Power distribution network bidirectional power flow coordination control method based on V2G aggregation

Abstract

The invention relates to the technical field of power distribution network bidirectional power flow coordination control, and discloses a power distribution network bidirectional power flow coordination control method based on V2G aggregation, and the method comprises the following steps: S1, constructing a logic energy storage unit composed of a plurality of V2G charging piles and vehicles supporting reverse discharge; s2, aggregating the logic energy storage units into virtual power plant nodes which can be dispatched by a power grid through an information communication technology (ICT); and S3, bidirectional power flow sensing and real-time state estimation: utilizing an intelligent electric meter, a sensor and a topology identification algorithm. According to the method, a logic energy storage unit composed of a plurality of V2G charging piles and vehicles supporting reverse discharging is constructed, the logic energy storage unit is aggregated into a virtual power plant node capable of being dispatched by a power grid through an information communication technology (ICT), and specific charging and discharging actions are decomposed and executed, so that dispersed V2G resources can be effectively integrated, and the V2G resources can be effectively integrated. And collaborative optimization with operation states such as voltage and power flow of the power distribution network is carried out.

Description

Technical Field

[0001] This invention relates to the field of bidirectional power flow coordination control technology for distribution networks, specifically a bidirectional power flow coordination control method for distribution networks based on V2G aggregation. Background Technology

[0002] Traditional power distribution networks are designed with a unidirectional radial structure, where electrical energy flows unidirectionally from substations to user loads. However, with the large-scale integration of distributed generation (such as solar and wind power) and electric vehicles (EVs), power distribution networks are undergoing fundamental changes. On the one hand, distributed generation feeds power back into the grid during peak power generation periods; on the other hand, vehicle-to-grid (V2G) technology enables EVs to not only charge (G2V) but also discharge when the grid needs it (V2G), aggregating a massive number of dispersed EV batteries into "virtual power plants," becoming dispatchable distributed energy storage resources. This bidirectional energy flow breaks the original unidirectional power flow pattern, causing the power distribution network to transform from "passive" to "active," with the direction and magnitude of power flow becoming highly dynamic and unpredictable.

[0003] This transformation currently presents severe technical challenges: voltage over-limit risk: reverse power flow in local areas may cause abnormal voltage rise at the feeder end, exceeding the safe operating range; scheduling and control difficulties: massive, dispersed, and randomly generated V2G resources (such as user travel habits and charging intentions) make unified scheduling of the power grid extremely complex. Therefore, there is an urgent need for a V2G aggregation-based bidirectional power flow coordination and control method for distribution networks. Summary of the Invention

[0004] (a) Technical problems to be solved

[0005] To address the shortcomings of existing technologies, this invention provides a bidirectional power flow coordination control method for distribution networks based on V2G aggregation. This method effectively integrates dispersed V2G resources and coordinates optimization with the voltage, power flow, and other operating conditions of the distribution network, thus solving the aforementioned problems.

[0006] (II) Technical Solution

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for bidirectional power flow coordination control of a distribution network based on V2G aggregation, comprising the following steps: S1. Construct a "logic energy storage unit" consisting of multiple V2G charging piles and vehicles that support reverse discharge; S2. Using information and communication technology (ICT), "logical energy storage units" are aggregated into virtual power plant nodes that can be dispatched by the power grid; S3. Two-way power flow perception and real-time state estimation: Using smart meters, sensors and topology recognition algorithms, the power flow direction changes caused by V2G charging and discharging in the distribution network are perceived in real time, and the local network power flow map is reconstructed to provide input basis for control. S4. Multi-objective optimization scheduling engine design: Based on electricity price signals, grid operation status and user constraints, the optimal charging and discharging plan is solved to achieve a balance between peak shaving and valley filling, reducing grid losses and maintaining voltage stability. S5. Distributed local control and edge collaborative execution: Deploy edge controllers at the station or transformer area level. After receiving dispatch instructions from the superior, they decompose and execute specific charging and discharging actions based on local vehicle availability, battery status, line capacity, and other conditions.

