Low-voltage transformer area three-phase voltage flexible control method and system based on light storage and charging power coordination

Abstract

The invention relates to a low-voltage transformer area three-phase voltage flexible control method and system based on light storage and charging power coordination, and belongs to the technical field of new energy control management. According to the scheme, the method comprises the steps that the output power of a photovoltaic inverter, the output power of an energy storage system and the charging demand power of an electric vehicle on a dispatching day are predicted based on historical data; establishing a multi-objective optimization function by taking that the voltage out-of-limit degree, the three-phase imbalance out-of-limit degree and the operation cost of each node of the photovoltaic inverter, the energy storage system and the charged electric vehicle are all minimum as objectives; and setting a constraint condition, searching the multi-objective optimization function by using an improved particle swarm algorithm, and generating a voltage regulation and control strategy. The problem of single regulation and control means of the existing power regulation and control equipment is solved; the advantages of the photovoltaic inverter, the energy storage system and the electric vehicle are combined, the potential of the photovoltaic inverter, the energy storage system and the electric vehicle in economy and regulation and control capability is exerted to the maximum extent, and therefore voltage control is achieved more efficiently.

Description

Technical Field

[0001] The present invention relates to a three-phase voltage flexible control method and system for a low-voltage power distribution area based on the coordinated power of photovoltaic energy storage and charging, belonging to the technical field of new energy energy control management. Background Art

[0002] With the proposal of the "dual carbon" goal, renewable energy represented by photovoltaic power has gradually become an important energy source in the power system. At the same time, with the continuous maturity of energy storage technology and the gradual decline in cost, distributed energy storage systems have gradually been widely used in low-voltage power distribution areas. By storing excess electrical energy during peak power generation periods and releasing electrical energy when the load demand is high, energy storage devices can achieve voltage control, and can also reduce the operating pressure of the power system through peak shaving and valley filling, and optimize the electrical energy utilization efficiency. In addition, the penetration rate of electric vehicle charging facilities in low-voltage power distribution areas has increased significantly in recent years. As a flexible load with high flexibility, the charging process of electric vehicles can be flexibly adjusted according to the operating conditions of the power grid. When the grid load is too high or the voltage exceeds the limit, the charging of electric vehicles can be temporarily interrupted or delayed to relieve the grid pressure.

[0003] However, with the continuous increase in the penetration rate of photovoltaic power, the problems of temporal and spatial mismatch between user loads, charging pile loads and photovoltaic power generation have gradually emerged, resulting in two-way power flow in the low-voltage power distribution area, and further leading to risks such as frequent over-limitation of node voltages and aggravation of three-phase imbalance. Therefore, how to effectively control the three-phase voltage in a low-voltage power distribution area containing photovoltaic power, energy storage and electric vehicle charging has become a key problem that needs to be solved urgently.

[0004] At present, the problems of frequent over-limitation of three-phase voltages and three-phase imbalance in low-voltage power distribution areas are serious, and the control effects of existing control devices are not good, which are mainly reflected in the following aspects:

[0005] 1) With the increase in the penetration rate of photovoltaic power generation and charging piles in the power system, their intermittency and randomness pose challenges to the stability of the power grid. Photovoltaic power generation mainly occurs during the day, while the peak electricity consumption of users and charging piles is usually at night. This temporal and spatial mismatch leads to frequent over-limitation of voltages in the low-voltage power distribution area. At the same time, the access of single-phase photovoltaic power and charging piles may cause the power or voltage of some phases in the low-voltage power distribution area to be too large, increasing the risk of three-phase imbalance.

