Active and reactive collaborative optimization control method of photovoltaic energy storage system based on network-forming inverter

Abstract

The invention relates to an active and reactive collaborative optimization control method for a photovoltaic energy storage system based on a network-forming inverter, and the method achieves the effective control of active and reactive power through simulating the characteristics of a synchronous generator. And then collaborative optimization of active and reactive power control parameters is carried out. A system frequency response model is utilized to describe dynamic response of system frequency, and a dynamic equation of the system is converted into a discrete form in combination with the dynamic equation of the energy storage unit. Meanwhile, the optimization model is solved by considering system capacity limitation, so that transient frequency characteristics are improved, and frequency deviation after system disturbance is reduced. In addition, the minimum active power loss is taken as an optimization target, the voltage deviation is taken as a penalty term to be substituted into a target function, and a reactive power optimization target function is established. A PSO algorithm is adopted to solve a reactive power optimization function, and reactive power output of photovoltaic and energy storage is adjusted by adjusting VSG control parameters, so that the economical efficiency of a power grid is improved on the premise of ensuring qualified voltage.

Description

Technical Field

[0001] The invention relates to an active and reactive coordinated optimization control method for a photovoltaic energy storage system based on a grid-connected inverter, and belongs to the field of frequency and voltage dynamic characteristic optimization of a new power system. Background Art

[0002] The access of distributed photovoltaic power generation to the grid has become an important direction for the development of today's power system. With the rapid development of new power systems, grid-connected inverters have gradually replaced grid-following inverters due to their excellent frequency and voltage support capabilities. However, the access of large-scale new energy sources has led to a decrease in the voltage support capacity of the grid and a decrease in the short-circuit ratio, exacerbating stability issues such as sub- / super-synchronous oscillations dominated by phase-locked loops and static voltage stability.

[0003] In recent years, grid-forming control (GFM) technology and energy storage system (ESS) have provided new ideas for solving the above problems. Some scholars have proposed comprehensive performance indicators and test methods for connecting new energy to the grid-connected structure, established test models for follower and grid-connected converters, and defined test indicators and performance requirements for characterizing voltage and frequency support capabilities. Some scholars have also considered the impact of grid voltage support capabilities on small disturbance stability through the generalized short-circuit ratio indicator. However, none of the above methods can better support the grid voltage when the system load fluctuates, resulting in poor system reliability. Summary of the invention

[0004] The purpose of the present invention is to solve the problems existing in the prior art and to provide a method for coordinated optimization control of active and reactive power that improves the frequency and voltage stability of a new power system equipped with photovoltaic energy storage and can better support the grid voltage when the load fluctuates.

[0005] In order to achieve the above-mentioned purpose, the technical solution proposed by the present invention is: a method for coordinated optimization control of active and reactive power of a photovoltaic energy storage system based on a grid-connected inverter, comprising:

[0006] Constructing the power outer loop control model of grid-connected inverter based on virtual synchronous machine technology;

[0007] Establish the frequency dynamic response model of the grid-connected inverter and the dynamic equation of the energy storage unit;

[0008] Combining the power outer loop control model, frequency dynamic response model and dynamic equation of energy storage unit of the grid-connected inverter, the dynamic equation of the grid-connected inverter of the photovoltaic energy storage system is obtained.

[0009] According to the dynamic equation of the grid-connected inverter of the photovoltaic energy storage system, an active power optimization model is constructed with the goal of minimizing the frequency deviation of the photovoltaic energy storage system at the lowest point after the frequency is disturbed.

[0010] According to the dynamic equation of the grid-connected inverter of the photovoltaic energy storage system, the reactive power optimization model is constructed with the minimum active power loss as the optimization goal and combined with the voltage deviation penalty coefficient; the PSO algorithm is used to solve the reactive power optimization model.

