A power distribution network harmonic optimization method and system based on electrochemical energy storage

Abstract

The present application belongs to the field of power distribution network management, and relates to a power distribution network harmonic optimization method and system based on electrochemical energy storage, comprising: collecting initial harmonic data and preprocessing the initial harmonic data to obtain processed harmonic data; the harmonic data includes power grid current, energy storage system parameters, PCS power and power grid voltage; the preprocessing includes sequentially performing wavelet filtering processing and direct current component filtering processing on the initial harmonic data; based on the processed harmonic data, fundamental wave parameters are calculated; the fundamental wave parameters include fundamental wave active power, fundamental wave reactive power and fundamental wave apparent power; based on the fundamental wave parameters, fundamental wave components are calculated, and based on the fundamental wave components, a compensation current target value is determined; the fundamental wave components include fundamental wave active component, fundamental wave reactive component and harmonic component; based on the compensation current target value, control instructions are generated through model predictive control and fast repetitive control coordination; the overall power quality and the reliability of operation of the power distribution network are improved.

Description

Technical Field

[0001] This invention relates to the field of power distribution network management, and specifically discloses a method and system for optimizing power distribution network harmonics based on electrochemical energy storage. Background Technology

[0002] With the rapid development of power electronics technology, the proportion of nonlinear loads, new energy power generation equipment, and power conversion devices connected to the distribution network continues to increase, leading to increasingly prominent harmonic pollution problems within the distribution network. The presence of harmonics not only increases power loss in transmission lines, accelerates insulation aging of power equipment, and reduces equipment lifespan, but may also cause problems such as malfunction of relay protection devices and deviations in power metering, seriously threatening the safe, economical, and stable operation of the distribution network. To address harmonic issues in power distribution networks, existing technologies primarily employ passive and active filtering methods. Some solutions attempt to combine harmonic mitigation with electrochemical energy storage systems, but these approaches still have several shortcomings: First, while passive filters are simple in structure and low in cost, their filtering effect is easily affected by grid impedance parameters, making them prone to resonance with the grid. Furthermore, they can only compensate for harmonics of fixed frequencies, failing to adapt to scenarios with dynamic changes in harmonic frequency and amplitude. Second, active filters can achieve dynamic tracking and compensation of harmonics, but require additional power supply, resulting in relatively low reliability. Moreover, a single active filter control strategy struggles to consider multiple factors such as power distribution network load fluctuations and the operating status of the energy storage system. Third, existing harmonic optimization schemes combining electrochemical energy storage often focus solely on harmonic suppression, failing to adequately coordinate the needs of power quality improvement and the safe operation of the energy storage system. The control strategies lack synergy, and the compensation parameter solution logic is incomplete, leading to low harmonic compensation accuracy, slow response speed, and excessive fluctuations in the state of charge (SOC) of the energy storage system, affecting the safe service life of the energy storage equipment.

[0003] In view of this, the present invention proposes a method and system for harmonic optimization of distribution networks based on electrochemical energy storage. Through systematic harmonic data acquisition and processing, multi-dimensional fundamental parameter calculation, and multi-objective compensation parameter optimization, combined with a collaborative control strategy of model predictive control (MPC) and fast repetitive control (FRC), precise control commands are generated. This effectively controls the SOC fluctuation of energy storage, improves the operational stability of energy storage equipment, and accurately tracks the real-time changes in the harmonic characteristics of the distribution network. It significantly improves the accuracy and response speed of harmonic compensation, avoids the risks of overcompensation, undercompensation, or resonance in existing technologies, and improves the overall power quality and operational reliability of the distribution network. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for harmonic optimization in power distribution networks based on electrochemical energy storage. The problem addressed is how to generate precise control commands to effectively control energy storage SOC fluctuations and suppress harmonics, thereby improving the overall power quality and operational reliability of the power distribution network. The specific solution is as follows:

[0005] A method for harmonic optimization of distribution networks based on electrochemical energy storage includes: acquiring initial harmonic data and preprocessing the initial harmonic data to obtain processed harmonic data; the harmonic data includes grid current, energy storage system parameters, PCS power, and grid voltage; the preprocessing includes sequentially performing wavelet filtering and DC component filtering on the initial harmonic data; calculating fundamental parameters based on the processed harmonic data; the fundamental parameters include fundamental active power, fundamental reactive power, and fundamental apparent power; calculating fundamental components based on the fundamental parameters, and determining the target value of the compensation current based on the fundamental components; the fundamental components include fundamental active component, fundamental reactive component, and harmonic components; and generating control commands based on the target value of the compensation current through model predictive control and fast repetitive control.

[0006] Furthermore, the preprocessing of the initial harmonic data to obtain processed harmonic data includes: cleaning the initial harmonic data to obtain first harmonic data; the first harmonic data includes a first grid current, first energy storage system parameters, first PCS power, and first grid voltage; filtering out the DC components of the first grid current and first grid voltage using a second-order generalized integrator to obtain a second grid current, second grid voltage, orthogonal voltage signal, and analog voltage; and using the second grid current, first energy storage system parameters, first PCS power, second grid voltage, orthogonal voltage signal, and analog voltage as processed harmonic data.

[0007] Furthermore, the second grid current is:

[0008] ;

[0009] ;

[0010] ;

[0011] in, , and These represent the second grid currents in the complex frequency domain for phases A, B, and C, respectively. Represents a complex variable in the complex frequency domain; Represents the positive-order component transfer function; , and These represent the initial grid currents of phases A, B, and C in the complex frequency domain, respectively.

[0012] The voltage of the second power grid is:

[0013] ;

[0014] ;

[0015] ;

[0016] in, , and These represent the second grid voltages of phases A, B, and C in the complex frequency domain, respectively. , and These represent the initial grid voltages of phases A, B, and C in the complex frequency domain, respectively.

