Optimized operation method and system for network construction type converter

Abstract

The invention discloses an optimized operation method and system for a network-constructed converter. The minimum voltage amplitude deviation of a common coupling point, the maximum new energy power generation power, the maximum network-side power factor and the minimum line loss serve as nonlinear multiple targets in optimized operation of the network-constructed converter. The non-linear multi-target constraints comprise a common coupling point voltage constraint, a network side minimum power factor constraint, a converter output current constraint, a converter apparent power constraint, a photovoltaic output constraint and an energy storage state constraint; according to the priority of each target, determining a target function and a constraint condition of each priority target; according to the priority order, the optimal solutions of the target functions of the priority targets under the constraint condition are solved in sequence, and the optimal solutions serve as input data of the proxy model; and according to the output data of the agent model, optimizing the operation of the network construction type converter. Cooperative control of convergence speed, global optimization capability and dynamic adaptability is considered, and power supply quality and operation efficiency of an active power distribution area are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system optimized operation, and in particular, relates to a method and system for optimized operation of a grid-connected converter. Background Art

[0002] Photovoltaic (PV) and energy storage (Battery) systems are increasingly being used in active power distribution substations. However, the intermittent and fluctuating nature of PV and battery generation poses numerous challenges to the stable operation of these substations. Fluctuations in PV power generation can lead to voltage fluctuations and reduced power factor, impacting power quality. The dynamic coupling of grid-side power factor, system losses, and other indicators in multi-objective optimization scenarios makes conventional decoupling methods unable to meet real-time requirements, resulting in response delays of ≥500ms. Furthermore, with multiple energy sources working in concert, how can power be allocated rationally to minimize system losses and maximize renewable energy utilization?

[0003] Existing solutions to these problems often focus on algorithm improvement and model optimization. For example, the dynamic weighted particle swarm algorithm improves convergence speed by linearly adjusting the inertia weight coefficient with the number of iterations. However, the weight adjustment mechanism fails to account for sudden changes in photovoltaic power, such as step changes caused by cloud cover. This requires the algorithm to reconverge when operating conditions switch. Case tests show that the voltage recovery time is extended to 0.8 seconds, far exceeding the 0.3 seconds required by GB / T 30137-2013. Furthermore, the optimization objective is fixed as the weighted sum of maximizing grid-side power factor and minimizing losses, making it impossible to dynamically adjust priorities based on real-time operating conditions, such as prioritizing renewable energy consumption during periods of high generation. The multimodal operation model for photovoltaic storage systems divides typical scenarios into six operating modes, but the model lacks sensitivity analysis of the resistance and reactance parameters of transmission and distribution lines. Parameter drift, such as line impedance shifts caused by temperature changes, can lead to inaccurate optimization results, with an error rate exceeding 12%. The multi-objective optimization of distribution substations based on the improved NSGA-II uses a non-dominated sorting genetic algorithm. However, its Pareto optimal solution set generation mechanism consumes a large amount of computational time when the dimension exceeds 5, reaching more than 20 minutes, making it difficult to meet the requirements of online control. In addition, existing technologies have three common problems: the contradiction between convergence and stability: improving convergence speed is likely to sacrifice global search capabilities (for example, rapid convergence to local optimality leads to voltage oscillation); limited model generalization ability: no quantitative correlation is established between equipment parameters and target weights, and repeated parameter adjustment is required for cross-substation application; poor adaptability to dynamic working conditions: fixed priority strategies cannot respond to fluctuations in renewable energy penetration (for example, maximizing photovoltaic power consumption is required during peak photovoltaic power generation periods). This directly leads to a loss of 8% to 15% in overall system efficiency, seriously restricting the large-scale application of photovoltaic storage systems in distribution substations. Summary of the Invention

[0004] In order to address the deficiencies in the prior art, the present invention provides a method and system for optimizing the operation of a grid-type converter, which takes into account the coordinated control of convergence speed, global optimization capability and dynamic adaptability. By optimizing the operating parameters of the photovoltaic storage system, the line loss of the substation system is minimized, the grid-side power factor is maximized, the power generation power of the new energy power generation system is maximized, and the voltage amplitude deviation of the common coupling point is minimized, thereby improving the power supply quality and operating efficiency of the active distribution substation.

[0005] The present invention adopts the following technical solutions.

[0006] The present invention proposes a method for optimizing operation of a grid-connected converter, comprising:

[0007] Obtain active power difference, reactive power difference, actual grid voltage value, transmission line and transformer equivalent resistance and reactance, calculate grid current amplitude, line loss, grid-side power factor, and common coupling point voltage amplitude; use the active power of the load, the active power generated by the grid, and line loss to determine the power generation of renewable energy; use the common coupling point voltage amplitude and the nominal value of the grid voltage to determine the common coupling point voltage amplitude deviation;

[0008] The nonlinear multi-objectives in the optimized operation of the grid-type converter are to minimize the voltage amplitude deviation at the common coupling point, maximize the power of renewable energy generation, maximize the grid-side power factor, and minimize the line loss; the constraints of the nonlinear multi-objectives include: common coupling point voltage constraint, grid-side minimum power factor constraint, converter output current constraint, converter apparent power constraint, photovoltaic output constraint, and energy storage state constraint; according to the priority of each objective, the objective function and constraint conditions of each priority objective are determined; according to the priority order, the optimal solution of the objective function of each priority objective under the constraint conditions is solved in turn, and the optimal solution is used as the input data of the proxy model; according to the output data of the proxy model, the operation of the grid-type converter is optimized.

[0009] The difference between the active power of the load and the active power of the photovoltaic system and the active power of the energy storage system is used as the active power difference; the difference between the reactive power of the load and the reactive power of the photovoltaic converter is used as the reactive power difference.

[0010] The grid current amplitude is calculated using the active power difference, reactive power difference, actual grid voltage, and equivalent resistance and reactance of the transmission line and transformer to satisfy the following relationship:

[0011]

[0012] Where, I SG is the grid current amplitude, Z G is the equivalent impedance of the transmission line and transformer, is the nominal value of the grid voltage, is the actual value of the grid voltage, R G 、L G are the equivalent resistance and reactance of the transmission line and transformer respectively, ω is the grid angular frequency, λ1 is the active power difference, and λ2 is the reactive power difference.

[0013] Calculate line loss, grid-side power factor, and voltage amplitude at the point of common coupling using active power difference, reactive power difference, grid current amplitude, and equivalent resistance and reactance of transmission lines and transformers.

[0014] The line loss ρ satisfies the following relationship:

[0015] The grid-side power factor θ satisfies the following relationship:

[0016]

[0017] Common coupling point voltage amplitude V PCC Satisfies the following expression:

[0018]

[0019] Common coupling point voltage amplitude deviation ΔV L Satisfies the following expression:

[0020] The nonlinear multi-objective F satisfies the following relationship:

[0021]

[0022] Where, ΔV L is the voltage amplitude deviation at the common coupling point, P DG is the power generated by renewable energy, P DG,max is the upper limit of renewable energy power generation, ρ is the line loss, θ is the grid-side power factor, and abs() is the absolute value function.

[0023] The voltage constraint at the point of common coupling is: V PCC is the voltage amplitude at the common coupling point, is the nominal value of the grid voltage, They are the minimum and maximum values of the common coupling point voltage nominal values allowed by the active distribution network.

[0024] Obtain the real-time parameter K of reactive power-voltage droop control of grid-connected converter q (t), parameter lower limit K q,min , parameter upper limit K q,max , benchmark parameter K q,base , calculate the lower limit α1(t) and upper limit α2(t) of the voltage time-varying coefficient as follows:

[0025]

[0026] The lower limit α1(t) and upper limit α2(t) of the voltage time-varying coefficient are used to correct the ratio coefficient k between the actual value of the grid voltage and the nominal value of the grid voltage, and the lower limit k1 and upper limit k2 of the ratio coefficient are obtained, which satisfy the following relationship: k1=k(1+α1(t)), k2=k(1+α2(t)).

