Electric vehicle energy routing type charging control system and method

Abstract

The invention relates to the technical field of electric vehicle charging equipment, provides an electric vehicle energy routing type charging control system and method, and realizes photovoltaic, energy storage, power distribution, electric vehicle and other energy integrated cooperative scheduling control and electric vehicle charging real-time load balancing scheduling control in the charging process. The charging equipment realizes equipment layer multi-source cooperative scheduling control based on an electric vehicle energy routing type charging technology, and solves the problems of large-scale overcharging network impact, disordered discharging, photovoltaic absorption, low energy conversion efficiency, insufficient energy scheduling flexibility and the like of a traditional charging equipment layer.

Description

Technical Field

[0001] This invention relates to the technical field of electric vehicle charging equipment, specifically to an electric vehicle energy routing charging control system and method. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] With the accelerated green energy transition and transportation electrification, the number of electric vehicles continues to grow, leading to a surge in charging demand, especially with the increasing prevalence of high-power charging scenarios. This trend places higher demands on charging infrastructure. Traditional charging stations generally rely on a single power grid, which frequently causes severe grid load fluctuations and voltage disturbances under large-scale high-power charging scenarios, seriously affecting the stability of the power grid. Furthermore, with the rapid development of new energy sources such as distributed photovoltaic power, effectively improving local absorption capacity, reducing curtailment rates, and achieving efficient utilization of green energy have become critical issues that charging stations urgently need to address.

[0004] The inventors discovered in their research that existing charging station systems generally adopt a single power supply architecture based on the power grid, which is insufficient to cope with the control pressure brought about by the surge in instantaneous load during high-power charging scenarios. Especially during peak charging periods for electric vehicles, this can easily cause local grid overload, voltage fluctuations, and other problems, affecting power supply stability. Regarding energy utilization, existing distributed renewable energy sources (such as photovoltaics) are mostly connected through AC microgrids, and energy transmission is completed through multi-stage power conversion, resulting in low conversion efficiency and high energy loss. At the equipment structure level (such as charging piles), charging systems are mostly simple combinations of functional units, lacking a unified path management mechanism. The energy flow process is opaque and uncontrollable, making flexible and efficient energy dispatch impossible. Summary of the Invention

[0005] To address the aforementioned issues, this invention proposes an energy-routing charging control system and method for electric vehicles. This system achieves integrated coordinated scheduling and control of energy sources such as photovoltaics, energy storage, power distribution, and electric vehicles during the charging process, as well as real-time load balancing scheduling and control for electric vehicle charging. The charging equipment, based on electric vehicle energy-routing charging technology, realizes multi-source coordinated scheduling and control at the equipment layer, solving problems such as large-scale supercharging network impact, disordered discharge, photovoltaic absorption, low energy conversion efficiency, and insufficient energy scheduling flexibility at the traditional charging equipment layer.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: One or more embodiments provide an electric vehicle energy routing charging control system, comprising: The energy path planning layer is used to identify and construct energy routing paths between various available energy sources, and to select paths and determine the energy flow direction based on the operating status of the charging system and the characteristics of the energy sources.

[0007] The energy dispatch pool is used to dispatch energy from the dispatch bus of the energy path planning layer to the power zone; Power zones are used to distribute energy to different loads through power allocation; The energy path planning layer includes an energy bus, an energy conversion pool, and an energy path planning terminal. The energy path planning terminal includes: The path planning pool is used to construct multiple energy supply paths and establish corresponding topology models. The routing control module executes the energy routing distribution network stability control algorithm and the multi-objective optimal path control algorithm, and selects the optimal path based on the distribution network stability, energy conversion efficiency, response delay, and economic benefits of each path.

[0008] One or more embodiments provide an electric vehicle energy routing charging control system, comprising: The acquisition module is configured to acquire charging demand and charging system operation data; The distribution module is configured to take power smoothing, voltage stability, frequency stability and output power stability as control objectives, construct an energy routing distribution network stability control distribution model, and obtain a set of feasible energy supply strategies that meet the stability requirements after solving the model. The optimization module is configured to construct a multi-objective optimal path control algorithm model to optimize energy routing paths with the goal of maximizing efficiency and maximizing benefits, and to select the optimal energy routing path from the set of feasible energy supply strategies. The charging module is configured to charge the power layer's charging load based on the selected energy routing path.

