Photovoltaic inverter structure supporting V2G function and voltage optimization regulation and control method

Abstract

The invention discloses a photovoltaic inverter structure supporting a V2G function and a voltage optimization regulation and control method, and belongs to the technical field of voltage optimization regulation and control, and the method comprises the steps: enabling a photovoltaic topological structure and a V2G topological structure to be integrated; determining the power of the current maximum power point and the rated power of the photovoltaic inverter, and constructing a power output model; in combination with a battery state evolution equation, charging and discharging power constraint, an aggregation node coupling relationship and an optimization target, a battery state evolution and charging and discharging power constraint model of the single electric vehicle at a specific aggregation node and a specific moment is constructed by utilizing discrete time step length simulation and solving; on the basis of the voltage safety constraint and the regulation contribution degree, after space priority ranking is carried out between the at least two adjustable resources, coordinated regulation is carried out; a multi-time-scale-oriented layered optimization objective function system is constructed, multi-objective comprehensive optimization of stable voltage, minimum line loss, minimum abandoned light and battery health protection is realized, and safe, efficient and economic grid connection of distributed photovoltaic is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of voltage optimization and control technology, specifically to a photovoltaic inverter structure and voltage optimization and control method that supports V2G functionality. Background Technology

[0002] Currently, the penetration rate of distributed photovoltaic power in distribution networks is rapidly increasing. However, traditional distribution networks were originally designed with a unidirectional power flow structure (power is supplied from substations to users), which makes it difficult to adapt to the operational challenges brought about by the high proportion of distributed power sources.

[0003] Especially in rural or suburban areas with low local loads, solar power generation is high during the day while electricity demand is low. A large amount of reverse power flow flows back along the line impedance, which can easily cause the voltage at the end of the grid to exceed the upper limit (such as exceeding the national standard limit of 230V±10%). This can not only damage user-side equipment, but also trigger protection trips, resulting in curtailment of solar power and power outages.

[0004] Meanwhile, existing regulation methods have significant shortcomings: on the one hand, traditional photovoltaic inverters only have maximum power point tracking (MPPT) functionality and lack the ability to actively participate in grid regulation; on the other hand, although the large-scale development of electric vehicles makes their power batteries potential distributed energy storage resources, existing V2G (Vehicle-to-Grid) technologies mostly use independent devices, requiring additional grid connection interfaces and separate approvals. This is not only costly and space-consuming, but also lacks coordinated control with the photovoltaic system—when photovoltaic output exceeds capacity, causing voltage rise, V2G devices cannot be effectively utilized for local consumption, resulting in delayed regulation response and low efficiency. More importantly, existing methods generally lack awareness of spatial location differences, often adopting an "average effort" scheduling strategy, failing to accurately identify the key nodes with the greatest impact on voltage, leading to resource waste and poor regulation effects. Summary of the Invention

[0005] In view of the above-mentioned problems, the present invention provides a photovoltaic inverter structure and voltage optimization control method that supports V2G function.

[0006] Therefore, the technical problem solved by this invention is: the problem of voltage overrun, fluctuation and curtailment in the distribution network caused by high-penetration distributed photovoltaic access, as well as the defects of existing V2G systems such as independent deployment, lack of coordinated regulation with photovoltaics, complex grid connection and inefficient response.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a voltage optimization control method, comprising: Determine the current maximum power point power and the rated power of the photovoltaic inverter, and construct the output model of the active power and reactive power of the photovoltaic inverter; By combining the battery state evolution equation, charge and discharge power constraints, aggregation node coupling relationship and optimization objective, and using discrete time step simulation and solution, a battery state evolution and charge and discharge power constraint model of a single electric vehicle at a specific aggregation node and specific time is constructed. Based on voltage safety constraints and regulation contribution, coordinated regulation is carried out after spatial priority ranking among at least two adjustable resources; Based on the defined variables, constraints, and priority ranking, a hierarchical optimization objective function system oriented towards multiple time scales is constructed to achieve a comprehensive optimal balance of multiple objectives, including voltage stability, minimum line loss, minimum curtailment of solar power, and battery health protection.

[0008] As a preferred embodiment of the voltage optimization and control method described in this invention, the method involves: determining the current maximum power point power and the rated power of the photovoltaic inverter, and constructing an output model for the active and reactive power of the photovoltaic inverter; specifically, it includes the following steps: Complete the basic settings for maximum power point power and rated power of photovoltaic inverter in the system; The system regulates the actual active power output of the photovoltaic inverter by using the first decision variable. Based on the determination of the actual active power output and rated power remaining capacity of the photovoltaic inverter, the system regulates the reactive power output of the photovoltaic inverter through the second decision variable. Based on the collaborative constraints of the first and second decision variables, it is ensured that the total output power of the photovoltaic inverter does not exceed the rated power capacity of the photovoltaic inverter, and the output of active power does not exceed the maximum power that the photovoltaic inverter can currently generate.

[0009] As a preferred embodiment of the voltage optimization and control method described in this invention, the method comprises: by combining the battery state evolution equation, charge / discharge power constraints, aggregation node coupling relationship, and optimization objective, and using discrete time step simulation and solution, constructing a battery state evolution and charge / discharge power constraint model for a single electric vehicle at a specific aggregation node and a specific time; specifically including the following steps: Data on the change of electric vehicle battery capacity over time is collected to provide basic data support for subsequent battery state evolution and charge / discharge power constraint models. The total power is defined as consisting of two parts: charging and discharging. Power is allocated through binary variables to control the discharging behavior of electric vehicles. Physical constraints are imposed on the maximum charging power, maximum discharging power, upper limit of battery state of charge, and lower limit of battery state of charge to ensure that the calculation results of the model conform to actual engineering conditions.

[0010] As a preferred embodiment of the voltage optimization and control method described in this invention, the method involves: based on voltage safety constraints and control contribution, performing coordinated control after spatial priority ranking among at least two adjustable resources; specifically including the following steps: Based on the real-time acquisition of AC bus voltage at each node, the system performs voltage over-limit detection and safety boundary determination. If the limit is exceeded, the subsequent hierarchical control process is triggered; otherwise, the current operating state is maintained. Based on the active power reduction coefficient of the photovoltaic node and the total dispatch power of electric vehicles under the aggregator node, the contribution of the photovoltaic node and the contribution of the electric vehicle aggregator node are calculated respectively. All photovoltaic nodes and electric vehicle aggregation nodes are sorted from highest to lowest according to their respective contributions to form a control priority list, and control is carried out accordingly; After the control is executed, the AC bus voltage of each node is read again to confirm whether it no longer exceeds the limit. If it still exceeds the limit, the second-best resource is called according to priority. If it no longer exceeds the limit, the current round of control ends and the result is fed back.