[0006] 2) In terms of regulation measures, regulation measures such as adjusting the tap position of transformers and series capacitor banks are relatively traditional, with defects such as inability to operate frequently, slow adjustment speed, and low accuracy. The existing mainstream regulation schemes all adopt power control measures. For example, the reactive power regulation potential of photovoltaic inverters can be utilized to regulate the node voltage through reactive power compensation. However, in fact, this measure cannot achieve the accommodation of photovoltaic power. In addition, the continuous progress of energy storage technology has made it an emerging measure for photovoltaic grid-connected voltage control. However, in low-voltage distribution areas, the number of distributed energy storage is relatively small, the capacity is low, its voltage regulation ability is limited, the working life is limited by the charge and discharge frequency, and the current construction cost and regulation cost of energy storage devices are relatively high. In terms of flexible loads, as an interruptible and delayable load, electric vehicles can adjust their charging status according to the demand in the power grid to achieve load transfer. The above four regulation measures are not well combined in existing research. In the future, it is necessary to coordinate the control of voltage by combining multiple regulation measures to overcome the limitations of single active power voltage control or reactive power voltage control.

[0007] 3) In terms of voltage regulation objectives, the existing regulation measures mostly adopt rigid regulation methods. Although they can ensure that the voltage does not exceed the limit, the practice of accurately regulating the node voltage to be close to 1 pu often increases the output load of the regulation equipment, thus reducing the economy of regulation. In fact, it is only necessary to maintain the voltage within the allowable range to meet the operation requirements, and excessive regulation is unnecessary. In addition, since most of the photovoltaic systems in low-voltage distribution areas are single-phase grid-connected and the three-phase load distribution is unbalanced, the three-phase unbalance problem frequently occurs, often simultaneously with the voltage over-limit problem. However, this problem is often ignored and there are no targeted solutions.

[0008] 4) In terms of calculation methods, although the existing methods introduce intelligent algorithms and consider multi-objective functions, the global convergence of these algorithms is poor, and they are prone to falling into local optimal solutions and difficult to find the global optimal solution. Summary of the Invention

[0009] The purpose of the present invention is to provide a flexible control method and system for three-phase voltage in low-voltage distribution areas based on the coordination of photovoltaic, energy storage, and charging power, so as to solve the problem of single regulation means of existing power regulation equipment; it realizes the combination of the respective advantages of photovoltaic inverters, energy storage systems, and electric vehicles, and maximally exerts their potential in economy and regulation ability, so as to more efficiently achieve voltage control.

[0010] To achieve the above object, the solution of the present invention includes: predicting the output power of the photovoltaic inverter, the output power of the energy storage system, and the charging demand power of the electric vehicle on the scheduling day based on historical data; taking the voltage violation degree, three-phase imbalance violation degree, and operating cost of each node of the photovoltaic inverter, the energy storage system, and the charging electric vehicle as variables, and combining the actual application scenario to reach the optimal situation as the goal to establish a multi-objective optimization function; setting constraint conditions, and using an improved particle swarm algorithm to search for the solution of the multi-objective optimization function and generate a voltage regulation strategy.

[0011] A three-phase voltage flexible control method for a low-voltage power distribution area based on the coordination of photovoltaic, energy storage, and charging power includes the following steps:

[0012] 1) Predict the output power of the photovoltaic inverter, the output power of the energy storage system, and the charging demand power of the electric vehicle on the scheduling day based on historical data;

[0013] 2) Establish a multi-objective optimization function with the goal of minimizing the voltage violation degree, three-phase imbalance violation degree, and operating cost of each node of the photovoltaic inverter, the energy storage system, and the charging electric vehicle;

[0014] 3) Set constraint conditions, and use an improved particle swarm algorithm to search for the solution of the multi-objective optimization function and generate a voltage regulation strategy.

[0015] Further, in step 3), the improved particle swarm algorithm is a particle swarm optimization algorithm improved by a simulated annealing strategy.

[0016] Further, the adaptive adjustment of the learning factor of the particle swarm optimization algorithm improved by the simulated annealing strategy is realized through the following formula:

[0017]

[0018] In the formula, c1 is the individual learning factor; c2 is the group learning factor; c max and c min are the maximum and minimum values of the learning factor respectively; k is the current iteration number; k max is the maximum iteration number.