[0009] An electronic device includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, wherein the computer instructions, when executed by the processor, perform the steps described above in an electric vehicle energy routing charging control system.

[0010] A computer-readable storage medium for storing computer instructions, which, when executed by a processor, complete the steps described above in an electric vehicle energy routing charging control system.

[0011] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) This invention effectively solves the problems of existing charging stations relying on a single power supply path, untimely power supply response, and low energy conversion efficiency by constructing an energy routing-based energy dispatch mechanism. The multi-source fusion and path optimization control strategy improves the flexibility and robustness of the charging system and reduces grid impact caused by load surges. Simultaneously, path selection is evaluated based on comprehensive indicators, ensuring power supply stability while improving energy utilization and significantly reducing energy loss. Furthermore, this architecture enhances the system's local absorption capacity for new energy sources such as photovoltaics, promoting the efficient integration and utilization of green energy, and providing a better solution for charging infrastructure in high-power scenarios.

[0012] The advantages of the present invention, as well as its additional advantages, will be described in detail in the following specific embodiments. Attached Figure Description

[0013] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute a limitation thereof.

[0014] Figure 1 This is a block diagram of an electric vehicle energy routing charging control system according to Embodiment 1 of the present invention; Figure 2 This is an example structural diagram of an electric vehicle energy routing charging control system according to Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of the controller layout of the distributed control architecture in Embodiment 1 of the present invention; Figure 4 This is a schematic diagram illustrating the generation process of a set of feasible energy supply strategies according to Embodiment 1 of the present invention; Detailed Implementation The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0015] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0016] It should be noted that the terminology used herein is for describing particular embodiments only and is not intended to limit the exemplary embodiments of the present invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof. It should be noted that, without conflict, the various embodiments and features within those embodiments can be combined with each other. The embodiments will now be described in detail with reference to the accompanying drawings.

[0017] Example 1 In one or more of the technical solutions disclosed in the embodiments, such as Figures 1 to 4 As shown, an electric vehicle energy routing charging control system adopts a multi-source fusion and hierarchical scheduling architecture design to realize the access of multiple types of energy, energy path planning, precise allocation and dynamic optimization control of charging power, including an energy path planning layer, an energy scheduling pool and a power zone. The energy path planning layer is used to identify and construct energy routing paths between various available energy sources, and to select paths and determine the energy flow direction based on the operating status of the charging system and the characteristics of the energy sources.

[0018] The energy dispatch pool is used to dispatch energy from the dispatch bus of the energy path planning layer to the power zone; A power zone, or power distribution pool, is used to distribute energy to different loads through power allocation. In some embodiments, the energy path planning layer includes an energy combiner bus, an energy conversion pool, and an energy path planning terminal, wherein the energy path planning terminal includes: The path planning pool is used to construct multiple energy supply paths and establish corresponding topology models. The routing control module executes the energy routing distribution network stability control algorithm and the multi-objective optimal path control algorithm, and selects the optimal path based on indicators such as distribution network stability, energy conversion efficiency, response delay, and economic benefits of each path; The energy busbar includes AC busbars and DC busbars, which are used to connect various types of energy supply equipment. The energy conversion pool includes AC / DC modules, DC / DC modules, etc. The energy supply equipment can include grid power (via traditional transformers or power electronic transformers), flexible DC bus cross-regional power supply, distributed photovoltaic, wind power generation, energy storage battery packs, electric vehicle reversible energy supply systems (V2G), etc.; all energy access is through modular interfaces and energy conversion pools to the energy combiner bus, and then participates in path planning and scheduling. All energy sources in the system are connected to the energy conversion pool via modular interfaces and further fed into the energy combiner bus, enabling unified management and coordinated control of energy. The energy conversion pool has multi-port bidirectional conversion capabilities, supporting flexible conversion between AC and DC, and between multiple voltage levels, ensuring efficient connection and dynamic switching of various energy sources at the physical level. Compared with traditional solutions where distributed renewable energy (such as photovoltaics) is mostly connected through AC microgrids and requires multiple power conversion stages (such as DC-AC-DC) for energy transmission, resulting in low conversion efficiency and high losses, this system, through modular DC access and centralized combiner design, enables renewable energy to directly participate in the load power supply path in DC form, effectively reducing intermediate conversion links, significantly improving energy transmission efficiency, and reducing the total system energy consumption.

