Optimal control method of power supply and distribution network considering harmonic distortion rate

Abstract

The application discloses a kind of light storage and charging power distribution network optimization control method considering harmonic distortion rate. The fundamental wave current and harmonic spectrum coefficient of photovoltaic, electric vehicle and energy storage are obtained, and the complex impedance matrix of multiple frequency nodes is constructed. The comprehensive harmonic transfer coefficient is determined by the product of harmonic spectrum coefficient and impedance matrix element, and the constant real coefficient is obtained by complex multiplication expansion. The harmonic voltage is expressed as the first-order linear real part and imaginary part combination of equipment active and reactive decision variables. With the condition that the fundamental wave voltage amplitude is approximately rated value, the total harmonic distortion rate constraint is converted into standard second-order cone constraint, which is included in the second-order cone programming model of power distribution network for global optimization solution. The power regulation instruction of each device is obtained, and the closed-loop verification of power flow is carried out by Newton-Raphson method. The application breaks through the non-convexity obstacle of harmonic distortion rate constraint, realizes efficient and accurate solution under the condition of retaining the superposition and cancellation effect of multiple source harmonic complex phase, and meets the national standard 5% harmonic red line constraint.

Description

Technical Field

[0001] This invention relates to the field of power distribution network operation optimization and control technology, and more specifically, to a method for optimizing and controlling a photovoltaic-storage-charging power distribution network that takes into account harmonic distortion rate. Background Technology

[0002] With the integration of high-proportion distributed photovoltaic (PV) power generation units, large-scale electric vehicle (EV) charging facilities, and distributed energy storage systems into the distribution network, the power quality and operational safety of the distribution network face severe challenges. Power electronic devices such as PV inverters, EV chargers, and energy storage converters inject a large amount of harmonic current into the distribution network during the regulation of active and reactive power. The harmonic voltage at each node is determined by the harmonic current injected by all devices in the network and the network's harmonic impedance, exhibiting a complex domain coupling and superposition characteristic of multiple sources and multiple frequencies. The national standard GB / T14549 stipulates that the total harmonic distortion (THD) at each node of the distribution network must not exceed 5%.

[0003] Existing optimal power flow control methods for distribution networks that take into account harmonic constraints typically require the addition of THD (Total Harmonic Distortion) limits to the optimization model. However, the calculation of THD involves the root-mean-square (RMS) of the sum of squares of the magnitudes of each harmonic voltage, and the harmonic voltages are obtained by mapping the complex currents of all harmonic sources to the network's complex impedance matrix. This makes the THD constraint a highly nonlinear and non-convex function with respect to the device power decision variables. Conventional convex optimization solvers cannot handle this directly.

[0004] To circumvent the aforementioned nonlinear and nonconvex computational challenges, a common approach in existing technologies is to ignore the complex phase relationships between different harmonic sources and directly estimate the total harmonic distortion (THD) rate by arithmetically summing the harmonic current amplitudes of each device. While this simplifies the calculation, it completely loses the inherent superposition and cancellation effects of harmonic phasors in the complex domain, leading to inflated THD estimates and overly conservative safety boundaries, thus severely limiting the actual connectable capacity of photovoltaic power generation units and electric vehicle charging stations. Another common approach is to use heuristic intelligent algorithms such as genetic algorithms and particle swarm optimization to perform black-box optimization on models with THD constraints. However, these methods are extremely time-consuming to solve, cannot guarantee a globally optimal solution, and lack determinism and stability in the computational process.

[0005] Therefore, how to transform the THD constraint into a convex form while preserving the complex phase information of multi-source harmonics and accurately assessing THD, so that it can be solved efficiently and deterministically, and at the same time achieve the optimal synergy between fundamental voltage security and new energy consumption, is a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0006] The purpose of this invention is to provide an optimized control method and system for photovoltaic-storage-charging-distribution networks that takes into account harmonic distortion rate, so as to solve the problems in the prior art where THD constraints are difficult to incorporate into the convex optimization framework and where harmonic evaluation distortion and non-convex model solving difficulties coexist.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A method for optimizing the control of a photovoltaic-storage-charging-distribution network that takes into account harmonic distortion rate includes:

[0009] Obtain the fundamental current and harmonic spectrum coefficients corresponding to photovoltaic power generation units, electric vehicle charging stations and energy storage systems in the power distribution network;

[0010] Construct the complex impedance matrix of multi-frequency network nodes under each harmonic order based on the distribution network topology parameters;

[0011] The harmonic spectrum coefficients and the complex impedance matrix of the multi-frequency network nodes, and the device access nodes With target assessment nodes The product of the elements of the node impedance matrix between them determines the overall harmonic transmission coefficient;

[0012] The harmonic voltage in the total harmonic distortion rate constraint of the target node is expanded by complex multiplication using the comprehensive harmonic transmission coefficient to obtain constant real coefficients. The real and imaginary parts of the harmonic voltage are respectively expressed as a first-order linear combination of the active and reactive power decision variables of each device and the corresponding real coefficients.