[0011] As a preferred embodiment of the voltage optimization and control method described in this invention, the method comprises: constructing a hierarchical optimization objective function system oriented towards multiple time scales based on the defined variables, constraints, and priority ranking, to achieve a comprehensive optimal balance among multiple objectives, including voltage stability, minimum line loss, minimum solar power curtailment, and battery health protection; specifically including the following steps: Based on the day-ahead forecast data, combined with the photovoltaic active / reactive power decision variables, the electric vehicle scheduled discharge capacity and voltage safety hard constraints, a day-ahead hour-level reactive power and voltage optimization objective function is constructed. Based on real-time measurement data, and integrating the rated capacity constraint of photovoltaic inverter, real-time charging and discharging feasibility, voltage regulation contribution and three-level response logic, an objective function for intraday minute-level voltage regulation is constructed. Based on the battery state evolution and charge / discharge power constraint model, the battery aging process is quantified into an optimizable equivalent cost, and an equivalent cost model for electric vehicles considering battery degradation is constructed.

[0012] The present invention also provides a photovoltaic inverter structure that supports V2G functionality.

[0013] To solve the above-mentioned technical problems, the present invention also provides the following technical solution: a photovoltaic inverter structure supporting V2G function, which applies the voltage optimization and control method described above, including: an AC bus for connecting local loads and external power grid; A grid connection switch is installed between the AC bus and the external power grid for grid-connected / off-grid switching. A bidirectional DC / AC inverter connects the DC bus and the AC bus to achieve bidirectional conversion of AC and DC energy. The DC bus serves as a shared DC platform for energy collection and distribution in the intelligent energy management system. A photovoltaic DC / DC converter, with its input terminal connected to a photovoltaic string and its output terminal connected to the DC bus, is used for maximum power point tracking. The V2G bidirectional DC / DC converter is provided in one set, with its DC side connected to the DC bus and an external interface for connecting to the electric vehicle power battery.

[0014] As a preferred embodiment of the photovoltaic inverter structure supporting V2G function described in this invention, the intelligent energy management system communicates with the grid-connected switch, the bidirectional DC / AC inverter, the photovoltaic DC / DC converter, and the V2G bidirectional DC / DC converter, respectively.

[0015] As a preferred embodiment of the photovoltaic inverter structure supporting V2G function described in this invention, the V2G bidirectional DC / DC converter adopts an isolated or non-isolated topology.

[0016] As a preferred embodiment of the photovoltaic inverter structure supporting V2G function described in this invention, the V2G bidirectional DC / DC converter interacts with the battery state of charge of the electric vehicle, and supports charging and discharging modes including constant voltage charging, constant current charging, and constant power charging and discharging.

[0017] As a preferred embodiment of the photovoltaic inverter structure supporting V2G function described in this invention, the intelligent energy management system has a built-in voltage detection module to monitor the voltage of the AC bus in real time.

[0018] The beneficial effects of this invention are as follows: This invention not only achieves deep integration of photovoltaics and V2G in terms of topology, but also constructs an intelligent control system with spatiotemporal coordination and multi-objective optimization in terms of control strategy, providing a complete solution for safe, efficient, and economical grid connection of high-penetration distributed photovoltaics; specifically as follows: (1) Highly integrated hardware significantly reduces costs and grid connection thresholds: By transforming the unidirectional DC / DC converter of the photovoltaic MPPT port into a bidirectional structure and reusing its DC bus to connect to electric vehicles, there is no need to add independent V2G equipment, which greatly saves hardware costs and installation space; more importantly, V2G, as a "functional extension" of the photovoltaic system rather than an independent power unit, can be connected to the grid based on the original grid connection point, which completely avoids the complicated separate grid connection approval process and clears policy obstacles for large-scale promotion; (2) Achieve “local consumption and precise regulation” to effectively suppress voltage over-limit: When the voltage of the distribution network rises due to the large amount of photovoltaic power generation, the system can respond quickly according to the three-level strategy of “reactive power regulation, photovoltaic active power reduction and V2G local charging”; by introducing “voltage regulation contribution”, a sensitivity index based on the physical topology of the power grid, the photovoltaic nodes and electric vehicle aggregation nodes are spatially prioritized to ensure that the resources with the greatest impact on voltage are called first, and the maximum voltage improvement is achieved with the minimum power adjustment, which significantly improves the regulation efficiency and economy. (3) Construct a multi-timescale collaborative scheduling mechanism of "day-ahead-intraday" to take into account safety, economy and user-friendliness: In the day-ahead stage, based on high-precision forecasts, users are guided to make reservations for V2G through the App platform to form the global optimal scheduling baseline; in the intraday stage, based on actual measurement data, rolling corrections are made to dynamically balance multiple objectives such as line loss, curtailment, voltage deviation and battery degradation costs. While ensuring grid safety, the consumption of renewable energy is maximized, and user assets are protected through battery health constraints to enhance the willingness to participate. (4) Supports seamless switching between grid connection and off-grid, enhancing power supply resilience: In the event of a grid failure, the system can quickly switch to off-grid mode, with photovoltaic and electric vehicles jointly constructing an island microgrid to continuously supply power to important loads, significantly improving local power supply reliability and disaster resistance. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a general flowchart of a voltage optimization control method provided in one embodiment of the present invention.

[0021] Figure 2 This is a flowchart illustrating a voltage optimization control method for addressing voltage rise in grid-connected mode, as provided in one embodiment of the present invention.

[0022] Figure 3 This is a topology diagram of a photovoltaic inverter supporting V2G function connected to the power distribution network according to an embodiment of the present invention.

[0023] Figure 4 This is a topology diagram of a conventional photovoltaic inverter provided in one embodiment of the present invention.

[0024] Figure 5 This is a flowchart illustrating the grid-connected and off-grid operation modes of a photovoltaic inverter supporting V2G functionality, provided as an embodiment of the present invention.

[0025] Figure 6 A flowchart illustrating a method for a photovoltaic inverter supporting V2G functionality to respond to grid dispatch and local voltage regulation, provided as an embodiment of the present invention. Detailed Implementation

[0026] To make the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0027] Example 1, referring to Figure 1 and Figure 2 This is one embodiment of the present invention, which provides a voltage optimization control method, including: S1: Determine the current maximum power point power and the rated power of the photovoltaic inverter, and construct the output model of the active power and reactive power of the photovoltaic inverter.

[0028] It should be noted that step S1 specifically includes the following steps: S101: Complete the basic settings for maximum power point power and rated power of photovoltaic inverter in the system; S102: The system regulates the actual active power output of the photovoltaic inverter through the first decision variable; S103: Based on the determination of the actual active power output and rated power remaining capacity of the photovoltaic inverter, the system regulates the reactive power output of the photovoltaic inverter through the second decision variable; S104: Based on the collaborative constraints of the first and second decision variables, ensure that the total output power of the photovoltaic inverter does not exceed the rated power capacity of the photovoltaic inverter, and that the output of active power does not exceed the maximum power that the photovoltaic inverter can currently generate.

[0029] It should be noted that in step S101, the current maximum power point power is dynamically determined by the photovoltaic MPPT (maximum power point tracking) module based on the real-time collected output voltage and current of the photovoltaic string, through the maximum power point tracking algorithm; or it is estimated based on the current irradiance, ambient temperature, and photovoltaic module characteristic parameters.

