offset, and peak power of the planning scheme are shown in Table 1: Table 1. Planning results index plan Microgrid carrying capacity 4107.64kW Energy loss 878.31kWh Voltage offset 79.34％ Microgrid peak power 2464.83kW The V2G mode optimizes the power of the microgrid system and reduces energy loss. The energy loss of the planned solution is 878.31kWh and the carrying capacity is 4107.64kW. The scheduling of the planned solution is as follows: Figure 3As shown in the figure, renewable energy, especially wind power generation, can meet the load demand, with an output power of 33.69MWh. The peak power of the power grid in the scheme is 2464.83kW. Due to the increase in the charging and discharging of electric vehicles and the carrying capacity of the microgrid, the peak power of the power grid decreases. The electric vehicle discharges 390kW to the grid in the 19th hour, and the power injected by renewable energy is 1073.66kW. Therefore, the intelligent management of electric vehicles can be used to limit the peak demand of the power grid. This peak regulation improves the reliability of the system and maximizes the utilization of infrastructure. After the above steps are calculated, the comprehensive optimization of the access location and capacity of renewable energy, the output of renewable energy, the charging and discharging of electric vehicles, and the operation of microgrids is achieved.

[0041] The specific implementation described above is a preferred implementation of the vehicle-grid integrated renewable energy planning method that takes into account scenario uncertainty of the present invention, and is not intended to limit the specific implementation scope of the present invention. The scope of the present invention includes but is not limited to this specific implementation. All equivalent changes made in accordance with the shape and structure of the present invention are within the protection scope of the present invention.

Claims

1. A vehicle-grid integrated renewable energy planning method considering scenario uncertainty, characterized by: The following steps are involved: S1: Establish an uncertain model based on the operating characteristics of electric vehicles and the intermittent output of renewable energy; S2: Preset input parameters based on the historical operation data of electric vehicles and the historical power generation data of renewable energy, generate uncertain scenarios based on the input parameters using uncertain models, and process the uncertain scenarios to obtain target scenarios based on the Euclidean distance elimination method; S3: Based on the target scenario, an objective function is constructed with the goal of maximizing the microgrid carrying capacity and minimizing the microgrid energy loss, and the vehicle-grid integrated renewable energy is planned based on the objective function.

2. The vehicle-grid integrated renewable energy planning method considering scenario uncertainty according to claim 1 is characterized in that: The S1 includes: S11: Based on the state of charge in the operating characteristics, a distribution function of the electric vehicle is constructed using binary parameters existing in the electric vehicle; S12: Based on the characteristic parameters characterizing the intermittent nature of the output, a distribution function of a first renewable resource in the renewable resources and a distribution function of a second renewable resource in the renewable resources are respectively constructed.

3. The vehicle-grid integrated renewable energy planning method considering scenario uncertainty according to claim 2 is characterized in that: In S11, the distribution function of the electric vehicle is: f is the distribution function of electric vehicles, X represents the binary parameters existing in electric vehicles, N is the mean value of the state of charge, M 2 is the standard deviation of the state of charge, EV is the minimum value of the state of charge, is the maximum value of the state of charge, EV i is the initial state of charge of electric vehicle i.

4. The vehicle-grid integrated renewable energy planning method considering scenario uncertainty according to claim 2 is characterized in that: In S12, the distribution function of the first renewable resource is C is the distribution function of the first renewable resource, v t is the wind speed at time t, is the size parameter at time t, σ t is the shape parameter at time t.

5. The vehicle-grid integrated renewable energy planning method considering scenario uncertainty according to claim 2 is characterized in that: In S12, the distribution function of the second renewable resource is D is the distribution function of the second renewable resource, φ is the first shape coefficient, θ is the second shape coefficient, Q is the normalized value of solar irradiance, and B is the beta function.

6. The vehicle-grid integrated renewable energy planning method considering scenario uncertainty according to claim 1 is characterized in that: The S2 includes: S21: Based on the input parameters, use the uncertain model to randomly assign values ​​to each uncertain variable in the uncertain model; S22: Combining each of the randomly assigned uncertain variables to generate uncertain scenarios, and determining whether the number of uncertain scenarios meets a preset condition. If so, executing S23; if not, executing S21; S23: Based on the feature vectors of the uncertain scenarios, calculate the Euclidean distance between the uncertain scenarios, preset the initial probability of the uncertain scenarios based on the historical operation data and the historical power generation data, and obtain the target scenario based on the Euclidean distance between the uncertain scenarios and the initial probability of the uncertain scenarios.

7. The vehicle-grid integrated renewable energy planning method considering scenario uncertainty according to claim 6 is characterized in that: In S23, the expression for calculating the Euclidean distance between uncertain scenes based on the feature vector of the uncertain scene is: Where n is the number of features in the feature vector of the uncertain scene, X[j] is the jth feature in the feature vector of the xth uncertain scene, and Y[j] is the jth feature in the feature vector of the yth uncertain scene.

8. The vehicle-grid integrated renewable energy planning method considering scenario uncertainty according to claim 6 is characterized in that: In S23, acquiring the target scene based on the Euclidean distance between the uncertain scenes includes: The uncertain scenes whose Euclidean distance is less than or equal to the preset distance are eliminated, and the initial probability of the uncertain scenes whose Euclidean distance to the eliminated uncertain scenes is greater than and closest to the preset distance is added to the initial probability of the eliminated uncertain scenes, and then the initial probability of the uncertain scenes whose Euclidean distance to the eliminated uncertain scenes is greater than and closest to the preset distance is updated, and the uncertain scene whose initial probability is greater than the preset probability is the target scene.

9. The vehicle-grid integrated renewable energy planning method considering scenario uncertainty according to claim 6, characterized in that: The S3 includes: S31: Set constraints on electric vehicles and renewable energy based on the feature vector of the target scenario, and construct an objective function based on the constraints to maximize the microgrid carrying capacity and minimize the microgrid energy loss; S32: On the premise of satisfying the constraints, randomly generate the value of the independent variable in the objective function, obtain the value of the objective function based on the value of the independent variable, and judge whether the value of the objective function is greater than the preset maximum value. If it is greater, plan the vehicle-grid integrated renewable energy based on the value of the independent variable and the constraints. If it is less, execute S32.

10. The vehicle-grid integrated renewable energy planning method considering scenario uncertainty according to claim 9, characterized in that: The objective function is Where F is the objective function value, NB WT is the wind power node set, is the wind power rated capacity of the i-th wind power node, NB PV is the photovoltaic node set, is the PV rated capacity of the jth PV node, C is the target scenario, π c is the initial probability of the cth target scenario, T is the energy loss of the microgrid within the preset time range, NL is the branch set, is the square of the current of branch ij in the branch set at time t under the cth target scenario, R ij is the equivalent resistance of branch ij.

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