[0027] Benchmark parameter K q,base The ratio of the maximum reactive capacity that the converter can provide or absorb to the allowable voltage deviation is based on the reference parameter K q,base , parameter lower limit K q,min is the benchmark parameter K q,base 0.3 times of the upper limit of parameter K q,max is the benchmark parameter K q,base 2 times.

[0028] The grid-side power factor constraint is: θ min ≤θ≤1,θ min The minimum power factor allowed by the power grid; the minimum power factor in the constraint condition is not a fixed value, but is the power factor with the minimum line loss determined based on the optimal power flow model, the equivalent resistance and reactance of the transmission line and transformer.

[0029] The output current of the converter is constrained to be: 0≤I FL ≤I FL,max , I FL is the output current of the grid-type energy storage converter, I FL,max is the maximum output current of the grid-type energy storage converter;

[0030] The apparent power constraint of the converter is: |S PV | is the apparent power of the grid-type energy storage converter, is the nominal capacity value of the grid-type energy storage converter;

[0031] Photovoltaic output constraint: 0≤P PV ≤P PV,max , P PV is the active power of the photovoltaic system, P PV,max The maximum active power that can be generated by the photovoltaic system;

[0032] The energy storage state constraint is: P BA,min ≤P BA ≤P BA,max , P BA is the active power of the energy storage system, P BA,max 、P BA,min They are respectively the upper limit when the discharge power of the energy storage system is positive and the upper limit when the charging power is negative.

[0033] The objective function and constraints with the minimum common coupling point voltage amplitude deviation as the first-level goal satisfy the following relationship:

[0034]

[0035] Voltage constraints at the point of common coupling: When the first-level target is to minimize the voltage amplitude deviation at the common coupling point, the control target of the grid-type converter is to ensure that the voltage amplitude at the common coupling point does not exceed the limit. At this time, the real-time parameter of the reactive power-voltage droop control of the grid-type converter is K q ′(t), the lower limit of the modified ratio coefficient is k3 and the upper limit is k4, They are respectively the minimum and maximum values of the common coupling point voltage nominal values allowed by the active distribution network when controlling the common coupling point voltage amplitude not to exceed the limit;

[0036] Grid-side power factor constraint: θ min ≤θ≤1; Converter output current constraint: 0≤I FL ≤I FL,max ;Converter apparent power constraint: Photovoltaic output constraint: 0≤P PV ≤P PV,max ; Energy storage state constraint: P BA,min ≤P BA ≤P BA,max .

[0037] The objective function and constraints of the second-level goal, which takes the maximum power generation of renewable energy, satisfy the following relationship:

[0038] F2=min(P DG -P DG,max ) 2

[0039] Voltage constraints at the point of common coupling: When the second-level target is to maximize the power generation of renewable energy, the control target of the grid-type converter is to keep the voltage fluctuation at the common coupling point within the range. At this time, the real-time parameter of the reactive power-voltage droop control of the grid-type converter is K q ″(t), the lower limit of the modified ratio coefficient is k5 and the upper limit is k6, They are respectively the minimum and maximum values of the common coupling point voltage nominal values allowed by the active distribution network when controlling the common coupling point voltage fluctuation within the range;

[0040] Grid-side power factor constraint: θ min ≤θ≤1; Converter output current constraint: 0≤I FL ≤I FL,max ;Converter apparent power constraint: Photovoltaic output constraint: 0≤P PV ≤P PV,max ; Energy storage state constraint: P BA,min ≤P BA ≤P BA,max .

[0041] The objective function and constraints of the third-level goal, which is to minimize line loss and maximize grid-side power factor, satisfy the following relationship:

[0042]

[0043] Voltage constraints at the point of common coupling: When the second-level target is to maximize the power generation of renewable energy, the control target of the grid-type converter is to keep the voltage fluctuation at the common coupling point within the range. At this time, the real-time parameter of the reactive power-voltage droop control of the grid-type converter is K q ″(t), the lower limit of the modified ratio coefficient is k5 and the upper limit is k6, so, They are respectively the minimum and maximum values of the common coupling point voltage nominal values allowed by the active distribution network when controlling the common coupling point voltage fluctuation within the range;

[0044] Grid-side power factor constraint: θ min ≤θ≤1; Converter output current constraint: 0≤I FL ≤I FL,max ;Converter apparent power constraint: Photovoltaic output constraint: P PV =P PV,max ; Energy storage state constraint: P BA,min ≤P BA ≤P BA,max .

[0045] According to the priority order, the optimized PSO algorithm is used to solve the optimal solution of the objective function of each priority target under the constraint conditions in turn, including: taking the operating conditions corresponding to the optimal solution of the objective function of the first-level target under the constraint conditions as the input data of the proxy model; if the objective function of the first-level target has no solution under the constraint conditions, then taking the operating conditions corresponding to the optimal solution of the objective function of the second-level target under the constraint conditions as the input data of the proxy model; if the objective function of the first-level target has no solution under the constraint conditions and the objective function of the second-level target has no solution under the constraint conditions, then taking the operating conditions corresponding to the optimal solution of the objective function of the third-level target under the constraint conditions as the input data of the proxy model; the optimal solution of the objective function of each priority target under the constraint conditions includes the active power P of the photovoltaic system. PV , the active power P of the energy storage system BA .

[0046] In the optimized PSO algorithm, the Lorenz attractor is used to generate a chaotic sequence, and the dimensions of the chaotic variables are set according to the number of priorities. The first-level goal is to minimize the voltage amplitude deviation at the common coupling point, the second-level goal is to maximize the power generation of renewable energy, and the third-level goal is to minimize line losses and maximize the grid-side power factor. Chaotic dimension matching is achieved based on the priority.

[0047] The chaotic sequence corresponding to the state parameter c of the Lorentz system Generate the initial population; take the chaotic sequence {a k} and {b k}Replace the random numbers r1 and r2 to update the particle speed of the PSO algorithm; on the basis of realizing chaos dimension matching based on priority, the particle swarm search radius is adjusted in real time by adjusting the system parameters when the priority is switched;

[0048] The iteration termination threshold of each priority target is associated with the Lyapunov exponent of the chaotic sequence.

[0049] Based on the active power of the photovoltaic system and the active power of the energy storage system, the operating conditions of the system are determined as follows:

[0050] 1) Operating condition 1: P PV =P BA = 0kW, the photovoltaic system and energy storage system are both disconnected, and the grid alone supplies energy to the load;

[0051] 2) Operating condition 2: P BA =0kW, P PV >0: The energy storage system is disconnected, and the grid and photovoltaic system jointly supply energy to the load;

[0052] 3) Operating condition 3: P BA =0kW, P PV >0: The energy storage system is disconnected and the photovoltaic system supplies energy to the grid and loads;

[0053] 4) Operating condition 4: P BA >0,P PV >0: The photovoltaic system, energy storage system and power grid jointly supply energy to the load;

[0054] 5) Operating condition 5: P BA <0, P PV >0: The photovoltaic system supplies energy to the load and the energy storage system charges;

[0055] 6) Operating condition 6: P BA >0,P PV When >0, the grid is disconnected, and the photovoltaic system and energy storage system jointly supply energy to the load.

[0056] The present invention also proposes a grid-type converter optimization operation system, comprising:

[0057] The parameter acquisition module is used to obtain the active power difference, reactive power difference, actual grid voltage value, transmission line and transformer equivalent resistance and reactance, calculate the grid current amplitude, line loss, grid-side power factor, and common coupling point voltage amplitude; use the active power of the load, the active power generated by the grid, and the line loss to determine the power generation power of renewable energy; use the common coupling point voltage amplitude and the grid voltage nominal value to determine the common coupling point voltage amplitude deviation;

[0058] The optimization operation module is used to optimize the nonlinear multi-objectives of the grid-type converter in the operation, with the minimum common coupling point voltage amplitude deviation, the maximum renewable energy power generation power, the maximum grid-side power factor, and the minimum line loss as the nonlinear multi-objectives; the constraints of the nonlinear multi-objectives include: common coupling point voltage constraint, grid-side minimum power factor constraint, converter output current constraint, converter apparent power constraint, photovoltaic output constraint, and energy storage status constraint; according to the priority of each objective, the objective function and constraint conditions of each priority objective are determined; according to the priority order, the optimal solution of the objective function of each priority objective under the constraint conditions is solved in turn, and the optimal solution is used as the input data of the proxy model; according to the output data of the proxy model, the operation of the grid-type converter is optimized.