[0011] A further design of the above technical solution is as follows: the power outer loop control model of the grid-connected inverter is:

[0012]

[0013] Among them, H is the virtual inertia coefficient, ω is the angular frequency, δ is the virtual power angle output by the active power-frequency control loop in the outer loop of the network control, t is the time, D is the virtual damping coefficient, P 0 is the active power setting value, P is the actual active power, K D is the VSG primary frequency modulation coefficient, s is the Laplace operator, f is the frequency reference value in the control outer loop, and f 0 To control the actual value of the frequency in the outer loop, P 0 is the active power reference value, E 0 is the initial internal potential amplitude, E is the output reference voltage, U 0 is the rated voltage value, U is the actual voltage, K is the reactive power integral coefficient, K q is the reactive voltage drop coefficient, Q 0 is the reactive power setting value, and Q is the actual reactive power.

[0014] The frequency dynamic response model of the grid-connected inverter is:

[0015]

[0016] Where Δf is the system frequency deviation, H g F is the virtual inertia coefficient of the synchronous machine, and R is the adjustment coefficient. H is the working proportional coefficient of the high pressure cylinder, T R is the reheater time constant, D g Indicates the equivalent damping coefficient of the load in the system, ΔP m and ΔP e They are the mechanical active power deviation and electromagnetic active power deviation of the synchronous machine respectively.

[0017] The dynamic equation of the energy storage unit is:

[0018]

[0019] Among them, SOC represents the state of charge, Ppv represents the active power generated by the photovoltaic array, Ppv0 represents the maximum tracking power of the photovoltaic system, and Q represents the rated charging capacity of the energy storage unit capacitor.

[0020] The dynamic equation of the grid-connected inverter of the photovoltaic energy storage system is:

[0021]

[0022] Where k is any discrete point in the discrete equation, k+1 is the next discrete point, ΔP m is the mechanical power deviation of the synchronous machine, S OC is the state of charge and h is the discrete step length.

[0023] The active power optimization model is:

[0024] min|Δf bottom |

[0025]

[0026] Where Δf bottom is the frequency deviation corresponding to the lowest point of the disturbed frequency, P max is the maximum active output of the inverter, P pv0 is the active power output of the photovoltaic power generation system at the maximum power point, P Emax It is the upper limit of the active power of the energy storage unit.

[0027] The reactive power optimization model is:

[0028]

[0029] Where P loss is the active power loss, k is the branch number, m is the total number of branches, I k is the current of the kth branch, λ is the voltage deviation penalty coefficient, U max is the voltage upper limit, U min is the voltage lower limit, U n is the rated voltage value, R k is the resistance of the kth branch, X k is the reactance of the kth branch, and j represents the imaginary part of the complex number.

[0030] The constraints of the reactive power optimization model include power balance equation constraints, system reactive power inequality constraints, photovoltaic output inequality constraints and energy storage output inequality constraints.

[0031] The power balance equation constraint is:

[0032]

[0033] Among them, P VSG and Q VSG are the active power and reactive power output values ​​of the inverter controlled by VSG, P ESS and Q ESS are the active power and reactive power output values ​​of the energy storage device after VSG control and regulation, P loss and Q loss is the active power and reactive power loss in the system, P load and Q load Respectively represent the active power and reactive power consumed by the load;

[0034] The system reactive power inequality constraint is:

[0035] Q VSG,max +Q ESS,max ≥Q load,max +Q loss

[0036] Among them, Q VSG,max , Q ESS,max are the maximum reactive power output limits of the photovoltaic and energy storage converters under VSG control, Q load,max is the maximum reactive power required by the load;

[0037] The photovoltaic output inequality constraint is:

[0038]

[0039] Among them, S PV is the total photovoltaic output capacity, S PVmax and P VSGmax They are the maximum limits of total photovoltaic output capacity and active power output respectively;

[0040] The energy storage output inequality constraint is:

[0041]

[0042] Among them, P ch and P dis are the active power of the energy storage device in the charging and discharging state, Q ESS is the reactive power output of the energy storage battery pack, S ESSmax It is the maximum limit value of energy storage capacity. Since energy storage devices can emit reactive power or absorb reactive power, it is necessary to limit their active and reactive power output during discharge and charging.