[0013] With the fundamental voltage amplitude approximately equal to the rated value, the constraint that the total harmonic distortion rate does not exceed the specified limit is transformed into an Euclidean norm constraint on the vector of the first-order linear real part and the first-order linear imaginary part, which constitutes a standard second-order cone form.

[0014] The standard second-order cone form constraint is incorporated into the second-order cone programming model of the optimal power flow of the distribution network. The global optimization solution is performed to obtain the power control commands of each device, while simultaneously satisfying the fundamental voltage safety constraint and the total harmonic distortion rate limit.

[0015] The active and reactive power commands of each device obtained after optimization are substituted into the Newton-Raphson method for AC power flow calculation to obtain the accurate fundamental voltage amplitude and phase angle of each node. The accurate fundamental voltage is used to recalculate the true harmonic current and the true harmonic voltage to obtain the true total harmonic distortion (THD). The approximate THD value used in the optimization process is compared with the true THD. If the deviation exceeds a preset threshold, the margin of the fundamental voltage approximation is adjusted and the optimization is performed again.

[0016] Through the above steps, this invention overcomes the non-convexity barrier of total harmonic distortion (THD) constraints in the prior art, and for the first time realizes the strict transformation of THD constraints containing complex phase information into a combination of linear real and linear imaginary parts, and embeds it into a second-order cone programming model, ensuring the acquisition of the global optimal solution and efficient solution.

[0017] Furthermore, the distribution network topology parameters include the fundamental resistance of each branch. and fundamental frequency reactance The construction of the complex impedance matrix of the multi-frequency network nodes includes:

[0018] For the Subharmonics, connecting nodes With nodes The branch harmonic impedance is expressed as:

[0019] ;

[0020] in, branch road The fundamental frequency resistance, branch road The fundamental reactance, For harmonic order, The imaginary unit;

[0021] Based on the distribution network branch topology, construct the first... Network node admittance matrix under subharmonics Invert the admittance matrix of the node to obtain the admittance of the entire network. Subharmonic node impedance matrix .

[0022] Furthermore, the harmonic spectrum coefficients of the photovoltaic power generation unit, the electric vehicle, and the energy storage system are all expressed in complex form, including the amplitude percentage and phase angle information of the harmonic current relative to the fundamental current.

[0023] The comprehensive harmonic transmission coefficient By the device at the node The Subharmonic spectral coefficient With target assessment nodes and nodes Between the first Nodal impedance matrix elements under subharmonics The product is determined as follows: .

[0024] Furthermore, the step of using the comprehensive harmonic transmission coefficient to represent the real and imaginary parts of the harmonic voltage as a first-order linear combination of the active and reactive power decision variables of each device specifically includes:

[0025] The comprehensive harmonic transmission coefficient is expanded into a real part coefficient by complex multiplication. With imaginary part coefficient :

[0026] ;

[0027] The active power of each device and reactive power As a decision variable, node The Real part of the second harmonic voltage and the virtual part They are determined by the following set of first-order linear expressions:

[0028] ;

[0029] ;

[0030] In the formula, and The combined harmonic transmission coefficients are respectively After expansion, it corresponds to the node. constant real coefficients, and They are nodes The active and reactive power decision variables of the equipment. This represents the summation over all access nodes containing harmonic injection devices;

[0031] The fundamental voltage amplitude is approximated as the rated value. , will node Total harmonic distortion Not exceeding the maximum allowed value The constraint is represented as a standard second-order cone constraint:

[0032] ;

[0033] In the formula, Let represent the Euclidean norm of the vector, which encompasses the real and imaginary parts of the harmonic voltages at all assessed frequencies. Its beneficial effect is that, through rigorous complex algebraic transformations, the originally highly nonlinear and non-convex total harmonic distortion rate constraint is equivalently transformed into a standard second-order cone inequality, enabling commercial convex optimization solvers to directly process it. This completely solves the dual dilemmas of instability in heuristic algorithms and distortion in amplitude simplification methods in existing technologies.

[0034] Furthermore, the step of substituting the active and reactive power commands of each device obtained after optimization into the Newton-Raphson method AC power flow calculation for closed-loop verification specifically includes:

[0035] Substitute the active and reactive power commands of each device obtained after optimization into the Newton-Raphson method AC power flow calculation to obtain the accurate fundamental voltage amplitude and phase angle of each node.

[0036] The true harmonic current and true harmonic voltage are recalculated using the precise fundamental voltage to obtain the true total harmonic distortion rate.

[0037] The approximate total harmonic distortion rate used in the optimization process is compared with the actual total harmonic distortion rate. If the deviation exceeds the preset threshold, the margin of the fundamental voltage approximation is adjusted and the optimization solution is performed again.