[0059] The present invention also provides a terminal, comprising a processor and a storage medium; the storage medium is used to store instructions; and the processor is used to operate according to the instructions to execute steps of the method.

[0060] The present invention also relates to a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method when the program is executed by a processor.

[0061] The beneficial effects of the present invention are that, compared with the prior art, it at least includes the following: the method proposed by the present invention, which targets the grid-type photovoltaic storage access to the active distribution substation, analyzes the system structure and operating conditions, establishes an accurate mathematical model, and forms a multi-objective optimization problem, including minimizing system losses, maximizing the grid-side power factor, maximizing the power generation of renewable energy, and minimizing the load voltage deviation; based on the hierarchical optimization idea, the multi-objective optimization problem is converted into a single-objective optimization problem; and the optimization problem is solved by using an improved particle swarm optimization algorithm. In the specific implementation process, the system parameters are first obtained and the algorithm is initialized to solve the multi-objective optimization problem, and finally the control parameters are adjusted according to the optimization results to achieve adaptive management. Compared with the prior art, the present invention has significant advantages such as strong adaptability, high new energy absorption rate and good voltage quality, which effectively improves the power supply quality and operating efficiency of the distribution substation, and is suitable for the new energy optimization and power quality optimization of all types of active distribution substations with grid-type photovoltaic storage access. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 This is a schematic diagram of the structure of an active distribution station area connected to a photovoltaic storage system in an embodiment of the present invention;

[0063] Figure 2 This is a flow chart of a method for optimizing operation of a grid-type converter proposed by the present invention. DETAILED DESCRIPTION

[0064] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. The embodiments described in this application are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0065] The active distribution network structure of the photovoltaic storage access involved in the present invention is as follows: Figure 1 As shown, the public power grid is connected to the distribution substation through transmission lines and transformers to supply power to the substation. The grid voltage is v Ga 、v Gb 、v Gc , the current on the transmission line is i SGa 、i SGb 、i SGc , the photovoltaic system and energy storage system are connected to the DC bus, and then connected to the AC grid through the grid-type converter. Figure 1 The photovoltaic array is connected to the DC bus through the DC / DC converter, and the battery is connected to the DC bus through the DC / DC converter. The photovoltaic power generation power is P PV , the output power of the energy storage battery is P BA , the AC load is connected to the AC bus, and the voltage at the common coupling point is v PCCa 、v PCCb 、v PCCc , load current i La 、i Lb 、i Lc , the output current of the grid-type converter is i FL , the reactive power Q output by the grid-connected converter FL Used to compensate for load reactive demand and maintain high power factor operation of the power grid.

[0066] Depending on the output of the photovoltaic system and energy storage system, the system operating conditions include:

[0067] In operating condition 1, both the PV system and the energy storage system are disconnected, with the grid providing energy alone. In operating condition 2, the energy storage system is disconnected, with the grid and PV system providing energy to the load. In operating condition 3, the energy storage system is disconnected, with the PV system providing energy to the grid and load. In operating condition 4, the PV system, energy storage system, and grid all provide energy. In operating condition 5, the PV system provides energy while the energy storage system charges. In operating condition 6, the grid is disconnected, with the PV system and energy storage system providing energy. The integration of energy storage brings significant advantages to the system. It not only stores excess energy generated by PV, achieving peak load shifting and valley filling, and improving the absorption capacity of renewable energy, but also releases energy when needed, enhancing system operating efficiency. Furthermore, by working in conjunction with the grid-connected converter, it can effectively improve the supply voltage quality and suppress current harmonics.

[0068] The present invention proposes a method for optimizing the operation of a grid-type converter. Figure 2 As shown, including:

[0069] Step 1: Obtain the active power difference, reactive power difference, actual grid voltage value, and equivalent resistance and reactance of the transmission line and transformer, and calculate the grid current amplitude, line loss, grid-side power factor, and common coupling point voltage amplitude. Determine the renewable energy power generation power using the active power of the load, the active power generated by the grid, and the line loss. Determine the common coupling point voltage amplitude deviation using the common coupling point voltage amplitude and the nominal grid voltage value.

[0070] Specifically, the active power of the load, the active power of the photovoltaic system, the active power of the energy storage system, the reactive power of the load, and the reactive power of the photovoltaic converter are obtained. Based on the equivalent circuit of the active distribution network connected to the grid-type photovoltaic storage, the line loss, grid-side power factor and common coupling point voltage amplitude are calculated respectively; the common coupling point voltage amplitude deviation is determined by using the common coupling point voltage amplitude and the nominal value of the grid voltage; the active power of the load, the active power emitted by the grid and the line loss are used to determine the power of the new energy power generation system.

[0071] Specifically, step 1 includes:

[0072] Step 1.1: The difference between the active power of the load and the active power of the PV system and the active power of the energy storage system is used as the active power difference λ1; the difference between the reactive power of the load and the reactive power of the PV converter is used as the reactive power difference λ2;

[0073] λ1=P L -P PV -P BA

[0074] λ2=Q L -Q FL

[0075] Where, P L With QL are the active power and reactive power of the load, P PV is the active power of the photovoltaic system, P BA is the active power of the energy storage system, Q FL is the reactive power of the photovoltaic converter;

[0076] Step 1.2, calculate the grid current amplitude using the active power difference, reactive power difference, actual grid voltage, and equivalent resistance and reactance of transmission lines and transformers;

[0077] The load voltage is the voltage at the common coupling point and satisfies the following relationship:

[0078]

[0079] Where, v PCCa 、v PCCb 、v PCCc is the three-phase voltage at the common coupling point, R G 、L G are the equivalent resistance and reactance of the transmission line and transformer respectively, i SGa 、i SGb 、i SGc is the three-phase current on the transmission line, v Ga 、v Gb 、v Gc is the three-phase voltage of the power grid.

[0080] The current output by the grid-type converter satisfies the following relationship:

[0081]

[0082] Where i FLa 、i FLb 、i FLc is the three-phase current output by the grid-type converter, i La 、i Lb 、i Lc is the three-phase current of the load.

[0083] The load demand is the complex power of the load, which satisfies the following relationship:

[0084]

[0085] Where S L is the load complex power, v PCC is the voltage phasor at the point of common coupling, is the conjugate phasor of the grid output current phasor;

[0086] in,

[0087]

[0088] Where, v PCCbc 、v PCCca 、v PCCab is the line voltage of the load.

[0089] The complex power output by the converter satisfies the following relationship:

[0090]

[0091] Where S FL is the complex power output by the inverter, is the conjugate phasor of the phasor of the output current of the grid-type converter;

[0092] The complex power generated by the power grid satisfies the following relationship:

[0093]

[0094] Where S G is the complex power generated by the grid, v G is the grid voltage phasor, is the conjugate phasor of the grid current phasor, P G With Q G are the active power and reactive power of the power grid respectively;

[0095] in,

[0096]

[0097] Where, v Gbc 、v Gca 、v Gcb is the line voltage of the grid.

[0098] According to the law of conservation of power, the complex power generated by the power grid also satisfies the following relationship:

[0099]

[0100] Where, I SG is the grid current amplitude.