[0030] It should be noted that in step S102, the active power reduction coefficient is defined as the first decision variable to regulate the output of actual active power. The active power reduction coefficient is dynamically generated by a multi-time-scale optimization algorithm (such as day-ahead planning or intraday rolling correction).

[0031] It should be noted that in step S103, based on the determined active power output, the remaining capacity of the photovoltaic inverter is used to support the reactive power output, and the reactive power reduction coefficient is defined as the second decision variable to regulate the actual reactive power output.

[0032] It should be noted that in step S104, the output of active power and the output of reactive power are coupled to the rated power capacity of the photovoltaic inverter to ensure that the photovoltaic inverter operates in a safe condition at any time, avoid overload, and reserve a controllable margin for subsequent V2G (vehicle-to-grid) coordinated regulation.

[0033] Furthermore, the output models of the photovoltaic inverter at the j-th photovoltaic node in the distribution network and at time t are expressed by the following formulas: ; in, Indicates photovoltaic node At any moment The actual contribution of the individual; Indicates photovoltaic node At any moment The actual unproductive output; Indicates photovoltaic node The active power reduction factor; Indicates photovoltaic node The reactive power reduction factor; Indicates photovoltaic node At any moment Maximum power point power; This indicates the rated maximum power of the photovoltaic inverter.

[0034] It should be noted that, It refers to the active power actually delivered by a photovoltaic inverter to the grid or local load at a specific time and location, representing the portion of photovoltaic power generation that is effectively utilized. It is one of the core objects of regulation; by adjusting its size, it can affect line power flow and node voltage. In this set of formulas, It is not simply equal to the power at the maximum power point, but multiplied by an active power reduction factor. This means that the system can actively deviate the photovoltaic inverter from its maximum power point as needed, thereby achieving power reduction.

[0035] It should be noted that, It is the reactive power that a photovoltaic inverter actually delivers to the grid or local load at a specific time and place (used to establish an electromagnetic field and maintain voltage stability). Although it does no work, it is crucial for maintaining the grid voltage level. Generating inductive reactive power (negative value) can absorb voltage, and is the primary means of solving voltage limit exceedance problems. In this set of formulas, The calculation is based on the rated power capacity limit of the photovoltaic inverter; the total output power of the photovoltaic inverter cannot exceed its rated power capacity. Therefore, when the actual output active power Once set, the remaining capacity of the rated power can be used to output reactive power. In the formula It refers to the current actual output active power. Based on this, the maximum reactive power amplitude that a photovoltaic inverter can theoretically provide is multiplied by the reactive power reduction factor. To determine the final output reactive power. .

[0036] It should be noted that, It is a decision variable used to control the active power output of the photovoltaic inverter at time t. The proportion of its maximum power output; The range of values ​​is: 0≤ ≤1; =1 indicates that the photovoltaic inverter is operating at its maximum power point and generating electricity at full capacity.

[0037] <1 indicates that the photovoltaic inverter actively reduces its output power, also known as "curtailment," which is a measure taken to protect the power grid when the voltage is too high.

[0038] Better In the voltage optimization and control algorithm of this invention, the amount of photovoltaic energy allowed to be injected into the grid is directly determined.

[0039] It should be noted that, It is also a decision variable used to control the proportion of reactive power output by the photovoltaic inverter at time t to its theoretical maximum reactive power.

[0040] The range of values ​​is: 0≤ ≤1; In practical applications, to achieve voltage reduction, the inverter needs to generate inductive reactive power. It should be a negative value; therefore, It should generally be understood as the magnitude ratio of reactive power, and its direction (capacitive / inductive) is determined by the control system based on the voltage conditions; that is, a negative value represents inductive reactive power (voltage absorption), and a positive value represents capacitive reactive power (voltage boost).

[0041] It is a key variable in the control algorithm used for rapid response to voltage changes and is used preferentially over active power reduction.

[0042] It should be noted that, This refers to the maximum active power that a photovoltaic module can output under current environmental conditions such as sunlight and temperature. It is calculated in real time by the MPPT algorithm of the photovoltaic inverter. In this set of formulas, As a benchmark value, is The object on which the coefficient acts This reflects the potential of photovoltaic resources.

[0043] It should be noted that, This is the upper limit of the design capacity of the photovoltaic inverter itself, representing the maximum value of the vector sum of active and reactive power it can handle simultaneously. This is a hard constraint condition in the formula for calculating the upper limit of reactive power. Any combination of active and reactive power cannot exceed this capacity, otherwise the equipment will be damaged.

[0044] Preferably, step S1 constructs a controlled, multi-degree-of-freedom power output model, which is the basis of the entire voltage optimization and control algorithm, transforming the traditional photovoltaic inverter into a controllable intelligent unit with active voltage regulation capabilities.

[0045] S2: By combining the battery state evolution equation, charge and discharge power constraints, aggregation node coupling relationship and optimization objective, and using discrete time step simulation and solution, a battery state evolution and charge and discharge power constraint model of a single electric vehicle at a specific aggregation node and specific time is constructed.

[0046] It should be noted that step S2 specifically includes the following steps: S201: Collect data on the change of electric vehicle battery capacity over time to provide basic data support for subsequent battery state evolution and charge / discharge power constraint models; S202: Defines the total power as consisting of two parts: charging and discharging, and uses binary variables to allocate power and control the discharge behavior of electric vehicles; S203: Physical constraints are imposed on the maximum charging power, maximum discharging power, upper limit of battery state of charge, and lower limit of battery state of charge to ensure that the calculation results of the model conform to actual engineering conditions.

[0047] It should be noted that in step S201, by collecting data on the capacity changes of the electric vehicle battery in different time periods, the law of battery capacity evolution over time can be revealed. This data is the basis for building a battery state evolution model. Recording the specific performance of the battery during charging and discharging, including key parameters such as charging rate, depth of discharge, and battery temperature, provides accurate input data for the subsequent charge and discharge power constraint model.

[0048] Furthermore, data acquisition involves installing high-precision sensors on electric vehicles to monitor key parameters such as battery voltage, current, temperature, and state of charge in real time, and recording various battery parameters at fixed time intervals (such as every minute or hour) to form continuous time series data.

[0049] Furthermore, by collecting data under different driving conditions (such as urban roads, highways, congested sections, etc.), a variety of real-world usage scenarios are covered, ensuring the comprehensiveness and representativeness of the data.

[0050] Furthermore, the collected data is preprocessed to remove outliers and noise, such as erroneous readings caused by sensor malfunctions or external interference; the raw data is normalized so that data of different dimensions can be compared and analyzed on the same scale; and missing data points are filled in using linear interpolation or other interpolation methods to ensure the integrity of the time series.

[0051] It should be noted that in step S202, the total power consists of two parts: charging power and discharging power. Charging power represents the energy that the battery obtains from the grid or charging pile, while discharging power represents the energy that the battery releases to the load. A binary variable is introduced to indicate whether the battery is in a discharging state at the current moment, ensuring the mutual exclusivity of the charging and discharging modes. That is, only one mode can be selected at the same time. This power allocation mechanism is incorporated into the optimization model as one of the constraints to ensure that the calculation results of the model meet the actual charging and discharging requirements.