[0043] The steps of solving the reactive power optimization model using the PSO algorithm are as follows:

[0044] Step 1: operate the system in an initial state, and then subject the system to a sudden load disturbance, causing reactive power shortage and voltage fluctuation;

[0045] Step 2, performing virtual synchronous machine control parameter training;

[0046] Step 3, generating a set of control parameter values, inputting the control parameter values ​​into the virtual synchronous machine controller, adjusting the power output of photovoltaic and energy storage and operating the system at this moment to obtain the voltage and current of the line and the voltage and current of the load point;

[0047] Step 4: Calculate the active network loss P loss value, by judging whether the load point voltage meets U min <U<U max The range is used to determine the value of the penalty factor λ and optimize the fitness of the objective function;

[0048] Step 5: Determine whether it reaches the number of iterations, otherwise return to step 3;

[0049] Step 6: Output the optimal values ​​of the variables at this time as well as the voltage U, current I and active network loss value P loss , complete the VSG parameter setting and input the parameter values ​​into the virtual synchronous machine controller.

[0050] The beneficial effects of the present invention are:

[0051] Based on the virtual synchronous machine (VSG) technology, a grid-type control model is established. As a preferred solution, mathematical modeling is used to convert the actual problems of the power system into mathematical problems, providing a basis for optimization control. The present invention is based on the virtual synchronous generator (VSG) control method, as a classic method for grid-type inverter control. By simulating the characteristics of synchronous generators, effective control of active and reactive power is achieved. Then, the active and reactive power control parameters are coordinated and optimized. The system frequency response model is used to describe the dynamic response of the system frequency, and the system dynamic equation is converted into a discrete form in combination with the dynamic equation of the energy storage unit. At the same time, the optimization model is solved considering the system capacity limitation to improve the transient frequency characteristics and reduce the frequency deviation after the system disturbance. In addition, with the minimum active power loss as the optimization goal, the voltage deviation is brought into the objective function as a penalty term to establish the objective function of reactive optimization. The PSO algorithm is used to solve the reactive optimization function, and the reactive output of photovoltaic and energy storage is adjusted by adjusting the VSG control parameters to improve the economy of the power grid under the premise of ensuring qualified voltage.

[0052] The present invention effectively improves the frequency and voltage stability of the new power system equipped with photovoltaic energy storage, and at the same time enables it to better support the grid voltage when the load fluctuates, thereby improving the reliability of the new power system and providing strong support for the stable operation of the new power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is the flow chart of PSO algorithm;

[0054] Figure 2 This is a comparison chart of the VSG1 frequency waveform;

[0055] Figure 3 This is a comparison chart of the voltage waveform of VSG1;

[0056] Figure 4 This is a comparison chart of VSG2 frequency waveform;

[0057] Figure 5 This is a comparison chart of VSG2 voltage waveform. DETAILED DESCRIPTION

[0058] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0059] Embodiment 1

[0060] The active and reactive power coordinated optimization control method of the photovoltaic energy storage system based on the grid-connected inverter of the present invention specifically comprises the following steps:

[0061] The power outer loop control model of grid-connected inverter is constructed based on virtual synchronous generator (VSG) technology;

[0062] Establish the frequency dynamic response model of the grid-connected inverter and the dynamic equation of the energy storage unit;

[0063] Combining the power outer loop control model, frequency dynamic response model and dynamic equation of energy storage unit of the grid-connected inverter, the dynamic equation of the grid-connected inverter of the photovoltaic energy storage system is obtained.

[0064] According to the dynamic equation of the grid-connected inverter of the photovoltaic energy storage system, an active power optimization model is constructed with the goal of minimizing the frequency deviation of the photovoltaic energy storage system at the lowest point after the frequency is disturbed.

[0065] According to the dynamic equation of the grid-connected inverter of the photovoltaic energy storage system, the reactive power optimization model is constructed with the minimum active power loss as the optimization goal and combined with the voltage deviation penalty coefficient; the PSO algorithm is used to solve the reactive power optimization model.