[0101] Assume that the grid voltage phasor v G =V G ∠0, V G is the grid voltage amplitude, grid current vector is the phase angle, and the grid voltage vector and grid current vector are substituted into the two formulas of the complex power generated by the grid, and the following is derived:

[0102]

[0103] Combining the above two equations, eliminating The derived grid current amplitude satisfies the following relationship:

[0104]

[0105] Where, I SG is the grid current amplitude, Z G is the equivalent impedance of the transmission line and transformer, is the nominal value of the grid voltage, is the actual value of the grid voltage, R G 、L G are the equivalent resistance and reactance of the transmission line and transformer respectively, ω is the grid angular frequency, and k is the ratio coefficient of the actual grid voltage to the nominal grid voltage.

[0106] Step 1.3: Calculate the line loss, grid-side power factor, and voltage amplitude at the point of common coupling using the active power difference, reactive power difference, grid current amplitude, and equivalent resistance and reactance of the transmission line and transformer.

[0107] The line loss satisfies the following relationship:

[0108]

[0109] Where, ρ is the line loss;

[0110] The grid-side power factor θ satisfies the following relationship:

[0111]

[0112] Where, θ is the grid-side power factor, and abs() is the absolute value function;

[0113] Depend on where i SGd ,i SGq are the dq axis components of the grid current vector respectively;

[0114] Also because V Gq =0;

[0115]

[0116] Among them, V Gd 、V Gq are the dq axis components of the grid voltage vector, V PCCd 、V PCCq They are the dq axis components of the voltage vector at the common coupling point, and are substituted into The voltage amplitude of the common coupling point can be obtained as V PCC Satisfies the following expression:

[0117]

[0118] Where V PCC is the voltage amplitude at the common coupling point;

[0119] In step 1.4, the active power of the load, the active power generated by the grid, and the line loss are used to determine the power generated by the renewable energy source, satisfying the following relationship:

[0120] P DG =P L -P G +ρ

[0121] Where, P DG Power generation for new energy;

[0122] In step 1.5, the voltage amplitude deviation at the point of common coupling is determined using the voltage amplitude at the point of common coupling and the nominal value of the grid voltage, satisfying the following expression:

[0123]

[0124] Where ΔV L is the voltage amplitude deviation at the common coupling point.

[0125] Step 2: Minimize the common coupling point voltage amplitude deviation, maximize the renewable energy power generation power, maximize the grid-side power factor, and minimize the line loss as the nonlinear multi-objectives in the grid-type converter optimization operation; the constraints of the nonlinear multi-objectives include: common coupling point voltage constraint, grid-side minimum power factor constraint, converter output current constraint, converter apparent power constraint, photovoltaic output constraint, and energy storage status constraint.

[0126] Specifically, step 2 includes:

[0127] Step 2.1, taking the minimum voltage deviation amplitude at the common coupling point, the maximum renewable energy power generation, the maximum grid-side power factor, and the minimum line loss as the nonlinear multi-objectives in the optimization operation of the grid-type converter;

[0128] The optimization operation of a grid-connected converter is a process of solving nonlinear multi-objective optimization problems. The nonlinear multi-objectives include but are not limited to: minimizing the voltage amplitude deviation at the common coupling point, maximizing the power generation of renewable energy, maximizing the grid-side power factor, and minimizing the line loss, satisfying the following relationship:

[0129]

[0130] Where, P DG,max is the upper limit of renewable energy power generation, and F is a nonlinear multi-objective;

[0131] When the power generation of new energy is the largest, P DGThe upper limit of renewable energy power generation power P DG,max When the power factor on the grid side is the largest, the absolute value of the difference between θ and 1 is the smallest.

[0132] Step 2.2: The nonlinear multi-objective constraints include the common coupling point voltage constraint, the grid-side power factor constraint, the converter output current constraint, the converter apparent power constraint, the photovoltaic output constraint, and the energy storage status constraint.

[0133] The constraints of nonlinear multi-objective satisfy the following relationship:

[0134] 1) Voltage constraints at the point of common coupling: V PCC is the voltage amplitude at the common coupling point, is the nominal value of the grid voltage, are the minimum and maximum values of the common coupling point voltage nominal values allowed by the active distribution network respectively;

[0135] Among them, the increase in renewable energy power generation will increase the common coupling point voltage amplitude deviation, but an excessively high common coupling point voltage amplitude deviation may trigger the limit control, forcing the renewable energy to operate at a reduced rating; therefore, the real-time parameter K of the reactive power-voltage droop control of the grid-connected converter is obtained. q (t), parameter lower limit K q,min , parameter upper limit K q,max , benchmark parameter K q,base , calculate the lower limit α1(t) and upper limit α2(t) of the voltage time-varying coefficient as follows:

[0136]

[0137] Among them, the real-time parameter of reactive power-voltage droop control represents the relationship between voltage change and reactive power change. The benchmark parameter K q,base The ratio of the maximum reactive capacity that the converter can provide or absorb to the allowable voltage deviation is based on the reference parameter K q,base To avoid the voltage / frequency support capability being reduced due to the droop coefficient being too small, the lower limit of the parameter K is determined. q,min is the benchmark parameter K q,base The upper limit K of the parameter is determined to be 0.3 times of the original value, in order to prevent the droop coefficient from being too large, causing over-adjustment or oscillation. q,max is the benchmark parameter K q,base 2 times.

[0138] The voltage time-varying coefficient lower limit α1(t) and upper limit α2(t) are used to correct the ratio coefficient k between the actual grid voltage value and the nominal grid voltage value, and the ratio coefficient lower limit k1 and upper limit k2 are obtained, which satisfy the following relationship:

[0139] k1=k(1+α1(t)), k2=k(1+α2(t))

[0140] Under different system operating conditions, the reactive power-voltage droop control parameters of the grid-type converter vary. The present invention adjusts the minimum and maximum values of the common coupling point voltage nominal values allowed by the active distribution network by introducing real-time changes in the reactive power-voltage droop control parameters of the grid-type converter, thereby achieving the common coupling point voltage constraint under the optimal coordination of the common coupling point voltage amplitude deviation and the renewable energy power generation power. Under such constraints, the obtained optimal solution is more accurate and reliable, thereby improving the operating characteristics of the grid-type converter.

[0141] , grid-side power factor constraint: θ min ≤θ≤1,θ min The minimum power factor allowed by the power grid. The minimum power factor in the constraint condition is not a fixed value. It is based on the optimal power flow (OPF) model. According to the equivalent resistance and reactance of the transmission line and transformer, it determines the power factor when the line loss is minimized, thereby realizing the dynamic constraint of the grid-side power factor when the line loss is minimized.

[0142] ,Converter output current constraint: 0≤I FL ≤I FL,max , I FL is the output current of the grid-type energy storage converter, I FL,max is the maximum output current of the grid-type energy storage converter;

[0143] 4) Converter apparent power constraint: |S PV | is the apparent power of the grid-type energy storage converter, is the nominal capacity value of the grid-type energy storage converter;

[0144] 5) Photovoltaic output constraint: 0≤P PV ≤P PV,max , P PV is the active power of the photovoltaic system, P PV,max The maximum active power that can be generated by the photovoltaic system;

[0145] 6) Energy storage state constraint: P BA,min ≤P BA ≤P BA,max , P BA is the active power of the energy storage system, P BA,max 、P BA,min They are respectively the upper limit when the discharge power of the energy storage system is positive and the upper limit when the charging power is negative.

[0146] The constraints include the rated parameters of each device in the system (capacity limitations of transformers, inverters, etc.), power balance constraints (ensuring system input and output power balance), and the safe range of system operation (such as voltage and current cannot exceed the maximum value allowed by the equipment), ensuring that the minimum loss solution is sought within the feasible range. At the same time, the reasonable range of the power factor itself is taken into account to ensure that the optimization result is within the practical operational range and maximize the power factor.

[0147] Step 3: Determine the objective function and constraints of each priority target based on the priority of each target.