[0038] Furthermore, the objective function of the second-order cone programming model for optimal power flow in the distribution network is... This includes the cost of solar curtailment, the cost of electric vehicle power curtailment, and the active power loss of the grid.

[0039] Furthermore, the second-order cone programming model for optimal power flow in the distribution network includes the following constraints:

[0040] Node power balance constraints, nodes Net value of injected active power and net reactive power It is determined by the algebraic sum of substation grid-connected power, distributed power output, photovoltaic power output, energy storage discharge power, conventional load, electric vehicle charging power, energy storage charging power, controllable distribution area regulation, and reactive power compensation.

[0041] The DistFlow branch power flow constraint, relaxed by a second-order cone, introduces squared variables of voltage magnitude and squared variables of branch current magnitude, transforming the nonlinear power flow equation into a convex constraint containing second-order cone inequalities.

[0042] Energy storage system charge and discharge operation constraints include dynamic changes in state of charge (SOC), energy storage margin constraints, maximum charge and discharge power limits, and charge and discharge mutual exclusion constraints.

[0043] Furthermore, the power control commands for each device include:

[0044] The curtailment power command and reactive power command issued to the photovoltaic power generation unit, wherein the curtailment power command is calculated by subtracting the optimized actual output power from the predicted maximum output power of the photovoltaic unit;

[0045] Power restriction instructions and reactive power instructions issued to electric vehicle charging stations;

[0046] The charging and discharging power commands issued to the energy storage system specify that the charging power is a positive value and the discharging power is a negative value.

[0047] Furthermore, obtaining the fundamental current corresponding to the photovoltaic power generation unit, electric vehicle charging station, and energy storage system in the distribution network includes:

[0048] In a per-unit system, the system reference power is taken. Reference voltage Branch impedance and node power are converted to per-unit values;

[0049] Based on the flat voltage start-up assumption, the fundamental voltage amplitude of each node is approximated as... Using the complex power formula Derivation of fundamental current In the formula Apparent power, and These are active power and reactive power, respectively. It is the conjugate value of the fundamental voltage.

[0050] On the other hand, the present invention provides a photovoltaic-storage-charging-distribution network optimization control system that takes into account harmonic distortion rate. Each module in this system corresponds one-to-one with each step in the photovoltaic-storage-charging-distribution network optimization control method that takes into account harmonic distortion rate, including:

[0051] The data acquisition module is used to obtain the fundamental power and harmonic spectrum coefficients of photovoltaic power generation units, electric vehicle charging stations and energy storage systems in the power distribution network;

[0052] The impedance matrix construction module is used to construct complex impedance matrices for multi-frequency network nodes under various harmonic orders based on distribution network topology parameters. ;

[0053] The transmission coefficient generation module is used to generate the transmission coefficients from the harmonic spectrum coefficients and the device access node. and target assessment nodes The nodal impedance matrix elements between The product of these factors, combined into the overall harmonic transmission coefficient. ;

[0054] The linear transformation module is used to perform complex multiplication expansion using the comprehensive harmonic transmission coefficients to obtain constant real coefficients. and The real and imaginary parts of the harmonic voltage in the total harmonic distortion rate constraint of the target node are expressed as the following first-order linear combination:

[0055] ,

[0056] ;

[0057] The second-order cone constraint construction module is used to transform the constraint that the total harmonic distortion rate does not exceed a specified limit into a Euclidean norm constraint on the vector of the first-order linear real part and the first-order linear imaginary part, with the condition that the fundamental voltage is approximately equal to the rated value. This constraint constitutes a standard second-order cone form.

[0058] The optimization solution module is used to incorporate the standard second-order cone form constraints into the second-order cone programming model of the optimal power flow of the distribution network, call a commercial second-order cone solver to perform global optimization solution, and obtain the power control commands of each device; the optimization solution module is also used to substitute the active power and reactive power commands of each device obtained after optimization solution into the Newton-Raphson method AC power flow calculation for closed-loop verification. If the deviation exceeds the preset threshold, the margin of the fundamental voltage approximation assumption is adjusted and the optimization solution is performed again.

[0059] The above modules are logically connected in sequence. Data flows in from the front-end acquisition module, and is processed progressively by the impedance matrix construction module, the transfer coefficient generation module, the linear transformation module, and the second-order cone constraint construction module. Finally, the optimization solution module outputs power control commands that can be directly sent to field equipment, forming a complete closed loop for the optimization control of photovoltaic energy storage and charging in the distribution network.

[0060] Example 3

[0061] This embodiment provides a computer-readable storage medium storing a computer program. When executed by a processor, this computer program implements all the steps of the photovoltaic-storage-charging-distribution network optimization control method considering harmonic distortion rate as described in Embodiment 1. The computer-readable storage medium can be a disk storage device, solid-state storage device, optical disk storage device, or other non-volatile storage medium. The processor can be a server central processing unit of a distribution network dispatch automation system, a distributed computing node processor, or a microprocessor of an embedded controller, etc. Attached Figure Description

[0062] To more clearly illustrate the technical solutions in this invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without any creative effort.