[0017] The quadrature voltage signal is:

[0018] ;

[0019] ;

[0020] ;

[0021] in, , and These represent the quadrature voltage signals of phases A, B, and C, respectively. Represents the transfer function of orthogonal components;

[0022] The analog voltage is:

[0023] ;

[0024] ;

[0025] ;

[0026] in, , and These represent the analog voltages of phases A, B, and C, respectively. Indicates the analog voltage amplitude; This represents the real part of the complex signal; , and These represent the initial grid voltages of phases A, B, and C in the time domain, respectively.

[0027] Furthermore, the fundamental active power is:

[0028] ;

[0029] in, This represents the fundamental active power of phase X, which is a three-phase variable including phases A, B, and C; T represents the power grid cycle. This represents the second grid current in phase X in the time domain; The voltage of the second grid in phase X is represented in the time domain; t represents the time variable. This represents the integral over time t over one power grid cycle;

[0030] The fundamental reactive power is:

[0031] ;

[0032] in, This represents the fundamental reactive power of phase X; This represents the quadrature voltage signal of phase X;

[0033] The fundamental apparent power is:

[0034] ;

[0035] in, This represents the apparent power of the fundamental frequency.

[0036] Furthermore, the fundamental active component is:

[0037] ;

[0038] in, This represents the fundamental active component of phase X; This represents the fundamental active power of phase X; Represents the Euclidean norm; Represents the analog voltage of phase X;

[0039] The fundamental reactive component is:

[0040] ;

[0041] in, This represents the fundamental reactive component of phase X; This represents the quadrature voltage signal of phase X in the time domain; This represents the fundamental reactive power of phase X;

[0042] The harmonic components are:

[0043] ;

[0044] in, Represents the harmonic components of phase X; This represents the second grid current in phase X in the time domain.

[0045] Furthermore, determining the target value of the compensation current based on the fundamental component includes: determining initial compensation parameters based on scenario requirements and energy storage status; the initial compensation parameters include initial reactive power compensation weight, initial harmonic compensation weight, initial three-phase energy storage fundamental active current, and fundamental zero-sequence current compensation coefficient; based on the initial compensation parameters, constructing a multi-objective optimization model, and obtaining the optimal compensation parameters through an optimization algorithm; the optimal compensation parameters include optimal reactive power compensation weight, optimal harmonic compensation weight, and optimal three-phase energy storage fundamental active current. Based on the fundamental component and the optimal compensation parameters, the target value of the compensation current is calculated.

[0046] Furthermore, the objective function of the multi-objective optimization model is:

[0047] ;

[0048] Where min represents minimization; Represent the objective function; Indicates taking the absolute value; Indicates the target total harmonic distortion rate; This indicates the calculation of total harmonic distortion (THD). Indicates the weight coefficient of the SOC term; Indicates the target state of charge of the energy storage; Indicates the current state of charge of the energy storage; This represents the weighting coefficient of the reactive power compensation item; Indicates the reactive power compensation weight; This represents the fundamental reactive component of phase X; This represents the weighting coefficient of the harmonic compensation term; Indicates the harmonic compensation weight; Represents the harmonic components of phase X;

[0049] The constraints of the multi-objective optimization model include weight constraints and energy storage current constraints:

[0050] The weight constraints are:

[0051] ;

[0052] ;

[0053] The energy storage current constraint is:

[0054] .

[0055] The target value for the compensation current is:

[0056] ;

[0057] in, This indicates the target value of the compensation current for phase X; This represents the fundamental active current of the energy storage phase X; Indicates the reactive power compensation weight; This represents the fundamental reactive component of phase X; Indicates the harmonic compensation weight; Represents the harmonic components of phase X; This represents the fundamental zero-sequence current compensation coefficient; , and These represent the second grid currents in the time domain for phases A, B, and C, respectively.

[0058] Furthermore, based on the target value of the compensation current, control commands are generated collaboratively through model predictive control and fast repetitive control, including: predicting the future actual compensation current based on the target value of the compensation current and the LCL discrete model of MFGTI; constructing a control objective function and generating a basic control quantity by optimizing the control objective function; processing the harmonic components through fast repetitive control to obtain the output correction quantity; and superimposing the basic control quantity and the output correction quantity to obtain the control command.

[0059] Furthermore, the LCL discrete model of MFGTI is as follows:

[0060] ;

[0061] ;

[0062] in, This represents the predicted state at time k+i. This represents the predicted state one step before time k; This represents the temporary control quantity of the previous step k+i-1; This represents the actual compensation current predicted at time k+i steps. Represents the discrete state transition matrix of the system; Represents a discrete input matrix; Indicates the output matrix;

[0063] The control objective function is:

[0064] ;

[0065] Where J represents the control objective function; min represents minimization; Indicates the prediction time domain; i represents the prediction time step variable; Indicates control over the time domain; Indicates the current deviation term; This represents the control change at the (k+i)th step to be optimized; Indicates the coefficient of synergy; Represents the z-domain discrete transfer function of FRC; This represents the control change at step k to be optimized;

[0066] The output correction is:

[0067] ;

[0068] ;

[0069] in, This represents the output correction amount at step k; This represents the control change at the k-th step after optimization; Represents a complex variable in the complex frequency domain; T represents the power grid period; Indicates the fundamental frequency of the power grid; It is Discretized z-domain transfer function; Represents the transfer function;

[0070] The control commands are:

[0071] ;

[0072] in, This represents the control command for step k; This represents the control instruction for step k-1; This represents the control change at the k-th step after optimization; This represents the conversion factor.