[0066] Embodiment 2

[0067] This embodiment is further designed on the basis of the first embodiment, specifically:

[0068] Networked control modeling based on virtual synchronous machine technology:

[0069] The grid-type inverter control method includes a power outer loop and a voltage and current inner loop. This embodiment uses a virtual synchronous machine control method for analysis. The VSG control method is a classic method for grid-type grid-connected inverter control. Considering that the inner loop responds faster, the inner loop dynamic process of the inverter control can be ignored. Therefore, the dynamic process of the grid-type control method in this embodiment can be characterized by an outer loop control model:

[0070]

[0071] Among them, H is the virtual inertia coefficient, D is the virtual damping coefficient, P 0 is the active power setting value, P is the actual active power, K D is the VSG primary frequency modulation coefficient. 0 is the initial internal potential amplitude, E is the output reference voltage, U 0 is the rated voltage value, U is the actual voltage, and K is the reactive power integral coefficient. q is the reactive voltage drop coefficient, Q 0 is the reactive power setting value, Q is the actual reactive power, s is the Laplace operator, which is used in Laplace transform. ω is the angular frequency, δ is the virtual power angle output by the active power-frequency control loop in the mesh control outer loop, and t is the time. f is the frequency reference value in the control outer loop, f 0 is the actual value of the frequency in the control outer loop, P 0 is the active power reference value.

[0072] Based on the above outer loop control model, active power control parameters are collaboratively optimized;

[0073] The active parameter optimization target is the output frequency of the active power-frequency loop in the grid-connected inverter control. The optimization parameters are the active power capacity of the photovoltaic system converter and energy storage. The optimization model is solved to improve the transient frequency characteristics and reduce the frequency deviation after the system disturbance.

[0074] The frequency dynamic response of the grid-connected inverter described by the system frequency response (SFR) model can be expressed as:

[0075]

[0076] Where Δf is the system frequency deviation, H g F is the virtual inertia coefficient of the synchronous machine, and R is the adjustment coefficient. H is the working proportional coefficient of the high pressure cylinder, T R is the reheater time constant. ΔP m and ΔP e They are the mechanical active power deviation and electromagnetic active power deviation of the synchronous machine respectively. g Represents the equivalent damping coefficient of the load in the system.

[0077] The frequency dynamic response of the grid-connected inverter can be described by equation (2). The dynamic equation of the energy storage unit is established as:

[0078]

[0079] Among them, S OC Indicates the state of charge, P pv Represents the active power generated by the photovoltaic array, P pv0 It represents the maximum tracking power of the photovoltaic system, and Q represents the rated charging capacity of the energy storage unit capacitor.

[0080] By integrating equations (1), (2) and (3), the dynamic equation of the grid-connected inverter of the photovoltaic energy storage system is obtained as follows:

[0081]

[0082] Among them, H vsg is the inertia coefficient of VSG, R G is the adjustment coefficient.

[0083] The task of determining the analytical expression of the lowest frequency point of the system under study according to equation (4) is very challenging. To alleviate this problem, this paper transforms equation (4) into a discrete form. This discrete equation is then used as a constraint for the numerical solution of the optimization problem. The Euler method is used to derive equation (4), and the following results are obtained:

[0084]

[0085] Where k is any discrete point in the discrete equation, and k+1 is the next discrete point, which is one unit higher than the current point. m is the mechanical power deviation of the synchronous machine, S OC is the state of charge and h is the discrete step length.

[0086] The discretized model (5) can be expressed as follows:

[0087] x(k+1)=F(x(k))+B(u(k)) (6)

[0088] Where x = [Δf, ΔP m ,S OC ] T , u=[ΔP d ,P pv ] T .

[0089] Considering the inverter capacity limitation, under the premise that the capacity does not exceed the limit, the additional active power P ad That is the maximum active output of the inverter Pmax and the inverter active power setting value P 0 Similarly, considering the charging and discharging power limit of the energy storage system, the additional active power P that the energy storage can generate Ead P is the maximum active output of the energy storage system Emax and the energy storage active setting value P E0 The maximum active power that can be generated by the photovoltaic energy storage system is determined by the smaller value of the active power that can be generated by the inverter and the active power that can be generated by the energy storage system.