[0063] Figure 1 This is an overall flowchart of the photovoltaic-storage-charging-distribution network optimization control method that takes into account harmonic distortion rate, as described in this invention.

[0064] Figure 2 This is a diagram of the IEEE 33-node network topology of the power distribution network in an embodiment of the present invention.

[0065] Figure 3The diagram shows the fundamental voltage distribution at each node of the distribution network after optimization according to an embodiment of the present invention.

[0066] Figure 4 This is a distribution diagram of the total harmonic distortion rate of each node after optimization in an embodiment of the present invention.

[0067] Figure 5 This is a comparison and error verification diagram between the estimated total harmonic distortion rate and the actual physical value in an embodiment of the present invention. Detailed Implementation

[0068] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only for explaining the present invention and are not intended to limit the present invention.

[0069] Terminology Explanation

[0070] Photovoltaic power generation unit: The English name is Photovoltaic, abbreviated as PV. It is a distributed power generation system that directly converts light energy into electrical energy through solar cells. In this application, it specifically refers to a distributed photovoltaic power generation unit that is connected to the distribution network and has the ability to reduce active power and regulate reactive power.

[0071] Electric vehicle charging station: The English name is Electric Vehicle, abbreviated as EV. It refers to transportation vehicles that use electricity as a power source. In this application, it specifically refers to electric vehicle charging loads that are connected to the power distribution network through charging stations and have the ability to regulate charging and discharging power and provide reactive power support.

[0072] Energy storage system: The English name is Energy Storage System, abbreviated as ESS. It is a device that realizes bidirectional storage and release of electrical energy through electrochemical or other means. In this application, it specifically refers to a distributed energy storage device that is connected to a distribution network node and has the ability to independently control charging and discharging power.

[0073] Total Harmonic Distortion (THD) is a core power quality indicator characterizing the degree of voltage waveform distortion. It is defined as the ratio of the root mean square (RMS) value of each harmonic voltage to the RMS value of the fundamental voltage, usually expressed as a percentage. According to the national standard GB / T14549 "Power Quality - Harmonics in Public Power Grids," the total harmonic distortion (THD) at each node of the distribution network must not exceed five percent.

[0074] Second-Order Cone Programming (SOCP) is a class of convex mathematical optimization problems whose constraints include second-order cone inequalities. This application utilizes this convex optimization framework to achieve a globally optimal solution for the optimal power flow in a harmonic-constrained distribution network.

[0075] DistFlow branch power flow model: a steady-state power flow calculation model applicable to radial distribution networks. It uses branch active power, reactive power, and node voltage amplitude as state variables, and can be transformed into convex optimization constraints after second-order cone relaxation.

[0076] Comprehensive harmonic transmission coefficient: Based on the construction of the complex impedance matrix of multi-frequency network nodes, this application derives a constant coefficient that characterizes the complex mapping relationship between the injected harmonic current of each device and the harmonic voltage of the target node. It is determined by the product of the device harmonic spectrum coefficient and the corresponding node impedance matrix element.

[0077] The Newton-Raphson method is a high-precision numerical method for solving nonlinear equations. In this application, it is used for AC power flow calculations to accurately obtain the fundamental voltage amplitude and phase angle at each node.

[0078] Controllable area: The area supplied by a distribution transformer in a distribution network that can accept dispatch instructions to flexibly adjust active and reactive power loads.

[0079] State of Charge (SOC): Characterized by the proportion of remaining energy to the rated capacity of the energy storage system, it serves as a core state variable for the operational constraints of the energy storage system in this application.

[0080] Node admittance matrix: A complex matrix constructed based on the distribution network topology and branch parameters. The diagonal elements of the matrix are the node self-admittances, and the off-diagonal elements are the negative values ​​of the mutual admittances between nodes. It represents the frequency domain relationship between current and voltage between nodes in the network.

[0081] Node impedance matrix: the inverse of the node admittance matrix, whose elements Indicates from node When injecting unit current at the node The generated voltage is a key mathematical tool for analyzing the propagation of harmonics in networks.

[0082] Imaginary unit: denoted by the mathematical symbol . ,satisfy , is used to represent the imaginary part of a complex number. In this application, it is used to construct complex impedance and harmonic phasors.

[0083] Per-unit (PU): A dimensionless representation commonly used in power system analysis, obtained by dividing the actual value of a physical quantity by a selected reference value.

[0084] Calculation Example (Step-by-Step Numerical Verification)

[0085] Before detailing the seven-step process, a simplified numerical example of a three-node system will be used to visually demonstrate the complete derivation process of this invention from raw data to THD constraint values. This will also help in understanding the physical meaning of subsequent steps.