[0052] It should be noted that in step S203, the charging power is limited by the maximum output power of the battery charger, which is usually determined by the technical specifications of the charging equipment. The charging power is also affected by the charging efficiency. The actual charging power should take into account the conversion efficiency of the charger. The discharging power is limited by the battery's discharge capacity, which is usually determined by the battery's chemical characteristics. The discharging power is also affected by the battery's depth of discharge. Excessive discharge will shorten the battery's lifespan, so it is necessary to reasonably control the depth of discharge.

[0053] For electric vehicles (EVs) that choose to participate in V2G, let each EV arrive at time τarr,i and leave at time τdep,i, with a parking duration of Tstay = τdep – τarr; for aggregator node i, define the set Ωi = {1,…,Ki,t} as all vehicles present at that node at time t, and the charging / discharging power of the corresponding charging pile at time t can be calculated by the battery state evolution and charging / discharging power constraint model; the calculation expression is as follows: ; in, This represents the available battery capacity of the k-th electric vehicle under the i-th aggregator node at time t; Indicates charging efficiency; Indicates discharge efficiency; Indicates the time step; This represents the total scheduling power of the k-th electric vehicle under the i-th aggregator node at time t; This represents the charging power of the k-th EV under the i-th aggregator node at time t; This represents the discharge power of the k-th EV under the i-th aggregator node at time t; Represents the binary variable of V2G discharge. ∈{0,1}; Indicates the maximum discharge power; Indicates the maximum charging power; This indicates the minimum state of charge at which the battery can operate safely. This indicates the highest state of charge at which the battery can operate safely. This indicates the rated total capacity of the electric vehicle's battery.

[0054] It should be noted that, This represents the remaining energy available for charging and discharging of the k-th electric vehicle at time t. It is a direct reflection of the battery's energy state, determining how much power the k-th electric vehicle can provide or absorb in the next moment. In this set of formulas, The calculation formula is a state equation, the value of which is determined by the capacity at the previous time step. The result is obtained by adding the charge amount and subtracting the discharge amount; the charge amount is... The discharge amount is .

[0055] It should be noted that charging efficiency The discharge efficiency represents the efficiency at which electrical energy input into the battery is converted into chemical energy. This represents the efficiency of converting the battery's chemical energy into output electrical energy; losses are considered when calculating energy changes, making the model closer to reality. For example, when 1 kWh of electricity is charged, only 0.95 kWh is stored; when 1 kWh of electricity is released, 1.05 kWh of battery energy is consumed.

[0056] It should be noted that the time step This refers to the time resolution of the optimization algorithm, such as 5 minutes, 10 minutes, or 1 hour, in hours (h). For example, if Δt = 5 / 60 = 1 / 12 hours, it means that the state is updated every 5 minutes, converting power (kW) into energy (kWh), because energy = power × time.

[0057] It should be noted that, This represents the system's total power dispatch instruction for the k-th electric vehicle. Although mathematically written as c+d, it is physically impossible for it to charge and discharge simultaneously. Therefore, in actual control, and There is one and only one non-zero value; In the optimization model, it is used as an intermediate variable to construct the objective function or constraints; in engineering implementation, c or d is actually executed, not the sum of the two. It is used to report the net power interaction status of the kth electric vehicle when the energy management system communicates with the "Four-Knowledge" platform.

[0058] It should be noted that, =c+d is a mathematical modeling technique (for unified handling of charging / discharging power), and does not represent simultaneous physical charging and discharging. The actual control logic is determined by... Conditions such as SOC (State of Charge) determine whether a vehicle is in one of three states: charging, discharging, or idle.

[0059] It should be noted that, It is the active power absorbed by the k-th electric vehicle from the power grid or photovoltaic system at time t; The range of values ​​is: 0≤ ≤ ; The maximum charging power for the kth electric vehicle model; when When the value is 0, it means no charging.

[0060] It should be noted that, Let be the active power released by the k-th electric vehicle to the grid or local load at time t; The range of values ​​is: 0≤ ≤ ⋅ ; The maximum discharge power of the kth electric vehicle model; It should be noted that, It is a decision variable used to control whether the kth electric vehicle is allowed to participate in discharge at time t; its value range is {0,1}. =1 indicates that discharge is permitted. It can be greater than 0; =0 indicates: Discharge is prohibited. =0.

[0061] Better It can avoid frequent charging and discharging switching of the same electric vehicle, protect battery life, and meet the user's wish of "charging only and not discharging".

[0062] It should be noted that, The maximum external discharge power supported by the k-th electric vehicle. As an upper limit constraint on discharge power; The maximum charging power supported for the k-th electric vehicle. As an upper limit constraint on charging power; The minimum state of charge set to protect battery health. The maximum state of charge set to protect battery health. and To ensure that the battery always operates within a safe range, prevent overcharging or over-discharging, and extend battery life, this is also the reason why this embodiment emphasizes that the electric vehicle battery can only participate in regulation when its state of charge meets the requirements. This refers to the total energy capacity of the battery when fully charged. Used to express absolute capacity This is converted to a relative state of charge, i.e., SOC = / This facilitates standardized management and constraints.

[0063] Preferably, step S2 constructs an accurate, controlled, and fully considers the battery health status (such as battery state of charge limits, charge and discharge efficiency, maximum power boundary, etc.) dynamic model of electric vehicles. This model fully characterizes the availability and scheduling potential of electric vehicles in the time dimension (parking time) and spatial dimension (access node location) by acquiring key data such as the arrival / departure time, battery available capacity, upper and lower limits of charge and discharge power, safe range of battery state of charge, charge and discharge efficiency, and V2G control logic (such as binary discharge permission variables) of each electric vehicle participating in V2G.

[0064] Therefore, this model provides the underlying data support required for day-ahead planning and intraday rolling optimization of the intelligent energy management system. In the day-ahead phase, the system can formulate a 24-hour scale charging and discharging reservation scheduling scheme based on the predicted electric vehicle access plan and the initial state of battery charge. In the intraday phase, the system can dynamically correct the charging and discharging power commands for each vehicle by combining real-time battery charge status, grid voltage and photovoltaic power output fluctuations.

[0065] Through this modeling process, electric vehicles are no longer regarded as passive loads that only consume electrical energy in the traditional sense, but are transformed into distributed energy storage units with clear spatiotemporal constraints, accurate prediction, and flexible scheduling. This enables the system to accurately aggregate and schedule massive, dispersed EV resources. When excess photovoltaic power generation leads to local voltage overruns, it prioritizes guiding eligible EVs to charge locally, thereby achieving the coordinated control objectives of "locally absorbing excess photovoltaic power, suppressing voltage overruns, and reducing line losses," ultimately improving the safety, economy, and renewable energy absorption capacity of the active power distribution network.

[0066] S3: Based on voltage safety constraints and regulation contribution, coordinated regulation is carried out after spatial priority ranking among at least two adjustable resources.