[0008] Preferably, the "logical energy storage unit" in S1 is not a physically centralized battery pack, but rather a distributed energy storage network that connects geographically dispersed electric vehicles and charging piles with V2G functionality through a software platform (such as a virtual power plant). This network can be remotely monitored, coordinated, and scheduled. This model treats each parked electric vehicle as a "mobile power bank," and together they provide peak shaving, frequency regulation, and other services to the power grid. A complete "logical energy storage unit" requires the following four key components to work together: Electric vehicles that support V2G functionality: The vehicle itself must have bidirectional charging and discharging capabilities, allowing electrical energy to be transmitted from the battery back to the power grid; V2G bidirectional charging pile: This is the physical interface for energy flow. It must have both charging and discharging functions and be able to perform bidirectional energy conversion with the power grid. Intelligent communication and protocols: Vehicles, charging piles, and platforms need to exchange data through a unified communication protocol (such as OCPP, ISO 15118) to ensure accurate command issuance and real-time status feedback; Resource aggregator / virtual power plant platform: This is the brain of the "logical unit", responsible for aggregating massive and scattered vehicle resources and packaging them into a large-capacity "virtual power plant" to participate in electricity market transactions and grid dispatch.

[0009] Preferably, S2 aggregates multiple "logical energy storage units" through a software platform to form a "single entity" that can be dispatched by the power grid. This enables a leap from individual uncontrollable to group controllable, develops an edge-coordinated aggregation architecture, improves response speed and security, and does not rely on a central platform to directly issue charging and discharging commands. Instead, it achieves loosely coupled collaboration through incentive signals and local autonomous decision-making agents, distinguishing it from centralized control logic. The virtual power plant node operation mechanism includes resource aggregation and optimization: integrating dispersed "logical energy storage units" and achieving the following through the virtual power plant platform: load forecasting: combining peak and valley electricity prices (e.g., valley electricity 0.3 yuan / kWh, peak electricity 3 yuan / kWh) and user behavior data to optimize charging and discharging timing; dynamic response: dispatching units to discharge during peak power grid periods; participation in the electricity market; peak shaving ancillary services: accepting power grid dispatching commands to participate in peak shaving and valley filling.

[0010] Preferably, S3 ensures the grid remains safe and stable when absorbing a large number of distributed power sources. Data acquisition includes: smart meters installed on transformers or user sides to measure voltage, current, power, and other data in real time; sensors deployed at key nodes to monitor grid frequency, power quality, etc.; topology identification and power flow reconstruction: using the collected data and combined with topology identification algorithms, the real-time connection structure of the network is determined (e.g., which V2G piles are feeding back power to the grid). Based on the reconstructed topology and real-time data, state estimation is performed to calculate the complete state of each node in the grid, such as voltage and phase angle, forming a "local network power flow map." This provides accurate input for multi-objective optimized scheduling, avoiding problems such as voltage exceeding limits and line overload caused by disordered discharge. By installing voltage acquisition terminals, the distribution network voltage is incorporated into the AVC system input, solving the voltage regulation lag problem caused by bidirectional power flow. IoT meters that support high-frequency data acquisition can achieve minute-level or even second-level measurements to meet bidirectional metering requirements. The "automatic topology identification" function has been implemented in the new generation of IoT meters, ensuring that the power flow direction can still be accurately determined after system wiring changes.

[0011] Preferably, in S4, the scheduling engine is the "decision-making brain" of the system, and the objective function is designed as follows: Economic efficiency: ( (Time-of-use electricity pricing).