[0148] When there is a conflict between nonlinear multi-objectives, the optimization requirements of the high-priority objective are prioritized. Therefore, the priority of each objective is defined as follows:

[0149] 1) The voltage stability at the common coupling point directly affects the safety of electrical equipment. To reduce the risk of voltage exceeding the limit, the priority of the common coupling point with the smallest voltage amplitude deviation is set as the first level;

[0150] The objective function and constraints with the minimum common coupling point voltage amplitude deviation as the first-level goal satisfy the following relationship:

[0151]

[0152] Voltage constraints at the point of common coupling: When the first-level target is to minimize the voltage amplitude deviation at the common coupling point, the control target of the grid-type converter is to ensure that the voltage amplitude at the common coupling point does not exceed the limit. At this time, the real-time parameter of the reactive power-voltage droop control of the grid-type converter is K q ′(t), the lower limit of the modified ratio coefficient is k3 and the upper limit is k4, so, They are respectively the minimum and maximum values of the common coupling point voltage nominal values allowed by the active distribution network when controlling the common coupling point voltage amplitude not to exceed the limit;

[0153] Grid-side power factor constraint: θ min ≤θ≤1; Converter output current constraint: 0≤I FL ≤I FL,max ;Converter apparent power constraint: Photovoltaic output constraint: 0≤P PV ≤P PV,max ; Energy storage state constraint: P BA,min ≤P BA ≤P BA,max ;

[0154] The constraints include the allowable voltage fluctuation range of the load (determined according to different load types), the power transmission capacity of the system (to ensure that the load demand can be met under various working conditions), and maintaining the stability of the load terminal voltage.

[0155] 2) Increasing the renewable energy power generation capacity and grid-side power factor under the condition of stable voltage at the common coupling point can effectively improve the photovoltaic utilization rate and ensure the absorption of renewable energy power. Therefore, the priority of renewable energy power generation with the highest power is set as the second level;

[0156] The objective function and constraints of the second-level goal, which takes the maximum power generation of renewable energy, satisfy the following relationship:

[0157] F2=min(P DG -P DG,max ) 2

[0158] Voltage constraints at the point of common coupling: When the second-level target is to maximize the power generation of renewable energy, the control target of the grid-type converter is to keep the voltage fluctuation at the common coupling point within the range. At this time, the real-time parameter of the reactive power-voltage droop control of the grid-type converter is K q ″(t), the lower limit of the modified ratio coefficient is k5 and the upper limit is k6, so, They are respectively the minimum and maximum values of the common coupling point voltage nominal values allowed by the active distribution network when controlling the common coupling point voltage fluctuation within the range;

[0159] Grid-side power factor constraint: θ min ≤θ≤1; Converter output current constraint: 0≤I FL ≤I FL,max ;Converter apparent power constraint: Photovoltaic output constraint: 0≤P PV ≤P PV,max ; Energy storage state constraint: P BA,min ≤P BA ≤P BA,max ;

[0160] Under normal power system operation, voltage fluctuations should be controlled within certain limits to ensure the normal operation of power equipment and loads. Generally speaking, for most power systems, the voltage fluctuation range should be kept within ±5% of the rated voltage.

[0161] The constraints include the maximum power tracking range of the photovoltaic array (affected by factors such as light intensity and temperature) and the charge and discharge limits of the energy storage battery (to prevent overcharging and over-discharging), ensuring the stable operation of the system while fully utilizing photovoltaic resources.

[0162] 3) Optimizing line loss and increasing the grid-side power factor can effectively improve the economy of the power grid, so the priority of minimizing line loss is set to the third level.

[0163] The objective function and constraints of the third-level goal, which is to minimize line loss and maximize grid-side power factor, satisfy the following relationship:

[0164]

[0165] Voltage constraints at the point of common coupling: When the second-level target is to maximize the power generation of renewable energy, the control target of the grid-type converter is to keep the voltage fluctuation at the common coupling point within the range. At this time, the real-time parameter of the reactive power-voltage droop control of the grid-type converter is K q ″(t), the lower limit of the modified ratio coefficient is k5 and the upper limit is k6, so, They are respectively the minimum and maximum values of the common coupling point voltage nominal values allowed by the active distribution network when controlling the common coupling point voltage fluctuation within the range;

[0166] Grid-side power factor constraint: θ min ≤θ≤1; Converter output current constraint: 0≤I FL ≤I FL,max ;Converter apparent power constraint: Photovoltaic output constraint: P PV =P PV,max ; Energy storage state constraint: P BA,min ≤P BA ≤P BA,max .

[0167] Step 4: Solve the optimal solution of the objective function of each priority target under the constraint conditions in order of priority. The optimal solution is used as the input data of the agent model; according to the output data of the agent model, the operation of the grid-connected converter is optimized.

[0168] The proxy model is a dynamic operating condition mapper. The optimal solution of the objective function of each priority target under the constraint conditions is actually a working condition decision rule generated by offline optimization. The calculation result of the proxy model is very close to the original model, but the solution calculation amount is small. The proxy model compresses the working condition decision rule generated by offline optimization into a lightweight control strategy that can be executed in real time in the control system of the grid-type converter. The proxy model is essentially an approximate expression of the solution space of nonlinear multi-objectives through machine learning, realizing dimensionality reduction mapping from high-dimensional calculation (PSO algorithm iteration) to low-dimensional response (millisecond-level control).

[0169] Specifically, according to the priority order, the optimized PSO algorithm is used to solve the optimal solution of the objective function of each priority target under the constraint conditions in turn, including: using the operating conditions corresponding to the optimal solution of the objective function of the first-level target under the constraint conditions as the input data of the proxy model; if the objective function of the first-level target has no solution under the constraint conditions, then using the operating conditions corresponding to the optimal solution of the objective function of the second-level target under the constraint conditions as the input data of the proxy model; if the objective function of the first-level target has no solution under the constraint conditions and the objective function of the second-level target has no solution under the constraint conditions, then using the operating conditions corresponding to the optimal solution of the objective function of the third-level target under the constraint conditions as the input data of the proxy model;

[0170] The optimal solution of the objective function of each priority target under the constraint conditions includes but is not limited to the active power P of the photovoltaic system PV , the active power P of the energy storage system BA Based on the active power of the photovoltaic system and the active power of the energy storage system, the operating conditions of the system are determined as follows:

[0171] 1) Operating condition 1: P PV =P BA = 0kW, the photovoltaic system and energy storage system are both disconnected, and the grid alone supplies energy to the load;

[0172] 2) Operating condition 2: P BA =0kW, P PV >0: The energy storage system is disconnected, and the grid and photovoltaic system jointly supply energy to the load;

[0173] 3) Operating condition 3: P BA =0kW, P PV >0: The energy storage system is disconnected and the photovoltaic system supplies energy to the grid and loads;

[0174] 4) Operating condition 4: P BA >0,P PV >0: The photovoltaic system, energy storage system and power grid jointly supply energy to the load;

[0175] 5) Operating condition 5: P BA <0, P PV >0: The photovoltaic system supplies energy to the load and the energy storage system charges;

[0176] 6) Operating condition 6: P BA >0,P PV When >0, the grid is disconnected, and the photovoltaic system and energy storage system jointly supply energy to the load.

[0177] Based on the priority of each objective, the nonlinear multi-objective optimization problem is converted into a single-objective optimization problem. The optimization objectives in the single-objective optimization problem cover a variety of situations, such as minimizing line losses, maximizing the grid-side power factor, maximizing the power of the renewable energy generation system, and minimizing the voltage amplitude deviation at the common coupling point. Moreover, since each operating condition is determined based on the optimal solution of the objective function of each priority objective under the constraints, the priority of each objective determines the priority of the system's operating conditions. For example, based on the optimal solution of the objective function of the first-level objective under the constraints, the system is determined to be in operating condition 3, and based on the optimal solution of the objective function of the second-level objective under the constraints, the system is determined to be in operating condition 2. Then, the priority of operating condition 3 is greater than that of operating condition 2. Based on the above analysis, the input data of the proxy model not only covers various operating conditions of the system, but also distinguishes them according to priority. The output data of the proxy model is conducive to achieving a dynamic balance between multiple objectives such as voltage stability, loss optimization, and renewable energy consumption, solving the complex optimization problem of the power system.