[0019] This charging control system dynamically identifies and plans energy routing paths between various energy sources (such as mains power, photovoltaics, and energy storage) by setting up an energy path planning layer. The path planning pool establishes multiple energy supply paths based on the current status information of the connected energy sources (including power level, voltage status, and available capacity), forming a corresponding topology model. Based on this model, the routing control module combines an energy routing-based distribution network stability control algorithm with a multi-objective optimal path control algorithm to comprehensively evaluate the stability, conversion efficiency, response delay, and economic cost of each path, dynamically selecting the optimal energy supply path. Energy flows into the energy busbar via the selected path and is distributed to the power zone through the energy dispatch pool. The power zone connects to charging terminals or other load devices, flexibly allocating resources to meet load demands at different power levels, achieving precise energy supply and efficient utilization.

[0020] This system effectively addresses the problems of existing charging stations relying on a single power source, untimely power supply response, and low energy conversion efficiency by constructing an energy routing-based energy dispatch mechanism. Employing a multi-source fusion and path optimization control strategy enhances the flexibility and robustness of the charging system, reducing grid impact caused by load surges. Simultaneously, path selection is evaluated based on comprehensive indicators, ensuring power supply stability while improving energy utilization and significantly reducing energy loss. Furthermore, this architecture enhances the system's local absorption capacity of new energy sources such as photovoltaics, promoting the efficient integration and utilization of green energy and providing a superior solution for charging infrastructure in high-power scenarios.

[0021] In some embodiments, the energy scheduling pool collects the current energy status information of the system and determines the scheduling strategy by combining load demand, grid-side constraints, equipment capacity, etc. In some embodiments, the power area includes an AC / DC power area, a DC / DC power conversion module, a charging control device, and an end-load interface, the end-load interface including devices such as a charging gun and a vehicle connector.

[0022] A further technical solution involves a routing control module in the energy path planning layer. This module executes an energy routing-based distribution network stability control algorithm and a multi-objective optimal path control algorithm. Based on indicators such as distribution network stability, energy conversion efficiency, response delay, and economic benefits for each path, it selects the optimal path using the following steps: Step 1: Obtain charging demand and charging system operation data; Step 2: With power smoothing, voltage stability, frequency stability, and output power stability as control objectives, construct an energy routing distribution network stability control and allocation model, and solve it to obtain a set of feasible energy supply strategies that meet the stability requirements. Step 3: With the goal of maximizing efficiency and maximizing benefits, construct a multi-objective optimal path control algorithm model to optimize energy routing paths, and select the optimal energy routing path from the set of feasible energy supply strategies. Step 4: Based on the selected energy routing path, charge the charging load of the power layer; In this embodiment, firstly, based on the system's operating status (such as electric vehicle charging demand, grid fluctuations, and available energy status), a multi-objective control objective function for energy routing distribution network stability is formed by setting weighted parameters. Solving this system yields a set of feasible energy supply strategies that meet stability requirements. Step 2 involves microgrid-level control, where the microgrid's state is obtained through data collection and formula calculations, and real-time adjustments are made to achieve microgrid stability. In a stable state, if there is energy transmission demand, the path control algorithm in step 3 selects a suitable source to reach the appropriate load. Based on the obtained strategy set, a multi-objective optimal path control algorithm is further invoked, comprehensively evaluating factors such as energy conversion efficiency and economic benefits of each path, ultimately selecting the optimal energy routing path to achieve the best balance between system stability and efficiency.

[0023] Unlike traditional fixed-path or single-strategy scheduling methods, this embodiment introduces a two-layer control architecture of "first solving and then optimizing" in the control process. Feasible paths are initially screened through the distribution network stability model, and then a comprehensive judgment is made through the path optimization algorithm, thereby realizing path-level energy scheduling optimization.

[0024] Traditional station-level monitoring systems centrally access and control all distributed devices, relying on communication and interaction between monitors. However, this approach is limited by communication speed and data update frequency, resulting in a loss of rapid response capability. The usual practice is for the device layer to collect and execute data, then transmit and aggregate the data to the station control system, which then adjusts the system based on the status of each device. This approach has the following problems: first, the transmission is slow because the system is distributed; second, the data collection is inconsistent, and the resulting strategies may not be immediately effective, requiring multiple adjustments. Further technical solutions, such as... Figure 3 As shown, a distributed control architecture is constructed, with the control terminals from the upper level to the lower level including multiple routing control modules, edge coordination controllers, and instantaneous control controllers; The routing control module directly schedules and controls the devices by acquiring high-frequency data from the edge side. Multiple instantaneous control controllers are configured, each independently performing instantaneous control based on the characteristics of its respective power electronic device. For example, an energy storage PCS can achieve micro-level control by simulating generator droop control. An edge coordination controller utilizes data from the instantaneous controllers and its own high-frequency acquisition to coordinate system stability. The next level up is the energy routing controller, which focuses on resource coordination control to create economic value. Market platform control primarily addresses external support needs. The principle of reserving a buffer for the next level of control is consistently applied at each level.

