Power distribution network multi-type load joint planning method, system and equipment based on micro-topographic information and medium

Abstract

The invention discloses a power distribution network multi-type load joint planning method, system and device based on micro-topographic information and a medium. The method comprises the steps that basic data of a power distribution network and the access position and power characteristic parameters of a device to be accessed are acquired; acquiring micro-topographic data for the access position of the distributed wind turbine generator in the to-be-accessed equipment, calculating a topographic correction factor based on the micro-topographic data, correcting the reference wind speed according to the topographic correction factor to obtain a calibrated wind speed, and determining actual power generation power according to the calibrated wind speed and the power characteristic parameters; constructing a multi-stage optimization model according to the basic data of the power distribution network and the actual generated power, and setting constraint conditions in the optimization model; and solving the multi-stage optimization model by adopting a mixed integer programming method. The method solves the problems that an existing planning method does not consider the influence of microtopography on distributed wind power modeling, lacks a comprehensive model for collaborative optimization of multi-class facility investment, and is difficult to realize optimal global cost and safe and reliable operation of a system under extreme working conditions.

Description

Technical Field

[0001] This invention relates to the field of power system planning technology, and in particular to a method, system, equipment and medium for joint planning of multiple types of loads in a distribution network based on micro-topography information. Background Technology

[0002] With the increasing penetration of high-demand loads such as distributed wind power and ultra-fast charging stations for electric vehicles into the power grid, the planning of distribution networks faces increasingly severe challenges. The inherent volatility and uncertainty of these new power sources and loads put enormous pressure on traditional power flow management and voltage control methods.

[0003] Existing technologies have proposed reducing investment and operating costs by coordinating the planning of distributed power sources and electric vehicle charging stations. Other studies have integrated traffic flow, load growth, and renewable energy uncertainties to develop more practical distribution network expansion strategies. However, existing planning methods often fail to adequately consider the impact of local micro-topography on accurate modeling of distributed wind power generation. They also lack a comprehensive model capable of coordinating and optimizing investments in various key facilities such as power lines, reactive power compensation equipment, and transformers to achieve global cost optimization and ensure the safe and reliable operation of the system under various extreme conditions. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a method, system, equipment, and medium for joint planning of multiple types of loads in a distribution network based on micro-topography information. This addresses the problems that existing planning methods do not fully consider the impact of micro-topography on distributed wind power modeling, lack a comprehensive model for collaboratively optimizing the investment of multiple types of facilities, and are difficult to achieve global cost optimization and safe and reliable system operation under extreme conditions.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for joint planning of multiple types of loads in a distribution network based on micro-topographic information, comprising: acquiring basic data of the distribution network and the access location and power characteristic parameters of the equipment to be connected; collecting micro-topographic data for the access location of distributed wind turbines among the equipment to be connected, calculating a terrain correction factor based on the micro-topographic data, and correcting the reference wind speed according to the terrain correction factor to obtain a calibrated wind speed, and determining the actual power generation based on the calibrated wind speed and power characteristic parameters; constructing a multi-stage optimization model based on the basic data of the distribution network and the actual power generation, and setting constraints in the optimization model; solving the multi-stage optimization model using a mixed integer programming method, and outputting the planning results.

[0007] As a preferred embodiment of the multi-type load joint planning method for distribution networks based on micro-topography information described in this invention, the steps of calculating a terrain correction factor based on the micro-topography data, correcting the reference wind speed according to the terrain correction factor to obtain a calibrated wind speed, and determining the actual power generation based on the calibrated wind speed and power characteristic parameters include: obtaining the ground slope angle, horizontal distance from the ridge, and altitude of the access location; determining the terrain slope influence coefficient based on the ground slope angle, determining the horizontal distance modulation coefficient based on the horizontal distance, and determining the vertical height influence coefficient based on the altitude; calculating the terrain correction factor based on the terrain slope influence coefficient, the horizontal distance modulation coefficient, and the vertical height influence coefficient; multiplying the terrain correction factor by the reference wind speed to obtain the calibrated wind speed; and querying the power characteristic parameters of the distributed wind turbine based on the calibrated wind speed to determine the actual power generation.

[0008] The beneficial effects of this preferred technical solution are as follows: It acquires the ground slope angle, horizontal distance from the ridge, and altitude of the access location, ensuring complete coverage of terrain-related parameters at the distributed wind turbine access location. Then, based on the ground slope angle, horizontal distance, and altitude, it determines the terrain slope influence coefficient, horizontal distance modulation coefficient, and vertical height influence coefficient, allowing for the individual quantification of the influence of terrain factors in different dimensions. Based on these three types of coefficients, a terrain correction factor is calculated. Multiplying the terrain correction factor by the reference wind speed yields the calibration wind speed, ensuring that the wind speed data matches the actual terrain conditions of the access location. Finally, based on the calibration wind speed, it queries the power characteristic parameters of the distributed wind turbine to determine the actual power generation, ensuring that the determination of the actual power generation fully considers the role of terrain factors and closely matches the actual terrain conditions of the access location.

[0009] As a preferred embodiment of the multi-type load joint planning method for distribution networks based on micro-topography information described in this invention, the step of constructing an optimization model based on the basic data of the distribution network and the actual power generation includes: determining the minimization of the net present value of the total cost within the planning period as the optimization objective, wherein the total cost includes the investment cost and operating cost of each stage; calculating the investment cost of each planning stage, wherein the investment cost includes the construction cost of distribution lines, the installation cost of static var compensators, and the expansion cost of transformers; calculating the operating cost of each planning stage, wherein the operating cost is weighted and summed according to the probability of occurrence of different operating scenarios and the network loss cost of the distribution network system; setting decision variables, wherein the decision variables include the construction decision variables of distribution lines, the configuration decision variables of static var compensators, and the expansion decision variables of transformers; setting constraints, and evaluating the constraints under extreme operating scenarios to ensure that the planning scheme still meets all constraints under extreme operating conditions, wherein the constraints include power flow constraints, equipment capacity constraints, and investment decision constraints; and integrating the optimization objective, decision variables, and constraints to form a multi-stage optimization model.