[0073] This application also provides a distribution network harmonic optimization system based on electrochemical energy storage, according to the above-described method for harmonic optimization of distribution networks based on electrochemical energy storage. The system includes a preprocessing module, a fundamental frequency calculation module, a compensation current calculation module, and a control command generation module. The preprocessing module collects initial harmonic data and preprocesses it to obtain processed harmonic data. The harmonic data includes grid current, energy storage system parameters, PCS power, and grid voltage. Preprocessing includes sequentially performing wavelet filtering and DC component filtering on the initial harmonic data. The fundamental frequency calculation module calculates fundamental frequency parameters based on the processed harmonic data. The fundamental frequency parameters include fundamental active power, fundamental reactive power, and fundamental apparent power. The compensation current calculation module calculates fundamental frequency components based on the fundamental frequency parameters and determines the target value of the compensation current based on these components. The fundamental frequency components include fundamental active component, fundamental reactive component, and harmonic components. The control command generation module generates control commands based on the target value of the compensation current through model predictive control and fast repetitive control.

[0074] The present invention has the following advantages and beneficial effects:

[0075] This invention employs a two-stage preprocessing strategy using wavelet filtering and a second-order generalized integrator (SOGI): it can effectively filter out high-frequency noise in the initial harmonic data, avoiding noise interference in the separation of the fundamental and harmonic frequencies; and accurately filter out DC components (such as zero-point drift) in the grid current and voltage, solving the integration error problem caused by DC components in traditional schemes, resulting in noise-free and DC-free processed harmonic data, reducing the impact of data distortion on subsequent compensation effects.

[0076] This invention combines the quadrature voltage signal output by SOGI to accurately separate the fundamental active component, reactive component and harmonic component, so that subsequent compensation can focus on improving the power factor of reactive component and suppressing the distortion rate of harmonic component, avoiding the problems of overcompensation or undercompensation, and improving the accuracy of harmonic control.

[0077] When compensating for the target current value, this invention balances power quality and energy storage safety by combining scenario requirements and a multi-objective optimization model. This avoids the shortcomings of focusing only on harmonic suppression and neglecting the safety of energy storage devices. In order to improve the power quality of the distribution network, control the fluctuation of energy storage SOC, avoid energy storage current exceeding the limit, and extend the service life of the energy storage system.

[0078] This invention utilizes an LCL discrete model based on MFGTI (Multifunctional Grid-Connected Inverter) to generate precise output correction values ​​while ensuring dynamic response of fundamental frequency compensation and energy storage charging and discharging, thus compensating for the lag in high-frequency harmonic response of MPC. It achieves both rapid response for fundamental frequency compensation and precise suppression of higher-order harmonics. Furthermore, the introduction of the LCL filtering model further filters out high-frequency harmonics from inverter switching, significantly improving the overall power quality of the distribution network. Attached Figure Description

[0079] Figure 1 An exemplary flowchart of a power distribution network harmonic optimization method based on electrochemical energy storage provided by the present invention;

[0080] Figure 2 An exemplary block diagram of a power distribution network harmonic optimization system based on electrochemical energy storage provided by the present invention;

[0081] Figure 3 The tracking curve of the compensation current provided by the present invention. Detailed Implementation

[0082] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0083] This embodiment applies to a 10kV distribution network scenario. This distribution network has a high proportion of nonlinear loads, and the total harmonic distortion (THD) at the point of common coupling (PCC) exceeds the national standard limit. Furthermore, the connected electrochemical energy storage system (lithium battery energy storage) suffers from excessive fluctuations in its state of charge (SOC). This embodiment achieves the dual objectives of harmonic suppression in the distribution network and safe operation of the energy storage system through data acquisition, fundamental parameter calculation, compensation current optimization, and coordinated control command generation.

[0084] Figure 1 This is an exemplary flowchart of a power distribution network harmonic optimization method based on electrochemical energy storage provided by the present invention. Figure 1 As shown, the distribution network harmonic optimization method based on electrochemical energy storage includes the following:

[0085] Step 1: Collect initial harmonic data and preprocess the initial harmonic data to obtain processed harmonic data.

[0086] Initial harmonic data includes initial grid current, energy storage system parameters, PCS (Power Conversion System) power, and initial grid voltage. Initial harmonic data can be acquired using various feasible sensors. Hall current sensors are used to acquire the three-phase initial grid current at the point of common coupling (PCC) between the distribution network and the electrochemical energy storage system; voltage transformers are used to acquire the three-phase initial grid voltage at the PCC; and the real-time state of charge (SOC) of the electrochemical energy storage system is acquired through the battery management system (BMS). The sampling frequency is set to 2kHz to ensure data coverage of the grid's power frequency cycle (0.02s). The acquired initial grid current is denoted as a complex frequency domain A / B / C three-phase current signal, and the initial grid voltage is denoted as a complex frequency domain A / B / C three-phase voltage signal. The initial grid current refers to the original three-phase current of the distribution network acquired by the sensors. For example, the three-phase current at the point of common coupling (PCC) between the distribution network and the energy storage system can be acquired using Hall current sensors. , and The system then performs a complex domain transformation. Energy storage system parameters can include the State of Charge (SOC) of the stored energy. For example, the SOC of electrochemical energy storage can be acquired through a Battery Management System (BMS). The initial grid voltage refers to the three-phase voltage at the point of common coupling (PCC) between the distribution network and the energy storage system. For example, the three-phase voltage at the PCC can be acquired using a voltage transformer. , and And denoted in complex frequency domain form.

