Distributed grid-connected photovoltaic reactive power optimization method and system of intelligent measurement and control terminal

Abstract

The invention discloses a distributed grid-connected photovoltaic reactive power optimization method and system for an intelligent measurement and control terminal, and relates to the technical field of photovoltaic reactive power optimization, and the method comprises the steps: collecting and uploading the electric energy data of a photovoltaic inverter and each user through a collection device; the intelligent measurement and control terminal receives the data of the acquisition equipment, processes and analyzes the data, and monitors the operation state of the photovoltaic inverter; and the intelligent measurement and control terminal starts a reactive power optimization scheme and issues a control instruction to the photovoltaic inverter. According to the invention, data processing calculation and control signal issuing can be realized, heterogeneous data from different devices, sensors and monitoring systems acquired by acquisition equipment can be received, then the data are integrated and analyzed, and real-time detection and management of various devices and facilities in a transformer area can be realized. According to the reactive power optimization scheme stored in the intelligent measurement and control terminal, information of the whole transformer area can be processed and calculated, the photovoltaic inverter is guided to change output reactive power, and the voltage out-of-limit problem is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of photovoltaic reactive power optimization, in particular to a distributed grid-connected photovoltaic reactive power optimization method and system of an intelligent measurement and control terminal. BACKGROUND

[0002] Due to its own advantages and the strong support of national policy, distributed photovoltaic power generation has formed a large-scale grid-connected pattern. However, after a large number of distributed photovoltaic power generation enters the power grid, the output power of distributed photovoltaic power generation is unstable due to the influence of continuously changing natural conditions such as illumination and irradiance, causing the voltage to be in an unstable state. The higher the penetration rate, the higher the instability, resulting in voltage and frequency fluctuations in the power grid. Distributed inverters usually have anti-islanding function and do not have high-low voltage ride-through function. When the power grid fluctuates, the inverter may shut down for protection, causing greater impact on the power grid. To cope with the adverse effects of power grid voltage fluctuation and excessive system loss caused by this change, it has become a key problem for the reliable operation of the current distribution system. Solving this challenge is crucial to ensuring the stability and reliability of the power system.

[0003] The current low-voltage distributed photovoltaic power generation system meets the technical standards of the electric energy meter. Through the low-voltage collection system concentrator set in the transformer area, the electric energy meter data is uploaded to the power grid metering automation system, thereby realizing functions such as bidirectional metering, time-sharing metering, and electric quantity freezing. The system supports multiple communication methods, including carrier wave, RS485, and wireless communication. In addition, the electric energy meter can feed back the collected current, voltage, power, and power factor information to the project owner, realizing mutual detection and management.

[0004] The smart meter lacks functions such as AC sampling, state quantity acquisition, and event reporting, and cannot realize comprehensive monitoring of transformer area operation data, cannot monitor and control photovoltaic grid-connected intelligent circuit breakers and inverters, and therefore has no detection capability for low-voltage users. It cannot support optimization of photovoltaic grid-connected group control, comprehensive treatment of power quality, fast information exchange and analysis between transformer areas.

[0005] At the same time, the metering automation system in the current smart meter has obvious deficiencies in monitoring functions for low-voltage user side. There are obvious defects in the range and frequency of data acquisition, and there is a lack of effective control means and ability in control.

[0006] To address the shortcomings of smart meters in detection and control methods, intelligent monitoring and control terminals were designed and manufactured, providing the equipment foundation for active support of the distribution network. By integrating and analyzing heterogeneous data from different devices, sensors, and monitoring systems, various devices and facilities in the energy system can be better monitored and managed. This allows for the timely detection and resolution of potential problems, thereby improving the security and stability of the energy system and achieving better monitoring of the distribution area.

[0007] The distributed photovoltaic reactive power optimization scheme stored inside the intelligent monitoring and control terminal can achieve reasonable allocation of reactive power, thereby reducing potential losses in the power grid. It can also improve the voltage quality of the power grid area to maintain stable grid operation. Summary of the Invention

[0008] In view of the above-mentioned problems, the present invention is proposed.

[0009] Therefore, the problem that this invention aims to solve is: how to address the lack of detection and control methods in smart meters.