[0010] The beneficial effects of this preferred technical solution are as follows: It determines that the optimization objective is to minimize the net present value of the total cost within the planning period, encompassing investment and operating costs at each stage. This ensures that cost considerations cover different expenditure types throughout the planning period. When calculating investment costs at each planning stage, the costs of power distribution line construction, static var compensator installation, and transformer expansion are included, allowing investment cost statistics to cover multiple key facilities. Then, based on the probability of different operating scenarios, the network loss costs of the power distribution system are weighted and summed to obtain the operating cost, ensuring that the operating cost calculation matches the likelihood of actual scenarios. Decision variables for power distribution line construction, static var compensator configuration, and transformer expansion are set, allowing for clear and quantifiable facility configuration choices in the planning. Constraints on power flow, equipment capacity, and investment decisions are set and evaluated under extreme operating scenarios, extending the applicability of constraints to extreme conditions. Finally, the optimization objective, decision variables, and constraints are integrated to form a multi-stage optimization model, enabling the multi-stage optimization model to simultaneously coordinate costs, facility configuration, and operating condition constraints.

[0011] As a preferred embodiment of the multi-type load joint planning method for distribution networks based on micro-topography information described in this invention, the power flow constraints include node power balance constraints, branch power flow constraints, voltage constraints, and radial topology constraints of the distribution network; the equipment capacity constraints include transformer capacity constraints, static reactive power compensation device capacity constraints, and line capacity constraints; the investment decision constraints include the constraint that the capacity of the static reactive power compensation device must be between the minimum and maximum values ​​if it is installed.

[0012] The beneficial effects of this preferred technical solution are as follows: Power flow constraints are clearly defined as node power balance constraints, branch power flow constraints, voltage constraints, and radial topology constraints of the distribution network. This ensures that the power distribution and voltage status of the distribution network are consistent with the actual operating logic of the radial structure. Furthermore, equipment capacity constraints limit the capacity range of transformers, static var compensators, and lines, ensuring that the operating parameters of each device are within its own carrying capacity. Simultaneously, investment decision constraints specify that if a static var compensator is installed, its capacity must be between its minimum and maximum values, ensuring that the configuration of the static var compensator conforms to the actual usable capacity range. These different types of constraints cover the power interaction, equipment carrying capacity, and investment configuration dimensions of the distribution network operation, ensuring that the parameters of each dimension corresponding to the planning scheme are within a reasonable range, and guaranteeing that the operating state of the distribution network corresponding to the plan matches the actual system's operating requirements.

[0013] As a preferred embodiment of the multi-type load joint planning method for distribution networks based on micro-topography information described in this invention, the extreme operating scenarios include high load scenarios and high injection scenarios; the high load scenario corresponds to the combined operating condition of minimum wind power output, maximum background load, and maximum charging demand; the high injection scenario corresponds to the combined operating condition of maximum wind power output, minimum background load, and zero charging demand; the constraints are evaluated under the extreme operating scenarios to ensure that the voltage of all nodes and the capacity of equipment meet the safe operation requirements under extreme conditions.

[0014] The beneficial effects of this preferred technical solution are as follows: It clarifies extreme operating scenarios into high-load and high-injection scenarios. The high-load scenario corresponds to the combination of minimum wind power output, maximum background load, and maximum charging demand. The high-injection scenario corresponds to the combination of maximum wind power output, minimum background load, and zero charging demand. This ensures that the extreme scenarios cover extreme combinations of wind power output and load demand. Furthermore, it evaluates constraints under these extreme operating scenarios to ensure that all node voltages and equipment capacities meet safe operating requirements under extreme conditions. This allows the distribution network operation state corresponding to the planning scheme to adapt to these extreme combinations of operating conditions, ensuring that the planned power system remains within a safe operating range even under extreme circumstances. Simultaneously, it integrates safety considerations for extreme operating conditions into the constraint evaluation stage of the planning, ensuring that the planning results align with the extreme operating conditions that may occur in the actual operation of the distribution network.

[0015] As a preferred embodiment of the multi-type load joint planning method for distribution networks based on micro-topography information described in this invention, the calculation formula for the terrain correction factor is as follows: ; ; ; ; In the formula, For terrain correction factor, This is the influence coefficient of terrain slope. The horizontal distance modulation coefficient, The vertical height influence coefficient. The ground slope angle, This is the horizontal distance from the top of the ridge. The coefficient that affects the horizontal range, The characteristic horizontal distance associated with the half-height of the ridge, Altitude This is the vertical attenuation coefficient.

[0016] The beneficial effects of this preferred technical solution are as follows: The calculation formula for the terrain correction factor uses 2.9tanβ to calculate the terrain slope influence coefficient, transforming the influence of the ground slope angle into a quantifiable coefficient. The horizontal distance modulation coefficient is then calculated by combining the horizontal distance from the ridge top, the coefficient of the horizontal range of influence, and the characteristic horizontal distance related to the ridge half-height, thus concretely presenting the influence of horizontal distance. Simultaneously, the vertical height influence coefficient is calculated using altitude, vertical attenuation coefficient, and horizontal distance, incorporating the influence of altitude. Finally, the terrain correction factor is obtained by adding 1 to the product of these three coefficients. This integrates the three dimensions of terrain factors—ground slope, horizontal distance, and altitude—into the calculation of the correction factor, enabling the calculation to cover the multi-dimensional terrain parameters of the installation location. This allows the obtained terrain correction factor to match the comprehensive influence of the actual terrain, ensuring that the terrain correction factor corresponds to the specific terrain conditions of the installation location.