[0178] The integration of energy storage brings significant advantages to the system. It not only stores excess photovoltaic energy, shaving peak loads and filling valleys, improving the absorption capacity of new energy, but also releases energy when needed, improving system efficiency. Furthermore, by working in conjunction with grid-connected converters, it effectively improves the quality of the supply voltage and suppresses current harmonics.

[0179] In the present invention, an optimized PSO algorithm is used to sequentially solve the optimal solution of the objective function of each priority target under the constraints in order of priority; wherein, the optimized PSO algorithm uses the Lorenz attractor to generate a chaotic sequence in the PSO algorithm to replace the random number in the traditional PSO algorithm, and iterates according to the steps of initial population generation, particle velocity update, etc. to solve the optimal solution of the objective function of each priority target under the constraints. In view of the shortcomings of the traditional PSO algorithm in dealing with complex optimization problems, such as slow convergence speed and easy to fall into local optimality, especially the limitations shown in the face of multi-peak and high-dimensional problems commonly seen in power systems, combining this calculation method with single-objective optimization problems based on target priority can significantly improve the speed and efficiency of calculation.

[0180] In the embodiment, the optimized PSO algorithm is used to sequentially solve the optimal solution of the objective function of each priority target under the constraint conditions, including:

[0181] Step A1, using Lorenz attractor to generate chaotic sequence;

[0182] The Lorenz attractor is described by the following differential equation:

[0183]

[0184] Where a, b, and c are the state parameters of the Lorentz system, δ, κ, and β are system parameters, t is time, and F1(a, b, c), F2(a, b, c), and F3(a, b, c) are functions of the Lorentz system state parameters a, b, and c.

[0185] The dimension of the chaotic variable is set according to the number of priorities. In the present invention, there are three priorities, so the dimension of the chaotic variable is set to 3, which respectively correspond to the first-level goal of minimizing the voltage amplitude deviation at the common coupling point, the second-level goal of maximizing the power generation of renewable energy, and the third-level goal of minimizing line loss and maximizing the grid-side power factor. Chaotic dimension matching is achieved based on the priority.

[0186] In the embodiment, δ, κ and β are 10, 28 and 8 / 3 respectively, so that the Lorentz system exhibits chaotic behavior. The above differential equation is numerically solved using the fourth-order Runge-Kutta (RK4) method.

[0187] Moreover, on the basis of realizing chaos dimension matching based on priority, the particle swarm search radius is adjusted in real time by adjusting the system parameter β when the priority is switched. The adjustment range of β is

[0188] Set the chaotic sequence {a k}、{b k} and {c k} initial conditions a0, b0, c0, a k 、b k 、c k At time t k The value of is calculated as follows:

[0189]

[0190] Where k = 1, 2, ..., K, K is the maximum number of iterations, m ij For the intermediate slope, i = 1, 2, 3, j = 1, 2, 3, 4;

[0191] a k 、b k 、c k At time t k The calculation method for the intermediate slope given in the value calculation formula is as follows:

[0192]

[0193] Where Δt is the time step, Δt = t k -t k-1 , F i (x k-1 ,y k-1 ,z k-1) is the parameter x at the k-1th iteration k-1 、y k-1 、z k-1 The i-th function of ;

[0194] In calculating m i1 and m i2 After that, use m i1 and m i2 Calculate m i3 , and finally according to m i3 Calculate m i4 .

[0195] In the above iterative process, the iteration termination threshold of each priority target is associated with the Lyapunov exponent of the chaotic sequence. In the embodiment, the first-level target allows a smaller exponent (<0.01) to ensure voltage stability;

[0196] For the chaotic sequence {a k}、{b k} and {c k} is normalized, each chaotic sequence of the Lorenz attractor {h k} are converted into canonical chaotic sequences The calculation method is as follows:

[0197]

[0198] Where, are the state parameters h of the Lorentz system at the kth iteration k The maximum and minimum values of h=a,b,c, is the state parameter h of the Lorentz system at the kth iteration k The standard value of .

[0199] Step A2: The chaotic sequence corresponding to the state parameter c of the Lorentz system Generate the initial population;

[0200] For D-dimensional problems, the size of the chaotic sequence must be greater than or equal to D|POP|, where |POP| is the population size. The corresponding matrix is as follows:

[0201]

[0202] Where Lc is the chaotic sequence The corresponding matrices, c1 and c |POP| are the state parameters of the Lorentz system at the 1st and |POP|th iterations, c (D-1)|POP| and c D|POP| are the state parameters of the Lorentz system at the |POP|(D-1)th and D|POP|th iterations respectively;

[0203] The initial population is generated as follows:

[0204] X ij =X j,min +(X j,max -X j,min )Lc ij

[0205] Where, X ij is the value of the j-th decision variable of the i-th particle, X j,min and X j,max is the search lower limit and search upper limit of the j-th decision variable, Lc ij Chaotic sequence The corresponding element in the i-th row and j-th column of the matrix, i = 1, 2, ..., D, j = 1, 2, ..., |POP|;

[0206] Step A3, the chaotic sequence {a k} and {b k}Replace the random numbers r1 and r2 to update the particle speed of the PSO algorithm;

[0207] Chaotic sequence {a k} and {b k The corresponding matrices La and Lb are both of size T max ×|POP|, satisfies the following relationship:

[0208]

[0209] Where a1 and a |POP| are the state parameters of the Lorentz system at the 1st and |POP|th iterations, b1 and b |POP| are the state parameters of the Lorentz system at the 1st and |POP|th iterations, respectively. and They are respectively |POP|(T max -1) and T max The state parameters of the Lorentz system at |POP| iterations, and They are respectively |POP|(T max -1) and T max The state parameters of the Lorentz system at |POP| iterations, T max is the maximum value of the iteration cycle;

[0210] At each iteration m, for each particle i, the values of random numbers r1 and r2 are extracted from matrices La and Lb as follows:

[0211]

[0212] Where, La mi , Lb mi is a chaotic sequence {a k} and {b k}The element in the mth row and ith column of the corresponding matrix;

[0213] The present invention adopts chaotic sequences to replace the equations describing particle generation in the initial solution and the random numbers involved in the particle iteration formula of the traditional PSO algorithm for updating particle velocity.

[0214] Step A4, the iterative equation for optimizing the PSO algorithm is:

[0215]

[0216] Where, I=1,2,…,|POP|,E I (n) and F I (n) are the position and velocity of the I-th particle in the n-th iteration, n = 1, 2, ..., T max , μ1 and μ2 are acceleration coefficients, η is the inertia coefficient, is the optimal position of the I-th particle in the n-th iteration, is the current global best position, that is, the optimal solution.

[0217] The maximum number of iterations of RK4 should be greater than or equal to the sequence {a k}、{b k} and {c k}length. The maximum value of the sequence {c k} has a length of D|POP|, and the sequence {a k} and {b k}, with T max The length of |POP|. Therefore, the maximum number of iterations of the RK4 method is expressed as follows:

[0218] K=max(D|POP|,T max |POP|)

[0219] As described in step 4, the improved PSO algorithm is used to solve the single-objective optimization problem based on target priority described in step 3. During the iteration process, the fitness of each particle is calculated according to the objective function, and the individual optimal position and the global optimal position are updated; the iteration is continued until the maximum number of iterations is reached or the convergence condition is met. The optimal optimization results of the operating parameters of the grid-type photovoltaic storage system are output, including the inverter output reactive power Q FL , Photovoltaic output P PV , energy storage battery output power P BA .

[0220] The purpose of converting to single-objective optimization and adopting optimized algorithms is to improve the calculation speed during actual runtime.