[0067] It should be noted that in step S3, the adjustable resources include photovoltaics and electric vehicles.

[0068] It should be noted that step S3 specifically includes the following steps: S301: Based on the real-time acquisition of AC bus voltage at each node, the system performs voltage over-limit sensing and safety boundary determination. If the limit is exceeded, the subsequent hierarchical control process is triggered; otherwise, the current operating state is maintained. S302: Based on the active power reduction factor of the photovoltaic node and the total dispatch power of electric vehicles under the aggregator node, calculate the contribution of the photovoltaic node and the contribution of the electric vehicle aggregator node respectively. S303: Sort all photovoltaic nodes and electric vehicle aggregation nodes according to their respective contributions from high to low to form a control priority list, and implement control accordingly; S304: After the control is executed, read the AC bus voltage of each node again to confirm whether it no longer exceeds the limit. If it still exceeds the limit, continue to call the second-best resource according to the priority. If it no longer exceeds the limit, end this round of control and provide feedback on the result.

[0069] It should be noted that when peak shaving and valley filling or voltage stabilization are required, the resources with the highest contribution will be used first. For example, when the voltage is too high, the photovoltaic nodes that contribute the most power should be prioritized to reduce their output, or the electric vehicle clusters that contribute the most power should be prioritized to charge.

[0070] The constraints on the operation of the power system are the voltage security and regulation capability constraints of each node at time t; specifically expressed as: ; in, This represents the lower voltage safety limit for node i; This represents the upper limit of the safe voltage for node i; This represents the actual voltage of node i at time t.

[0071] It should be noted that, This represents the minimum allowable voltage value for node i. This represents the highest allowable voltage value for node i. and It is a mandatory safety constraint for power grid operation. At any time and at any node, the voltage must fall within this range; otherwise, it will damage user equipment, trigger protection devices to trip, or even cause a power outage. and The standard for the value is usually set according to the national or regional power grid standard. For example, the Chinese standard stipulates that the allowable deviation of the voltage of the low voltage distribution network is ±10% of the nominal voltage of 230V, that is, 207V~253V. and This is the hard constraint of the entire optimization model in this embodiment. The ultimate goal of all control strategies (such as adjusting reactive power, reducing active power, and starting V2G charging) is to ensure that this condition is met.

[0072] It should be noted that, It is the AC bus voltage value of node i at the current moment, which is monitored in real time by the voltage detection module of the intelligent energy management system. As a feedback signal, it is used to determine whether the current system is in a safe state and to trigger corresponding control actions. The system will continuously monitor this. Once it is found to exceed [ If the range is exceeded, a tiered response mechanism will be activated.

[0073] Voltage regulation can be achieved by reducing the active power of photovoltaic nodes; therefore, a voltage regulation contribution is introduced for spatial ranking. The expressions for calculating the contribution of photovoltaic nodes and the contribution of electric vehicle aggregation nodes are as follows: ; ; in, This represents the voltage regulation contribution of the j-th photovoltaic node at time t; This represents the voltage regulation contribution of the j-th electric vehicle aggregation node at time t; This represents the active power reduction factor of the j-th photovoltaic node; This represents the sum of the charging and discharging power of all electric vehicles at the j-th photovoltaic node.

[0074] It should be noted that, It is a partial derivative, mathematically representing the sensitivity of voltage to the active power control variable, that is, when the active power reduction factor of the photovoltaic node... Even a tiny change can affect the voltage at local and upstream nodes. To what extent does the change occur; the absolute value sign indicates that only the magnitude of the effect matters, without distinguishing the direction (pressure increase or decrease). The larger the value, the more significant the impact of the photovoltaic node on the voltage; for example, the photovoltaic node located at the end of the line has the greatest impact on the voltage of that node due to changes in its power output. The value is usually very high.

[0075] Ideally, when multiple photovoltaic nodes need to participate in regulation simultaneously, priority should be given to selecting... Adjust the largest node to achieve the greatest voltage improvement with the least power adjustment.

[0076] It should be noted that, Similarly, partial derivatives measure the sensitivity of voltage to the total power of EVs, when the total dispatched power of electric vehicles connected to the photovoltaic node... Even a tiny change can affect the voltage at local and upstream nodes. To what extent has it changed?

[0077] It should be noted that, It is the sum of the charging and discharging power of all vehicles present at that node (see the formula above). ), The higher the value, the stronger the impact of the electric vehicle cluster on voltage in that area; generally, areas near voltage limits or with high line impedance have a stronger impact. With a higher value, it should be prioritized when V2G resources need to be utilized. The highest node performs charging or discharging to achieve the most effective local consumption or grid support.

[0078] It should be noted that, These are the decision variables defined earlier, used to control the proportion of active power output by the photovoltaic inverter to its maximum generateable power; in this formula, It is a calculation The independent variable, that is, by changing... To observe the changes in voltage; These are also variables defined earlier. This represents the net power interaction of all vehicles present at aggregation node j (positive for charging, negative for discharging, or considering only the absolute value). In this formula, It is a calculation The independent variable.

[0079] Furthermore, such as Figure 2As shown, when excessive photovoltaic power generation leads to a rise in local voltage: the controller first regulates the inverter to output inductive reactive power. If the voltage remains high and exceeds the limit, the photovoltaic power is simultaneously limited. Then, the excess power is used to charge electric vehicles via the V2G interface. When the electric vehicle's battery state of charge meets the requirements, local consumption is achieved. The discharge power of the electric vehicle must be less than the rated power of the photovoltaic node, as expressed by: ; in, Indicates the charging / discharging power of the electric vehicle; This indicates the rated power of the photovoltaic inverter.

[0080] It should be noted that, It is a power capacity constraint, meaning that the discharge power (or charging power) of the electric vehicle should not exceed the rated power of the photovoltaic inverter to which it is connected; it can protect the equipment, prevent the inverter from being overloaded and damaged due to excessive EV power, and ensure that the power scale of V2G matches the main system.

[0081] Preferably, the set of formulas in step S3 constructs a spatial priority screening mechanism based on physical characteristics. Voltage constraint is the goal, and voltage regulation contribution is the means. Together, they ensure that the regulation strategy is not blind, but targeted and precise, thereby maximizing the use of distributed resources and achieving economical, efficient and reliable operation while ensuring grid security.

[0082] S4: Based on the variables, constraints and priority ordering defined in steps S1 to S3, construct a hierarchical optimization objective function system for multiple time scales to achieve a comprehensive optimal balance of multiple objectives, including voltage stability, minimum line loss, minimum curtailment of light, and battery health protection.