[0087] Preprocessing includes sequentially performing wavelet filtering and DC component removal on the initial harmonic data. Noise removal can refer to cleaning the initial harmonic data using wavelet energy spectrum entropy formed by combining wavelet transform and information entropy, obtaining the first harmonic data. For example, using a db4 wavelet basis and a 3-level decomposition wavelet energy spectrum entropy algorithm, wavelet filtering is performed on the acquired initial grid current and voltage signals to remove high-frequency noise above 2kHz, obtaining filtered first harmonic data, which includes the filtered grid current and voltage signals. Filtering the initial grid current and voltage removes high-frequency noise, yielding the first harmonic data. The first harmonic data includes the first grid current, first energy storage system parameters, first PCS power, and first grid voltage. DC component removal can refer to processing the first grid current and first grid voltage in the first harmonic data using a second-order generalized integrator to remove the DC component, obtaining the second grid current, second grid voltage, orthogonal voltage signals, and analog voltage. The second grid current, first energy storage system parameters, first PCS power, second grid voltage, orthogonal voltage signal, and analog voltage from the first harmonic data are used as processed harmonic data. The damping coefficient of SOGI is set to 0.5, and the fundamental angular frequency is set to 314 rad / s (corresponding to the 50Hz power frequency of the grid). The grid current signal after processing and filtering through the positive sequence component transfer function of SOGI is used to obtain the second grid current (three phases A / B / C) in the complex frequency domain; the grid voltage signal after processing and filtering through the same positive sequence component transfer function is used to obtain the second grid voltage (three phases A / B / C) in the complex frequency domain; the grid voltage signal after processing and filtering through the orthogonal component transfer function of SOGI is used to obtain the orthogonal voltage signal (three phases A / B / C) in the complex frequency domain. The second grid voltage in the complex frequency domain is converted into a time-domain signal through inverse Laplace transform. The real part of this time-domain signal is taken and multiplied by 10kV (the rated voltage amplitude of the 10kV distribution network) to obtain the three-phase analog voltage signal A / B / C in the time domain. The second grid current includes the grid currents of phases A, B, and C, specifically:

[0088] ;

[0089] ;

[0090] ;

[0091] in, , and These represent the second grid currents in the complex frequency domain for phases A, B, and C, respectively. Represents a complex variable in the complex frequency domain; The positive-order component transfer function is obtained through a second-order generalized integrator, for example... k=0.5 is the SOGI damping coefficient. =314 rad / s is the fundamental angular frequency of the power grid; , and The initial grid currents of phases A, B, and C in the complex frequency domain are represented by these values, which can be obtained by performing a Laplace transform on the initial grid currents acquired by the sensors.

[0092] The second grid voltage includes the grid voltages of phases A, B, and C, specifically:

[0093] ;

[0094] ;

[0095] ;

[0096] in, , and These represent the second grid voltages of phases A, B, and C in the complex frequency domain, respectively. Represents a complex variable in the complex frequency domain; The positive-order component transfer function is obtained through a second-order generalized integrator. , and The initial grid voltages of phases A, B, and C in the complex frequency domain are represented by these values, which can be obtained by performing a Laplace transform on the initial grid voltages acquired by the sensors.

[0097] The quadrature voltage signals of phases A, B, and C are:

[0098] ;

[0099] ;

[0100] ;

[0101] in, , and These represent the quadrature voltage signals of phases A, B, and C, respectively. The orthogonal component transfer function is obtained through a second-order generalized integrator, for example... .

[0102] The analog voltages for phases A, B, and C are:

[0103] ;

[0104] ;

[0105] ;

[0106] in, , and These represent the analog voltages of phases A, B, and C, respectively. Indicates the analog voltage amplitude; This represents the real part of the complex signal; , and These represent the initial grid voltages of phases A, B, and C in the time domain, respectively.

[0107] Step 2: Based on the processed harmonic data, calculate the fundamental wave parameters; the fundamental wave parameters include fundamental wave active power, fundamental wave reactive power, and fundamental wave apparent power.

[0108] The fundamental active power is:

[0109] ;

[0110] in, This represents the fundamental active power of phase X, which is a three-phase variable including phase A, phase B, and phase C; T represents the power grid period (T=0.02s). The second grid current in phase X in the time domain can be obtained by performing an inverse Laplace transform on the second grid current in phase X in the complex frequency domain. The second grid voltage of phase X in the time domain can be obtained by performing an inverse Laplace transform on the second grid voltage of phase X in the complex frequency domain; t represents the time variable. This represents the integration over time t within one power grid cycle. For example, using the power grid frequency cycle of 0.02s as the integration time range, the trapezoidal numerical integration method is used to integrate the product of the second power grid current time domain signal and the second power grid voltage time domain signal. The integration result is then divided by 0.02s to obtain the fundamental active power of that phase.

[0111] The fundamental reactive power is:

[0112] ;

[0113] in, This represents the fundamental reactive power of phase X; This represents the orthogonal voltage signal of phase X. For example, using the trapezoidal numerical integration method with an integration time range of 0.02s, the product of the time-domain signal of the second grid current and the time-domain signal of the orthogonal voltage is integrated. The integration result is then divided by 0.02s to obtain the fundamental reactive power of that phase.

[0114] The fundamental apparent power is:

[0115] ;

[0116] in, This represents the fundamental apparent power. The fundamental active power and fundamental reactive power of each phase are squared and summed. The square root of the sum is then taken to obtain the fundamental apparent power of that phase.

[0117] Step 3: Calculate the fundamental component based on the fundamental parameters, and determine the target value of the compensation current based on the fundamental component; the fundamental component includes the fundamental active component, the fundamental reactive component, and the harmonic component.

[0118] The fundamental active component is:

[0119] ;

[0120] in, This represents the fundamental active component of phase X; Represents the Euclidean norm; This represents the analog voltage of phase X.

[0121] The fundamental reactive component is:

[0122] ;

[0123] in, This represents the fundamental reactive component of phase X; The orthogonal voltage signal of phase X in the time domain can be obtained by performing an inverse Laplace transform on the orthogonal voltage signal of phase X in the complex frequency domain.

