Stock power distribution network charging load power balance control method, device and equipment

Abstract

The invention discloses a stock power distribution network charging load power balance control method, device and equipment, and relates to the technical field of power distribution network control, and the method comprises the following steps: obtaining a travel mode through clustering analysis according to an obtained multi-source database; allocating a travel mode to each electric vehicle based on a Monte Carlo method according to the regional electric vehicle inventory, and generating a travel chain and a charging demand scene; obtaining a reference power limit value based on the charging demand scene, correcting the reference power limit value to obtain analog power setting, and outputting the analog power setting to a power converter for execution; and correcting the reference power limit value according to the local primary response after execution of the power converter to obtain final power setting. The method is used for solving the problems of inaccurate power grid load prediction and insufficient power balance control accuracy caused by the charging demand characteristics of the electric vehicle.

Description

Technical Field

[0001] This invention relates to the field of power distribution network control technology, and more specifically, to a method, apparatus, and equipment for power balance control of charging loads in existing power distribution networks. Background Technology

[0002] Currently, power grid load forecasting mainly relies on historical overall electricity consumption data and macro-period factors, treating the load as a relatively stable and regular overall trend extrapolation. Power grid power balance control depends on centralized dispatch of large generating units, supplemented by a small number of traditional energy storage facilities to follow load changes. For electric vehicle charging, a disordered charging mode is generally adopted, that is, the vehicle is charged at its rated power immediately after being connected, and there is a lack of real-time information exchange and power regulation capabilities between the charging pile and the power grid.

[0003] For example, the invention patent announcement CN114723284B discloses a method for assessing the reliability of a distribution network that includes distributed power sources and electric vehicles. By constructing probabilistic models of wind turbines and photovoltaic generators and the behavioral characteristics of electric vehicle users, and combining them with an orderly charging and discharging strategy based on dynamic time-of-use pricing, the method uses Monte Carlo simulation to assess the reliability of the distribution network. This solves the problem of reliability assessment after the simultaneous access of distributed power sources and electric vehicles, and provides a more accurate assessment and optimization strategy, which is suitable for large-scale distribution network planning.

[0004] The aforementioned traditional technological systems have revealed serious shortcomings when faced with the large-scale integration of electric vehicles. Electric vehicle charging demand exhibits high spatiotemporal randomness and user behavior dependence; its strong volatility directly impacts load forecasting models based on historical patterns, leading to a significant decrease in forecast accuracy. More importantly, electric vehicle charging loads, in unstructured modes, are highly likely to overlap with traditional peak electricity consumption, drastically increasing the pressure on the power grid balance. Existing centralized dispatching methods struggle to detect, accurately regulate, and balance grid power, proving slow and costly in responding to rapid power fluctuations, ultimately increasing the safety risks and reducing the economic efficiency of power grid operation.

[0005] To address the above problems, this invention proposes a solution. Summary of the Invention

[0006] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a method, apparatus, and equipment for power balance control of charging loads in existing distribution networks. By building adaptive scenarios based on Monte Carlo and travel chains, and repeatedly correcting power limits based on control correlation simulation and control response, the present invention addresses the problems of inaccurate grid load forecasting and insufficient accuracy of power balance control caused by the characteristics of electric vehicle charging demand.

[0007] To achieve the above objectives, the present invention provides the following technical solution: The power balance control method for charging loads in existing power distribution networks includes the following steps: obtaining travel patterns through cluster analysis based on the acquired multi-source database; assigning travel patterns to each electric vehicle based on the regional electric vehicle ownership using the Monte Carlo method to generate trip chains and charging demand scenarios; obtaining a baseline power limit based on the charging demand scenarios, and correcting the baseline power limit to obtain a simulated power setting, which is then output to the power converter for execution; and further correcting the baseline power limit based on the local primary response after execution by the power converter to obtain the final power setting.

[0008] In a preferred embodiment, obtaining the travel pattern through cluster analysis specifically involves: using a clustering algorithm to perform cluster analysis on the travel trajectory data to identify the travel pattern; establishing a travel pattern probability model based on the characteristic data of each travel pattern, wherein the characteristic data includes departure time, mileage, travel route, and dwell time; constructing a complete itinerary chain template containing the origin, intermediate stops, and destination, and correspondingly associating several travel pattern probability models.