[0019] Further, in step 3), the constraint conditions include a power balance constraint, and the power balance constraint is as follows:

[0020]

[0021] In the formula, and are the active power and reactive power injected into the φ phase of node i respectively; is the charge and discharge power of the energy storage system in the φ phase of node i; and are the active power load and reactive power load of the φ-phase at node i, respectively; and are the active power output of the household distributed PV and the reactive power output of the PV inverter of the φ-phase at node i, respectively; is the electric vehicle charging load of the φ-phase at node i.

[0022] Further, in step 3), the constraint conditions include the output constraint of the PV inverter, and the output constraint of the PV inverter is as follows:

[0023]

[0024] In the formula, is the reactive power output of the PV inverter of the φ-phase at node i; is the reactive power output limit of the PV inverter of the φ-phase at node i; is the capacity of the PV inverter of the φ-phase at node i.

[0025] Further, in step 3), the constraint conditions include the energy storage system constraint, and the energy storage system constraint includes the state of charge constraint, and the state of charge constraint is as follows:

[0026]

[0027] In the formula, is the state of charge of the energy storage of the φ-phase at node i; SOC min and SOC max are the upper and lower limits of SOC, respectively.

[0028] Further, the energy storage system constraint includes the charge and discharge power constraint, and the charge and discharge power constraint is as follows:

[0029]

[0030] In the formula, is the rated energy storage charge and discharge power, is for node i is the charge and discharge power of the energy storage system of the φ-phase.

[0031] Further, the energy storage system constraint includes the state of charge change constraint, and the state of charge change constraint is as follows:

[0032]

[0033] In the formula, is at time t Δt is the time scale; S ESS is the rated capacity of the energy storage system; η ESS is the charge and discharge efficiency of the ESS.

[0034] Furthermore, in step 3), the constraint conditions include the flexible charging constraint of electric vehicles, and the flexible charging constraint of electric vehicles is as follows:

[0035]

[0036] In the formula, and are the maximum and minimum energies of the electric vehicle respectively; is the energy of the electric vehicle at time t; and are the charging and discharging powers of the electric vehicle at time t respectively; and are the maximum charging and discharging powers of the electric vehicle respectively; α EV and β EV are the start and end times of the charging and discharging periods of the electric vehicle respectively.

[0037] A three-phase voltage flexible control system for low-voltage power distribution areas based on the coordinated power of photovoltaic energy storage and charging of the present invention includes a processor, and the processor is used to execute a computer program to implement the steps of the above-mentioned three-phase voltage flexible control method for low-voltage power distribution areas based on the coordinated power of photovoltaic energy storage and charging.

[0038] The beneficial effects of the present invention are as follows:

[0039] The three-phase voltage flexible control method for low-voltage power distribution areas based on the coordinated power of photovoltaic energy storage and charging of the present invention can combine the respective constraint characteristics and advantages of photovoltaic inverters, energy storage systems, and electric vehicles, and use an improved multi-objective particle swarm optimization algorithm to achieve a better control method for voltage control, maximizing the potential of photovoltaic inverters, energy storage systems, and electric vehicles in terms of economy and regulation ability, so as to more efficiently achieve voltage control. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is a flowchart of the three-phase voltage flexible control method for low-voltage power distribution areas based on the coordinated power of photovoltaic energy storage and charging of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0041] In order to make the purpose, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be described in detail below with reference to the drawings and embodiments.

[0042] Method Embodiment:

[0043] The three-phase voltage flexible control method for low-voltage power distribution areas based on the coordinated power of photovoltaic energy storage and charging of the present invention can combine the respective advantages of photovoltaic inverters, energy storage systems, and electric vehicles, and maximize their potential in terms of economy and regulation ability, so as to more efficiently achieve voltage control.