[0012] Preferably, the cloud-based optimization instructions in S5 need to be dynamically adjusted based on local conditions. The deployment of the edge controller is crucial to ensuring execution efficiency. The edge controller's functional positioning includes local decision-making priority: when cloud communication is interrupted or there is a sudden power grid failure (such as a voltage drop), the edge controller automatically switches to "island mode" and independently schedules based on predefined strategies (such as prioritizing heavy loads and limiting power) to avoid cascading failures; dynamic resource adaptation: real-time updates of EV availability (such as users temporarily canceling charging reservations), battery health status (SOH), and charging pile fault information to dynamically adjust charging and discharging plans; refined execution of charging and discharging actions, including power allocation strategies: using a "capacity-efficiency" dual-factor weighting method, EVs participating in scheduling are sorted according to SOC (high SOC prioritizes discharge), charging and discharging efficiency (such as priority scheduling for those with over 90%), and user responsiveness (such as priority for contracted users) to ensure instruction fairness and execution efficiency; smooth control technology: by limiting the ramp rate (such as ≤5kW / s), voltage fluctuations caused by sudden changes in charging and discharging power are avoided, and PID feedback control is used to correct the deviation between the actual output power and the instruction in real time.

[0013] Compared with existing technologies, this invention provides a method for bidirectional power flow coordination control of distribution networks based on V2G aggregation, which has the following advantages: 1. This invention constructs a "logical energy storage unit" consisting of multiple V2G charging piles and vehicles supporting reverse discharge. Through information and communication technology (ICT), this "logical energy storage unit" is aggregated into a virtual power plant node that can be dispatched by the power grid. The "logical energy storage unit" is not a physically centralized battery pack, but rather connects geographically dispersed electric vehicles and charging piles with V2G capabilities through a software platform (such as a virtual power plant), forming a distributed energy storage network that can be remotely monitored, coordinated, and dispatched. Utilizing smart meters, sensors, and topology recognition algorithms, it can perceive in real-time the V2G-enabled energy in the distribution network. The power flow changes caused by V2G charging and discharging reconstruct the local network power flow map, providing input basis for control. Based on electricity price signals, grid operation status and user constraints, the optimal charging and discharging plan is solved to achieve a balance between peak shaving and valley filling, reducing network losses and maintaining voltage stability. Edge controllers are deployed at the substation or distribution area level. After receiving the upper-level dispatch instructions, they decompose and execute specific charging and discharging actions in combination with local vehicle availability, battery status, line capacity and other conditions. Thus, this method can effectively integrate the dispersed V2G resources and perform collaborative optimization with the voltage, power flow and other operation status of the distribution network. 2. A complete "logical energy storage unit" in this invention requires the following four key components to work together: Electric vehicles supporting V2G functionality: The vehicle itself must have bidirectional charging and discharging capabilities, allowing electrical energy to be transmitted from the battery back to the grid; V2G bidirectional charging piles: This is the physical interface for energy flow, and must have both charging and discharging functions, and be able to perform bidirectional energy conversion with the grid; Intelligent communication and protocols: The vehicle, charging piles, and platform must exchange data through a unified communication protocol (such as OCPP, ISO15118) to ensure accurate command issuance and real-time status feedback; Resource aggregator / virtual power plant platform: This is the brain of the "logical unit," responsible for aggregating massive, dispersed vehicle resources, packaging them into a large-capacity "virtual power station," and participating in electricity market transactions and grid dispatch. 3. This invention utilizes data acquisition: smart meters installed on transformers or user sides to measure voltage, current, power, and other data in real time; sensors deployed at key nodes to monitor grid frequency, power quality, etc.; topology identification and power flow reconstruction: using the acquired data and a topology identification algorithm, the real-time connection structure of the network is determined (e.g., which V2G piles are feeding back power to the grid). Based on the reconstructed topology and real-time data, state estimation is performed to calculate the complete state of each node in the grid, including voltage and phase angle, forming a "local network power flow map." This provides accurate input for multi-objective optimized scheduling, avoiding problems such as voltage exceeding limits and line overload caused by disordered discharge. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of the method steps of the present invention. Detailed Implementation