[0090] When the frequency drops to the lowest point, the frequency deviation is the largest, and the power of the photovoltaic storage system for primary frequency modulation reaches the maximum value; since the frequency change rate at the lowest point is zero, the power of the photovoltaic storage system for inertial support is also zero. In order to improve the transient frequency characteristics and reduce the frequency deviation after the system disturbance, the following active power optimization model is established to solve the corresponding optimal inertia coefficient H bottom :Combining the previous equations (1) and (2), Δf can be expressed as a function related to the inertia coefficient H, from H bottom Get Δf bottom ;

[0091]

[0092] Where Δf bottom is the frequency deviation corresponding to the lowest point of the disturbed frequency, P max is the maximum active output of the inverter, P pv0 is the active power output of the photovoltaic power generation system at the maximum power point, P Emax is the upper limit of the active power of the energy storage unit. The first constraint is the converter capacity limitation, the second constraint is the energy storage unit (ESS) power limitation, and the third constraint is the discrete form of the dynamic equation of the photovoltaic-energy storage grid-connected system.

[0093] The method for collaborative optimization of reactive power control parameters is:

[0094] The reactive parameter optimization target is the output voltage of the reactive power-voltage loop in the grid-type inverter control. The optimized control parameter is the output reactive power of photovoltaic and energy storage. The reactive optimization function is solved by the particle swarm optimization (PSO) algorithm. By adjusting the VSG control parameters, the reactive output of photovoltaic and energy storage is adjusted to improve the economy of the power grid while ensuring the qualified voltage.

[0095] In order to achieve the purpose of optimizing the reactive power control of the system, the VSG control parameters are adjusted to adjust the reactive output of photovoltaic and energy storage, and the economic efficiency of the power grid is improved under the premise of ensuring qualified voltage. The optimization goal is to minimize the active power loss, and the voltage deviation is brought into the objective function as a penalty term. The following objective function of reactive power optimization is established:

[0096]

[0097] Where P loss is the active power loss, k is the branch number, and m is the total number of branches. k is the current of the kth branch, λ is the voltage deviation penalty coefficient, U max is the voltage upper limit, U min is the voltage lower limit, U n is the rated voltage value, R k is the resistance of the kth branch, X k is the reactance of the kth branch, j represents the imaginary part of the complex number; when the voltage meets the interval requirements, λ is 0; when the voltage exceeds the limit, λ is λ 0 .

[0098] In order to ensure the normal operation of the system and the optimal solution of the objective function, the parameters need to be constrained. According to the system power balance, the relationship between the reactive power and voltage controlled by the VSG, the equation constraints are constructed, and the inequality constraints of reactive power and energy storage reactive power are established.

[0099] (1) Power balance equation constraints

[0100]

[0101] Among them, P VSG and Q VSG are the active power and reactive power output values ​​of the inverter controlled by VSG, P ESS and Q ESS are the active power and reactive power output values ​​of the energy storage device after VSG control and regulation. loss and Q loss is the active power and reactive power loss in the system, P load and Q load They represent the active power and reactive power consumed by the load respectively, that is, after VSG is connected to the grid, the total active power and reactive power output of the system is equal to the sum of the line loss power and the load consumption power.

[0102] (2) System reactive power inequality constraints

[0103] The system uses VSG control technology to adjust and control reactive power within the system. Therefore, the reactive power compensation source must be able to meet the reactive power shortage caused by load fluctuations. The specific expression is as follows:

[0104] Q VSG,max +Q ESS,max ≥Q load,max +Q loss (10)

[0105] Among them, QVSG,max , Q ESS,max are the maximum reactive power output limits of the photovoltaic and energy storage converters under VSG control, Q load,max is the maximum reactive power required by the load.