[0025] In step 1, the charging demand is the charging request data of the power zone, including the electric vehicle's battery capacity, current state of charge (SOC), expected charging completion time, and user-set charging priority. The charging system operation data includes dynamic information such as renewable energy power generation, remaining energy storage battery pack capacity, grid electricity price signal, and available energy power of electric vehicle reversible energy supply system. In step 2, a distribution model for energy routing-based distribution network stability control is constructed with power smoothing, voltage stability, frequency stability, and output power stability as control objectives. The formula is: ; in, , , These are weighting coefficients, which are derived from dynamic learning. By continuously adjusting these coefficients, the system eventually reaches equilibrium. This is the upper limit of power for power smoothing scheduling; This is the power value required to stabilize the voltage of the power grid; This is the power value required for stable grid frequency; This is the load-side power demand value; With the goal of smoothing the power distribution network, the power output of the distribution network is calculated, and the control formula is as follows: ; in, Set value; , represents the coefficient; This represents the change in power demand at the current moment. Set the average power of the distribution network over a specified time period; The formula in the above implementation method performs de-jitter processing on sudden efficiency peak fluctuations, changing the power curve into a gentle upward curve. When the power change rate is less than the power mutation threshold, the change in power relative to the average change rate is divided into equal parts according to the K1 coefficient, so that the power changes evenly. When the power change rate is greater than or equal to the mutation threshold, it indicates that a large fluctuation has occurred, and the change in power relative to the average change rate is divided into equal parts according to the K2 coefficient, so that the power changes evenly. The reason for the K1 and K2 coefficients is to avoid the risk of large fluctuations even when the values ​​are equal.

[0026] The power grid voltage stability control formula, which is the adjustment amount of electrical energy calculated based on voltage stability, is as follows: ; in, It is a controllable input power value to the distribution network, such as energy storage, photovoltaic, wind power, or mutual assistance energy between distribution substations; This is the maximum power demand value on the load side; It is a dynamic coefficient; It is the voltage fluctuation value; This is the required value for controlling voltage fluctuations.

[0027] Voltage fluctuations on the distribution network side are closely related to active power output. By adjusting adjustable power sources such as energy storage, the voltage in the distribution area can be kept within a reasonable range, thus avoiding damage to equipment and impact on grid stability caused by voltage fluctuations.

[0028] The formula for distribution network frequency stability control, which calculates the adjustment amount of electrical energy based on frequency stability, is as follows: ; in, It is a controllable input power value to the distribution network, such as energy storage, photovoltaic, wind power, or mutual assistance energy between distribution substations; This is the maximum power demand value on the load side; It is a dynamic coefficient; It is the frequency fluctuation value; This is the power value required for stable grid frequency; Frequency fluctuations on the distribution network side are closely related to active power output. By adjusting adjustable power sources such as energy storage, the frequency of the distribution area can be kept within a reasonable range to avoid damage to equipment and impact on grid stability caused by voltage fluctuations.