[0221] In terms of optimization algorithm performance, this approach addresses the premature convergence problem of existing PSO algorithms in power system optimization by creatively incorporating fractal chaotic mapping into an iterative mechanism. A three-dimensional chaotic sequence generated by a Lorenz attractor enables parallel search of the population in multidimensional space, effectively improving the algorithm's convergence and global search capabilities. When dealing with complex, multi-peak, and high-dimensional power system optimization problems, it can more quickly find the optimal solution, avoid being trapped in local optima, and improve the accuracy and reliability of the optimization results, surpassing traditional PSO algorithms and other conventional optimization algorithms in this field. Regarding the multi-objective coordination mechanism, this approach breaks through the inherent weighted summation of conventional multi-objective optimization and transforms the multi-objective optimization problem into a single-objective optimization problem based on optimization objective priorities. It comprehensively considers multiple key factors, including line loss, grid-side power factor, renewable energy generation power, and load voltage deviation, for a grid-connected active distribution network system with solar-powered storage. This approach achieves autonomous coordination among grid-side power factor, renewable energy consumption ratio, and voltage deviation indicators, resolving the technical challenge of multiple optimization objectives constrained by each other in traditional methods and effectively improving the overall power quality and operational efficiency of distribution substations.

[0222] This invention creatively combines a multi-objective hierarchical optimization mechanism with a chaos-modified PSO algorithm. Rather than simply improving computational speed, it addresses the complexity optimization challenges of power systems from three perspectives: model construction, algorithm improvement, and dynamic coordination. System parameters under all operating conditions are selected as input, and the solution to a single-objective optimization problem is used as output. A target proxy model is trained and obtained, and its output serves as the control input for the system, enabling real-time control of its operation.

[0223] The present invention also proposes a grid-type converter optimization operation system, comprising:

[0224] The parameter acquisition module is used to obtain the active power difference, reactive power difference, actual grid voltage value, transmission line and transformer equivalent resistance and reactance, calculate the grid current amplitude, line loss, grid-side power factor, and common coupling point voltage amplitude; use the active power of the load, the active power generated by the grid, and the line loss to determine the power generation power of renewable energy; use the common coupling point voltage amplitude and the grid voltage nominal value to determine the common coupling point voltage amplitude deviation;

[0225] The optimization operation module is used to optimize the nonlinear multi-objectives of the grid-type converter in the operation, with the minimum common coupling point voltage amplitude deviation, the maximum renewable energy power generation power, the maximum grid-side power factor, and the minimum line loss as the nonlinear multi-objectives; the constraints of the nonlinear multi-objectives include: common coupling point voltage constraint, grid-side minimum power factor constraint, converter output current constraint, converter apparent power constraint, photovoltaic output constraint, and energy storage status constraint; according to the priority of each objective, the objective function and constraint conditions of each priority objective are determined; according to the priority order, the optimal solution of the objective function of each priority objective under the constraint conditions is solved in turn, and the optimal solution is used as the input data of the proxy model; according to the output data of the proxy model, the operation of the grid-type converter is optimized.

[0226] The present disclosure may be a system, method and / or computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0227] A computer-readable storage medium can be a tangible device that can hold and store instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination thereof. As used herein, a computer-readable storage medium is not to be construed as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through an electrical wire.

[0228] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions to be stored in the computer-readable storage medium in each computing / processing device.

[0229] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages such as Smalltalk, C++, and conventional procedural programming languages such as "C" language or similar programming languages. Computer-readable program instructions may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., utilizing an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be personalized by utilizing the state information of the computer-readable program instructions. The electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0230] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing operation of a grid-connected converter, characterized in that: include: Obtain active power difference, reactive power difference, actual grid voltage, equivalent resistance and reactance of transmission lines and transformers, calculate grid current amplitude, line loss, grid-side power factor, and common coupling point voltage amplitude; use the active power of the load, the active power generated by the grid, and line loss to determine the power generation capacity of renewable energy; Determine the voltage amplitude deviation of the common coupling point by using the voltage amplitude of the common coupling point and the nominal value of the grid voltage; The nonlinear multi-objectives in the optimization operation of the grid-type converter are to minimize the voltage amplitude deviation at the common coupling point, maximize the power generation of renewable energy, maximize the grid-side power factor, and minimize the line loss. The constraints of nonlinear multi-objectives include: common coupling point voltage constraint, grid-side minimum power factor constraint, converter output current constraint, converter apparent power constraint, photovoltaic output constraint, and energy storage status constraint; according to the priority of each objective, the objective function and constraint conditions of each priority objective are determined; in order of priority, the optimal solution of the objective function of each priority objective under the constraint conditions is solved in turn, and the optimal solution is used as the input data of the proxy model; according to the output data of the proxy model, the operation of the grid-connected converter is optimized.

2. The method for optimizing operation of a grid-connected converter according to claim 1, wherein: The difference between the active power of the load and the active power of the photovoltaic system and the active power of the energy storage system is used as the active power difference; the difference between the reactive power of the load and the reactive power of the photovoltaic converter is used as the reactive power difference.

3. The method for optimizing operation of a grid-connected converter according to claim 2, wherein: The grid current amplitude is calculated using the active power difference, reactive power difference, actual grid voltage, and equivalent resistance and reactance of the transmission line and transformer to satisfy the following relationship: Where, I SG is the grid current amplitude, Z G is the equivalent impedance of the transmission line and transformer, is the nominal value of the grid voltage, is the actual value of the grid voltage, R G 、L G are the equivalent resistance and reactance of the transmission line and transformer respectively, ω is the grid angular frequency, λ1 is the active power difference, and λ2 is the reactive power difference.

4. The method for optimizing operation of a grid-connected converter according to claim 3, wherein: Calculate line loss, grid-side power factor, and voltage amplitude at the point of common coupling using active power difference, reactive power difference, grid current amplitude, and equivalent resistance and reactance of transmission lines and transformers. The line loss ρ satisfies the following relationship: The grid-side power factor θ satisfies the following relationship: Common coupling point voltage amplitude V PCC Satisfies the following expression: Common coupling point voltage amplitude deviation ΔV L Satisfies the following expression:

5. The method for optimizing operation of a grid-connected converter according to claim 1, characterized in that: The nonlinear multi-objective F satisfies the following relationship: Where, ΔV L is the voltage amplitude deviation at the common coupling point, P DG is the power generated by renewable energy, P DG,max is the upper limit of renewable energy power generation, ρ is the line loss, θ is the grid-side power factor, and abs( ) is the absolute value function.

6. The method for optimizing operation of a grid-connected converter according to claim 5, characterized in that: The voltage constraint at the point of common coupling is: V PCC is the voltage amplitude at the common coupling point, is the nominal value of the grid voltage, They are the minimum and maximum values of the common coupling point voltage nominal values allowed by the active distribution network.

7. The method for optimizing operation of a grid-connected converter according to claim 6, characterized in that: Obtain the real-time parameter K of reactive power-voltage droop control of grid-connected converter q (t), parameter lower limit K q,min , parameter upper limit K q,max , benchmark parameter K q,base , calculate the lower limit α1(t) and upper limit α2(t) of the voltage time-varying coefficient as follows: The lower limit α1(t) and upper limit α2(t) of the voltage time-varying coefficient are used to correct the ratio coefficient k between the actual value of the grid voltage and the nominal value of the grid voltage, and the lower limit k1 and upper limit k2 of the ratio coefficient are obtained, which satisfy the following relationship: k1=k(1+α1(t)), k2=k(1+α2(t)).

8. The method for optimizing operation of a grid-connected converter according to claim 7, characterized in that: Benchmark parameter K q,base The ratio of the maximum reactive capacity that the converter can provide or absorb to the allowable voltage deviation is based on the reference parameter K q,base , parameter lower limit K q,min is the benchmark parameter K q,base 0.3 times of the upper limit of parameter K q,max is the benchmark parameter K q,base 2 times.

9. The method for optimizing operation of a grid-connected converter according to claim 5, wherein: The grid-side power factor constraint is: θ min ≤θ≤1,θ min The minimum power factor allowed by the power grid; the minimum power factor in the constraint condition is not a fixed value, but is the power factor with the minimum line loss determined based on the optimal power flow model, the equivalent resistance and reactance of the transmission line and transformer.