[0106] (3) Photovoltaic output inequality constraints

[0107] The constraints on photovoltaic active and reactive output are as follows:

[0108]

[0109] Due to the limitations of photovoltaic panels and inverters, it is necessary to set a maximum constraint on photovoltaic power generation. PV is the total photovoltaic output capacity, S PVmax and P VSGmax They are the maximum limits of total photovoltaic output capacity and active power output respectively.

[0110] (4) Energy storage output inequality constraints

[0111] The active and reactive output constraints of the energy storage battery are as follows:

[0112]

[0113] Among them, P ch and P dis are the active power of the energy storage device in the charging and discharging state, Q ESS is the reactive power output of the energy storage battery pack, S ESSmax It is the maximum limit value of energy storage capacity. Since energy storage devices can emit reactive power or absorb reactive power, it is necessary to limit their active and reactive power output during discharge and charging.

[0114] The present invention adopts the PSO algorithm to solve the reactive power optimization function established above. Since the voltage deviation penalty term is added to the main optimization objective, it needs to be added to the fitness of the algorithm to transform the soft constraint problem into a traditional unconstrained problem.

[0115] System control ideas and parameter setting steps are as follows Figure 1 shown.

[0116] The specific steps for parameter training and tuning are as follows:

[0117] Step 1: The system operates in a stable initial state. Then the system is disturbed by a sudden load change, which causes a large reactive power shortage in the system and generates voltage fluctuations.

[0118] Step 2: Perform virtual synchronous machine control parameter training, individual and group learning factors c of the PSO algorithm 1 and c 2are all assigned a value of 2, the initial value of the inertia weight w is 0.9, the dimension n of the solution space, i.e. the number of decision variables, is 4, and the size of a set of particles is 12.

[0119] Step 3: Generate a set of control parameter values ​​and input them into the VSG controller to adjust the power output of photovoltaic and energy storage and operate the system at this moment to obtain the voltage and current of the line and the voltage and current of the load point.

[0120] Step 4: Calculate the active network loss P based on this loss value, by judging whether the load point voltage meets U min <U<U max The range is used to determine the value of the penalty factor λ and optimize the fitness of the objective function.

[0121] Step 5: Determine whether the number of iterations i has been reached, otherwise return to step 2, re-update a set of variable values, and continue iterating; if the number of iterations i reaches the maximum of 1000, stop iterating and continue to step 6;

[0122] Step 6: Output the optimal values ​​of the variables at this time as well as the voltage U, current I and active network loss value P loss , thus completing the setting of VSG parameters, and then inputting the parameter values ​​into the VSG controller, and the PSO algorithm ends.

[0123] Simulation Example

[0124] This example is based on the virtual synchronous machine control technology to build a new grid-type control model of the power system integrating VSG control. Through MATLAB / Simulink simulation experiments, a photovoltaic storage system including two VSGs is simulated to verify the effectiveness of the active and reactive collaborative optimization control method of the photovoltaic storage system based on the grid-type inverter proposed in the above embodiment. Through mathematical modeling and considering the capacity limitation of the photovoltaic storage system, the frequency support control parameters are adjusted according to the disturbance size, and the parameters of the active power control are optimized. In addition, the particle swarm optimization algorithm is used, with the minimum active power loss as the optimization goal, and the voltage deviation is introduced into the objective function as a penalty term to establish the objective function of reactive power optimization. Considering the constraints such as the inverter capacity limitation and the energy storage unit power limitation, the normal operation of the system and the optimal solution of the optimization goal are ensured, thereby optimizing the parameters of the reactive power control. The comparison results of the dynamic characteristics of the output frequency and voltage of the two VSGs before and after parameter optimization are shown in the figure. Figure 2 , Figure 3 , Figure 4 and Figure 5 As shown: Figure 2 This is a comparison chart of VSG1 frequency waveform. Figure 3 This is a comparison chart of the VSG1 voltage waveform. Figure 4 This is a comparison chart of VSG2 frequency waveform. Figure 5This is a comparison chart of VSG2 voltage waveform.