[0083] It should be noted that step S4 specifically includes the following steps: S401: Based on day-ahead forecast data (including distributed photovoltaic power output forecast, load forecast, and electric vehicle user reservation information), combined with photovoltaic active / reactive power decision variables, electric vehicle reserved discharge capacity, and voltage safety hard constraints, construct a day-ahead hourly reactive voltage optimization objective function; S402: Based on real-time measurement data (actual photovoltaic output, node voltage, real-time SOC of electric vehicles, and line status), and integrating the rated capacity constraints of photovoltaic inverters, the feasibility of real-time charging and discharging, and the contribution of voltage regulation and the three-level response logic (reactive power, light clipping and V2G charging), a target function for intraday minute-level voltage regulation is constructed. S403: Based on the modeling of battery energy state, charge and discharge efficiency, SOC safe range and cycle life in S2, the battery aging process is quantified into an optimizable equivalent cost, and an equivalent cost model for electric vehicles considering battery degradation is constructed.

[0084] It should be noted that with the popularization and development of existing smart distribution network photovoltaic power generation forecasting and load forecasting technologies, the accuracy of power forecasting can usually exceed 90%. Through the "PV Four-Capability" system, power flow planning is carried out on distributed active distribution networks with more than 24 hours of operation through optimized regulation. This allows for advance planning of power generation and load for the photovoltaic inverters supporting V2G functionality in this invention. Private electric vehicles can use online software to understand the charging and discharging capacity requirements, charging prices, and discharging benefits of different photovoltaic inverter charging nodes. Based on distance, travel habits, electric vehicle capacity, and SOC status, they can plan ahead and make appointments for charging and discharging, thereby obtaining low-cost charging and high-cost discharging benefits.

[0085] Furthermore, the objective function for optimizing hourly reactive power and voltage is expressed as follows: ; in, The objective function to be minimized is... ; Represents the objective function for voltage deviation; Represent the objective function for voltage fluctuation; and They are and The corresponding weights; This represents the voltage at node i at time t; Represents the theoretical voltage; This represents the voltage at node i at the previous moment.

[0086] It should be noted that, It is the core instruction of the entire optimization problem, namely, to find a set of decision variables such that Minimum, this set of decision variables includes the photovoltaic active power reduction coefficient, the photovoltaic reactive power reduction coefficient, and the scheduled discharge power of electric vehicles; This measures the deviation between the voltage at each node of the distribution network and the ideal reference voltage over a predicted time period (e.g., 24 hours), by minimizing... To ensure that the system voltage remains as stable as possible within a safe range during future operation, avoiding exceeding limits; if If the voltage is relatively large, the optimization algorithm will prioritize ensuring voltage stability; conversely, if the voltage is relatively small, it will sacrifice some voltage accuracy in exchange for improvements in other objectives (such as economy). Adjustments can be made according to actual needs; The dynamic stability of voltage measures the drastic change in voltage over a predicted time period, calculated as the sum of squares of voltage changes between two adjacent time points; this is achieved by minimizing... It can suppress frequent and large voltage fluctuations, which is crucial for protecting sensitive electrical equipment and improving user comfort. To balance the priorities among different objectives, for example, in areas with drastic load changes, it will assign... A higher value.

[0087] It should be noted that due to the random and unpredictable nature of power grid operation, local voltage control is required during daytime operation through an intelligent energy management system.

[0088] Furthermore, the objective function for intraday minute-level voltage regulation is expressed as: ; in, The objective function to be minimized is... ; Represent the objective function for line loss; Represents the objective function for voltage offset; Represent the objective function for photovoltaic power reduction; This represents the objective function for the equivalent cost of electric vehicles, taking into account battery degradation. , , , P represents the weights of each objective function; Total P represents the total power consumed by the transformer substation. K This indicates that node p consumes active power; P PV Indicates the active power generated by photovoltaic node p; N represents the number of distribution network nodes in the assessment area; U N This indicates the rated value of the node voltage.

[0089] It should be noted that, It is the objective function of the intraday rolling optimization phase, which aims to revise the day-ahead plan based on real-time data on a timescale of several minutes to tens of minutes to cope with the randomness and uncertainty in power grid operation.

[0090] It should be noted that, It measures the active power loss caused by line resistance during the operation of the distribution network. Minimizing line loss can reduce the operating cost of the system and improve energy efficiency. The measure of the amount of photovoltaic power that must be forgone to maintain voltage stability, known as "curtailment," is to minimize it. This means absorbing as much renewable energy as possible to reduce economic losses and resource waste; Measuring the instantaneous deviation between the voltage of each node and the reference voltage at the current moment is the most direct goal of intraday optimization, ensuring that the system is within the safe voltage range at every moment; The aging loss of electric vehicle batteries due to charge-discharge cycles is converted into a quantifiable economic cost to reflect its impact on the user's battery life.

[0091] Furthermore, the equivalent cost model for electric vehicles considering battery degradation is expressed as: ; in, κ1 represents the equivalent cycle depth of the k-th vehicle due to V2G discharge during time period t; κ in κ1, κ2, and κ3 are coefficients related to the charging and discharging power and the equivalent cycle depth.

[0092] Preferably, the core architecture of the multi-timescale operation algorithm used in step S4 divides the entire voltage regulation process into two levels: a day-ahead level focusing on the future, and an intraday level focusing on the present. Specifically, it can be broken down into two parts for understanding: First node: Day-ahead layer; Define the time range and resolution: The next 24 hours is the planning cycle of the day-ahead layer; NT=96, Δt=15min means that within 24 hours, it is divided into 96 time steps (because 24 hours x 60 minutes / 15 minutes = 96), and each time step is 15 minutes, which is a relatively rough but comprehensive time scale; Define the optimization objective: The core safety objective is to ensure that the voltage of all nodes does not exceed the limit. This means that through advance planning, we can ensure that the voltage of every distribution network node at every point in time within the next 24 hours is kept within the safe upper and lower limits. As mentioned above, the day-ahead optimization objective of step S4 is to minimize both voltage deviation and voltage fluctuation. Determine decision variables: (1) The photovoltaic active power reduction factor determines whether photovoltaic power needs to be actively curtailed; (2) The photovoltaic reactive power reduction factor determines how much inductive or capacitive reactive power the photovoltaic inverter generates to support the voltage; (3) The scheduled discharge power plan for electric vehicles is based on the travel intentions and electricity price signals submitted by the vehicle owner through the App.

[0093] The system utilizes high-precision forecast data (PV output and load) to develop a comprehensive, preventative operating plan for the entire system one day in advance. This plan determines which PV nodes need to "generate less electricity" or "generate more reactive power" at what time, and which electric vehicles can "generate some electricity" at night to help the grid. This plan will be released to the user's app for the vehicle owner to confirm.

[0094] Second node: Rolling intraday layer; Define the time range and resolution: The minute-level is the execution cycle of the rolling intraday layer, which focuses on the current state and a very short period of time in the future; the prediction time domain NP=12, Δt=5min means that each time only 12 time steps in the future are optimized, and each step is 5 minutes (that is, the total prediction time domain is 12x5=60 minutes). After completing one optimization, the system will roll forward one step (for example, after 5 minutes), and then use the latest real-time data to re-optimize the next 12 time steps, which is rolling optimization.