[0029] Furthermore, regarding the method for constructing an energy routing-based distribution network stability control and allocation model, and solving it to obtain a set of feasible energy supply strategies that meet stability requirements, such as... Figure 3As shown, it includes the following steps: Step 21: When there is a new load, the energy is distributed to the power supply equipment of each energy source according to the set step size based on the energy routing distribution network stability control and distribution model. Step 22: After each allocation step, obtain the voltage and frequency of the distribution network and determine whether any abnormalities have occurred. If no abnormalities have occurred, continue to the next long energy allocation step; otherwise, calculate the adjustment amount of the power allocated to the distribution network based on the power grid voltage stability control formula and the distribution network frequency stability control formula. Specifically, when voltage and frequency are abnormal, first determine whether the load is too high. Then, the power adjustment is calculated using the grid voltage stability control formula and the distribution network frequency stability control formula. When the load is low, reduce the power generation of new energy sources; when the load is high, recalculate the charging power and redistribute the load. Step 23: Record the energy allocation scheme corresponding to the adjustment amount after each adjustment as a feasible energy supply strategy. After multiple adjustments, a set of feasible energy supply strategies is obtained. In this embodiment, energy allocation is carried out based on the distribution model constructed on the stability of the distribution network. Under the condition of meeting the stability requirements, multiple feasible energy supply strategies can be obtained. However, the allocation may not be the optimal solution even if it meets the stability requirements of the system. Therefore, it is necessary to further introduce a multi-objective optimization mechanism. On the basis of balancing voltage and frequency stability, the mechanism comprehensively considers indicators such as economic efficiency, energy efficiency ratio and renewable energy penetration rate, and selects the energy allocation scheme with better coordination through Pareto optimal solution set.

[0030] In step 3, a multi-objective optimal path control algorithm model is constructed, expressed by the following formula: ; in, To improve the efficiency of branch energy routing paths. The value can be 0 or 1, indicating whether to execute; The delay response loss coefficient, The return coefficient takes values ​​of [0,1]. Output capability coefficient Let T be the scheduling process loss in terms of time, and T be the standard process loss.

[0031] Path efficiency The efficiency is calculated dynamically because each path passes through different power conversion units. The more power conversions a path passes through, the lower the efficiency. Furthermore, the conversion efficiency of different power conversion units is a fixed value. Therefore, the final conversion efficiency can be determined by dynamically checking the conversion units the path passes through. Path efficiency = n × conversion unit efficiency.

[0032] The scheduling process loss is measured in terms of time. The value is calculated based on the losses caused by the delay in meeting the current load demand. For example, if the load of electric vehicles is not met, resulting in low charging efficiency and long occupancy time, these costs are converted into specific values ​​for calculation. T is an empirical value, the loss value within a large cycle.

[0033] In this embodiment, through (Most efficient path) (Highest returns) through and Take the maximum value and perform a weighted sum according to a certain ratio to obtain the optimal control path.

[0034] Factors influencing the scheduling path mainly include energy transmission efficiency and the revenue from power sources. The overall scheduling adheres to the principle of optimal energy utilization. When there is a charging demand on the load side, the source of currently available energy is first determined, and then utilized... (Maximum Efficiency Path) Identify the several paths with the best conversion efficiency, and then calculate the target benefit based on the energy source, thereby finding the optimal path.

[0035] For each energy supply strategy in the set of feasible energy supply strategies, calculate... and The weighted sum is used to determine the optimal path, and the energy routing path corresponding to the maximum value of the sum is selected as the optimal path.

[0036] Example 2 Based on Embodiment 1, this embodiment provides an energy routing-based charging control method for electric vehicles, configured to be implemented in the routing control module of the energy path planning layer, including the following steps: Step 1: Obtain charging demand and charging system operation data; Step 2: With power smoothing, voltage stability, frequency stability, and output power stability as control objectives, construct an energy routing distribution network stability control and allocation model, and solve it to obtain a set of feasible energy supply strategies that meet the stability requirements. Step 3: With the goal of maximizing efficiency and maximizing benefits, construct a multi-objective optimal path control algorithm model to optimize energy routing paths, and select the optimal energy routing path from the set of feasible energy supply strategies. Step 4: Based on the selected energy routing path, charge the charging load of the power layer; This method first collects current electric vehicle charging request information and operational status data of various energy devices within the station (including power, voltage, frequency, and other parameters of mains power, photovoltaic, and battery storage) through a system detection module. Based on this, an energy routing-based distribution network stability control and allocation model is constructed, constrained by power output smoothing and voltage and frequency fluctuation control. Solving this model yields multiple energy supply strategies that meet the distribution network stability requirements. Subsequently, a multi-objective optimal path control algorithm is used to optimize paths within the aforementioned strategy set, comprehensively considering energy conversion efficiency and economic benefits to select the optimal energy flow path. Finally, the control system performs energy scheduling based on this optimal path and supplies energy to the power layer to charge electric vehicle loads, achieving intelligent path-driven energy allocation.