10. The method for optimizing operation of a grid-connected converter according to claim 5, characterized in that: The output current of the converter is constrained to be: 0≤I FL ≤I FL,max , I FL is the output current of the grid-type energy storage converter, I FL,max is the maximum output current of the grid-type energy storage converter; The apparent power constraint of the converter is: |S PV | is the apparent power of the grid-type energy storage converter, is the nominal capacity value of the grid-type energy storage converter; Photovoltaic output constraint: 0≤P PV ≤P PV,max , P PV is the active power of the photovoltaic system, P PV,max The maximum active power that can be generated by the photovoltaic system; The energy storage state constraint is: P BA,min ≤P BA ≤P BA,max , P BA is the active power of the energy storage system, P BA,max 、P BA,min They are respectively the upper limit when the discharge power of the energy storage system is positive and the upper limit when the charging power is negative.

11. The method for optimizing operation of a grid-connected converter according to claim 1, wherein: The objective function and constraints with the minimum common coupling point voltage amplitude deviation as the first-level goal satisfy the following relationship: Voltage constraints at the point of common coupling: When the first-level target is to minimize the voltage amplitude deviation at the common coupling point, the control target of the grid-type converter is to ensure that the voltage amplitude at the common coupling point does not exceed the limit. At this time, the real-time parameter of the reactive power-voltage droop control of the grid-type converter is K q ′(t), the lower limit of the modified ratio coefficient is k3 and the upper limit is k4, They are respectively the minimum and maximum values of the common coupling point voltage nominal values allowed by the active distribution network when controlling the common coupling point voltage amplitude not to exceed the limit; Grid-side power factor constraint: θ min ≤θ≤1; Converter output current constraint: 0≤I FL ≤I FL,max ;Converter apparent power constraint: Photovoltaic output constraint: 0≤P PV ≤P PV,max ; Energy storage state constraint: P BA,min ≤P BA ≤P BA,max .

12. The method for optimizing operation of a grid-connected converter according to claim 1, wherein: The objective function and constraints of the second-level goal, which takes the maximum power generation of renewable energy, satisfy the following relationship: F2=min(P DG -P DG,max ) 2 Voltage constraints at the point of common coupling: When the second-level target is to maximize the power generation of renewable energy, the control target of the grid-type converter is to keep the voltage fluctuation at the common coupling point within the range. At this time, the real-time parameter of the reactive power-voltage droop control of the grid-type converter is K q ″(t), the lower limit of the modified ratio coefficient is k5 and the upper limit is k6, They are respectively the minimum and maximum values of the common coupling point voltage nominal values allowed by the active distribution network when controlling the common coupling point voltage fluctuation within the range; Grid-side power factor constraint: θ min ≤θ≤1; Converter output current constraint: 0≤I FL ≤I FL,max ;Converter apparent power constraint: Photovoltaic output constraint: 0≤P PV ≤P PV,max ; Energy storage state constraint: P BA,min ≤P BA ≤P BA,max .

13. The method for optimizing operation of a grid-connected converter according to claim 1, wherein: The objective function and constraints of the third-level goal, which is to minimize line loss and maximize grid-side power factor, satisfy the following relationship: Voltage constraints at the point of common coupling: When the second-level target is to maximize the power generation of renewable energy, the control target of the grid-type converter is to keep the voltage fluctuation at the common coupling point within the range. At this time, the real-time parameter of the reactive power-voltage droop control of the grid-type converter is K q ″(t), the lower limit of the modified ratio coefficient is k5 and the upper limit is k6, so, They are respectively the minimum and maximum values of the common coupling point voltage nominal values allowed by the active distribution network when controlling the common coupling point voltage fluctuation within the range; Grid-side power factor constraint: θ min ≤θ≤1; Converter output current constraint: 0≤I FL ≤I FL,max ;Converter apparent power constraint: Photovoltaic output constraint: P PV =P PV,max ; Energy storage state constraint: P BA,min ≤P BA ≤P BA,max .

14. The method for optimizing operation of a grid-connected converter according to claim 1, wherein: According to the priority order, the optimized PSO algorithm is used to solve the optimal solution of the objective function of each priority target under the constraint conditions in turn, including: taking the operating conditions corresponding to the optimal solution of the objective function of the first-level target under the constraint conditions as the input data of the proxy model; if the objective function of the first-level target has no solution under the constraint conditions, then taking the operating conditions corresponding to the optimal solution of the objective function of the second-level target under the constraint conditions as the input data of the proxy model; if the objective function of the first-level target has no solution under the constraint conditions and the objective function of the second-level target has no solution under the constraint conditions, then taking the operating conditions corresponding to the optimal solution of the objective function of the third-level target under the constraint conditions as the input data of the proxy model; the optimal solution of the objective function of each priority target under the constraint conditions includes the active power P of the photovoltaic system. PV , the active power P of the energy storage system BA .

15. The method for optimizing operation of a grid-connected converter according to claim 14, characterized in that: In the optimized PSO algorithm, the Lorenz attractor is used to generate a chaotic sequence, and the dimensions of the chaotic variables are set according to the number of priorities. The first-level goal is to minimize the voltage amplitude deviation at the common coupling point, the second-level goal is to maximize the power generation of renewable energy, and the third-level goal is to minimize line losses and maximize the grid-side power factor. Chaotic dimension matching is achieved based on the priority. The chaotic sequence {c k ％ }Generate the initial population; use the chaotic sequence {a k } and {b k }Replace the random numbers r1 and r2 to update the particle speed of the PSO algorithm; on the basis of realizing chaos dimension matching based on priority, the particle swarm search radius is adjusted in real time by adjusting the system parameters when the priority is switched; The iteration termination threshold of each priority target is associated with the Lyapunov exponent of the chaotic sequence.

16. The method for optimizing operation of a grid-connected converter according to claim 14, wherein: Based on the active power of the photovoltaic system and the active power of the energy storage system, the operating conditions of the system are determined as follows: 1) Operating condition 1: P PV =P BA = 0kW, the photovoltaic system and energy storage system are both disconnected, and the grid alone supplies energy to the load; 2) Operating condition 2: P BA =0kW, P PV >0: The energy storage system is disconnected, and the grid and photovoltaic system jointly supply energy to the load; 3) Operating condition 3: P BA =0kW, P PV >0: The energy storage system is disconnected and the photovoltaic system supplies energy to the grid and loads; 4) Operating condition 4: P BA >0,P PV >0: The photovoltaic system, energy storage system and power grid jointly supply energy to the load; 5) Operating condition 5: P BA <0, P PV >0: The photovoltaic system supplies energy to the load and the energy storage system charges; 6) Operating condition 6: P BA >0,P PV When >0, the grid is disconnected, and the photovoltaic system and energy storage system jointly supply energy to the load.

17. A grid-type converter optimization operation system, used to implement the grid-type converter optimization operation method according to any one of claims 1 to 16, characterized in that: include: The parameter acquisition module is used to obtain the active power difference, reactive power difference, actual grid voltage value, transmission line and transformer equivalent resistance and reactance, calculate the grid current amplitude, line loss, grid-side power factor, and common coupling point voltage amplitude; and use the active power of the load, the active power generated by the grid, and the line loss to determine the power generation power of renewable energy; Determine the voltage amplitude deviation of the common coupling point by using the voltage amplitude of the common coupling point and the nominal value of the grid voltage; The optimization operation module is used to optimize the operation of the grid-type converter with the minimum common coupling point voltage amplitude deviation, maximum renewable energy power generation, maximum grid-side power factor, and minimum line loss as nonlinear multi-objectives; The constraints of nonlinear multi-objectives include: common coupling point voltage constraint, grid-side minimum power factor constraint, converter output current constraint, converter apparent power constraint, photovoltaic output constraint, and energy storage status constraint; according to the priority of each objective, the objective function and constraint conditions of each priority objective are determined; in order of priority, the optimal solution of the objective function of each priority objective under the constraint conditions is solved in turn, and the optimal solution is used as the input data of the proxy model; according to the output data of the proxy model, the operation of the grid-connected converter is optimized.

18. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 16.

19. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 16 are implemented.

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