[0095] Define the optimization objective: Minimizing voltage deviation and fluctuation in the distribution network is the direct objective. Unlike the day-ahead layer, which only focuses on preventing over-limits, the goal is to keep the voltage as stable as possible near the ideal value and avoid drastic fluctuations. Real-time correction is the core action. It is not about starting a new plan from scratch, but about making fine adjustments based on the current plan and real-time measured data (such as actual voltage, actual photovoltaic output, EV real-time SOC, etc.). Determine the revised decision variables: The photovoltaic active power reduction factor planned in the current period will be revised in real time. The planned EV discharge power has been revised. The additional charging power is a decision variable. At the intraday level, the system can not only adjust the discharge according to the actual situation, but also dynamically start EV charging (especially when the voltage rises due to excess photovoltaic power generation). This is an immediate response that is usually not considered at the intraday level.

[0096] The rolling intraday layer is responsible for handling unpredictable emergencies (such as a sudden appearance of a dark cloud causing a sharp drop in photovoltaic output, or a sudden start of a large load), refreshing the plan at an extremely high frequency (every 5 minutes) to ensure that the system can operate safely and smoothly at all times.

[0097] Ideally, the day-ahead layer and the rolling intraday layer constitute a complete closed loop of prediction, planning, and correction. The day-ahead layer is based on predictions to make long-term plans and formulate a macro framework; the intraday layer is based on actual measurements to make real-time adjustments and correct micro details. The collaborative approach of the day-ahead layer and the rolling intraday layer not only ensures the long-term stability of the system, but also gives it strong flexibility to cope with instantaneous disturbances. It is a key technical means to achieve safe, economical, and efficient operation under a high proportion of distributed energy grid connection.

[0098] Ideally, based on the voltage regulation optimization model for transforming multi-timescale photovoltaics into photovoltaic plus electric vehicle nodes, it can be seen that the essence of the voltage regulation optimization problem is a mixed integer nonlinear optimization problem with multiple constraints and variables. By using artificial intelligence algorithms, the optimization problem of voltage regulation commands for photovoltaic and electric vehicle nodes can be solved, ultimately achieving the goal of stabilizing the entire grid voltage within the qualified range at the lowest economic or technical cost, while maximizing the absorption of renewable energy and meeting the travel needs of car owners as much as possible.

[0099] Example 2, refer to Figures 3-6 This invention provides a photovoltaic inverter structure that supports V2G functionality, which is applied to a voltage optimization and control method in Embodiment 1.

[0100] Specifically, in this embodiment, after the photovoltaic inverter supporting V2G function is connected to the distribution network, the topology connection architecture is as follows: Figure 3 As shown, it includes: AC bus 1 is used to connect the local load to the external power grid; Grid connection switch 2 is installed between AC bus 1 and power grid A, and is used for grid connection / off-grid switching; The bidirectional DC / AC inverter 3 connects the DC bus 4 and the AC bus 1 to achieve bidirectional conversion of AC and DC energy. DC bus 4 serves as a shared DC platform for system energy collection and distribution; Photovoltaic DC / DC converter 5, with its input terminal connected to photovoltaic string B and its output terminal connected to DC bus 4, is used for maximum power point tracking; The V2G bidirectional DC / DC converter 6 is provided with one set, whose DC side is connected to the DC bus 4, and has an external interface for connecting to the electric vehicle power battery. The intelligent energy management system 7 communicates with the grid-connected switch 2, the bidirectional DC / AC inverter 3, the photovoltaic DC / DC converter 5, and the V2G bidirectional DC / DC converter 6, and can be connected to the distributed photovoltaic "four-capable" system (observable, measurable, controllable, and adjustable) to achieve coordinated scheduling of the entire system's operating mode and power flow.

[0101] It should be noted that the intelligent energy management system 7 collects voltage and current signals at various points and executes the built-in control algorithm to send PWM (pulse width modulation) control signals to the grid-connected switch 2, the bidirectional DC / AC inverter 3, the photovoltaic DC / DC converter 5, and the V2G bidirectional DC / DC converter 6, respectively, thereby achieving precise control of the system power flow.

[0102] Furthermore, the V2G bidirectional DC / DC converter 6 can adopt an isolated or non-isolated topology.

[0103] It should be noted that the V2G bidirectional DC / DC converter 6 can be achieved through low-cost modification of the existing photovoltaic inverter MPPT module. The diodes in the original unidirectional Boost circuit can be replaced with fully controlled devices (such as IGBTs, insulated gate bipolar transistors or MOSFETs, metal oxide semiconductor field-effect transistors) to form a bidirectional DC / DC converter. The rated power of the V2G bidirectional DC / DC converter 6 is less than that of the main photovoltaic inverter to match the charging and discharging requirements of typical electric vehicles and ensure system safety.

[0104] Furthermore, the V2G bidirectional DC / DC converter 6 supports multiple charging and discharging modes, including constant voltage charging (CV), constant current charging (CC), and constant power charging and discharging (CP), through interaction with the SOC information of the electric vehicle, ensuring that the charging and discharging process is safe, efficient, and meets battery health management requirements.

[0105] Furthermore, the intelligent energy management system 7 obtains information such as photovoltaic power output prediction, load prediction, and grid impedance through the "four-capable" system.

[0106] It should be noted that during the day-ahead phase (24-hour scale): potential voltage over-limit risks and power regulation needs are analyzed, and time-of-use charging prices and discharge benefits are released to users through the App platform. Electric vehicle users are guided to make reservations in advance to participate in V2G, forming a day-ahead dispatch plan, realizing intelligent planning and operation of the microgrid, and improving the ability of renewable energy consumption and grid interaction.

[0107] Furthermore, the intelligent energy management system 7 has a built-in voltage detection module to monitor the voltage of AC bus 1 in real time.

[0108] It should be noted that, under grid connection mode: If the voltage is too high (e.g., due to excess photovoltaic power), the output of the bidirectional DC / AC inverter 3 should be adjusted first to absorb the voltage. If the limit is still exceeded, then the reduction of photovoltaic active power output and the guidance of electric vehicle charging (local consumption) will be initiated in sequence. All operations are subject to voltage safety constraints as hard boundaries.

[0109] It should be noted that in off-grid mode (during grid failure): The intelligent energy management system 7 automatically switches to an islanded microgrid, powered by a combination of photovoltaic and electric vehicle batteries; the intelligent energy management system 7 maintains the voltage and frequency stability of AC bus 1, ensuring uninterrupted power supply to critical loads.

[0110] It should be noted that, Figure 4The diagram shows the topology of a traditional photovoltaic inverter. By replacing the unidirectional Boost converter diodes in the photovoltaic MPPT port with fully controlled devices (IGBTs, MOSFETs), a bidirectional DC / DC converter can be realized, which can then be connected to the DC charging port of an electric vehicle. Therefore, the cost of modifying a traditional inverter to support the V2G port in this embodiment is extremely low. Currently, the MPPT port voltage is 500-1000V, which is well matched to the output voltage of a DC charging pile.