[0010] To address the aforementioned technical problems, this invention provides the following technical solution: a distributed grid-connected photovoltaic reactive power optimization method using an intelligent monitoring and control terminal, comprising: a data acquisition device collecting and uploading power data from the photovoltaic inverter and each user; an intelligent monitoring and control terminal receiving and processing the data from the data acquisition device to monitor the operating status of the photovoltaic inverter; and the intelligent monitoring and control terminal activating the reactive power optimization scheme and sending control commands to the photovoltaic inverter.

[0011] As a preferred embodiment of the distributed grid-connected photovoltaic reactive power optimization method of the intelligent monitoring and control terminal described in this invention, the power data of the photovoltaic inverter and each user includes active power, reactive power, power factor, and grid connection point voltage; the data upload includes: the intelligent monitoring and control terminal sending a request message; the acquisition device receiving the request message sent by the intelligent monitoring and control terminal; the acquisition device parsing the received request message and identifying the request content; after identifying the request content, the acquisition device uploading the data content to be collected to the intelligent monitoring and control terminal through the communication serial port.

[0012] As a preferred embodiment of the distributed grid-connected photovoltaic reactive power optimization method of the intelligent monitoring and control terminal described in this invention, the operating state includes: the intelligent monitoring and control terminal processing the received information, monitoring the operating state of the power grid network in the distribution area, and determining whether the voltage of each node exceeds the limit.

[0013] As a preferred embodiment of the distributed grid-connected photovoltaic reactive power optimization method of the intelligent monitoring and control terminal described in this invention, the reactive power optimization scheme includes: the intelligent monitoring and control terminal regulating the operating status and output power of the photovoltaic inverter based on the monitored operating status; the reactive power optimization scheme further includes establishing an objective function, optimization constraints, an optimization algorithm, and solving the optimization algorithm; the objective function includes establishing a function with minimum grid loss and voltage deviation as the objective function, expressed as:

[0014]

[0015] Where i and j are node numbers, P loss For the system's active power loss, G ij Let θ be the conductance between nodes i and j. ij Let N be the phase difference between nodes i and j. b U represents the total number of network branches. i Let U be the voltage value at node i. j U is the voltage value at node j, U0 is the rated voltage at node j, and U max U is the maximum node voltage. min The minimum node voltage is defined as follows; the optimization constraints include power flow balance constraints, node voltage constraints, and photovoltaic output constraints; the power flow balance constraints are expressed as follows:

[0016]

[0017] Among them, P i Let Q be the active power at node i. i Let G be the reactive power of node i. ij Let B be the real part of the admittance between nodes i and j. ij Let N be the imaginary part of the admittance between nodes i and j, and N represent the total number of nodes; the node voltage constraint is expressed as:

[0018] U imin ≤U i ≤U imax

[0019] Among them, U imin U is the lower limit of the voltage at node i. imax The voltage upper limit at node i; the photovoltaic output constraint is expressed as:

[0020] P t =P MPPT

[0021]

[0022] Among them, P t Contributing to photovoltaic power, Q tThese represent the reactive power output of photovoltaic systems, and S represents the photovoltaic capacity.

[0023] As a preferred embodiment of the distributed grid-connected photovoltaic reactive power optimization method for the intelligent measurement and control terminal described in this invention, the optimization algorithm is represented by the population G in the particle swarm optimization algorithm (PSO) as follows:

[0024]

[0025] Among them, P u Let N(D) be a particle in the population, and N(D) be the search dimension of the population space. The position of a particle in the population represents the solution to the objective problem, and the particle velocity represents the direction of the particle in the next iteration. The particle fitness is determined by the objective function to determine whether the iteration process meets the requirements, expressed as:

[0026]

[0027] Z u =f G (L u )

[0028] Among them, V u Z is the velocity vector of particle u. u L represents the fitness value of particle u. u Let f be the position vector of particle u. G Let V represent the objective function of the problem to be solved. The optimal solution for this iteration is determined by calculating the particle fitness and comparing the solutions. The optimal solution is used to determine the optimal position of the individual and the population as a whole. The particle's V is then calculated using the new parameter values. u and L u , is represented as:

[0029]

[0030]

[0031] Where w is the inertia weight factor, k is the number of iterations, c1 is the particle's learning ability from an individual, c2 is the particle's learning ability from a group, and r1 and r2 are random numbers between [0,1] to increase randomness. best For the particle's own historical optimal position, g best The global historical optimal position is determined by an improved inertia-decreasing weighting factor, expressed as follows:

[0032]

[0033] Among them, w min w is the minimum inertia weighting factor. max Let t be the maximum inertia weight factor, and t be the current iteration number.max This represents the maximum number of iterations.