[0124] The harmonic components are:

[0125] ;

[0126] in, Represents the harmonic components of phase X; This represents the second grid current in phase X in the time domain.

[0127] The target compensation current value is a reference value used to guide the energy storage converter (PCS) to output the optimal comprehensive compensation current. In some embodiments, determining the target compensation current value includes:

[0128] Based on scenario requirements and energy storage status, initial compensation parameters are determined. These parameters include initial reactive power compensation weight, initial harmonic compensation weight, initial three-phase energy storage fundamental active current, and fundamental zero-sequence current compensation coefficient. For example, taking the characteristics of a 10kV distribution network in an industrial scenario (35% nonlinear load), the initial reactive power compensation weight is set to 0.95, and the initial harmonic compensation weight is set to 1.0. According to the safe operation requirements of the energy storage system, when SOC ≤ 20% or ≥ 80%, the initial three-phase energy storage fundamental active current is set to 0.2kA; when 20% < SOC < 80%, the initial three-phase energy storage fundamental active current is set to 0. The fundamental zero-sequence current compensation coefficient is experimentally calibrated and set to 0.95. The reactive power compensation weight is a coefficient used to adjust the reactive power compensation amplitude of the distribution network. The initial reactive power compensation weight can be determined based on scenario requirements. For example, the historically selected reactive power compensation weight for the current scenario can be used as the initial reactive power compensation weight for this iteration. Specifically, for industrial scenarios, the initial reactive power compensation weight can be set to 0.95 to prioritize harmonic suppression; for commercial scenarios, the initial reactive power compensation weight can be set to 0.98 to prioritize improving the power factor. Harmonic compensation weight refers to the coefficient used to adjust the harmonic compensation level of the distribution network. The initial harmonic compensation weight can be set according to the load type. For example, when the proportion of nonlinear loads is greater than or equal to 30%, the initial harmonic compensation weight can be set to 1; when all loads are ordinary loads, the initial harmonic compensation weight can be set to 0.95. The three-phase energy storage fundamental active current refers to the current of the electrochemical energy storage system participating in the fundamental active power regulation of the distribution network. A positive value represents the active power injected into the grid by the energy storage discharge, and a negative value represents the active power absorbed from the grid by the energy storage charging, used to balance the energy storage SOC and the fundamental power demand of the distribution network. The initial three-phase energy storage fundamental active current can be set according to the state of charge of the energy storage. For example, when the State of Charge (SOC) is less than or equal to 20%, the initial three-phase fundamental active current of the energy storage is -0.2kA; when the SOC is greater than or equal to 80%, the initial three-phase fundamental active current of the energy storage is 0.2kA; and when the SOC is between 20% and 80%, the initial three-phase fundamental active current of the energy storage is 0. The fundamental zero-sequence current compensation coefficient can be calibrated experimentally. For example, the fundamental zero-sequence current compensation coefficient can be a fixed value of 0.95.

[0129] Based on the initial compensation parameters, a multi-objective optimization model is constructed, and the optimal compensation parameters are obtained through iterative optimization using a particle swarm optimization algorithm. The optimal compensation parameters include the optimal reactive power compensation weight, the optimal harmonic compensation weight, and the optimal three-phase energy storage fundamental active current. A multi-objective optimization model is constructed with the objectives of optimal power quality and controllable energy storage safety. The objective function includes four core sub-terms: first, the absolute value of the deviation between the THD at the distribution network PCC and the national standard target value (2.5%); second, the absolute value of the deviation between the real-time SOC of energy storage and the target SOC (50%) (the weight coefficient is set to 0.3 to balance energy storage safety and power quality); third, the reactive power compensation term (weight coefficient 0.1); and fourth, the harmonic compensation term (weight coefficient 0.2). The model constraints are: the reactive power compensation weight ranges from 0.8 to 1.0, the harmonic compensation weight ranges from 0.95 to 1.0, and the absolute value of the energy storage fundamental active current does not exceed 0.5 kA (rated current of the energy storage system). The particle swarm optimization (PSO) algorithm was used to solve the optimization model. The algorithm parameters were set as follows: 20 particles, inertia weight of 0.7, learning factors c1 and c2 of 1.49, and the iteration termination condition was that the number of iterations reached 100 or the objective function deviation was ≤0.01%. Through iterative solution using this algorithm, the optimal reactive power compensation weight, optimal harmonic compensation weight, and optimal three-phase energy storage fundamental active current were obtained.

[0130] Considering power quality, energy storage safety, and compensation accuracy, the objective function of the multi-objective optimization model is:

[0131] ;

[0132] Where min represents minimization; Represent the objective function; Indicates taking the absolute value; Represents the target total harmonic distortion (THD), which refers to the THD limit that the PCC point of the distribution network needs to achieve; This indicates the calculated total harmonic distortion rate, specifically the theoretical total harmonic distortion rate at the PCC point obtained from the decomposition. This represents the weighting coefficient of the SOC term, used to balance the priorities of energy storage security and power quality optimization; Indicates the target state of charge of energy storage, which refers to the target state of charge for the safe and efficient operation of the energy storage system; This indicates the current state of charge of the energy storage system, referring to the actual state of charge of the energy storage system at the optimization point. This represents the weighting coefficient of the reactive power compensation item; Indicates the reactive power compensation weight; This represents the fundamental reactive component of phase X; This represents the weighting coefficient of the harmonic compensation term; Indicates the harmonic compensation weight; This represents the harmonic components of phase X.

[0133] The constraints of the multi-objective optimization model include weight constraints and current constraints. The weight constraints are:

[0134] ;

[0135] ;

[0136] The energy storage current constraint is:

[0137] .