[0037] This charging control method realizes a closed-loop control process from data perception to path optimization and charging execution, significantly improving the energy dispatch efficiency and operational stability of the system under multi-energy integration. Through the combined application of a stability control model and an optimal path model, the system can simultaneously meet the dual requirements of distribution network operation safety and economic benefits, effectively avoiding grid fluctuations caused by sudden load changes. Combining energy cost and conversion efficiency factors during path optimization enables the selection of efficient and low-consumption energy supply strategies, helping to reduce operating costs. This method further enhances the participation of new energy sources in the charging system, strengthening the controllability and dispatch flexibility of green energy.

[0038] In step 1, the charging demand is the charging request data of the power zone, including the electric vehicle's battery capacity, current state of charge (SOC), expected charging completion time, and user-set charging priority. The charging system operation data includes dynamic information such as renewable energy power generation, remaining energy storage battery pack capacity, grid electricity price signal, and available energy power of electric vehicle reversible energy supply system. In step 2, a distribution model for energy routing-based distribution network stability control is constructed with power smoothing, voltage stability, frequency stability, and output power stability as control objectives. The formula is: ; in, , , These are weighting coefficients, which are derived from dynamic learning. By continuously adjusting these coefficients, the system eventually reaches equilibrium. This is the upper limit of power for power smoothing scheduling; This is the power value required to stabilize the voltage of the power grid; This is the power value required for stable grid frequency; This is the load-side power demand value; With the goal of smoothing the power distribution network, the power output of the distribution network is calculated, and the control formula is as follows: ; The power surge threshold is a set value. , represents the coefficient; This represents the change in power demand at the current moment. Set the average power of the distribution network over a specified time period; The formula in the above implementation method performs de-jitter processing on sudden efficiency peak fluctuations, changing the power curve into a gentle upward curve. When the power change rate is less than the power mutation threshold, the change in power relative to the average change rate is divided into equal parts according to the K1 coefficient, so that the power changes evenly. When the power change rate is greater than or equal to the mutation threshold, it indicates that a large fluctuation has occurred, and the change in power relative to the average change rate is divided into equal parts according to the K2 coefficient, so that the power changes evenly. The reason for the K1 and K2 coefficients is to avoid the risk of large fluctuations even when the values ​​are equal.

[0039] The power grid voltage stability control formula, which is the adjustment amount of electrical energy calculated based on voltage stability, is as follows: ; in, It is a controllable input power value to the distribution network, such as energy storage, photovoltaic, wind power, or mutual assistance energy between distribution substations; This is the maximum power demand value on the load side; It is a dynamic coefficient; It is the voltage fluctuation value; This is the required value for controlling voltage fluctuations.

[0040] Voltage fluctuations on the distribution network side are closely related to active power output. By adjusting adjustable power sources such as energy storage, the voltage in the distribution area can be kept within a reasonable range, thus avoiding damage to equipment and impact on grid stability caused by voltage fluctuations.

[0041] The formula for distribution network frequency stability control, which calculates the adjustment amount of electrical energy based on frequency stability, is as follows: ; in, It is a controllable input power value to the distribution network, such as energy storage, photovoltaic, wind power, or mutual assistance energy between distribution substations; This is the maximum power demand value on the load side; It is a dynamic coefficient; It is the frequency fluctuation value; This is the power value required for stable grid frequency; Frequency fluctuations on the distribution network side are closely related to active power output. By adjusting adjustable power sources such as energy storage, the frequency of the distribution area can be kept within a reasonable range to avoid damage to equipment and impact on grid stability caused by voltage fluctuations.

[0042] Furthermore, regarding the method for constructing an energy routing-based distribution network stability control and allocation model, and solving it to obtain a set of feasible energy supply strategies that meet stability requirements, such as... Figure 4 As shown, it includes the following steps: Step 21: When there is a new load, the energy is distributed to the power supply equipment of each energy source according to the set step size based on the energy routing distribution network stability control and distribution model. Step 22: After each allocation step, obtain the voltage and frequency of the distribution network and determine whether any abnormalities have occurred. If no abnormalities have occurred, continue to the next long energy allocation step; otherwise, calculate the adjustment amount of the power allocated to the distribution network based on the power grid voltage stability control formula and the distribution network frequency stability control formula. Specifically, when voltage and frequency are abnormal, first determine whether the load is too high. Then, the power adjustment is calculated using the grid voltage stability control formula and the distribution network frequency stability control formula. When the load is low, reduce the power generation of new energy sources; when the load is high, recalculate the charging power and redistribute the load. Step 23: Record the energy allocation scheme corresponding to the adjustment amount after each adjustment as a feasible energy supply strategy. After multiple adjustments, a set of feasible energy supply strategies is obtained. In this embodiment, energy allocation is carried out based on the distribution model constructed on the stability of the distribution network. Under the condition of meeting the stability requirements, multiple feasible energy supply strategies can be obtained. However, the allocation may not be the optimal solution even if it meets the stability requirements of the system. Therefore, it is necessary to further introduce a multi-objective optimization mechanism. On the basis of balancing voltage and frequency stability, the mechanism comprehensively considers indicators such as economic efficiency, energy efficiency ratio and renewable energy penetration rate, and selects the energy allocation scheme with better coordination through Pareto optimal solution set.