[0015] 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 skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Please see Figure 1 A method for coordinated control of bidirectional power flow in a distribution network based on V2G aggregation includes the following steps: S1. Construct a "logic energy storage unit" consisting of multiple V2G charging piles and vehicles that support reverse discharge; S2. Using information and communication technology (ICT), "logical energy storage units" are aggregated into virtual power plant nodes that can be dispatched by the power grid; S3. Two-way power flow perception and real-time state estimation: Using smart meters, sensors and topology recognition algorithms, the power flow direction changes caused by V2G charging and discharging in the distribution network are perceived in real time, and the local network power flow map is reconstructed to provide input basis for control. S4. Multi-objective optimization scheduling engine design: Based on electricity price signals, grid operation status and user constraints, the optimal charging and discharging plan is solved to achieve a balance between peak shaving and valley filling, reducing grid losses and maintaining voltage stability. S5. Distributed local control and edge collaborative execution: Deploy edge controllers at the station or transformer area level. After receiving dispatch instructions from the superior, they decompose and execute specific charging and discharging actions based on local vehicle availability, battery status, line capacity, and other conditions.

[0017] In S1, the "logic energy storage unit" is not a physically centralized battery pack, but rather a distributed energy storage network that connects geographically dispersed electric vehicles and charging piles with V2G capabilities through a software platform (such as a virtual power plant). This network can be remotely monitored, coordinated, and dispatched. This model treats each parked electric vehicle as a "mobile power bank," and together they provide peak shaving, frequency regulation, and other services to the power grid. A complete "logic energy storage unit" requires the following four key components to work together: Electric vehicles that support V2G capabilities: The vehicle itself must have bidirectional charging and discharging capabilities, allowing electrical energy to be transmitted from the battery back to the grid; V2G bidirectional charging pile: This is the physical interface for energy flow. It must have both charging and discharging functions and be able to perform bidirectional energy conversion with the power grid. Intelligent communication and protocols: Vehicles, charging piles, and platforms need to exchange data through a unified communication protocol (such as OCPP, ISO 15118) to ensure accurate command issuance and real-time status feedback; Resource aggregator / virtual power plant platform: This is the brain of the "logical unit", responsible for aggregating massive and scattered vehicle resources and packaging them into a large-capacity "virtual power plant" to participate in electricity market transactions and grid dispatch.

[0018] S2 aggregates multiple "logical energy storage units" through a software platform, forming a "single entity" that can be dispatched by the power grid. This enables a leap from individual uncontrollable to group controllable, developing an edge-coordinated aggregation architecture to improve response speed and security. It does not rely on a central platform to directly issue charging and discharging commands, but achieves loosely coupled collaboration through incentive signals and local autonomous decision-making agents. Unlike centralized control logic, the virtual power plant node operation mechanism includes resource aggregation and optimization: integrating dispersed "logical energy storage units" and achieving the following through the virtual power plant platform: load forecasting: combining grid peak and valley electricity prices (e.g., valley electricity 0.3 yuan / kWh, peak electricity 3 yuan / kWh) and user behavior data to optimize charging and discharging timing; dynamic response: dispatching units to discharge during grid peak hours; participation in the electricity market: peak shaving ancillary services: accepting grid dispatch commands to participate in peak shaving and valley filling.

[0019] S3 ensures the grid remains safe and stable even when absorbing a large number of distributed power sources. Data acquisition includes: smart meters (installed on transformers or user sides) that measure voltage, current, power, and other data in real time; sensors (deployed at key nodes) that monitor grid frequency and power quality; and topology identification and power flow reconstruction. Using the collected data and topology identification algorithms, it determines the real-time network connection structure (e.g., which V2G piles are feeding back power to the grid). Based on the reconstructed topology and real-time data, it performs state estimation, calculating the complete state of each node in the grid, including voltage and phase angle, forming a "local network power flow map." This provides accurate input for multi-objective optimized scheduling, avoiding problems such as voltage exceeding limits and line overload caused by disordered discharge. By installing voltage acquisition terminals, the distribution network voltage is incorporated into the AVC system input, solving the voltage regulation lag problem caused by bidirectional power flow. IoT meters supporting high-frequency data acquisition achieve minute-level or even second-level measurements, meeting bidirectional metering requirements. The "automatic topology identification" function has been implemented in the new generation of IoT meters, ensuring accurate determination of power flow direction even after system wiring changes.