[0017] As a preferred embodiment of the multi-type load joint planning method for distribution networks based on micro-topography information described in this invention, the formula for calculating the net present value of the total cost within the planning period is: ; ; In the formula, NPV is the net present value, P is the set of planning periods, p is the planning stage index, and r is the annual discount rate. The discount factor for stage p. For the investment cost of p during the planning phase, For the operating cost of p during the planning phase, For scene collection, Let be the probability of scenario sc occurring. For the collection of routes, Cost per unit of electricity Let L be the resistance of line l. Let be the square of the current in line l of p during the planning phase in scenario sc.

[0018] The beneficial effects of this preferred technical solution are as follows: The net present value (NPV) calculation formula for the total cost within the planning period adds the investment cost and operating cost of each planning stage, and then combines the annual discount rate to discount according to the planning stage, allowing the costs of different stages to be taken into account in terms of time value. The calculation of operating costs is weighted according to the probability of occurrence of different operating scenarios, and integrates the unit energy cost of each line, line resistance, and the square of the current of the line under the corresponding scenario and planning stage. This makes the calculation of operating costs cover the actual parameters of the probability of occurrence of different operating scenarios and the line network loss. This allows the calculation of NPV to take into account the costs, time value, and operating loss factors of each stage of the entire planning period, so that the NPV of the total cost can fit the actual cost composition and scenario differences within the planning period, and make the NPV result correspond to the actual cost situation of the planning.

[0019] Secondly, the present invention provides a multi-type load joint planning system for distribution networks based on micro-topography information, comprising: Data acquisition module: used to acquire basic data of the distribution network and the access location and power characteristic parameters of the equipment to be connected; Wind power modeling module: used to collect micro-topographic data for distributed wind turbine generators in the devices to be connected, calculate the topographic correction factor, correct the reference wind speed to obtain the calibrated wind speed, and determine the actual power generation based on the calibrated wind speed and power characteristic parameters; Optimization model construction module: used to construct a multi-stage optimization model based on the basic data of the distribution network and the actual power generation, and to set constraints; Solution output module: Used to solve the multi-stage optimization model using the mixed integer programming method and output the planning results.

[0020] Thirdly, the present invention provides an electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the distribution network multi-type load joint planning method based on micro-terrain information.

[0021] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the method for joint planning of multiple types of loads in a distribution network based on micro-terrain information.

[0022] Compared with existing technologies, the beneficial effects of this invention are as follows: For the characteristics of distributed wind turbines, micro-topographic data is introduced to calculate a terrain correction factor to calibrate wind speed, ensuring that the modeling of actual wind power generation fully matches the terrain conditions of the connection location. This avoids the output estimation deviation caused by ignoring local terrain in traditional planning, making the modeling results of distributed power sources closer to the actual operating state. Simultaneously, the constructed multi-stage optimization model, aiming to minimize the net present value of total cost over the planning period, integrates the investment and operating costs of multiple key facilities such as distribution lines, static var compensators, and transformers. This achieves collaborative planning of multiple facilities, breaking the limitations of single-facility planning and enabling a more systematic balance of cost allocation across various stages, achieving the optimal global cost for distribution network planning. Furthermore, the multi-stage optimization model incorporates constraint evaluations for extreme operating scenarios such as high load and high injection, ensuring that the planning scheme still meets constraints such as power flow and equipment capacity under various extreme conditions. This effectively addresses the volatility and uncertainty of new power sources and loads, improving the safe and reliable operation capability of the distribution network under complex conditions, and making the planning results more adaptable to the actual operation needs of the power grid. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a schematic diagram of the overall process of a multi-type load joint planning method for distribution networks based on micro-topography information according to an embodiment of the present invention. Detailed Implementation

[0025] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0026] Example 1, referring to Figure 1 As an embodiment of the present invention, a method for joint planning of multiple types of loads in a distribution network based on micro-topography information is provided, including steps S100 to S400: S100: Obtain basic data of the distribution network and the access location and power characteristic parameters of the equipment to be connected.

[0027] S200: Collect micro-topographic data for the access location of distributed wind turbine units in the equipment to be connected, calculate the terrain correction factor based on the micro-topographic data, and obtain the calibrated wind speed by correcting the reference wind speed according to the terrain correction factor. Determine the actual power generation based on the calibrated wind speed and power characteristic parameters.

[0028] S300: Construct a multi-stage optimization model based on the basic data of the distribution network and the actual power generation, and set constraints in the optimization model.

[0029] S400: Use mixed integer programming to solve the multi-stage optimization model and output the planning results.

[0030] It should be noted that the connection location of distributed wind turbines in the distribution network is often affected by local micro-topographic conditions. Traditional planning methods do not fully incorporate this micro-topographic information, which can easily lead to discrepancies between the estimation of actual wind power generation and the actual operating status. At the same time, there are many types of equipment to be connected to the distribution network, and the investment and configuration of facilities such as lines, reactive power compensation devices, and transformers lack coordinated planning. Furthermore, the costs and constraints under different operating scenarios are not systematically integrated, which makes the planning scheme either insufficient in cost control or difficult to adapt to actual operating conditions, affecting the operating efficiency and long-term economics of the distribution network.

[0031] Therefore, to address the issues of wind power output estimation errors and lack of coordination in facility planning in the aforementioned distribution network planning, steps S100~S400 are used to first obtain basic distribution network data and the access locations and power characteristic parameters of the equipment to be connected. Then, micro-topographic data is collected for the access locations of distributed wind turbines to calibrate and obtain the actual power generation, ensuring that the calculated wind power output is consistent with the actual terrain of the access location. Furthermore, a multi-stage optimization model is constructed by combining the basic distribution network data and the actual power generation, and constraints are set. The mixed integer programming method is used to solve the model, and the output planning results can coordinate the configuration of various facilities, allowing the planning scheme to simultaneously consider cost and operational constraints, and adapt to the actual operational needs of the distribution network.