[0043] In step 3, a multi-objective optimal path control algorithm model is constructed, expressed by the following formula: ; in, To improve the efficiency of branch energy routing paths. The value can be 0 or 1, indicating whether to execute; The delay response loss coefficient, The return coefficient takes values ​​of [0,1]. Output capability coefficient Let T be the scheduling process loss in terms of time, and T be the standard process loss.

[0044] Path efficiency The efficiency is calculated dynamically because each path passes through different power conversion units. The more power conversions a path passes through, the lower the efficiency. Furthermore, the conversion efficiency of different power conversion units is a fixed value. Therefore, the final conversion efficiency can be determined by dynamically checking the conversion units the path passes through. Path efficiency = n * conversion unit efficiency.

[0045] The scheduling process loss is measured in terms of time. The value is calculated based on the losses caused by the delay in meeting the current load demand. For example, if the load of electric vehicles is not met, resulting in low charging efficiency and long occupancy time, these costs are converted into specific values ​​for calculation. T is an empirical value, the loss value within a large cycle.

[0046] In this embodiment, through (Most efficient path) (Highest returns) through and Take the maximum value and perform a weighted sum according to a certain ratio to obtain the optimal control path.

[0047] Factors influencing the scheduling path mainly include energy transmission efficiency and the revenue from power sources. The overall scheduling adheres to the principle of optimal energy utilization. When there is a charging demand on the load side, the source of currently available energy is first determined, and then utilized... (Maximum Efficiency Path) Identify the several paths with the best conversion efficiency, and then calculate the target benefit based on the energy source, thereby finding the optimal path.

[0048] For each energy supply strategy in the set of feasible energy supply strategies, calculate... and The weighted sum is used to determine the optimal path, and the energy routing path corresponding to the maximum value of the sum is selected as the optimal path.

[0049] Example 3 Based on Embodiment 1, this embodiment provides an electric vehicle energy routing charging control system, including: The acquisition module is configured to acquire charging demand and charging system operation data; The distribution module is configured to take power smoothing, voltage stability, frequency stability and output power stability as control objectives, construct an energy routing distribution network stability control distribution model, and obtain a set of feasible energy supply strategies that meet the stability requirements after solving the model. The optimization module is configured to construct a multi-objective optimal path control algorithm model to optimize energy routing paths with the goal of maximizing efficiency and maximizing benefits, and to select the optimal energy routing path from the set of feasible energy supply strategies. The charging module is configured to charge the power layer's charging load based on the selected energy routing path.

[0050] It should be noted that each module in this embodiment corresponds one-to-one with each step in embodiment 2, and their specific implementation processes are the same, so they will not be repeated here.

[0051] Example 4 Based on Embodiment 1, this embodiment provides an electronic device, including a memory and a processor, as well as computer instructions stored in the memory and running on the processor. When the computer instructions are executed by the processor, they complete the steps in the electric vehicle energy routing charging control system described in Embodiment 2.

[0052] Example 5 A computer-readable storage medium for storing computer instructions, which, when executed by a processor, complete the steps in the electric vehicle energy routing charging control system described in Embodiment 2.

[0053] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0054] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. An electric vehicle energy routed charging control system, characterized by, Comprise: Energy path planning layer, for identifying and constructing energy routing paths between multiple available energy sources, and selecting paths according to the operating state of the charging system and the energy characteristics to determine the energy flow direction; Energy scheduling pool, for scheduling the energy of the scheduling bus of the energy path planning layer to the power area; Power area, for delivering energy to different loads through power distribution; The energy path planning layer comprises an energy bus, an energy conversion pool, and an energy path planning terminal, and the energy path planning terminal comprises: Path planning pool, for constructing multiple energy supply paths and establishing corresponding topological models; Routing control module, for executing energy routing type distribution network stability control algorithm and multi-objective optimal path control algorithm, and selecting the optimal path according to the indicators of the distribution network stability, energy conversion efficiency, response delay, and economic benefit of each path.