[0044] For example Figure 1As shown in the figure, the three-phase voltage flexible control method for low-voltage power distribution areas based on the coordinated power of photovoltaic energy storage and charging includes the following steps:

[0045] 1) Predict the output power of the photovoltaic inverter, the output power of the energy storage system, and the charging demand power of electric vehicles on the scheduling day based on historical data:

[0046] By analyzing the historical data of photovoltaic power generation, load, and electric vehicle charging in the power distribution area, accurately depict the spatio-temporal distribution characteristics of the source-load in the power distribution area. Based on the time-series characteristics of the photovoltaic inverter, energy storage system, and electric vehicle charging on multiple time scales, and combined with the probability distribution model, realize the multi-time scale prediction of photovoltaic power generation, energy storage system, and electric vehicle charging demand in the power distribution area during the scheduling day.

[0047] 2) Establish a multi-objective optimization function with the voltage over-limit degree, three-phase imbalance over-limit degree, and operating cost of each node of the photovoltaic inverter, energy storage system, and charging electric vehicle as variables, and achieve the optimal situation in combination with the actual application scenario as the goal;

[0048] Use power flow calculation to obtain the voltage of each node of the photovoltaic inverter, energy storage system, and electric vehicle charging; the three-phase imbalance over-limit degree refers to the sum of the differences between the degree of three-phase imbalance over-limit of each node and the three-phase imbalance limit value, as specifically shown in formulas (1) to (3):

[0049]

[0050] In the formula, VUF i represents the three-phase voltage imbalance degree of node i; VUF i ′ represents the three-phase imbalance over-limit of node i; VUF max represents the maximum limit value of the three-phase voltage imbalance degree; U i,ave represents the average voltage of node i; U i,A , U i,B and U i,C respectively represent the ABC three-phase voltages of node i;

[0051] The operating cost refers to the sum of the power purchase and sale cost and the equipment maintenance cost in the power distribution area, as shown in formula (4):

[0052] f3 = C Grid +C Main (4)

[0053] C Grid = C Buy P Buy -C Sell P Sell (5)

[0054]

[0055] In the formula, C Grid represents the purchase and sale electricity cost between the low-voltage distribution area and the main grid; C Main represents the operation and maintenance cost of equipment; C Buy and C Sell respectively represent the main grid's electricity purchase price and electricity sale price; P Buy and P Sell respectively represent the main grid's electricity purchase power and electricity sale power; C PV and C ESS respectively represent the operation and maintenance costs of unit photovoltaic equipment and energy storage systems; P PV,nPV and P ESS,nESS respectively represent the active power of photovoltaic at node n PV and the charging and discharging power of energy storage at node n ESS ; N PV and N ESS respectively represent the numbers of photovoltaic and energy storage systems in the low-voltage distribution area.

[0056] Considering the safety and stability in the three-phase low-voltage distribution area, the present invention comprehensively considers the degree of voltage over-limit at each node, the degree of three-phase imbalance over-limit, and the operation cost, and constructs a multi-objective optimization function as shown in Equation (7):

[0057] F = [minf1, minf2, minf3] (7)

[0058] In the formula, F represents the multi-objective optimization function; f1 represents the degree of voltage over-limit at nodes; f2 represents the degree of three-phase imbalance over-limit; f3 represents the operation cost.

[0059] The degree of voltage over-limit at nodes refers to the sum of the differences between the over-limit voltage amplitudes of each phase at each node and their corresponding voltage limits, specifically as shown in Equations (8) and (9):

[0060]

[0061] In the formula, represents the voltage over-limit value of phase φ at node i; N represents the total number of nodes; i is the i-th node; is one of the ABC three phases; φ represents the set of ABC three phases; U min and U max respectively represent the lower limit and upper limit of the node voltage; represents the per-unit value of the node voltage of phase φ at node i.

[0062] 3) Set constraint conditions, and use the improved particle swarm optimization algorithm to search for the solution of the multi-objective optimization function and generate a voltage regulation strategy.