[0032] Example 2, refer to Figure 1 This is one embodiment of the present invention. Based on the above embodiment, a method for joint planning of multiple types of loads in a distribution network based on micro-topography information is provided.

[0033] In this embodiment of the application, step S100 obtains basic data of the power distribution network and the access location and power characteristic parameters of the equipment to be connected; First, the basic data of the distribution network and the access locations and power characteristic parameters of the devices to be connected are obtained. In this embodiment, the basic data of the distribution network includes the topology, line parameters, and load data of the distribution network. Specifically, this embodiment uses a 24-node distribution network as the planning object. The voltage level of this distribution network is 13.8kV, containing 24 nodes and 34 branches, where node 1 is a substation, and nodes 2 to 24 are load nodes. The devices to be connected include 4 distributed wind turbines and 3 electric vehicle charging stations. The planning period is 15 years, divided into 3 planning phases. The access information of the 4 distributed wind turbines is as follows: the first distributed wind turbine is planned to be connected to node 6, with a rated capacity of 500kW, and is planned to be connected in the first phase; the second is planned to be connected to node 11, with a rated capacity of 500kW, and is planned to be connected in the first phase; the third is planned to be connected to node 18, with a rated capacity of 500kW, and is planned to be connected in the second phase; the fourth is planned to be connected to node 21, with a rated capacity of 500kW, and is planned to be connected in the third phase. Power characteristic parameters are used to define the power characteristic curves of each distributed wind turbine. Taking the first distributed wind turbine as an example, its power characteristic parameters include: cut-in wind speed of 3 m / s, rated wind speed of 12 m / s, cut-out wind speed of 25 m / s, and rated power of 500 kW. These power characteristic parameters define the power characteristic curve of the distributed wind turbine, that is, when the wind speed is lower than the cut-in wind speed, the power is zero; when the wind speed is between the cut-in wind speed and the rated wind speed, the power increases with the wind speed; when the wind speed is between the rated wind speed and the cut-out wind speed, the power remains at the rated value; and when the wind speed exceeds the cut-out wind speed, the power is zero.

[0034] In this embodiment of the application, step S200 collects micro-topographic data for the access location of the distributed wind turbine in the device to be connected, calculates the terrain correction factor based on the micro-topographic data, corrects the reference wind speed according to the terrain correction factor to obtain the calibration wind speed, and determines the actual power generation based on the calibration wind speed and power characteristic parameters. It should be noted that step S200 only performs micro-topography correction for distributed wind turbines. This is because the power generation of distributed wind turbines is significantly affected by wind speed, which in turn is greatly affected by terrain. For electric vehicle charging stations, since their power demand is not affected by terrain, micro-topography correction is unnecessary, and they can be directly included in the distribution network basic data as high-demand loads. In this embodiment, the specific process of micro-topography correction is illustrated using the first distributed wind turbine to be connected to node 6 as an example.

[0035] The steps of calculating a terrain correction factor based on the micro-topography data, correcting the reference wind speed according to the terrain correction factor to obtain the calibrated wind speed, and determining the actual power generation based on the calibrated wind speed and power characteristic parameters include A1~A3: A1. Obtain the ground slope angle, horizontal distance from the ridge, and altitude of the access location; For the first distributed wind turbine to be connected to node 6, a topographic map of its current location is obtained through a surveying department, and micro-topographic data is extracted from the map. In this embodiment, the ground slope angle... Horizontal distance from the ridge altitude In addition, it is necessary to obtain parameters related to terrain features, including coefficients that affect the horizontal range. Characteristic horizontal distances related to ridge half-height and vertical attenuation coefficient Assuming , , .

[0036] A2. Determine the terrain slope influence coefficient based on the ground slope angle, determine the horizontal distance modulation coefficient based on the horizontal distance, and determine the vertical height influence coefficient based on the altitude. The terrain slope influence coefficient C1 is used to quantify the impact of ground slope on the wind speed acceleration effect. It is obtained by multiplying the tangent of the ground slope angle by an empirical coefficient, which is determined to be 2.9 according to the American Society of Civil Engineers standard. The horizontal distance modulation coefficient C2 is used to modulate the wind speed acceleration effect based on the horizontal distance between the distributed wind turbine and the ridge. It decreases exponentially with the increase of the horizontal distance from the ridge. The vertical height influence coefficient C3 is used to adjust the wind speed acceleration effect according to the altitude. It increases exponentially with the increase of the altitude.

[0037] A3. The terrain correction factor is calculated based on the terrain slope influence coefficient, horizontal distance modulation coefficient and vertical height influence coefficient. The terrain correction factor is multiplied by the reference wind speed to obtain the calibration wind speed. The power characteristic parameters of the distributed wind turbine are queried according to the calibration wind speed to determine the actual power generation. In this embodiment, the terrain correction factor is obtained by multiplying the terrain slope influence coefficient, the horizontal distance modulation coefficient, and the vertical height influence coefficient and then adding 1. This calculation method comprehensively considers the multi-dimensional influence of terrain features on wind speed. Then, the terrain correction factor is multiplied by the reference wind speed to obtain the calibrated wind speed after terrain effect correction. Next, based on the calibrated wind speed, the power characteristic curve defined by the power characteristic parameters obtained in step S100 is queried. Finally, by substituting the calibrated wind speed into the power characteristic curve, the power generation capacity of the distributed wind turbine under actual terrain conditions is determined.