[0086] System settings: Node 1 is a balanced node (infinite power supply), voltage is Node 2 is connected to a photovoltaic (PV) power generation unit, whose fundamental power is... (i.e., 0.5MW) The 5th harmonic current spectral coefficient of the PV is 20% amplitude and 30° phase angle, i.e. The branch impedance of line 1-2 This example only uses the 5th harmonic; the processing method for other harmonic frequencies is the same.

[0087] Step 1: Calculate the fundamental current of PV.

[0088] Using the flat voltage start-up assumption, the fundamental voltage at node 2... Fundamental current formula ,because Therefore (Note the sign change of the imaginary part after the conjugate operation of complex numbers; the result is given directly here.)

[0089] Step 2: Calculate the 5th harmonic current injected by the PV.

[0090] .

[0091] Real part ,

[0092] virtual part .

[0093] Therefore .

[0094] Step 3: Establish the 5th harmonic impedance matrix.

[0095] Harmonic impedance In this 3-node system, the 5th harmonic self-impedance viewed from node 2 is equal to the branch impedance, i.e. .

[0096] Step 4: Calculate the 5th harmonic voltage of node 2.

[0097] .

[0098] Real part ,

[0099] virtual part .

[0100] Therefore Amplitude Square .

[0101] If only the 5th harmonic is considered, the THD at node 2 is... The result is less than 5% below the national standard. This result verifies the basic calculation process. In practice, the 5th, 7th, 11th, and 13th harmonics need to be considered simultaneously, and second-order cone constraints need to be constructed according to the subsequent process five.

[0102] Example 1

[0103] This embodiment provides an optimized control method for a photovoltaic-storage-charging-distribution network that takes into account harmonic distortion rate. The overall technical process is as follows: Figure 1 It includes three core parts: building a harmonic current and impedance matrix calculation model, constructing a photovoltaic energy storage and charging optimization model that considers harmonics and multidimensional safety constraints, and executing photovoltaic energy storage and charging optimization control commands.

[0104] Step 1: Obtain the fundamental current and harmonic spectrum coefficients corresponding to the photovoltaic power generation unit, electric vehicle charging station, and energy storage system in the distribution network.

[0105] First, load data, network topology parameters, and access equipment information are collected from each node of the distribution network. Under a per-unit system, the system baseline power is then used. Reference voltage (RMS line voltage), branch impedance and node power are converted to per-unit values ​​to unify dimensions.

[0106] For any device connected to the distribution network, its injected apparent power is According to the definition of complex power Deriving the fundamental complex current: To simplify subsequent calculations, this embodiment adopts the classical engineering assumption: the fundamental voltage amplitude of each node is approximately the rated value, i.e., under the per-unit system. .

[0107] Based on this, the fundamental current of various distributed resources in the power distribution network can be calculated. (Fundamental current of photovoltaic power generation unit) Under the assumption of voltage-based startup Therefore Similarly, electric vehicle charging stations Energy storage system (because of its) ).

[0108] The harmonic emission characteristics of a device are determined by both the amplitude percentage and phase angle of the spectrum. This is achieved by converting the amplitude and phase angle into complex spectral coefficients. The first injection of the equipment The subharmonic current can be expressed as the product of the complex spectral coefficient and the fundamental current: , , .in , , All of these are known complex constants obtained in advance through measurement or equipment nameplate parameters.

[0109] Step 2: Constructing complex impedance matrices for multi-frequency network nodes under various harmonic orders based on distribution network topology parameters

[0110] For standard lines in a distribution network, in the first Under subharmonics, connecting nodes With nodes Branch harmonic impedance In the formula , These are the fundamental resistance and reactance, respectively.

[0111] Based on graph theory and distribution network branch topology, construct the first... Network node admittance matrix under subharmonics :node Self-admittance ,node and Mutual admittance (If direct branches exist). Obtain the entire network by matrix inversion. Subharmonic node impedance matrix .element Indicates from node Injection Unit Number At the node, the subharmonic current is present. The generated harmonic voltage.

[0112] Step 3: Synthesize the overall harmonic transfer coefficient from the harmonic spectrum coefficients and nodal impedance elements.

[0113] Target assessment nodes The Subharmonic voltage .Will Substitute the values ​​and apply the voltage equalization assumption. ,have to .

[0114] Define the composite harmonic transmission coefficient , is a constant complex number, and is related to the decision variable. , It is irrelevant and can be pre-calculated and stored.

[0115] Step 4: Represent harmonic voltage as a first-order linear combination of active and reactive power decision variables.

[0116] Will Expand into real and imaginary parts: Substitute Expression and After performing complex product and separating the real and imaginary parts, we get:

[0117] ,

[0118] .