[0125] like Figure 2 and Figure 4 As shown in Figure 1, after optimizing the active power control parameters according to the disturbance size by considering the capacity limitation of the photovoltaic storage system, the output frequency of the two VSGs changes less after the system load fluctuates, and the dynamic characteristics of the output frequency are significantly improved. Figure 3 and Figure 5 As shown in the figure, after the particle swarm algorithm optimizes the reactive power-voltage loop parameters, the voltage drop of the system is significantly reduced after the load fluctuates, the reactive power compensation of the system is faster, and the dynamic characteristics of the output voltage are significantly improved.

[0126] Therefore, the present invention proposes to consider the capacity limitation of the grid-type photovoltaic storage system and use the particle swarm optimization algorithm to coordinate the parameters of the active and reactive power of the grid-type inverter, which effectively improves the frequency and voltage stability of the new power system equipped with photovoltaic energy storage and provides strong support for the stable operation of the new power system.

[0127] The technical solutions of the present invention are not limited to the above-mentioned embodiments, and any technical solutions obtained by equivalent replacement methods fall within the scope of protection required by the present invention.

Claims

1. A method for coordinated optimization control of active and reactive power of a photovoltaic energy storage system based on a grid-connected inverter, characterized in that: include: Constructing the power outer loop control model of grid-connected inverter based on virtual synchronous machine technology; Establish the frequency dynamic response model of the grid-connected inverter and the dynamic equation of the energy storage unit; Combining the power outer loop control model, frequency dynamic response model and dynamic equation of energy storage unit of the grid-connected inverter, the dynamic equation of the grid-connected inverter of the photovoltaic energy storage system is obtained. According to the dynamic equation of the grid-connected inverter of the photovoltaic energy storage system, an active power optimization model is constructed with the goal of minimizing the frequency deviation of the photovoltaic energy storage system at the lowest point after the frequency is disturbed. According to the dynamic equation of the grid-connected inverter of the photovoltaic energy storage system, the reactive power optimization model is constructed with the minimum active power loss as the optimization goal and combined with the voltage deviation penalty coefficient; the PSO algorithm is used to solve the reactive power optimization model.

2. The active and reactive power coordinated optimization control method of the photovoltaic energy storage system based on the grid-connected inverter according to claim 1 is characterized in that: The power outer loop control model of the grid-connected inverter is: Among them, H is the virtual inertia coefficient, ω is the angular frequency, δ is the virtual power angle output by the active power-frequency control loop in the outer loop of the network control, t is time, D is the virtual damping coefficient, P0 is the active power setting value, P is the actual active power, K D is the VSG primary frequency modulation coefficient, s is the Laplace operator, f is the frequency reference value in the control outer loop, f0 is the actual frequency value in the control outer loop, P0 is the active power reference value, E0 is the initial internal potential amplitude, E is the output reference voltage, U0 is the rated voltage value, U is the actual voltage, K is the reactive integral coefficient, K q is the reactive voltage drop coefficient, Q0 is the reactive power setting value, and Q is the actual reactive power.

3. The active and reactive power coordinated optimization control method of the photovoltaic energy storage system based on the grid-connected inverter according to claim 2 is characterized in that: The frequency dynamic response model of the grid-connected inverter is: Where Δf is the system frequency deviation, H g F is the virtual inertia coefficient of the synchronous machine, and R is the adjustment coefficient. H is the high pressure cylinder working proportional coefficient, T R is the reheater time constant, D g Indicates the equivalent damping coefficient of the load in the system, ΔP m and ΔP e They are the mechanical active power deviation and electromagnetic active power deviation of the synchronous machine respectively.

4. The active and reactive power coordinated optimization control method of the photovoltaic energy storage system based on the grid-connected inverter according to claim 3 is characterized by: The dynamic equation of the energy storage unit is: Among them, SOC represents the state of charge, Ppv represents the active power generated by the photovoltaic array, Ppv0 represents the maximum tracking power of the photovoltaic system, and Q represents the rated charging capacity of the energy storage unit capacitor.