[0038] The formula for calculating the terrain correction factor is as follows: ; ; ; ; In the formula, For terrain correction factor, This is the influence coefficient of terrain slope. The horizontal distance modulation coefficient, The vertical height influence coefficient. The ground slope angle, This is the horizontal distance from the top of the ridge. The coefficient that affects the horizontal range, The characteristic horizontal distance associated with the half-height of the ridge, Altitude This is the vertical attenuation coefficient; In this embodiment, the calculation is performed using the first distributed wind turbine of the proposed node 6 as an example. Based on the data obtained in step A1, the terrain slope influence coefficient is first calculated: Then calculate the horizontal distance modulation coefficient: Since the calculated horizontal distance modulation coefficient is negative, it indicates that the location is far from the ridge. Therefore, we take... Next, calculate the vertical height influence coefficient: Finally, the terrain correction factor is calculated: The results show that, because the location is far from the ridge, the acceleration effect of the terrain on wind speed is offset by the horizontal distance modulation coefficient, and the terrain correction factor is close to 1, meaning that the influence of the terrain is small. Then, the terrain correction factor is multiplied by the reference wind speed to obtain the calibrated wind speed. Assuming the reference wind speed for this area is v0 = 6.5 m / s, the calibrated wind speed after terrain correction is: .

[0039] Based on the power characteristic parameters obtained in step S100, the cut-in wind speed of this distributed wind turbine is 3 m / s, the rated wind speed is 12 m / s, the cut-out wind speed is 25 m / s, and the rated power is 500 kW. Since the calibration wind speed of 6.5 m / s is between the cut-in wind speed and the rated wind speed, according to the power characteristic curve, the power generation increases with wind speed, and is calculated to be approximately 200 kW.

[0040] In one optional implementation, the calculation of a terrain correction factor based on the micro-topography data, and the correction of the reference wind speed according to the terrain correction factor to obtain the calibrated wind speed, can also consider wind speed variations at different times. Specifically, for different time periods within the planning cycle, reference wind speed data for each time period are collected, and the reference wind speed for each time period is corrected using the terrain correction factor to obtain the calibrated wind speed for each time period. For example, within a 24-hour day, the time period is divided into daytime high-wind-speed periods and nighttime low-wind-speed periods. The reference wind speed for each time period is obtained, corrected using the terrain correction factor to obtain the calibrated wind speed for each time period, and then the actual power generation for each time period is determined based on the power characteristic parameters of the distributed wind turbine generator set.

[0041] In another optional implementation, a terrain correction factor is calculated based on the micro-topography data, and the reference wind speed is corrected according to the terrain correction factor to obtain the calibrated wind speed. The actual power generation is determined based on the calibrated wind speed and power characteristic parameters, and the wake effect between multiple distributed wind turbines can also be considered. Specifically, when multiple distributed wind turbines are arranged adjacently in space, the operation of the upwind turbines will affect the wind speed of the downwind turbines, resulting in a decrease in the wind speed received by the downwind turbines. After calculating the terrain correction factor and obtaining the calibrated wind speed, the wake influence coefficient is further calculated based on the relative position and prevailing wind direction of each distributed wind turbine, and the calibrated wind speed of the downwind turbines is corrected a second time to obtain the calibrated wind speed considering the wake effect. For example, when the distributed wind turbine at node 5 is located upwind of the distributed wind turbine at node 8, the calibrated wind speed of node 8 needs to be corrected for the wake effect. Then, based on the corrected wind speed and power characteristic parameters, the actual power generation considering the wake effect is determined, thereby improving the accuracy of power generation prediction in multi-unit scenarios.

[0042] In this embodiment of the application, step S300 constructs a multi-stage optimization model based on the basic data of the distribution network and the actual power generation, and sets constraints in the optimization model; The steps for constructing an optimization model based on basic data of the distribution network and actual power generation include B1 to B6: B1. The optimization objective is to minimize the net present value of the total cost within the planning period. The total cost includes the investment cost and operating cost of each stage. Since the costs of different planning stages occur at different times, it is necessary to discount the costs of each stage back to the initial time based on the annual discount rate to obtain the net present value of the total cost. In this embodiment, an annual discount rate of 2.25% is used to reflect the time value of money. By minimizing the net present value, the optimal overall economic efficiency of investment and operation can be achieved while ensuring the safe and reliable operation of the distribution network.

[0043] The formula for calculating the net present value of the total cost within the planning period is as follows: ; ; In the formula, NPV is the net present value, P is the set of planning periods, p is the planning stage index, and r is the annual discount rate. The discount factor for stage p. For the investment cost of p during the planning phase, For the operating cost of p during the planning phase, For scene collection, Let be the probability of scenario sc occurring. For the set of routes, Cost per unit of electricity Let L be the resistance of line l. Let the square of the current in line l of p be the current during the planning phase in scenario sc. In this embodiment, the planning period is 15 years, divided into 3 planning phases, i.e., P={1, 2, 3}. The first phase corresponds to year 5, the second phase to year 10, and the third phase to year 15. The annual discount rate r=0.0225, and the unit electricity cost is... The value is set to 0.6 yuan / kW·h. The discount factors for each stage are as follows: , , .

[0044] Taking the first phase as an example, assuming two operating scenarios are considered: a high-load scenario with a probability of 0.3 and a high-injection scenario with a probability of 0.7; under the high-load scenario, the total network loss power of the distribution network is approximately 150kW, and assuming 8760 hours of operation per year, the annual network loss cost is 150 × 8760 × 0.6 = 788400 yuan; under the high-injection scenario, the total network loss power of the distribution network is approximately 80kW, and the annual network loss cost is 80 × 8760 × 0.6 = 420480 yuan. Therefore, the operating cost of the first phase is: Yuan; Assuming the investment cost for the first phase includes 1.2 million yuan for new line construction, 64,000 yuan for installing static var compensator, and 120,000 yuan for transformer expansion, then the investment cost is: The contribution of the first stage to the net present value is: Similarly, the costs for the second and third stages are calculated, assuming they are 1,350,000 yuan and 980,000 yuan respectively. The net present value of the total cost is then: .