[0119] Therefore, the real and imaginary parts of each harmonic voltage become decision variables. , It is a strictly first-order linear function with no nonlinear terms.

[0120] Step 5: Transform the THD constraint into a standard second-order cone form.

[0121] node Total harmonic distortion rate is defined as National standards require .

[0122] Under the assumption of constant voltage, the denominator Then the constraint is equivalent to ,Right now

[0123]

[0124] This expression represents a standard second-order cone constraint. The left side is the Euclidean norm of the linear expression, and the right side is a constant, indicating complete convexity.

[0125] Step 6: Incorporate into a second-order cone programming model for global optimization solution.

[0126] A second-order cone programming model for optimal power flow in a distribution network is constructed. Decision variables include the active charging and discharging power of energy storage at each node, the active / reactive power of electric vehicles, and the active / reactive power of photovoltaic systems. The objective function is... It is the sum of the costs of solar curtailment, electric vehicle power curtailment, and active power loss in the grid.

[0127] Constraints include: node power balance, DistFlow branch power flow via second-order cone relaxation, energy storage SOC and charge / discharge power limits, and node voltage safety range (e.g., ), and the THD second-order cone constraints of each node constructed in process five.

[0128] The solution is obtained by calling a commercial solver (such as CPLEX). Since the model is a standard convex problem, it can converge to the global optimum within milliseconds. After solving, the solution outputs power control commands for each device: photovoltaic curtailment power and reactive power commands, electric vehicle power curtailment power and reactive power commands, and energy storage charging and discharging power commands.

[0129] Step 7: Closed-loop verification

[0130] The optimized instructions are substituted into the Newton-Raphson method for AC power flow calculation to obtain the accurate fundamental voltage of each node. Then, the actual harmonic current, harmonic voltage, and actual THD are recalculated. The THD estimated by the optimized model is compared with the actual THD. If the maximum absolute error exceeds a preset threshold (e.g., 0.5%), the THD margin is appropriately tightened, and the optimization is repeated until the error meets the requirements.

[0131] Numerical Example Verification

[0132] A standard IEEE 33-node distribution network system was selected; the topology is shown below. Figure 2 The system voltage reference is 12.66kV, and the power reference is 1MVA. Photovoltaic power is connected to nodes 12 (300kW) and 20 (600kW), with adjustable power factor; electric vehicle charging stations are connected to nodes 16 (1000kW) and 26 (2000kW); energy storage is connected to nodes 18 (100kWh) and 23 (150kWh), with charge / discharge efficiencies ranging from 0.95 to 0.97. Harmonic frequencies of 5th, 7th, 11th, and 13th are considered, and the spectral coefficients are derived from actual equipment measurements.

[0133] The optimized fundamental voltages at each node are shown below. Figure 3 All are controlled within Internal. See THD for each node. Figure 4 All are below the national standard of 5%. Figure 5 A comparison is given between the THD estimated by the optimized model and the true THD by the Newton-Raphson method. The maximum absolute error is about 0.38%, which is less than 0.5%, thus verifying the accuracy of the proposed method.

[0134] Example 2

[0135] This embodiment provides a photovoltaic-storage-charging-distribution network optimization control system that takes into account harmonic distortion rate. The system includes:

[0136] The data acquisition module is used to obtain the fundamental power and harmonic spectrum coefficients of photovoltaic power generation units, electric vehicle charging stations and energy storage systems in the power distribution network;

[0137] The impedance matrix construction module is used to construct complex impedance matrices for multi-frequency network nodes under various harmonic orders based on distribution network topology parameters. ;

[0138] The transmission coefficient generation module is used to generate the transmission coefficients from the harmonic spectrum coefficients and the device access node. and target assessment nodes The nodal impedance matrix elements between The product of these factors, combined into the overall harmonic transmission coefficient. ;

[0139] The linear transformation module is used to perform complex multiplication expansion using the comprehensive harmonic transmission coefficients to obtain constant real coefficients. and The real and imaginary parts of the harmonic voltage in the total harmonic distortion rate constraint of the target node are expressed as the following first-order linear combination:

[0140] ,

[0141] ;

[0142] The second-order cone constraint construction module is used to transform the constraint that the total harmonic distortion rate does not exceed a specified limit into a Euclidean norm constraint on the vector of the first-order linear real part and the first-order linear imaginary part, with the condition that the fundamental voltage is approximately equal to the rated value. This constraint constitutes a standard second-order cone form.

[0143] The optimization solution module is used to incorporate the standard second-order cone form constraints into the second-order cone programming model of the optimal power flow of the distribution network, call a commercial second-order cone solver to perform global optimization solution, and obtain the power control commands of each device; the optimization solution module is also used to substitute the active power and reactive power commands of each device obtained after optimization solution into the Newton-Raphson method AC power flow calculation for closed-loop verification. If the deviation exceeds the preset threshold, the margin of the fundamental voltage approximation assumption is adjusted and the optimization solution is performed again.