[0009] In a preferred embodiment, the step of assigning travel modes to each electric vehicle based on the Monte Carlo method to generate trip chains and charging demand scenarios specifically involves: determining the total number of vehicles participating in the simulation based on the regional electric vehicle ownership; randomly assigning a combination of travel modes to each vehicle, generating specific departure times, mileage, travel routes, and dwell times according to the corresponding probability distributions to generate trip chains; calculating the power consumption of each trip segment based on the trip chains and the impact of temperature on battery energy consumption; calculating the available charging time and required charging amount for each vehicle at each dwell point based on the vehicle's battery capacity, initial state of charge, and preset charging strategy; and generating a set of charging demand scenarios and corresponding probability distributions.

[0010] In a preferred embodiment, obtaining the baseline power limit based on the charging demand scenario specifically involves: overlaying the charging demand scenario with the basic load forecast, and using a point estimation method to calculate the probabilistic power flow of each node, wherein the probabilistic power flow includes the voltage over-limit probability and the branch overload probability; identifying the vulnerable periods and vulnerable areas of the power grid based on the probabilistic power flow of each node; and determining the baseline power limit of each charging node based on the vulnerable periods and vulnerable areas of the power grid.

[0011] In a preferred embodiment, determining the reference power limit for each charging node based on the vulnerable periods and areas of the power grid specifically involves: acquiring electrical data of the local controller access point; analyzing the access point electrical data of the vulnerable periods and areas of the power grid based on a safety knowledge base to obtain a preset local voltage safety lower limit and a local associated branch current upper limit; calculating the local voltage offset and local current overrun based on the access point electrical data, the local voltage safety lower limit, and the local associated branch current upper limit; and calculating the reference power limit for the local controller by combining a preset local power constraint function.

[0012] In a preferred embodiment, the modified reference power limit is used to obtain an analog power setting, which is then output to the power converter for execution. Specifically, the local controller exchanges reference power limits and the acquired charging urgency with adjacent controllers. Based on the received reference power limits and charging urgency from all adjacent controllers, the local controller node uses its local reference power limit as the initial state value and the charging urgency as the weight of the corresponding node, and iteratively outputs a consistent power limit for each controller through an optimization algorithm. The consistent power limit is compared with the vehicle's current actual charging power demand to obtain the analog power setting, which is then output to the power converter for execution.

[0013] In a preferred embodiment, the method for obtaining the charging urgency is as follows: based on the remaining battery power, the expected dwell time, and the user-set priority, the weights of each dimension are determined using the analytic hierarchy process (AHP), and the charging urgency is obtained by comprehensive weighting.

[0014] In a preferred embodiment, the step of adjusting the reference power limit based on the local primary response after the power converter is executed to obtain the final power setting specifically involves: generating a nonlinear droop control curve based on the power converter execution process; executing control based on the droop control curve to obtain the local primary response of each controller; adjusting the reference power limit based on the local primary response, and performing simulation again to obtain the final power setting.

[0015] The power balance control device for charging loads in existing power distribution networks includes: a data analysis module for obtaining travel patterns through cluster analysis based on acquired multi-source databases; a scenario generation module for allocating travel patterns to each electric vehicle based on the regional electric vehicle ownership using the Monte Carlo method, generating trip chains and charging demand scenarios; a simulation execution module for obtaining a baseline power limit based on the charging demand scenarios, correcting the baseline power limit to obtain a simulated power setting, and outputting the simulated power setting to the power converter for execution; and a response adjustment module for further correcting the baseline power limit based on the local primary response after execution by the power converter to obtain the final power setting.

[0016] An electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the power balance control method for charging loads in the existing power distribution network.

[0017] The technical effects and advantages of the present invention regarding the power balance control method, device, and equipment for charging loads in existing power distribution networks are as follows: This invention analyzes the travel patterns of electric vehicle users through clustering and generates charging demand scenarios based on the regional electric vehicle ownership, using a combination of Monte Carlo method and travel chain method. This generates a large number of charging demand scenarios that are close to reality and have reasonable spatiotemporal distribution, effectively characterizing the spatiotemporal clustering and dynamic fluctuations of charging demand, significantly improving the accuracy and robustness of regional charging load prediction, and providing a reliable training basis for subsequent power grid balance scheduling. By combining charging demand scenarios with reference power limits, and adjusting the reference power limits based on control correlation and charging urgency, a simulated power setting is obtained, which further enhances the adaptability of regional power control to different load variations and the accuracy of regulation of each sub-controller. The analog power setting is output to the power converter for execution and then corrected twice based on the response of the local controller. The final power setting is output to control the power balance of the grid load, avoid the risk of possible abnormal analog values, and thus effectively reduce grid security risks and enhance the stability of grid collaborative control. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the power balance control method for charging loads in existing power distribution networks provided in an embodiment of the present invention.