[0045] B2. Calculate the investment cost for each planning stage. The investment cost includes the construction cost of power distribution lines, the installation cost of static var compensators, and the cost of transformer expansion. In this embodiment, the construction cost of the power distribution line is determined based on the length of the newly built or reinforced line and the conductor type. For example, the unit construction cost of the candidate line is between 30,000 and 50,000 yuan / km depending on the conductor type. The installation cost of the static reactive power compensation device is determined based on the installation capacity and unit cost. For example, the unit installation cost of the static reactive power compensation device is 80 yuan / kVar. The transformer expansion cost is determined based on the expansion capacity and unit cost. For example, the unit expansion cost of the transformer is 120 yuan / kVA. The basic capacity of the substation transformer is 5000kVA, and the selectable expansion levels are 1000kVA, 2000kVA, and 3000kVA.

[0046] B3. Calculate the operating costs for each planning stage. The operating costs are weighted and summed based on the probability of occurrence of different operating scenarios and the network loss costs of the distribution network system. In this embodiment, network loss costs are primarily determined by line resistance and the current flowing through the lines. The magnitude of the current is influenced by load levels, distributed wind turbine output, and electric vehicle charging demand. Since the output of distributed wind turbines and the charging demand of electric vehicles are uncertain, this embodiment considers two typical operating scenarios to reflect different operating states: a high-load scenario and a high-injection scenario. Each scenario is assigned a corresponding probability of occurrence, and by performing a probability-weighted summation of network loss costs under different scenarios, the actual cost level of the distribution network in operation can be more realistically reflected.

[0047] B4. Set decision variables, including decision variables for the construction of power distribution lines, decision variables for the configuration of static reactive power compensation devices, and decision variables for the expansion of transformer capacity; The decision variables for power distribution line construction are 0-1 variables, indicating whether to construct or reinforce a candidate line at a given planning stage. A value of 1 indicates construction of the candidate line, while a value of 0 indicates no construction. The decision variables for static var compensator (SVC) configuration include both 0-1 and continuous variables. The 0-1 variables indicate whether to install an SVC at a given node (a value of 1 indicates installation, and a value of 0 indicates no installation), while the continuous variables represent the installed capacity of the SVC. The decision variables for transformer capacity expansion are integer variables, representing the expansion level selected at a given planning stage. By optimizing these decision variables, the investment decisions to be made at each planning stage can be determined, thereby minimizing the net present value of total cost.

[0048] B5. Set constraints and evaluate them under extreme operating scenarios to ensure that the planning scheme still meets all constraints under extreme conditions. The constraints include power flow constraints, equipment capacity constraints, and investment decision constraints. By evaluating constraints under extreme operating scenarios, it can be ensured that the distribution network can still meet the safe operation requirements of voltage deviation and equipment capacity under the most unfavorable operating conditions, thereby improving the reliability and robustness of the planning scheme.

[0049] The power flow constraints include node power balance constraints, branch power flow constraints, voltage constraints, and radial topology constraints of the distribution network. In this embodiment, the DistFlow power flow equation is used to establish these constraints, and the second-order cone relaxation technique is used to transform the nonlinear constraints into convex optimization constraints.

[0050] Equipment capacity constraints include transformer capacity constraints, static var compensator capacity constraints, and line capacity constraints. Transformer capacity constraints ensure that the apparent power of the transformer in each scenario does not exceed the sum of its basic capacity and the cumulative expansion capacity at each stage. Static var compensator capacity constraints ensure that the reactive power generated or absorbed by the static var compensator in each scenario is within the positive and negative range of its own installed capacity. In this embodiment, the minimum installed capacity of the static var compensator is 50 kVar, and the maximum installed capacity of a single node is 1000 kVar. Line capacity constraints ensure that the current of each line in each scenario does not exceed its own current carrying capacity limit.

[0051] Investment decision constraints include the constraint that the capacity of the static var compensator must be between the minimum and maximum values ​​if it is installed. That is, when the 0-1 variable is 1, the value range of the continuous variable is 50~1000kVar, and when the 0-1 variable is 0, the value of the continuous variable is 0.

[0052] Extreme operating scenarios include high-load scenarios and high-injection scenarios; The high-load scenario corresponds to the combination of minimum wind power output, maximum background load, and maximum charging demand. In this embodiment, the high-load scenario is set as follows: the output of the two distributed wind turbines connected in the first phase is 10% of their rated power, that is, about 50kW each; the background load is 120% of the predicted value; and the charging demand of the electric vehicle charging station is the maximum value, that is, the power demand of the charging station connected in the first phase is 1400kW.

[0053] The high injection scenario corresponds to the combined operating condition of maximum wind power output, minimum background load, and zero charging demand. In this embodiment, the high injection scenario is set as follows: the two distributed wind turbines connected in the first stage operate according to the actual power output determined in step S200, that is, the output of the distributed wind turbine at node 6 to be connected is about 200kW, and the output of the distributed wind turbine at node 11 to be connected is determined according to the micro-topography correction results of its own location; the background load is 60% of the predicted value; and the charging demand of the electric vehicle charging station is zero.

[0054] Evaluate constraints under extreme operating scenarios to ensure that all node voltages and equipment capacities meet safe operation requirements under extreme conditions; In an optional implementation, step B5 further includes extreme operating scenarios that consider equipment failures. In addition to high-load and high-injection scenarios, equipment failure scenarios are further defined to assess the distribution network's operational capability in the event of critical equipment failure. For example, a scenario could be set where a major distribution line fails and goes out of service, or a static var compensator (SVC) is shut down for maintenance. Under these failure scenarios, the multi-stage optimization model needs to ensure that the distribution network can still meet power flow constraints, equipment capacity constraints, and investment decision constraints through power flow transfer from other lines or the introduction of backup equipment. By considering equipment failure scenarios in the multi-stage optimization model, the reliability and flexibility of the planning scheme can be improved, ensuring that the distribution network can maintain safe and stable operation even when some equipment fails. This planning method that considers failure scenarios can effectively reduce the risk of large-scale power outages caused by a single equipment failure in the distribution network and improve the distribution network's ability to respond to emergencies.