[0144] The above modules are logically connected in sequence. Data flows in from the front-end acquisition module, and is processed progressively by the impedance matrix construction module, the transfer coefficient generation module, the linear transformation module, and the second-order cone constraint construction module. Finally, the optimization solution module outputs power control commands that can be directly sent to field equipment, forming a complete closed loop for the optimization control of photovoltaic energy storage and charging in the distribution network.

[0145] Example 3

[0146] This embodiment provides a computer-readable storage medium storing a computer program. When executed by a processor, this computer program implements all the steps of the photovoltaic-storage-charging-distribution network optimization control method considering harmonic distortion rate as described in Embodiment 1. The computer-readable storage medium can be a disk storage device, solid-state storage device, optical disk storage device, or other non-volatile storage medium. The processor can be a server central processing unit of a distribution network dispatch automation system, a distributed computing node processor, or a microprocessor of an embedded controller, etc.

[0147] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural modifications made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for optimizing the control of a photovoltaic-storage-charging-distribution network considering harmonic distortion rate, characterized in that, include: The fundamental current and harmonic spectrum coefficients corresponding to photovoltaic power generation units, electric vehicle charging stations and energy storage systems in the distribution network are obtained respectively; the harmonic spectrum coefficients are in complex form and include the amplitude percentage and phase angle information of each harmonic current relative to the fundamental current. Construct the complex impedance matrix of multi-frequency network nodes under each harmonic order based on the distribution network topology parameters; The comprehensive harmonic transmission coefficient is determined by multiplying the harmonic spectrum coefficient with the node impedance matrix elements between the device access node and the target assessment node in the complex impedance matrix of the multi-frequency network nodes. The harmonic voltage in the total harmonic distortion rate constraint of the target node is expanded by complex multiplication using the comprehensive harmonic transmission coefficient to obtain constant real coefficients. The real and imaginary parts of the harmonic voltage are respectively expressed as a first-order linear combination of the active and reactive power decision variables of each device and the corresponding real coefficients. With the fundamental voltage amplitude approximately equal to the rated value, the constraint that the total harmonic distortion rate does not exceed the specified limit is transformed into an Euclidean norm constraint on the vector of the first-order linear real part and the first-order linear imaginary part, which constitutes a standard second-order cone form. The standard second-order cone form constraint is incorporated into the second-order cone programming model of the optimal power flow of the distribution network. The global optimization solution is performed to obtain the power control commands of each device, while simultaneously satisfying the fundamental voltage safety constraint and the total harmonic distortion rate limit.

2. The photovoltaic-storage-charging-distribution network optimization control method considering harmonic distortion rate according to claim 1, characterized in that, The distribution network topology parameters include the fundamental resistance of each branch. and fundamental frequency reactance The construction of the complex impedance matrix of the multi-frequency network nodes under each harmonic order includes: For the Subharmonics, connecting nodes With nodes The branch harmonic impedance is expressed as: ； in, branch road The fundamental frequency resistance, branch road The fundamental reactance, For harmonic order, The imaginary unit; Based on the distribution network branch topology, construct the first... Network node admittance matrix under subharmonics Inverting the admittance matrix of this node yields the nth node of the entire network. Network node complex impedance matrix at the second harmonic order .

3. The photovoltaic-storage-charging-distribution network optimization control method considering harmonic distortion rate according to claim 1, characterized in that, The harmonic spectrum coefficients of the photovoltaic power generation unit, the electric vehicle, and the energy storage system are all expressed in complex form, including the amplitude percentage and phase angle information of the harmonic current relative to the fundamental current. The comprehensive harmonic transmission coefficient By the device at the node The Subharmonic spectral coefficient With target assessment nodes and nodes Between the second Nodal impedance matrix elements under subharmonics The product is determined as follows: .

4. The photovoltaic-storage-charging-distribution network optimization control method considering harmonic distortion rate according to claim 3, characterized in that, The method of using the comprehensive harmonic transmission coefficient to express the real and imaginary parts of the harmonic voltage as a first-order linear combination of the active and reactive power decision variables of each device specifically includes: The comprehensive harmonic transmission coefficient is expanded into real and imaginary coefficients using complex multiplication: ； in, The comprehensive harmonic transmission coefficient The real coefficients after expansion The comprehensive harmonic transmission coefficient The imaginary part coefficients after expansion; Using the active and reactive power of each device as decision variables, the node... The Real part of the second harmonic voltage and the virtual part They are determined by the following set of first-order linear expressions: ； ； In the formula, and The combined harmonic transmission coefficients are respectively After expansion, it corresponds to the node. constant real coefficients, and They are nodes The active and reactive power decision variables of the equipment. This represents the summation over all access nodes containing harmonic injection devices; Using the fundamental voltage amplitude as an approximation of the rated value, the node... Total harmonic distortion Not exceeding the maximum allowed value The constraint is represented as a standard second-order cone constraint: ； In the formula, Represents a node The Subharmonic voltage Represents a node The Subharmonic voltage Indicates taking the real part, Indicates taking the imaginary part; This represents the Euclidean norm of the vector, which encompasses both the real and imaginary parts of the harmonic voltages at all assessed frequencies.