[0020] In S4, the scheduling engine is the system's "decision-making brain," and the objective function design is as follows: Economic efficiency: ( (Time-of-use electricity pricing).

[0021] In S5, cloud-based optimization commands need to be dynamically adjusted based on local conditions. The deployment of edge controllers is crucial to ensuring execution efficiency. The functional positioning of edge controllers includes local decision-making priority: when cloud communication is interrupted or there is a sudden power grid failure (such as a voltage drop), the edge controller automatically switches to "island mode" and independently schedules based on predefined strategies (such as prioritizing important loads and limiting power) to avoid cascading failures; dynamic resource adaptation: real-time updates on EV availability (such as users temporarily canceling charging reservations), battery health status (SOH), and charging pile fault information to dynamically adjust charging and discharging plans; refined execution of charging and discharging actions, including power allocation strategies: using a "capacity-efficiency" dual-factor weighting method, EVs participating in scheduling are ranked according to SOC (high SOC prioritizes discharging), charging and discharging efficiency (such as prioritizing scheduling above 90%), and user responsiveness (such as prioritizing contracted users) to ensure command fairness and execution efficiency; smooth control technology: by limiting the ramp rate (such as ≤5kW / s), voltage fluctuations caused by sudden changes in charging and discharging power are avoided, and PID feedback control is used to correct the deviation between actual output power and commands in real time.

[0022] In use, a "logical energy storage unit" is constructed, consisting of multiple V2G charging piles and vehicles supporting reverse discharge. Information and communication technology (ICT) aggregates the "logical energy storage unit" into a virtual power plant node that can be dispatched by the power grid. Using smart meters, sensors, and topology recognition algorithms, real-time changes in power flow caused by V2G charging and discharging in the distribution network are perceived, and the local network power flow map is reconstructed to provide input for control. Based on electricity price signals, grid operating status, and user constraints, the optimal charging and discharging plan is solved to achieve a balance between peak shaving and valley filling, reducing network losses, and maintaining voltage stability. Edge controllers are deployed at the substation or distribution area level. After receiving dispatch instructions from the upper level, they decompose and execute specific charging and discharging actions based on local vehicle availability, battery status, line capacity, and other conditions.

[0023] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for coordinated control of power flow in a distribution network based on V2G aggregation, characterized in that: Comprising the following steps: S1, constructing a "logical energy storage unit" composed of multiple V2G charging piles and supporting reverse discharge vehicles; S2, aggregating the "logical energy storage unit" into a virtual power plant node that can be dispatched by the power grid through information communication technology (ICT); S3, bidirectional flow awareness and real-time state estimation: using smart meters, sensors and topology identification algorithms, real-time awareness of the changes in power flow caused by V2G charging and discharging in the distribution network, and reconstruction of the local network flow atlas to provide input basis for control; S4, multi-objective optimization scheduling engine design: based on electricity price signals, power grid operating state and user constraints, solving the optimal charging and discharging plan to balance peak clipping, reducing network loss and maintaining voltage stability; S5, distributed local control and edge collaborative execution: deploying edge controllers at the station or district level, receiving superior dispatching instructions, combining local vehicle availability, battery status, line capacity and other conditions to decompose and execute specific charging and discharging actions. 2.The V2G aggregation-based power distribution network bidirectional power flow coordination control method of claim 1, wherein: The "logical energy storage unit" in S1 is not a physically concentrated battery pack, but a software platform (such as a virtual power plant) that connects geographically dispersed electric vehicles and charging piles with V2G functionality to form a distributed energy storage network that can be remotely monitored, coordinated and dispatched. This model treats each parked electric vehicle as a "mobile power bank", and together they provide peak shaving, frequency regulation and other services for the power grid. A complete "logical energy storage unit" requires the following four key components to work together: V2G-enabled electric vehicles: vehicles must have bidirectional charging and discharging capability to allow energy to be transferred from the battery back to the grid; V2G bidirectional charging pile: this is the physical interface for energy flow, and must have both charging and discharging functions, and be able to perform bidirectional energy conversion with the grid; Intelligent communication and protocol: vehicles, piles and platforms need to exchange data through a unified communication protocol (such as OCPP, ISO 15118) to ensure accurate issuance of instructions and real-time feedback of status; Resource aggregator / virtual power plant platform: this is the brain of the "logical unit", responsible for aggregating massive dispersed vehicle resources into a large-capacity "virtual power station" to participate in power market transactions and grid dispatching. 3.The V2G aggregation-based power distribution network bidirectional power flow coordinated control method of claim 1, wherein: The S2 aggregates multiple "logical energy storage units" through a software platform to form a "single entity" that can be scheduled by the grid, enabling the transition from individual uncontrollability to group controllability, developing edge collaborative aggregation architecture, improving response speed and safety, and not relying on the central platform to directly issue charging and discharging instructions, but through incentive signals + local autonomous decision agent (Local Agent) to achieve loose coupling collaboration, which is different from centralized control logic. The virtual power plant node operation mechanism includes resource aggregation and optimization: integrating dispersed "logical energy storage units", achieving load forecasting through the virtual power plant platform: combining grid peak and valley electricity prices (such as valley electricity 0.3 yuan / kWh, peak electricity 3 yuan / kWh) and user behavior data to optimize charging and discharging timing; dynamic response: scheduling unit discharging during grid peak periods; participating in the power market: peak shaving auxiliary services: accepting grid dispatching instructions to participate in peak shaving.