[0063] Constraints are added to the voltage regulation strategy of the present invention so that the voltage regulation strategy can meet the voltage regulation requirements of low-voltage power distribution areas. The constraints are as follows:

[0064] ① Power balance constraint: The active and reactive powers injected into each node are related to the photovoltaic, load, energy storage system, and electric vehicle charging load connected to the node, that is:

[0065]

[0066] In the formula, and respectively represent the active power and reactive power injected into the φ phase of node i; represents the charge and discharge power of the energy storage system in the φ phase of node i; and respectively represent the active load and reactive load in the φ phase of node i; and respectively represent the active power output of the household distributed photovoltaic and the reactive power output of the photovoltaic inverter in the φ phase of node i; represents the electric vehicle charging load in the φ phase of node i.

[0067] ② Reactive power output constraint of photovoltaic inverter: The reactive power output limit of the photovoltaic inverter depends on the capacity of the photovoltaic inverter and the current active power output of the photovoltaic, as shown in formula (11):

[0068]

[0069] In the formula, represents the reactive power output of the photovoltaic inverter in the φ phase of node i; represents the reactive power output limit of the photovoltaic inverter in the φ phase of node i; represents the capacity of the photovoltaic inverter in the φ phase of node i.

[0070] ③ Energy storage system constraint: The participation of the energy storage system in voltage control is a complex process, and multiple constraints need to be considered to ensure the stability of the system and the life of the energy storage system.

[0071] State of charge constraint: The state of charge of the energy storage system needs to be maintained within a safe range to avoid overcharging or discharging, as shown in formula (12):

[0072]

[0073] In the formula, represents the state of charge of the energy storage in the φ phase of node i; SOC min and SOC max respectively represent the upper and lower limits of SOC.

[0074] Charge and Discharge Power Constraint: The charge and discharge power of the energy storage system cannot exceed its rated value to avoid equipment overload, as shown in Equation (13):

[0075]

[0076] In the formula, represents the rated charge and discharge power of the energy storage.

[0077] State of Charge Change Constraint: The state of charge of the energy storage system, as shown in Equation (14):

[0078]

[0079] In the formula, represents the at time t, Δt represents the time scale; S ESS represents the rated capacity of the energy storage system; η ESS represents the charge and discharge efficiency of the ESS.

[0080] ④ Flexible Charging Constraint of Electric Vehicles: Most of the charging loads of electric vehicles do not require immediate (i.e., postponable) or continuous (i.e., interruptible) charging operations, as long as they can be charged to the required energy level by the end of their set charging period.

[0081] First, the charging power, discharging power, and total stored energy of the electric vehicle should be within its safe range, as shown in Equation (15) below:

[0082]

[0083] In the formula, and represent the maximum and minimum energies of the electric vehicle, respectively; represents the energy of the electric vehicle at time t; and represent the charging and discharging powers of the electric vehicle at time t, respectively; and represent the maximum charging and discharging powers of the electric vehicle, respectively; α EV and β EV represent the start and end times of the charging and discharging periods of the electric vehicle, respectively.

[0084] Second, the electric vehicle energy change constraint is as shown in Equation (16) below:

[0085]

[0086] In the formula, τ EV represents the self-decay rate of the electric vehicle; η EC and η ED represent the charging and discharging efficiencies of the electric vehicle, respectively.

[0087] Then, the start and end energy requirements of the electric vehicle during the charging period are constrained as shown in the following formula (17):

[0088]

[0089] In the formula, and respectively represent the initial and end energies in the electric vehicle.

[0090] Formula (18) ensures the non-simultaneous charging and discharging of the electric vehicle:

[0091]

[0092] The present invention adopts an improved particle swarm algorithm integrating multiple strategies. This improved particle algorithm combines the global search characteristics of the simulated annealing algorithm, effectively avoiding the problem that the algorithm converges prematurely to the local optimal solution.

[0093] At the same time, the method of adaptively adjusting the learning factor enables the algorithm to achieve a balance between global search and local search.

[0094] The particle swarm optimization algorithm improved by the simulated annealing strategy adopts a more prudent strategy when facing the update of particle positions.