2. The electric vehicle energy routing type charging control system of claim 1, wherein: The energy bus comprises an AC bus and a DC bus, and is used to connect various types of energy supply devices, and the energy conversion pool comprises an AC / DC module and a DC / DC module.

3. The electric vehicle energy routing type charging control system of claim 1, wherein: The energy supply device comprises grid power, distributed photovoltaic power, wind power, energy storage battery pack, and electric vehicle reversible energy supply system.

4. An electric vehicle energy routing charging control method, characterized by, Comprise the following steps: Obtain charging demand and charging system operation data; Construct an energy routing type distribution network stability control distribution model with power smoothing, voltage stability, frequency stability, and output power stability as control targets, and solve to obtain a feasible energy supply strategy set meeting the stability requirements; Construct a multi-objective optimal path control algorithm model to optimize the energy routing path, and select the optimal energy routing path from the energy supply strategy set; Based on the selected energy routing path, charge the charging load of the power layer.

5. The electric vehicle energy routing charging control method of claim 4, wherein, Comprise the following steps: With power smoothing, voltage stability, frequency stability, output power stability as control target, the distribution model of energy routing type distribution network stability control is constructed, and the output power , the formula is: ; wherein, , , is a weight coefficient; is a power smoothing scheduling power upper limit value; is a power value required for grid voltage stability; is a power value required for grid frequency stability; is a load side demand power value.

6. The electric vehicle energy routing type charging control method of claim 5, wherein: To control the grid power smoothing, calculate the grid power output, and the control formula is: ； Wherein, the power mutation threshold is a set value; , represents a coefficient; represents a current time power demand change value, is the average power in the set time period for the distribution network; The grid voltage stability control formula, i.e., the adjustment amount of the grid power based on voltage stability calculation, is: ； wherein, is a controllable input power value of the power distribution network; is a maximum power demand value of the load side; is a dynamic coefficient; is a voltage fluctuation value; is a demand value for controlling voltage fluctuation; The distribution network frequency stability control formula, i.e., the adjustment amount of the distribution network power based on frequency stability calculation, is: ； wherein is the frequency fluctuation value; is the power value required for grid frequency stabilization.

7. The electric vehicle energy routing type charging control method of claim 4, wherein: The method for constructing an energy routing type distribution network stability control distribution model and solving to obtain a feasible energy supply strategy set meeting the stability requirements comprises the following steps: When there is a new load, distribute the energy to each energy supply device according to the energy routing type distribution network stability control distribution model at a set step; After each step of distribution, obtain the voltage and frequency of the distribution network, and determine whether an abnormality occurs; if not, continue the energy distribution at the next step; otherwise, calculate the adjustment amount of the distribution network power based on the grid voltage stability control formula and the distribution network frequency stability control formula; Record the energy allocation scheme corresponding to the adjustment amount of each adjustment as a feasible energy supply strategy, and obtain a set of feasible energy supply strategies after multiple adjustments.

8. An electric vehicle energy routing charging control system, characterized by, The method comprises the steps of: An acquisition module is configured to acquire charging demand and charging system operation data; An allocation module is configured to take power smoothing, voltage stability, frequency stability, and output power stability as control targets, construct an energy routing distribution network stability control allocation model, and solve the model to obtain a set of feasible energy supply strategies that meet the stability requirements; An optimization module is configured to take maximum efficiency and maximum revenue as targets, construct a multi-objective optimal path control algorithm model to optimize the energy routing path, and select the optimal energy routing path from the set of feasible energy supply strategies; A charging module is configured to charge the power layer charging load based on the selected energy routing path.

9. An electronic device, comprising: A computer program product comprising a memory and a processor and computer instructions stored on the memory and running on the processor, when the computer instructions are run by the processor, complete the steps of the electric vehicle energy routing charging control system in any one of claims 4-7.

10. A computer-readable storage medium, characterized in that, A computer program product for storing computer instructions, when the computer instructions are executed by a processor, complete the steps of the electric vehicle energy routing charging control system in any one of claims 4-7.

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