5. The active and reactive power coordinated optimization control method of the photovoltaic energy storage system based on the grid-connected inverter according to claim 4 is characterized in that: The dynamic equation of the grid-connected inverter of the photovoltaic energy storage system is: Where k is any discrete point in the discrete equation, k+1 is the next discrete point, ΔP m is the mechanical power deviation of the synchronous machine, S OC is the state of charge and h is the discrete step length.

6. The active and reactive power coordinated optimization control method of the photovoltaic energy storage system based on the grid-connected inverter according to claim 5 is characterized in that: The active power optimization model is: min|Δf bottom | Where Δf bottom is the frequency deviation corresponding to the lowest point of the disturbed frequency, P max is the maximum active output of the inverter, P pv0 is the active power output of the photovoltaic power generation system at the maximum power point, P Emax It is the upper limit of the active power of the energy storage unit.

7. The active and reactive power coordinated optimization control method of the photovoltaic energy storage system based on the grid-connected inverter according to claim 6 is characterized in that: The reactive power optimization model is: Where P loss is the active power loss, k is the branch number, m is the total number of branches, I k is the current of the kth branch, λ is the voltage deviation penalty coefficient, U max is the voltage upper limit, U min is the voltage lower limit, U n is the rated voltage value, R k is the resistance of the kth branch, X k is the reactance of the kth branch, and j represents the imaginary part of the complex number.

8. The active and reactive power coordinated optimization control method of the photovoltaic energy storage system based on the grid-connected inverter according to claim 7 is characterized in that: The constraints of the reactive power optimization model include power balance equation constraints, system reactive power inequality constraints, photovoltaic output inequality constraints and energy storage output inequality constraints.

9. The active and reactive power coordinated optimization control method of the photovoltaic energy storage system based on the grid-connected inverter according to claim 8 is characterized in that: The power balance equation constraint is: Among them, P VSG and Q VSG are the active power and reactive power output values ​​of the inverter controlled by VSG, P ESS and Q ESS are the active power and reactive power output values ​​of the energy storage device after VSG control and regulation, P loss and Q loss is the active power and reactive power loss in the system, P load and Q load Respectively represent the active power and reactive power consumed by the load; The system reactive power inequality constraint is: Q VSG,max +Q ESS,max ≥Q load,max +Q loss Among them, Q VSG,max , Q ESS,max are the maximum reactive power output limits of the photovoltaic and energy storage converters under VSG control, Q load,max is the maximum reactive power required by the load; The photovoltaic output inequality constraint is: Among them, S PV is the total photovoltaic output capacity, S PVmax and P VSGmax They are the maximum limits of total photovoltaic output capacity and active power output respectively; The energy storage output inequality constraint is: Among them, P ch and P dis are the active power of the energy storage device in the charging and discharging state, Q ESS is the reactive power output of the energy storage battery pack, S ESSmax It is the maximum limit value of energy storage capacity. Since energy storage devices can emit reactive power or absorb reactive power, it is necessary to limit their active and reactive power output during discharge and charging.

10. The active and reactive power coordinated optimization control method of the photovoltaic energy storage system based on the grid-connected inverter according to claim 9 is characterized in that: The steps of solving the reactive power optimization model using the PSO algorithm are as follows: Step 1: operate the system in an initial state, and then subject the system to a sudden load disturbance, causing reactive power shortage and voltage fluctuation; Step 2, performing virtual synchronous machine control parameter training; Step 3, generating a set of control parameter values, inputting the control parameter values ​​into the virtual synchronous machine controller, adjusting the power output of photovoltaic and energy storage and operating the system at this moment to obtain the voltage and current of the line and the voltage and current of the load point; Step 4: Calculate the active network loss P loss value, by judging whether the load point voltage meets U min <U<U max The range is used to determine the value of the penalty factor λ and optimize the fitness of the objective function; Step 5: Determine whether it reaches the number of iterations, otherwise return to step 3; Step 6: Output the optimal values ​​of the variables at this time as well as the voltage U, current I and active network loss value P loss , complete the VSG parameter setting and input the parameter values ​​into the virtual synchronous machine controller.

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