[0138] Based on the fundamental component and optimal compensation parameters, the target value of the compensation current is calculated. The target value of the compensation current is:

[0139] ;

[0140] in, This indicates the target value of the compensation current for phase X; This represents the fundamental active current of the energy storage phase X; Indicates the reactive power compensation weight; This represents the fundamental reactive component of phase X; Indicates the harmonic compensation weight; Represents the harmonic components of phase X; This represents the fundamental zero-sequence current compensation coefficient; , and These represent the second grid currents in the time domain for phases A, B, and C, respectively.

[0141] Based on the optimal compensation parameters obtained from the solution, the target value of the compensation current for each phase is calculated: the target value of the compensation current is equal to the optimal three-phase energy storage fundamental active current, plus the product of the optimal reactive power compensation weight and the fundamental reactive power component, plus the product of the optimal harmonic compensation weight and the harmonic component, plus the product of the fundamental zero-sequence current compensation coefficient and the average value of the three-phase second grid current time domain signal.

[0142] Step 4: Based on the target value of the compensation current, control commands are generated collaboratively through model predictive control and fast repetitive control, including:

[0143] Based on the target value of the compensation current and the LCL discrete model of MFGTI, the actual future compensation current is predicted. The LCL discrete model of MFGTI refers to the transformation of the continuous-domain electromagnetic dynamic equations into a discrete-domain model solvable by digital control, based on the physical parameters (inductance and capacitance values) of the LCL (inductor-capacitor-inductor) filter built into the MFGTI (Multifunctional Grid-Connected Inverter). This is achieved through a zero-order hold discretization method. For example, the inductor on the inverter side can be... Filter capacitor and grid-side inductance For example, an LCL filter is configured between the MFGTI and the distribution network. A zero-order hold is used to discretize the continuous model of the LCL filter, and the discrete sampling period is set to 0.5ms (matching the MFGTI's PWM carrier frequency of 2kHz), resulting in a discrete state-space model. The state vector of this model includes three state quantities: inverter-side inductor current, compensation current, and filter capacitor voltage. The discrete state matrix, input matrix, and output matrix are all calculated based on circuit laws and discretization formulas, with the output matrix only extracting the compensation current as the output quantity. A discrete state-space model can be constructed based on the LCL filter:

[0144] ;

[0145] ;

[0146] in, and Let these represent the state vectors of the system at discrete times k and k+1, respectively, with a dimension of three. For example, for Specifically, it is , Inductance The current in the middle (main current); Inductance The current in (compensation current); and The voltage across capacitor C is represented by ; T represents the transpose matrix; Represents the discrete state transition matrix of the system; Represents a discrete input matrix; This indicates the control input, specifically the voltage control command of MFGTI; Indicates the output vector; This represents the output matrix. A discrete matrix can be represented by... , The values ​​of C and the sampling period are calculated.

[0147] Specifically, linear interpolation is performed based on the current current and the target value of the compensation current to obtain the target value for future multiple steps (e.g., 8 steps). , C and sampling period were calculated to obtain , and And obtain the LCL state vector at time k. Determine the temporary control quantity ; This represents the actual control quantity at time k; The variable representing the prediction step number, for example, for a control system with a prediction time domain of 8, the value range of i is 1, 2, 3, ..., 8; based on the temporary control quantity, determine the predicted state and predicted actual compensation current for the k+i steps:

[0148] ;

[0149] ;

[0150] in, This represents the predicted state at time k+i. This represents the predicted state one step ahead of the prediction at time k; This represents the temporary control quantity of the previous step k+i-1; This represents the actual compensation current predicted at time k+i steps. Multiple values ​​within the prediction time domain are iteratively determined to obtain the predicted actual compensation current at each future time, thus yielding the future actual compensation current. .

[0151] Construct a control objective function, and generate basic control variables by optimizing the control objective function.

[0152] The control objective function is:

[0153] ;

[0154] Where J represents the control objective function; min represents minimization; This indicates the prediction time domain; for example, if we take 8, we predict the next 8 steps. 'i' represents the prediction time step variable. This indicates control over the time domain, meaning optimization is only applied to the next two steps. Subsequent steps Set to 0 to balance control flexibility and stability; This represents the current deviation term, used to control power quality and ensure compensation accuracy. , This represents the target compensation current value obtained by interpolating the target compensation current value at the (k+i)th step. This represents the control change at the (k+i)th step to be optimized, in order to avoid system oscillation; The coefficient of synergy can be obtained from experiments; for example, it can be 0.2. Represents the z-domain discrete transfer function of FRC; This represents the control change at step k to be optimized, such as the inverter duty cycle change. The optimized control change at step k+i can be obtained by optimizing the control objective function. and the optimized control change at step k. For example, setting the prediction time domain of MPC to 8 steps and the control time domain to 2 steps, based on the constructed LCL discrete model, the compensation current for the next 8 steps is predicted, and the absolute value of the deviation between the predicted compensation current and the target compensation current value is calculated for each step. An MPC objective function is constructed, including a term summing the absolute values ​​of the compensation current deviations for the next 8 steps, a term summing the absolute values ​​of the control changes for the next 2 steps, and an FRC coordination term (weighted by a coefficient of 0.2). A quadratic programming algorithm is used to solve this objective function. During the solution process, the constraints of the absolute value of the compensation current deviation ≤ 0.1kA and the control change ≤ 5% can be transformed into inequality constraints in quadratic programming to obtain the optimal control change for the current step and the next step. The base duty cycle is calculated as follows: the base duty cycle for the current step equals the historical duty cycle executed in the previous step, plus the optimal control change for the current step, and the base duty cycle value is constrained to be between 0-100%.