[0019] Figure 2 This is a schematic diagram of the structure of the power balance control device for charging loads in the existing power distribution network provided in an embodiment of the present invention.

[0020] Figure 3 This is a schematic diagram of the structure of the power balance control device for charging loads in the existing power distribution network provided in an embodiment of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0022] Example 1, Figure 1 The present invention provides a power balance control method for charging loads in existing distribution networks, comprising the following steps: S1. Based on the acquired multi-source database, the travel patterns are obtained through cluster analysis; S2, based on the number of electric vehicles in the region, assigns travel modes to each electric vehicle using the Monte Carlo method, generating trip chains and charging demand scenarios; S3 obtains a reference power limit based on the charging demand scenario, corrects the reference power limit to obtain an analog power setting, and outputs the analog power setting to the power converter for execution; S4, based on the local primary response after the power converter is executed, further corrects the reference power limit to obtain the final power setting.

[0023] S1. Based on the acquired multi-source database, the travel patterns are obtained through cluster analysis.

[0024] In this embodiment, the process of constructing the multi-source database is as follows: Acquire travel trajectory data and extract features such as travel time, mileage, and stop locations; Retrieve historical charging records, including charging start time, end time, charging amount, and charging power; Obtain the power grid topology, line parameters, transformer capacity, and historical load curves; The above data is cleaned, aligned, and normalized to establish a unified multi-source database.

[0025] In this embodiment, obtaining the travel pattern through cluster analysis specifically involves: Clustering algorithms are used to perform cluster analysis on travel trajectory data to identify travel patterns; A probability model for each travel mode is established based on the characteristic data of each travel mode, including departure time, travel distance, travel route, and stay duration. Construct a complete itinerary chain template that includes the origin, intermediate stops and the destination, and associate it with several travel mode probability models.

[0026] S2, based on the number of electric vehicles in a region, assigns travel modes to each electric vehicle using the Monte Carlo method, generating trip chains and charging demand scenarios.

[0027] In this embodiment, the process of allocating travel modes to each electric vehicle based on the Monte Carlo method to generate trip chains and charging demand scenarios specifically includes: The total number of vehicles participating in the simulation is determined based on the number of electric vehicles in the region. For each vehicle, a combination of travel modes is randomly assigned, and specific departure time, mileage, travel route and stay duration are generated according to the corresponding probability distribution to generate a trip chain; Based on the travel chain, the power consumption of each travel segment is calculated in combination with the effect of temperature on battery power consumption. Based on the vehicle's battery capacity, initial state of charge, and preset charging strategy, calculate the available charging time and required charging amount for each vehicle at each stop point. Generate a set of charging demand scenarios and their corresponding probability distributions.

[0028] S3 obtains a reference power limit based on the charging demand scenario, corrects the reference power limit to obtain an analog power setting, and outputs the analog power setting to the power converter for execution.

[0029] In this embodiment, obtaining the baseline power limit based on the charging demand scenario specifically involves: The charging demand scenario is superimposed with the basic load forecast, and the point estimation method is used to calculate the probabilistic power flow of each node. The probabilistic power flow includes the voltage over-limit probability and the branch overload probability. Based on the probabilistic power flow at each node, identify vulnerable periods and vulnerable areas of the power grid; The baseline power limit for each charging node is determined based on the vulnerable periods and regions of the power grid.

[0030] In this embodiment, determining the reference power limit for each charging node based on the vulnerable periods and areas of the power grid specifically involves: Obtain electrical data from the local controller access point; Based on the analysis of the electrical data of the access point during vulnerable periods and in vulnerable areas of the power grid using a safety knowledge base, the preset local voltage safety lower limit and the local associated branch current upper limit are obtained. Based on the electrical data of the access point, the local voltage safety lower limit, and the local associated branch current upper limit, calculate the local voltage deviation and the local current exceeding the limit. The reference power limit of the local controller is calculated by combining the preset local power constraint function.