[0055] In another optional implementation, the extreme operating scenarios in step B5 also include operating scenarios that consider seasonal load changes. In addition to high-load and high-injection scenarios, summer high-temperature and winter low-temperature scenarios are further defined to evaluate the operating performance of the distribution network under different seasonal conditions. In the summer high-temperature scenario, charging demand at charging stations increases, while the actual power generation of distributed wind turbines decreases due to lower wind speeds, putting pressure on the distribution network. In the winter low-temperature scenario, background load increases significantly due to heating demand, while the actual power generation of distributed wind turbines increases due to higher wind speeds, requiring the distribution network to cope with the combined effects of peak load and power generation fluctuations. By considering seasonal scenarios in the multi-stage optimization model, it can be ensured that the distribution line construction scheme, static var compensator configuration scheme, and transformer expansion scheme can adapt to the operating needs of different seasons throughout the year, improving the adaptability of the planning scheme to seasonal load changes.

[0056] B6. Integrate the optimization objective, decision variables, and constraints to form a multi-stage optimization model; Based on steps B1-B5, the objective function is to minimize the net present value of the total cost within the planning period. The decision variables for distribution line construction, static var compensator configuration, and transformer expansion are used as variables to be optimized. Power flow constraints, equipment capacity constraints, and investment decision constraints are used as constraints, and these are integrated to construct the model. In this embodiment, for a 24-node distribution network system, the integrated multi-stage optimization model includes construction decision variables for 34 candidate lines, static var compensator configuration decision variables for 24 nodes, and transformer expansion decision variables for substations. Constraints include power balance constraints, voltage constraints, and equipment capacity constraints for each node under high load and high injection scenarios, thus completing the construction of the multi-stage optimization model.

[0057] In this embodiment of the application, step S400 uses a mixed integer programming method to solve the multi-stage optimization model and outputs the planning results.

[0058] First, the multi-stage optimization model constructed in step S300 is input into the optimization solver for solution. In this embodiment, the MATLAB R2024b environment is used, and the Gurobi solver is called to solve the multi-stage optimization model. Since the multi-stage optimization model contains integer variables representing investment decisions and continuous variables representing operating states, a mixed integer programming method is used for solution. During the solution process, the Gurobi solver searches for the optimal solution using a branch and bound algorithm, finding the investment decision scheme that minimizes the net present value of the total cost within the planning period, while satisfying all constraints.

[0059] After the solution is completed, the planning results are output, which include multi-stage power distribution line construction schemes, static var compensator (SVC) configuration schemes, and transformer capacity expansion schemes. In this embodiment, for a 15-year planning cycle of a 24-node power distribution network system, the planning results specifically include: in the first five-year planning stage, a new line is built between node 5 and node 12, and a 150kVar SVC is installed at node 8; in the second five-year planning stage, a new line is built between node 15 and node 18, and the substation transformer capacity is expanded from 5MVA to 8MVA; in the third five-year planning stage, the existing line between node 20 and node 23 is reinforced, and a 100kVar SVC is installed at node 16. The planning results also provide the total investment cost, operating cost, and net present value of the total cost over the planning period for each stage, providing a basis for decision-making in the actual construction of the power distribution network.

[0060] In summary, considering the characteristics of distributed wind turbines, micro-topographic data is introduced to calculate a terrain correction factor to calibrate wind speed. This ensures that the modeling of actual wind power generation fully matches the terrain conditions of the connection location, avoiding the output estimation deviation caused by neglecting local terrain in traditional planning. This makes the modeling results of distributed power sources closer to the actual operating conditions. Simultaneously, the constructed multi-stage optimization model, aiming to minimize the net present value of total cost over the planning period, integrates the investment and operating costs of multiple key facilities such as distribution lines, static var compensators, and transformers. This achieves collaborative planning of multiple facilities, breaking the limitations of single-facility planning and enabling a more systematic balance of cost allocation across various stages, achieving the optimal global cost for distribution network planning. Furthermore, the multi-stage optimization model incorporates constraint evaluations for extreme operating scenarios such as high load and high injection, ensuring that the planning scheme still meets constraints such as power flow and equipment capacity under various extreme conditions. This effectively addresses the volatility and uncertainty of new power sources and loads, improving the safe and reliable operation capability of the distribution network under complex conditions and making the planning results more adaptable to the actual operation needs of the power grid.

[0061] Example 3 illustrates a schematic scheme for a multi-type load joint planning method for distribution networks based on micro-topography information. It should be noted that the technical solution of this system for multi-type load joint planning of distribution networks based on micro-topography information is based on the same concept as the technical solution of the aforementioned method for multi-type load joint planning of distribution networks based on micro-topography information. Details not described in detail in this embodiment can be found in the description of the aforementioned method for multi-type load joint planning of distribution networks based on micro-topography information.

[0062] This embodiment also provides a multi-type load joint planning system for distribution networks based on micro-topography information, including: Data acquisition module: used to acquire basic data of the distribution network and the access location and power characteristic parameters of the equipment to be connected; Wind power modeling module: used to collect micro-topographic data for distributed wind turbine generators in the devices to be connected, calculate the topographic correction factor, correct the reference wind speed to obtain the calibrated wind speed, and determine the actual power generation based on the calibrated wind speed and power characteristic parameters; Optimization model construction module: used to construct a multi-stage optimization model based on the basic data of the distribution network and the actual power generation, and to set constraints; Solution output module: Used to solve the multi-stage optimization model using the mixed integer programming method and output the planning results.

[0063] This embodiment also provides an electronic device applicable to the joint planning of multiple types of loads in a distribution network based on micro-topography information, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the method for joint planning of multiple types of loads in a distribution network based on micro-topography information as proposed in the above embodiment.

[0064] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the method for joint planning of multiple types of loads in a distribution network based on micro-topography information as proposed in the above embodiments.