5. The photovoltaic-storage-charging-distribution network optimization control method considering harmonic distortion rate according to claim 1, characterized in that, It also includes a closed-loop verification process: Substitute the active and reactive power commands of each device obtained after optimization into the Newton-Raphson method AC power flow calculation to obtain the accurate fundamental voltage amplitude and phase angle of each node. The true harmonic current and true harmonic voltage are recalculated using the precise fundamental voltage to obtain the true total harmonic distortion rate. The approximate total harmonic distortion rate used in the optimization process is compared with the actual total harmonic distortion rate. If the deviation exceeds the preset threshold, the margin of the fundamental voltage approximation is adjusted and the optimization solution is performed again.

6. The photovoltaic-storage-charging-distribution network optimization control method considering harmonic distortion rate according to claim 1, characterized in that, The objective function of the second-order cone programming model for optimal power flow in the distribution network is... This includes the cost of solar curtailment, the cost of electric vehicle power curtailment, and the active power loss of the grid.

7. The photovoltaic-storage-charging-distribution network optimization control method considering harmonic distortion rate according to claim 1, characterized in that, The second-order cone programming model for optimal power flow in the distribution network includes the following constraints: Node power balance constraints, nodes Net value of injected active power and net reactive power It is determined by the algebraic sum of substation grid-connected power, distributed power output, photovoltaic power output, energy storage discharge power, conventional load, electric vehicle charging power, energy storage charging power, controllable distribution area regulation, and reactive power compensation. The DistFlow branch power flow constraint, relaxed by a second-order cone, introduces squared variables of voltage magnitude and squared variables of branch current magnitude, transforming the nonlinear power flow equation into a convex constraint containing second-order cone inequalities. Energy storage system charge and discharge operation constraints include dynamic changes in state of charge (SOC), energy storage margin constraints, maximum charge and discharge power limits, and charge and discharge mutual exclusion constraints.

8. The photovoltaic-storage-charging-distribution network optimization control method considering harmonic distortion rate according to any one of claims 1 to 7, characterized in that, The power control commands for each device include: The curtailment power command and reactive power command issued to the photovoltaic power generation unit, wherein the curtailment power command is calculated by subtracting the optimized actual output power from the predicted maximum output power of the photovoltaic unit; Power restriction instructions and reactive power instructions issued to electric vehicle charging stations; The charging and discharging power commands issued to the energy storage system specify that the charging power is a positive value and the discharging power is a negative value.

9. A photovoltaic-storage-charging-distribution network optimization control system considering harmonic distortion rate, characterized in that, Each module in this system corresponds one-to-one with each step in the photovoltaic-storage-charging-distribution network optimization control method considering harmonic distortion rate as described in claim 1, including: The data acquisition module is used to acquire the fundamental power and harmonic spectrum coefficients of photovoltaic power generation units, electric vehicle charging stations and energy storage systems in the distribution network; the harmonic spectrum coefficients are in complex form and include the amplitude percentage and phase angle information of each harmonic current relative to the fundamental current. Impedance matrix construction module, used to construct complex impedance matrices of multi-frequency network nodes under various harmonic orders based on distribution network topology parameters; The transmission coefficient generation module is used to synthesize the comprehensive harmonic transmission coefficient by multiplying the harmonic spectrum coefficient with the elements of the node impedance matrix between the device access node and the target assessment node. The linear transformation module is used to perform complex multiplication expansion using the comprehensive harmonic transmission coefficient to obtain constant real coefficients. It represents the real and imaginary parts of the harmonic voltage in the total harmonic distortion constraint of the target node as a first-order linear combination of the active and reactive power decision variables of each device and the corresponding real coefficients. The second-order cone constraint construction module is used to transform the constraint that the total harmonic distortion rate does not exceed a specified limit into a Euclidean norm constraint on the vector of the first-order linear real part and the first-order linear imaginary part, with the condition that the fundamental voltage is approximately equal to the rated value. This constraint constitutes a standard second-order cone form. The optimization solution module is used to incorporate the standard second-order cone form constraint into the second-order cone programming model of the optimal power flow of the distribution network, perform global optimization solution, and obtain the power control command of each device. The optimization solution module is also used to: substitute the active power and reactive power commands of each device obtained after optimization into the Newton-Raphson method AC power flow calculation for closed-loop verification.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the steps of the photovoltaic-storage-charging-distribution network optimization control method that takes into account harmonic distortion rate as described in any one of claims 1 to 8.

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