[0031] In this embodiment, the local power constraint function is specifically:

[0032]

[0033] In the formula, This is a power constraint function based on the lower voltage safety limit. This is the voltage offset. The preset voltage droop dead zone, Minimum power factor to prevent power from dropping to zero; This is a power constraint function based on the upper limit of branch current. For current exceeding the limit, To preset the current limiting gradient, This is the exponential decay coefficient.

[0034] In this embodiment, the modified reference power limit is used to obtain an analog power setting, which is then output to the power converter for execution. Specifically: The local controller exchanges the reference power limit and the acquired charging urgency with the adjacent controller; The local controller node uses the local reference power limit of each node as the initial state value and the charging urgency as the weight of the corresponding node, and iteratively outputs the consistent power limit of each controller through an optimization algorithm based on the received reference power limit of all adjacent controllers and the charging urgency. The consistent power limit is compared with the vehicle's current actual charging power demand to obtain a simulated power setting, which is then output to the power converter for execution.

[0035] In this embodiment, the method for obtaining the charging urgency is specifically as follows: Based on the remaining battery power, expected dwell time, and user-set priorities, the weights of each dimension are determined using the analytic hierarchy process (AHP), and the charging urgency is obtained by comprehensive weighting.

[0036] S4, based on the local primary response after the power converter is executed, further corrects the reference power limit to obtain the final power setting.

[0037] In this embodiment, specifically: Based on the power converter's execution process, a nonlinear droop control curve is generated; Control is executed based on the droop control curve to obtain the local primary response of each controller; The baseline power limit is adjusted based on the local primary response, and the simulation is performed again to obtain the final power setting.

[0038] In this embodiment, the step of executing control based on the droop control curve further includes: When a neighbor communication interruption is detected, switch to local droop control mode only, that is, adjust the reference power limit based on the local primary response as the final power setting; When an abnormal jump in electrical data is detected, the electrical data is processed through data quality assessment and filtering algorithms. When a control command execution error occurs, the current control is taken offline, and a new power control command is generated through the backup control logic.

[0039] Example 2, Figure 2 The present invention provides a power balance control device for charging loads in existing power distribution networks, comprising: The data analysis module is used to obtain travel patterns through cluster analysis based on the acquired multi-source databases; The scenario generation module is used to allocate travel modes to each electric vehicle based on the regional electric vehicle ownership using the Monte Carlo method, and generate trip chains and charging demand scenarios. The simulation execution module is used to obtain a reference power limit based on the charging demand scenario, correct the reference power limit to obtain a simulation power setting, and output the simulation power setting to the power converter for execution. The response adjustment module is used to further adjust the reference power limit based on the local primary response after the power converter is executed, so as to obtain the final power setting.

[0040] Example 3, Figure 3 The present invention provides a power balancing control device for charging loads in an existing power distribution network. An electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the power balancing control method for charging loads in the existing power distribution network.

[0041] The processor is the control core of the electronic device. It connects various components of the electronic device through various interfaces and lines. It performs various functions of the electronic device and processes data by running or executing programs or modules stored in the memory and calling data stored in the memory.

[0042] The figure only shows an electronic device with components. Those skilled in the art will understand that the structure shown in the figure does not constitute a limitation on the electronic device and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0043] It should be understood that the embodiments described are for illustrative purposes only and are not limited to this structure in the scope of the patent application.

[0044] The computer program stored in the memory of the electronic device is a combination of multiple instructions. When run in the processor, it can implement the steps in the above-mentioned power balance control method for charging loads in the existing power distribution network.

[0045] Specifically, the implementation system of the processor for the above instructions can be referred to the description of the relevant steps in the corresponding embodiments of the accompanying drawings, which will not be repeated here.

[0046] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0047] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0048] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0049] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0050] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0051] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for power balance control of charging loads in existing distribution networks, characterized in that, Includes the following steps: Based on the acquired multi-source databases, travel patterns are obtained through cluster analysis; Based on the number of electric vehicles in a region, travel modes are assigned to each electric vehicle using the Monte Carlo method, generating trip chains and charging demand scenarios. Based on the charging demand scenario, a reference power limit is obtained, and the reference power limit is corrected to obtain an analog power setting. The analog power setting is then output to the power converter for execution. The reference power limit is then corrected based on the local primary response after the power converter is executed, resulting in the final power setting.

2. The power balance control method for charging loads in existing distribution networks according to claim 1, characterized in that, The travel patterns obtained through cluster analysis are as follows: Clustering algorithms are used to perform cluster analysis on travel trajectory data to identify travel patterns; A probability model for each travel mode is established based on the characteristic data of each travel mode, including departure time, travel distance, travel route, and stay duration. Construct a complete itinerary chain template that includes the origin, intermediate stops and the destination, and associate it with several travel mode probability models.