[0155] Harmonic components are processed by rapid repetitive control to obtain the output correction. This output correction is used to correct the 3rd, 5th, and 7th harmonics. Utilizing the periodic error learning characteristics of the FRC (Fault-Resistant Control), a correction signal is generated to accurately cancel harmonics in the distribution network, ensuring the compensation current tracks the component reference. An FRC z-domain transfer function is designed for the 3rd / 5th / 7th harmonics. This transfer function is based on the power grid frequency cycle and sampling period, enabling accurate tracking and compensation of the 3rd / 5th / 7th harmonics in the distribution network. The optimal control change of the current step is input into this FRC z-domain transfer function to obtain the FRC harmonic compensation current correction. This current correction is converted into a duty cycle correction using a current-to-duty cycle conversion coefficient. The conversion coefficient is derived based on the inductor voltage, current relationship, and the DC side voltage of the MFGTI (1500V), and after derivation and experimental calibration, it is set to 0.1% / kA (i.e., 0.1% duty cycle change for every 1kA of FRC harmonic compensation current). The output correction is:

[0156] ;

[0157] ;

[0158] in, This represents the output correction amount at step k; represents the control change at the k-th step after optimization; s represents the Laplace complex frequency variable; and T represents the power grid cycle. Indicates the fundamental frequency of the power grid; It is The z-domain transfer function discretized by the zero-order hold (ZOH); This represents the transfer function.

[0159] The control command is obtained by superimposing the basic control value and the output correction value. The control command is as follows:

[0160] ;

[0161] in, This represents the control command at step k, i.e., the final control command. This represents the control instruction for step k-1; This represents the control change at the k-th step after optimization; The conversion factor, i.e., the current duty cycle conversion factor of the FRC correction, can be derived from the circuit laws based on the hardware parameters and digital control parameters (sampling period) of MFGTI. This represents the output correction amount at step k. The final duty cycle command value is constrained to between 0-100%. The final duty cycle command is converted into a 10kHz PWM pulse signal to drive the IGBT switch of MFGTI, causing MFGTI to output a compensation current to the distribution network that matches the target compensation current value. Figure 3 As shown in the figure, the actual compensation current is represented by the black curve, and the target compensation current is represented by the dashed line 0.25kA. As shown in the figure, the error between the final actual compensation current and the target compensation current is less than 0.1kA.

[0162] Figure 2 This is an exemplary block diagram of a power distribution network harmonic optimization system based on electrochemical energy storage, provided by the present invention. (See diagram below.) Figure 2 As shown, a distribution network harmonic optimization system based on electrochemical energy storage includes a preprocessing module, a fundamental frequency calculation module, a compensation current calculation module, and a control command generation module. The preprocessing module collects initial harmonic data and preprocesses it to obtain processed harmonic data. The harmonic data includes grid current, energy storage system parameters, PCS power, and grid voltage. Preprocessing involves sequentially performing wavelet filtering and DC component filtering on the initial harmonic data. The fundamental frequency calculation module calculates fundamental frequency parameters based on the processed harmonic data. These parameters include fundamental active power, fundamental reactive power, and fundamental apparent power. The compensation current calculation module calculates fundamental frequency components based on the fundamental frequency parameters and determines the target value of the compensation current based on these components. The fundamental frequency components include fundamental active power, fundamental reactive power, and harmonic components. The control command generation module generates control commands based on the target compensation current value through a combination of model predictive control and fast repetitive control.

[0163] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing harmonics in a distribution network based on electrochemical energy storage, characterized in that, include: Initial harmonic data is collected and preprocessed to obtain processed harmonic data. The harmonic data includes grid current, energy storage system parameters, PCS power, and grid voltage. The preprocessing includes wavelet filtering and DC component filtering of the initial harmonic data. Based on the processed harmonic data, the fundamental wave parameters are calculated; the fundamental wave parameters include fundamental wave active power, fundamental wave reactive power and fundamental wave apparent power. Based on the fundamental frequency parameters, the fundamental frequency component is calculated, and based on the fundamental frequency component, the target value of the compensation current is determined; fundamental frequency component Includes fundamental active component, fundamental reactive component, and harmonic components; The determination of the target value of the compensation current based on the fundamental frequency component includes: Based on scenario requirements and energy storage status, the initial compensation parameters are determined. The initial compensation parameters include the initial reactive power compensation weight, the initial harmonic compensation weight, the initial three-phase energy storage fundamental active current and fundamental zero-sequence current compensation coefficient. Based on the initial compensation parameters, a multi-objective optimization model is constructed, and the optimal compensation parameters are obtained through an optimization algorithm. The optimal compensation parameters include the optimal reactive power compensation weight, the optimal harmonic compensation weight, and the optimal three-phase energy storage fundamental active current. The objective function of the multi-objective optimization model is: ； Where min represents minimization; Represent the objective function; Indicates taking the absolute value; Indicates the target total harmonic distortion rate; This indicates the calculation of total harmonic distortion (THD). Indicates the weight coefficient of the SOC term; Indicates the target state of charge of the energy storage; Indicates the current state of charge of the energy storage; This represents the weighting coefficient of the reactive power compensation item; Indicates the reactive power compensation weight; This represents the fundamental reactive component of phase X; This represents the weighting coefficient of the harmonic compensation term; Indicates the harmonic compensation weight; Represents the harmonic components of phase X; The constraints of the multi-objective optimization model include weight constraints and energy storage current constraints: The weight constraints are: ； ； The energy storage current constraint is: ； Based on the fundamental component and the optimal compensation parameters, the target value of the compensation current is calculated; the target value of the compensation current is: ； in, This indicates the target value of the compensation current for phase X; This represents the fundamental active current of the energy storage phase X; Indicates the reactive power compensation weight; This represents the fundamental reactive component of phase X; Indicates the harmonic compensation weight; Represents the harmonic components of phase X; This represents the fundamental zero-sequence current compensation coefficient; , and These represent the second grid currents in the time domain for phases A, B, and C, respectively. Based on the target value of the compensation current, control commands are generated collaboratively through model predictive control and fast repetitive control, including: Based on the target value of the compensation current and the LCL discrete model of MFGTI, the future actual compensation current is predicted; the LCL discrete model of MFGTI is as follows: ； ； in, This represents the predicted state at time k+i. This represents the predicted state one step before time k; This represents the temporary control quantity of the previous step k+i-1; This represents the actual compensation current predicted at time k+i steps. Represents the discrete state transition matrix of the system; Represents a discrete input matrix; Indicates the output matrix; The control objective function is: ； Where J represents the control objective function; min represents minimization; Indicates the prediction time domain; i represents the prediction time step variable; Indicates control over the time domain; Indicates the current deviation term; This represents the control change at the (k+i)th step to be optimized; Indicates the coefficient of synergy; Represents the z-domain discrete transfer function of FRC; This represents the control change at step k to be optimized; The output correction is: ； ； in, This represents the output correction amount at step k; This represents the control change at the k-th step after optimization; Represents a complex variable in the complex frequency domain; T represents the power grid period; Indicates the fundamental frequency of the power grid; It is Discretized z-domain transfer function; Represents the transfer function; The control commands are: ； in, This represents the control command for step k; This represents the control instruction for step k-1; This represents the control change at the k-th step after optimization; Indicates the conversion factor; Construct a control objective function, and generate basic control variables by optimizing the control objective function; The harmonic components are processed by rapid repetitive control to obtain the output correction amount; By superimposing the basic control quantity and the output correction quantity, the control command is obtained.