[0034] As a preferred embodiment of the distributed grid-connected photovoltaic reactive power optimization method for the intelligent measurement and control terminal described in this invention, the optimization algorithm solution includes: introducing power system operation data, setting basic parameters of the optimization algorithm, and encoding control variables; calculating the velocity and position of the initial particles, performing power flow calculation using the forward-backward substitution method to obtain the corresponding active power loss and voltage deviation values; comparing the positions of global particles in the current population, determining the optimal position according to the global optimal particle selection principle, and defining it as p. best It then searches for dominance relationships between particles, placing the solution set of non-dominance relationships into an external archive, and determines g based on the global optimal particle selection criterion. best The formulas for calculating particle velocity and position are iteratively updated to obtain a new solution set through power flow calculations; p is determined using the individual optimal particle selection criterion. best Solution sets that are not related to dominance are placed in an external archive. Based on the principles of maintaining the external archive, the richness of the solution sets is preserved, and the total g is redefined. best If the algorithm reaches the maximum number of iterations or meets the convergence condition, it outputs the optimal solution set; otherwise, it returns to power flow calculation for iterative operation.

[0035] As a preferred embodiment of the distributed grid-connected photovoltaic reactive power optimization method of the intelligent measurement and control terminal described in this invention, the control command issuance includes: the intelligent terminal analyzing, processing, and calculating the data before issuing a control signal; the inverter receiving the control signal, performing corresponding processing and generating response data; the acquisition device receiving the inverter's response data; the acquisition device processing the received inverter response data to form a standard format reply frame; the acquisition device replying the processed data to the intelligent measurement and control terminal device; and the intelligent measurement and control terminal device parsing the data according to the standard format.

[0036] Another objective of this invention is to provide a system for optimizing distributed grid-connected photovoltaic reactive power in intelligent monitoring and control terminals. This system solves the problem of optimizing distributed grid-connected photovoltaic reactive power in intelligent monitoring and control terminals by constructing a grid-connected photovoltaic reactive power optimization system.

[0037] To address the aforementioned technical problems, this invention provides the following technical solution: a distributed grid-connected photovoltaic reactive power optimization system with an intelligent monitoring and control terminal, comprising a data acquisition module, a monitoring module, and a reactive power optimization module; the data acquisition module uses acquisition equipment to collect and upload power data from the photovoltaic inverter and each user; the monitoring module uses an intelligent monitoring and control terminal to receive and process the data from the acquisition equipment, monitoring the operating status of the photovoltaic inverter; the reactive power optimization module uses the intelligent monitoring and control terminal to activate the reactive power optimization scheme and send control commands to the photovoltaic inverter.

[0038] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the distributed grid-connected photovoltaic reactive power optimization method of the intelligent measurement and control terminal as described above.

[0039] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the distributed grid-connected photovoltaic reactive power optimization method of the intelligent measurement and control terminal as described above.

[0040] The beneficial effects of this invention are as follows: The distributed grid-connected photovoltaic reactive power optimization method provided by this invention, based on an intelligent monitoring and control terminal, can realize data processing and calculation as well as the issuance of control signals. For data processing and calculation, it can accept heterogeneous data collected by acquisition devices from different equipment, sensors, and monitoring systems, and then integrate and analyze this data to achieve real-time detection and management of various equipment and facilities within the distribution area. Secondly, the reactive power optimization scheme stored in the intelligent monitoring and control terminal can process and calculate information from the entire distribution area, guiding the photovoltaic inverter to change its output reactive power and solving voltage over-limit problems. Attached Figure Description

[0041] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 The flowchart shows the distributed grid-connected photovoltaic reactive power optimization method of the intelligent measurement and control terminal provided in the first embodiment of the present invention.