[0111] It should be noted that, referring to Figure 5 This demonstrates the overall flow of the coordinated voltage regulation method in this embodiment. After the system starts up, the controller continuously monitors the system status, first determining whether the power grid is normal (e.g., whether the voltage and frequency are within the allowable range). If normal, it enters the grid-connected mode and performs coordinated voltage regulation; if abnormal, it enters the off-grid mode, forming an islanded microgrid.

[0112] It should be noted that, referring to Figure 6 The process of responding to grid dispatch and local voltage regulation in this embodiment is described in detail. When an AC bus voltage over-limit is detected, the controller executes the following sequentially: Control the output inductive reactive power of the bidirectional DC / AC inverter; If the voltage still does not drop, the DC path of the V2G interface is activated simultaneously, and the excess photovoltaic power is stored in the electric vehicle battery through the bidirectional DC / DC converter. If the SOC of an electric vehicle exceeds 0.8, the output of the photovoltaic DC / DC converter needs to be reduced to deviate from the maximum power point, which can effectively regulate the voltage to the normal range.

[0113] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for voltage optimization regulation, the method comprising: comprising, ​ determining the current maximum power point power and the rated power of the photovoltaic inverter, and building an output model of active power and reactive power of the photovoltaic inverter; by combining the battery state evolution equation, the charging and discharging power constraint, the coupling relationship of the aggregation node and the optimization target, a battery state evolution and charging and discharging power constraint model of a single electric vehicle at a specific aggregation node and a specific time is built by using discrete time step simulation and solution; based on the voltage safety constraint and the regulation contribution degree, spatial priority ranking is performed between at least two adjustable resources, and then collaborative regulation is performed; based on the defined variables, constraints and priority ranking, a multi-time scale hierarchical optimization objective function system is built to realize the multi-objective comprehensive optimization of voltage stability, minimum line loss, minimum light rejection and battery health protection.

2. The voltage optimization method of claim 1, wherein: determining the current maximum power point power and the rated power of the photovoltaic inverter, and building an output model of active power and reactive power of the photovoltaic inverter; comprising the following steps: the basic setting of the maximum power point power and the rated power of the photovoltaic inverter is completed in the system; the system regulates the actual output of active power of the photovoltaic inverter through the first decision variable; based on the determination of the actual output of active power of the photovoltaic inverter and the residual capacity of the rated power, the system regulates the output of reactive power of the photovoltaic inverter through the second decision variable; based on the collaborative constraint of the first decision variable and the second decision variable, it is ensured that the total output power of the photovoltaic inverter does not exceed the capacity of the rated power of the photovoltaic inverter, and the output of active power does not exceed the maximum power that the photovoltaic inverter can currently generate.

3. The method of claim 2, wherein: by combining the battery state evolution equation, the charging and discharging power constraint, the coupling relationship of the aggregation node and the optimization target, a battery state evolution and charging and discharging power constraint model of a single electric vehicle at a specific aggregation node and a specific time is built by using discrete time step simulation and solution; comprising the following steps: collecting the data of the change of the battery capacity of the electric vehicle with time to provide basic data support for the subsequent battery state evolution and charging and discharging power constraint model; define that the total power is composed of charging and discharging, and allocate power through binary variables to control the discharging behavior of the electric vehicle; physically constrain the maximum charging power, the maximum discharging power, the upper limit of the battery state of charge and the lower limit of the battery state of charge respectively to ensure that the calculation results of the model meet the actual engineering conditions.

4. The voltage optimization method of claim 3, wherein: based on the voltage safety constraint and the regulation contribution degree, spatial priority ranking is performed between at least two adjustable resources, and then collaborative regulation is performed; comprising the following steps: based on the real-time collected node ac bus voltage, the system performs voltage out-of-limit sensing and safety boundary judgment, if out-of-limit, the subsequent hierarchical regulation process is triggered, otherwise the current running state is maintained; based on the active power reduction coefficient of the photovoltaic node and the total scheduling power of the electric vehicle under the aggregator node, the photovoltaic node contribution degree and the electric vehicle aggregator node contribution degree are calculated respectively; all photovoltaic nodes and electric vehicle aggregator nodes are sorted from high to low according to their respective contribution degrees to form a regulation priority list, and the regulation is performed based on the list. After the regulation is executed, the AC bus voltage of each node is read again to confirm whether it is no longer out of limit. If it is still out of limit, the next optimal resource is called according to the priority; if it is no longer out of limit, the current round of regulation is ended, and the result feedback is performed.

5. The method of claim 4, wherein: Based on the defined variables, constraints and priority ranking, a multi-time scale oriented hierarchical optimization objective function system is constructed to realize the multi-objective comprehensive optimization of voltage stability, minimum line loss, minimum light rejection and battery health protection; specifically including the following steps: Based on the day-ahead prediction data, the photovoltaic active / reactive decision variable, the electric vehicle reservation discharge capacity and the voltage safety hard constraint are combined to construct the day-ahead hour-level reactive voltage optimization objective function; Based on the real-time measurement data, the photovoltaic inverter rated capacity constraint, the real-time charging and discharging feasibility, and the voltage regulation contribution and three-level response logic are fused to construct the minute-level voltage regulation objective function in the day; Based on the battery state evolution and charging and discharging power constraint model, the battery aging process is quantified as an equivalent cost that can be optimized, and an equivalent cost model of electric vehicles considering battery degradation is constructed.

6. A photovoltaic inverter structure supporting V2G function, applying a voltage optimization control method as claimed in any one of claims 1-5, characterized in that, It includes: An AC bus (1) for connecting local loads and external power grids; A grid-connected switch (2) arranged between the AC bus (1) and the external power grid for grid-connected / off-grid switching; A bidirectional DC / AC inverter (3) connecting a DC bus (4) and the AC bus (1) to realize bidirectional conversion of AC and DC energy; A DC bus (4) as a common DC platform for energy collection and distribution of the intelligent energy management system (7); A photovoltaic DC / DC converter (5) with a photovoltaic string (A) connected to the input end and the DC bus (4) connected to the output end for maximum power point tracking; A V2G bidirectional DC / DC converter (6) arranged in a group, with the DC side connected to the DC bus (4) and an interface provided for connecting the electric vehicle power battery.

7. The photovoltaic inverter structure supporting V2G function according to claim 6, characterized in that, It also includes: An intelligent energy management system (7) in communication with the grid-connected switch (2), the bidirectional DC / AC inverter (3), the photovoltaic DC / DC converter (5) and the V2G bidirectional DC / DC converter (6).

8. The photovoltaic inverter structure supporting V2G function according to claim 7, characterized in that, It also includes: The V2G bidirectional DC / DC converter (6) uses an isolated or non-isolated topology.

9. The photovoltaic inverter structure supporting V2G function according to claim 8, characterized in that, It also includes: The V2G bidirectional DC / DC converter (6) supports charging and discharging modes including constant voltage charging, constant current charging and constant power charging through information interaction with the state of charge of the electric vehicle battery.

10. The photovoltaic inverter structure supporting V2G function according to claim 9, characterized in that, It also includes: The intelligent energy management system (7) has a built-in voltage detection module to monitor the voltage of the AC bus (1) in real time.

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