[0095] Specifically, if the new position of the particle is better than the current position, then the particle will directly move to the new position. However, if the quality of the new position is not as good as the current position, the particle will not immediately abandon the current position, but will decide whether to move according to a probability controlled by the initial temperature T0. This strategy allows the particle to "explore" new positions with a certain probability, rather than blindly jumping to new positions. In this way, as long as the temperature is decreased slowly enough, it is less likely for the particle to escape from the potential optimal search area too quickly, thereby enhancing the particle's search ability in the local area to a certain extent.

[0096] This method effectively avoids the problem that the traditional particle swarm optimization algorithm converges prematurely to the local optimal solution by combining the efficient local search ability of the particle swarm optimization and the global search characteristics of the simulated annealing algorithm, thereby ensuring the convergence speed of the algorithm while also improving the quality of the solution.

[0097] As an implementation, the multi-objective particle swarm optimization algorithm of the present invention combined with the simulated annealing algorithm can achieve adaptive adjustment by combining the learning factor.

[0098] Adaptive learning factors play a crucial role in the multi-objective particle swarm optimization algorithm, directly influencing the convergence behavior of the algorithm; the learning factors dynamically adjust their magnitudes according to the progress of the algorithm iteration. Specifically, the individual learning factor is set to a relatively large value in the early stage of iteration to avoid the algorithm prematurely converging to a local optimum; as the iteration progresses, the individual learning factor gradually decreases. In contrast, the swarm learning factor is relatively small in the initial stage of iteration, which helps enhance the global search ability of the algorithm; as the iteration deepens, the swarm learning factor gradually increases to improve the local search efficiency of the algorithm in the later stage. By this method of adaptively adjusting the learning factors, a balance can be achieved between global search and local search, thereby optimizing the convergence performance of the particle swarm algorithm. The adaptive adjustment of the learning factors is achieved through formula (19):

[0099]

[0100] In the formula, c1 is the individual learning factor; c2 is the swarm learning factor; c max and c min are the maximum and minimum values of the learning factors respectively; k is the current iteration number; k max is the maximum iteration number.

[0101] As other implementation manners, the low-voltage distribution network three-phase voltage flexible control method based on the coordinated power of photovoltaic energy storage and charging in the present invention is not limited to realizing the adaptive adjustment of the multi-objective particle swarm algorithm by combining learning factors. Existing adaptive algorithms can all be used in the present invention to achieve the adaptive adjustment of the multi-objective particle swarm algorithm.

[0102] System embodiment:

[0103] The low-voltage distribution network three-phase voltage flexible control system based on the coordinated power of photovoltaic energy storage and charging in this embodiment includes a processor, and the processor is used to execute instructions stored in the memory to implement the above-mentioned low-voltage distribution network three-phase voltage flexible control method.

[0104] The present invention proposes a comprehensive coordinated control strategy, which combines the respective advantages of photovoltaic inverters, energy storage systems, and electric vehicles, solves the problem of insufficient single control means in traditional power regulation methods, maximally exerts their potential in economy and regulation ability, and thus more efficiently realizes voltage control.

[0105] The above is only a preferred embodiment of the present invention and is not intended to limit the present invention. The patent protection scope of the present invention is subject to the claims. All equivalent structural changes made by using the description and drawings of the present invention should be equally included in the protection scope of the present invention.

Claims

1. A three-phase voltage flexible control method for low-voltage power distribution areas based on coordinated power of photovoltaic energy storage and charging, characterized in that The method includes the following steps: 1) Predict the output power of the PV inverter, the output power of the energy storage system, and the charging demand power of electric vehicles on the dispatching day based on historical data; 2) Establish a multi-objective optimization function with the objective that the voltage violation degree, the three-phase unbalance violation degree, and the operating cost of each node of the PV inverter, the energy storage system, and the charging electric vehicles can all reach the minimum value; 3) Set constraint conditions, and use an improved particle swarm algorithm to search for the solution of the multi-objective optimization function and generate a voltage regulation strategy.