[0065] The storage medium proposed in this embodiment and the method for joint planning of multiple types of loads in a distribution network based on micro-terrain information proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0066] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0067] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for joint planning of multiple load types in a distribution network based on micro-topography information, characterized in that, include: Acquire basic data of the power distribution network and the access location and power characteristic parameters of the equipment to be connected; Micro-topographic data is collected for the access location of distributed wind turbines in the equipment to be connected. A terrain correction factor is calculated based on the micro-topographic data, and the reference wind speed is corrected according to the terrain correction factor to obtain the calibration wind speed. The actual power generation is determined according to the calibration wind speed and power characteristic parameters. A multi-stage optimization model is constructed based on the basic data of the distribution network and the actual power generation, and constraints are set in the optimization model. The multi-stage optimization model is solved using a mixed integer programming method, and the planning results are output.

2. The method for joint planning of multiple load types in a distribution network based on micro-topography information as described in claim 1, characterized in that, The steps of calculating a terrain correction factor based on the micro-topographic data, correcting the reference wind speed according to the terrain correction factor to obtain the calibrated wind speed, and determining the actual power generation based on the calibrated wind speed and power characteristic parameters include: The ground slope angle, horizontal distance from the ridge, and altitude of the access location are obtained respectively. The terrain slope influence coefficient is determined based on the ground slope angle, the horizontal distance modulation coefficient is determined based on the horizontal distance, and the vertical height influence coefficient is determined based on the altitude. The terrain correction factor is calculated based on the terrain slope influence coefficient, horizontal distance modulation coefficient and vertical height influence coefficient. The terrain correction factor is multiplied by the reference wind speed to obtain the calibration wind speed. The power characteristic parameters of the distributed wind turbine are then queried based on the calibration wind speed to determine the actual power generation.

3. The method for joint planning of multiple load types in a distribution network based on micro-topography information as described in claim 2, characterized in that, The steps for constructing an optimization model based on the basic data of the power distribution network and the actual power generation include: The optimization objective is to minimize the net present value of the total cost within the planning period, where the total cost includes the investment cost and operating cost at each stage. Calculate the investment costs for each planning stage, including the construction costs of power distribution lines, the installation costs of static var compensators, and the costs of transformer capacity expansion. Calculate the operating costs for each planning stage, and the operating costs are weighted and summed based on the probability of occurrence of different operating scenarios and the network loss costs of the distribution network system. Decision variables are defined, including decision variables for the construction of power distribution lines, decision variables for the configuration of static reactive power compensation devices, and decision variables for the expansion of transformer capacity; Set constraints and evaluate them under extreme operating scenarios to ensure that the planning scheme still meets all constraints under extreme conditions. The constraints include power flow constraints, equipment capacity constraints, and investment decision constraints. The optimization objective, decision variables, and constraints are integrated to form a multi-stage optimization model.

4. The method for joint planning of multiple load types in a distribution network based on micro-topography information as described in claim 3, characterized in that, The power flow constraints include node power balance constraints, branch power flow constraints, voltage constraints, and radial topology constraints of the distribution network. The equipment capacity constraints include transformer capacity constraints, static reactive power compensation device capacity constraints, and line capacity constraints. The investment decision constraints include the constraint that the capacity of the static var compensator must be between the minimum and maximum values ​​if it is installed.

5. The method for joint planning of multiple load types in a distribution network based on micro-topography information as described in claim 4, characterized in that, The extreme operating scenarios include high-load scenarios and high-injection scenarios; The high-load scenario corresponds to the combined operating condition of minimum wind power output, maximum background load, and maximum charging demand. The high injection scenario corresponds to the combined operating condition of maximum wind power output, minimum background load, and zero charging demand. The constraints are evaluated under the extreme operating scenarios to ensure that the voltage of all nodes and the capacity of the equipment meet the requirements for safe operation under extreme conditions.

6. The method for joint planning of multiple load types in a distribution network based on micro-topography information as described in claim 5, characterized in that, The formula for calculating the terrain correction factor is: ； ； ； ； In the formula, For terrain correction factor, This is the influence coefficient of terrain slope. The horizontal distance modulation coefficient, This is the vertical height influence coefficient. The ground slope angle, This is the horizontal distance from the top of the ridge. The coefficient that affects the horizontal range, The characteristic horizontal distance associated with the half-height of the ridge, Altitude This is the vertical attenuation coefficient.

7. The method for joint planning of multiple load types in a distribution network based on micro-topography information as described in claim 6, characterized in that, The formula for calculating the net present value of total costs over the planning period is: ； ； In the formula, NPV is the net present value, P is the set of planning periods, p is the planning stage index, and r is the annual discount rate. The discount factor for stage p. For the investment cost of p during the planning phase, For the operating cost of p during the planning phase, For scene collection, Let be the probability of scenario sc occurring. For the collection of routes, Cost per unit of electricity Let L be the resistance of line l. Let be the square of the current in line l of p during the planning phase in scenario sc.

8. A multi-type load joint planning system for distribution networks based on micro-topography information, using the method described in any one of claims 1-7, characterized in that, include: Data acquisition module: used to acquire basic data of the distribution network and the access location and power characteristic parameters of the equipment to be connected; Wind power modeling module: used to collect micro-topographic data for distributed wind turbine generators in the devices to be connected, calculate the topographic correction factor, correct the reference wind speed to obtain the calibrated wind speed, and determine the actual power generation based on the calibrated wind speed and power characteristic parameters; Optimization model construction module: used to construct a multi-stage optimization model based on the basic data of the distribution network and the actual power generation, and to set constraints; Solution output module: Used to solve the multi-stage optimization model using the mixed integer programming method and output the planning results.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the distribution network multi-type load joint planning method based on micro-terrain information as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the distribution network multi-type load joint planning method based on micro-terrain information as described in any one of claims 1 to 7.

Images