4. The V2G aggregation-based power distribution network bidirectional power flow coordination control method of claim 1, characterized in that: The S3 ensures the safety and stability of the grid when absorbing a large number of distributed power sources. Data collection: smart meters: installed at the transformer or user side, measuring real-time voltage, current, power, and other data; sensors: deployed at key nodes to monitor grid frequency, power quality, etc. topology identification and flow reconstruction: using collected data, combining topology identification algorithms to determine the real-time connection structure of the network (for example, which V2G piles are sending electricity back to the grid), based on the reconstructed topology and real-time data, performing state estimation to calculate the voltage, phase angle, and complete state of each node in the grid, forming a "local network flow atlas" to provide accurate input for multi-objective optimization scheduling, avoiding voltage out-of-limit and line overload caused by disordered discharging, and solving the problem of voltage regulation lag caused by two-way flow by installing voltage collection terminals to include distribution network voltage into the AVC system input.

5. The V2G aggregation-based power distribution network bidirectional power flow coordinated control method of claim 1, characterized in that: The scheduling engine in S4 is the "decision brain" of the system, the objective function is designed: , economy: ( for time-of-use pricing).

6. The V2G aggregation-based power distribution network bidirectional power flow coordinated control method of claim 1, characterized in that: The cloud optimization instructions in S5 need to be dynamically adjusted in combination with local actual conditions, and the deployment of edge controllers is the key to ensuring execution efficiency: the functional positioning of edge controllers includes local decision priority: when cloud communication is interrupted or the grid experiences sudden failures (such as voltage drop), the edge controller automatically switches to "island mode" and independently schedules based on predefined strategies (such as preserving critical loads and limiting power), avoiding cascading failures; resource dynamic adaptation: real-time updating of EV availability (such as user temporary cancellation of charging reservations), battery state of health (SOH), and charging pile failure information, dynamically adjusting charging and discharging plans; fine execution of charging and discharging actions includes power allocation strategies: using a "capacity-efficiency" double-factor weighting method to sort participating EVs according to SOC (high SOC discharges first), charging and discharging efficiency (such as 90% or more for priority scheduling), and user responsiveness (such as priority for subscribers), ensuring instruction fairness and execution efficiency; smooth control technology: avoiding voltage fluctuations caused by sudden changes in charging and discharging power through slope rate limitation (such as ≤5kW / s), and combining PID feedback control to real-time correct the actual output power and instruction deviation.

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