2. The three-phase voltage flexible control method for low-voltage power distribution areas based on the coordinated power of photovoltaic energy storage and charging according to claim 1, wherein In step 3), the improved particle swarm algorithm is a particle swarm optimization algorithm improved by a simulated annealing strategy.

3. The three-phase voltage flexible control method for low-voltage power distribution areas based on the coordinated power of photovoltaic energy storage and charging according to claim 2, characterized in that, The adaptive adjustment of the learning factor of the particle swarm optimization algorithm improved by the simulated annealing strategy is realized through the following formula: Wherein, c1 is the individual learning factor; c2 is the group learning factor; c max and c min are the maximum and minimum values of the learning factor respectively; k is the current iteration number; k max is the maximum iteration number.

4. The three-phase voltage flexible control method for low-voltage power distribution areas based on the coordinated power of photovoltaic energy storage and charging according to claim 1, characterized in that, In step 3), the constraint conditions include a power balance constraint, and the power balance constraint is as follows: Wherein, and are respectively the active power and reactive power injected into phase of node i; is the charge and discharge power of the energy storage system in phase of node i; and are respectively the active load and reactive load in phase of node i; and are respectively the active power output of the household distributed photovoltaic and the reactive power output of the photovoltaic inverter in phase of node i; is the electric vehicle charging load in phase of node i.

5. The three-phase voltage flexible control method for low-voltage power distribution areas based on the coordinated power of photovoltaic energy storage and charging according to claim 1, wherein, In step 3), the constraint conditions include a PV inverter output constraint, and the PV inverter output constraint is as follows: In the formula, is the reactive power output of the -phase photovoltaic inverter at node i; is the reactive power output limit of the -phase photovoltaic inverter at node i; is the capacity of the -phase photovoltaic inverter at node i.

6. The three-phase voltage flexible control method for low-voltage power distribution areas based on the coordinated power of photovoltaics, energy storage, and charging, as claimed in claim 1, is characterized in that In step 3), the constraint conditions include an energy storage system constraint, and the energy storage system constraint includes a state of charge constraint, and the state of charge constraint is as follows: In the formula, is the state of charge of the energy storage of the i-th node ; SOC min and SOC max are the upper and lower limits of SOC, respectively.

7. The three-phase voltage flexible control method for low-voltage power distribution areas based on the coordinated power of photovoltaic energy storage and charging according to claim 6, characterized in that, The energy storage system constraint includes a charge and discharge power constraint, and the charge and discharge power constraint is as follows: Wherein, is the rated energy storage charge and discharge power, is the charge and discharge power of the energy storage system at node i phase.

8. The three-phase voltage flexible control method for low-voltage power distribution areas based on the coordinated power of photovoltaic energy storage and charging according to claim 6, characterized in that, The energy storage system constraint includes a state of charge change constraint, and the state of charge change constraint is as follows: In the formula, is at time t Δt is the time scale; S ESS is the rated capacity of the energy storage system; η ESS is the charge-discharge efficiency of the ESS.

9. The three-phase voltage flexible control method for low-voltage power distribution areas based on the coordinated power of photovoltaic energy storage and charging according to claim 1, characterized in that In step 3), the constraint conditions include an electric vehicle flexible charging constraint, and the electric vehicle flexible charging constraint is as follows: Wherein, and are the maximum and minimum energies of the electric vehicle, respectively; is the energy of the electric vehicle at time t; and are the charging and discharging powers of the electric vehicle at time t, respectively; and are the maximum charging and discharging powers of the electric vehicle, respectively; α EV and β EV are the start and end times of the charging and discharging periods of the electric vehicle, respectively.

10. A three-phase voltage flexible control system for a low-voltage power distribution area based on coordinated power of photovoltaic energy storage and charging, comprising a processor, characterized in that, The processor is used to execute a computer program to implement the steps of the three-phase voltage flexible control method for a low-voltage power distribution area based on the coordination of PV, energy storage, and charging power as described in any one of claims 1 to 9.

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