2. The method for optimizing distribution network harmonics based on electrochemical energy storage according to claim 1, characterized in that, The preprocessing of the initial harmonic data to obtain processed harmonic data includes: The initial harmonic data is cleaned to obtain the first harmonic data; the first harmonic data includes the first grid current, the first energy storage system parameters, the first PCS power, and the first grid voltage; The DC component of the first grid current and the first grid voltage are filtered out by a second-order generalized integrator to obtain the second grid current, the second grid voltage, the quadrature voltage signal and the analog voltage. The second grid current, the parameters of the first energy storage system, the power of the first PCS, the second grid voltage, the quadrature voltage signal, and the analog voltage are used as harmonic data for processing.

3. The method for optimizing distribution network harmonics based on electrochemical energy storage according to claim 2, characterized in that, The second grid current is: ； ； ； in, , and These represent the second grid currents in the complex frequency domain for phases A, B, and C, respectively. Represents a complex variable in the complex frequency domain; Represents the positive-order component transfer function; , and These represent the initial grid currents of phases A, B, and C in the complex frequency domain, respectively. The voltage of the second power grid is: ； ； ； in, , and These represent the second grid voltages of phases A, B, and C in the complex frequency domain, respectively. , and These represent the initial grid voltages of phases A, B, and C in the complex frequency domain, respectively. The quadrature voltage signal is: ； ； ； in, , and These represent the quadrature voltage signals of phases A, B, and C, respectively. Represents the transfer function of orthogonal components; The analog voltage is: ； ； ； in, , and These represent the analog voltages of phases A, B, and C, respectively. Indicates the analog voltage amplitude; This represents the real part of the complex signal; , and These represent the initial grid voltages of phases A, B, and C in the time domain, respectively.

4. The method for optimizing distribution network harmonics based on electrochemical energy storage according to claim 1, characterized in that, The fundamental active power is: ； in, This represents the fundamental active power of phase X, which is a three-phase variable including phases A, B, and C; T represents the power grid cycle. This represents the second grid current in phase X in the time domain; The voltage of the second grid in phase X is represented in the time domain; t represents the time variable. This represents the integral over time t over one power grid cycle; The fundamental reactive power is: ； in, This represents the fundamental reactive power of phase X; This represents the quadrature voltage signal of phase X; The fundamental apparent power is: ； in, This represents the apparent power of the fundamental frequency.

5. The method for optimizing distribution network harmonics based on electrochemical energy storage according to claim 1, characterized in that, The fundamental active component is: ； in, This represents the fundamental active component of phase X; This represents the fundamental active power of phase X; Represents the Euclidean norm; Represents the analog voltage of phase X; The fundamental reactive component is: ； in, This represents the fundamental reactive component of phase X; This represents the quadrature voltage signal of phase X in the time domain; This represents the fundamental reactive power of phase X; The harmonic components are: ； in, Represents the harmonic components of phase X; This represents the second grid current in phase X in the time domain.

6. The distribution network harmonic optimization system based on electrochemical energy storage according to any one of claims 1-5, characterized in that, It includes a preprocessing module, a fundamental frequency calculation module, a compensation current calculation module, and a control command generation module; The preprocessing module is used to collect initial harmonic data and preprocess the initial harmonic data to obtain processed harmonic data. The harmonic data includes grid current, energy storage system parameters, PCS power, and grid voltage. The preprocessing includes performing wavelet filtering and DC component filtering on the initial harmonic data in sequence. The fundamental wave calculation module is used to calculate the fundamental wave parameters based on the processed harmonic data; the fundamental wave parameters include fundamental wave active power, fundamental wave reactive power and fundamental wave apparent power. The compensation current calculation module is used to calculate the fundamental component based on the fundamental parameters, and to determine the target value of the compensation current based on the fundamental component; the fundamental component includes the fundamental active component, the fundamental reactive component, and the harmonic component. The control command generation module is used to generate control commands based on the target value of the compensation current through model predictive control and fast repetitive control.

Images