3. The power balance control method for charging loads in existing distribution networks according to claim 2, characterized in that, The method of assigning travel modes to each electric vehicle based on the Monte Carlo method to generate trip chains and charging demand scenarios is as follows: The total number of vehicles participating in the simulation is determined based on the number of electric vehicles in the region. For each vehicle, a combination of travel modes is randomly assigned, and specific departure time, mileage, travel route and stay duration are generated according to the corresponding probability distribution to generate a trip chain; Based on the travel chain, the power consumption of each travel segment is calculated in combination with the effect of temperature on battery power consumption. Based on the vehicle's battery capacity, initial state of charge, and preset charging strategy, calculate the available charging time and required charging amount for each vehicle at each stop point. Generate a set of charging demand scenarios and their corresponding probability distributions.

4. The power balance control method for charging loads in existing distribution networks according to claim 3, characterized in that, The baseline power limit obtained based on charging demand scenarios is specifically as follows: The charging demand scenario is superimposed with the basic load forecast, and the point estimation method is used to calculate the probabilistic power flow of each node. The probabilistic power flow includes the voltage over-limit probability and the branch overload probability. Based on the probabilistic power flow at each node, identify vulnerable periods and vulnerable areas of the power grid; The baseline power limit for each charging node is determined based on the vulnerable periods and regions of the power grid.

5. The power balance control method for charging loads in existing distribution networks according to claim 4, characterized in that, The reference power limit for each charging node is determined based on the vulnerable periods and areas of the power grid, specifically as follows: Obtain electrical data from the local controller access point; Based on the analysis of the electrical data of the access point during vulnerable periods and in vulnerable areas of the power grid using a safety knowledge base, the preset local voltage safety lower limit and the local associated branch current upper limit are obtained. Based on the electrical data of the access point, the local voltage safety lower limit, and the local associated branch current upper limit, calculate the local voltage deviation and the local current exceeding the limit. The reference power limit of the local controller is calculated by combining the preset local power constraint function.

6. The power balance control method for charging loads in existing distribution networks according to claim 5, characterized in that, The corrected reference power limit is used to obtain an analog power setting, which is then output to the power converter for execution. Specifically: The local controller exchanges the reference power limit and the acquired charging urgency with the adjacent controller; The local controller node uses the local reference power limit of each node as the initial state value and the charging urgency as the weight of the corresponding node, and iteratively outputs the consistent power limit of each controller through an optimization algorithm based on the received reference power limit of all adjacent controllers and the charging urgency. The consistent power limit is compared with the vehicle's current actual charging power demand to obtain a simulated power setting, which is then output to the power converter for execution.

7. The power balance control method for charging loads in existing distribution networks according to claim 6, characterized in that, The method for obtaining the charging urgency is as follows: Based on the remaining battery power, expected dwell time, and user-set priorities, the weights of each dimension are determined using the analytic hierarchy process (AHP), and the charging urgency is obtained by comprehensive weighting.

8. The power balance control method for charging loads in existing distribution networks according to claim 7, characterized in that, The process of revising the reference power limit based on the local primary response after the power converter's execution to obtain the final power setting is as follows: Based on the power converter's execution process, a nonlinear droop control curve is generated; Control is executed based on the droop control curve to obtain the local primary response of each controller; The baseline power limit is adjusted based on the local primary response, and the simulation is performed again to obtain the final power setting.

9. An apparatus for using the power balance control method for charging loads in an existing distribution network as described in any one of claims 1-8, comprising: The data analysis module is used to obtain travel patterns through cluster analysis based on the acquired multi-source databases; The scenario generation module is used to allocate travel modes to each electric vehicle based on the regional electric vehicle ownership using the Monte Carlo method, and generate trip chains and charging demand scenarios. The simulation execution module is used to obtain a reference power limit based on the charging demand scenario, correct the reference power limit to obtain a simulation power setting, and output the simulation power setting to the power converter for execution. The response adjustment module is used to further adjust the reference power limit based on the local primary response after the power converter is executed, so as to obtain the final power setting.

10. An electronic device, characterized in that, The electronic device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the power balance control method for charging loads in the existing distribution network as described in any one of claims 1 to 8.

Images