[0043] Figure 2 The image shows the appearance of the intelligent measurement and control terminal for the distributed grid-connected photovoltaic reactive power optimization method provided in the first embodiment of the present invention.

[0044] Figure 3 The first embodiment of the present invention provides a block diagram of the physical architecture of an intelligent measurement and control terminal for a distributed grid-connected photovoltaic reactive power optimization method.

[0045] Figure 4 This is a structural diagram of a distributed grid-connected photovoltaic reactive power optimization system for an intelligent measurement and control terminal provided in the second embodiment of the present invention.

[0046] Figure 5 The IEEE 33-node grid-connected model diagram of the distributed grid-connected photovoltaic reactive power optimization method for the intelligent measurement and control terminal provided in the third embodiment of the present invention.

[0047] Figure 6 Voltage node voltage diagrams before and after optimization of the distributed grid-connected photovoltaic reactive power optimization method for the intelligent measurement and control terminal provided in the third embodiment of the present invention. Detailed Implementation

[0048] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0049] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0050] Example 1

[0051] Reference Figures 1-3 This is the first embodiment of the present invention. This embodiment provides a distributed grid-connected photovoltaic reactive power optimization method using an intelligent monitoring and control terminal, including: a data acquisition device collecting and uploading power data of the photovoltaic inverter and each user; an intelligent monitoring and control terminal receiving and processing the data from the data acquisition device, and monitoring the operating status of the photovoltaic inverter; and the intelligent monitoring and control terminal activating the reactive power optimization scheme and sending control commands to the photovoltaic inverter.

[0052] S1, Data Acquisition.

[0053] The data acquisition equipment collects and uploads power data from photovoltaic inverters and various users; the intelligent monitoring and control terminal receives and processes the data, monitoring the operating status of the photovoltaic inverters; the intelligent monitoring and control terminal activates a reactive power optimization scheme and sends control commands to the photovoltaic inverters. The intelligent monitoring and control terminal can integrate and analyze heterogeneous data from different devices, sensors, and monitoring systems, and then send control signals to each photovoltaic inverter in the distribution area through an optimization scheme. The reactive power optimization scheme processes and calculates the operating status of the distribution area's power grid network, changing the reactive power injected by the photovoltaic inverters in the network, thus solving the overvoltage problem caused by distributed grid-connected photovoltaic inverters.

[0054] The intelligent monitoring and control terminal collects the active power, reactive power, power factor, and grid connection point voltage of the distributed grid-connected photovoltaic inverters within the transformer area.

[0055] The intelligent measurement and control terminal includes a core module, a data acquisition module, a remote signaling module, a Bluetooth module, a clock module, a RS-485 module, an Ethernet module, a local communication module, and a remote communication module. The core module is the core of the terminal, controlling the coordinated operation of various functional modules through peripheral interfaces. It uses an industrial-grade single-core multi-core CPU capable of running up to 1.2GHz, with highly decoupled hardware and software, supporting installation in various containers, and real-time management of the data acquisition module, Bluetooth module, and other functional modules. The data acquisition module is the metering unit, enabling the acquisition and processing of remote measurements with high accuracy and good real-time performance. The remote signaling module can acquire the status of external dry nodes. The clock module uses a high-precision clock chip to ensure system clock accuracy. The Bluetooth module is the terminal debugging interface, compatible with handheld devices for local debugging and maintenance. The RS-485 module is a functional circuit module that acquires data from external RS-485 interface devices, achieving electrical safety isolation from external interfaces through a digital isolator to ensure system safety and reliability. The Ethernet module is the interface for local and remote communication, with a built-in physical layer. The data transceiver supports gigabit communication via the RGMII interface; the local communication module uses a proprietary broadband carrier chip, transmitting carrier signals via power line media, supporting area or phase differentiation, proactive reporting, and automatic networking functions; the remote communication module adopts a dual 4G and GPS tri-module solution, supporting simultaneous login to both marketing and operation / maintenance master stations, with high integration and accurate positioning, serving as the transmission hub between the intelligent fusion terminal and the master station, providing a reliable uplink data channel; the data acquisition module and remote signaling module are integrated together, acting as a slave module connected to the core module via the SPI interface; the clock module connects to the core module via the IIC interface; the Bluetooth module connects to the core module via the UART interface; the 485 module connects to the digital isolator and to the core module via the UART interface; the Ethernet module connects to the core module via the RGMII interface; the local communication module communicates with the core module via the UART interface; the remote communication module communicates with the core module via GPS through the UART interface and dual 4G communication via the USB interface. The data acquisition and communication process between the intelligent measurement and control terminal and the photovoltaic inverter is as follows:

[0056] S1.1 Intelligent measurement and control terminal sends request: The intelligent measurement and control terminal sends a request message and sends a data interaction message in a standard format according to the specification.

[0057] S1.2 Acquisition device receives request: The acquisition device receives the request message sent by the intelligent measurement and control terminal.

[0058] S1.3 Message parsing: The acquisition device parses the received request messages and identifies the request content.

[0059] S1.4 Data Upload: After recognizing the request content, the acquisition device uploads the data to be acquired to the intelligent measurement and control terminal via the communication serial port.

[0060] The equipment appearance diagram of the intelligent measurement and control terminal equipment is as follows: Figure 2 As shown, the software structure is as follows Figure 3 As shown.

[0061] S2, Status Monitoring.

[0062] The intelligent monitoring and control terminal processes the received information, monitors the operating status of the power grid network in the distribution area, and determines whether the voltage of each node exceeds the limit. Taking a rated voltage of 220V as an example, the overvoltage threshold is 235.4V, the general overvoltage is 242V, and the severe overvoltage is 250V.

[0063] S3, System Control.

[0064] Based on the monitored operating status of the photovoltaic inverter, the intelligent monitoring and control terminal regulates the inverter's operating status and output power. A reactive power optimization scheme is adopted, the specific method of which is as follows:

[0065] S3.1. Establish the objective function.

[0066] From the perspectives of economy and quality, an objective function is established with minimizing network loss and voltage deviation as the objective function:

[0067]

[0068] Where i and j represent node numbers; P loss Indicates the system's active power loss; G ij θ represents the conductance between nodes i and j; ij N represents the phase difference between nodes i and j; b Indicates the total number of network branches; U i and U j U represents the voltage values ​​at nodes i and j; U0 represents the rated voltage at node i; U max and U min These represent the maximum and minimum values ​​of the node voltage, respectively.

[0069] S3.2 Optimize constraints.

[0070] The power flow balance constraint is expressed as:

[0071]

[0072] Among them, P i and Q i G represents the active and reactive power of node i, respectively. ij and B ij These are the real and imaginary parts of the admittance between nodes i and j, respectively, and N represents the total number of nodes.

[0073] The node voltage constraint is expressed as:

[0074] U imin ≤U i ≤U imax

[0075] Among them, U imin and U imax These are the upper and lower limits of the voltage at node i, respectively.

[0076] Photovoltaic output constraints are expressed as follows:

[0077] P t =P MPPT

[0078]

[0079] Among them, P t and Q t These represent the active and reactive power outputs of photovoltaic power, respectively, with S representing the photovoltaic capacity.

[0080] S3.3, Optimization Algorithm.

[0081] Particle Swarm Optimization (PSO) is an algorithm that seeks optimal solutions by simulating the optimal paths obtained by birds during foraging. PSO eliminates the need for complex operations such as crossover and mutation, has a clear and simple logical operation process, and its optimization goal is easily achievable with high sensitivity. PSO can not only satisfy optimization problems in scientific research but also achieve global random optimization. During the optimization process, the particle position represents the value of the result to be optimized, and by setting rules to update the optimal position of the particles, a better solution can be obtained. The population G in PSO is defined as follows:

[0082]

[0083] Among them, P u Let N(D) represent the particles in the population, and let N(D) represent the search dimension of the population space.

[0084] The position of a particle in the population represents the solution to the objective problem, and the particle velocity represents the particle's direction in the next iteration. The particle fitness is determined by the objective function to determine whether the iteration process meets the requirements. The specific calculation method is as follows:

[0085]

[0086] Z u =f G (L u )

[0087] Among them, V u Z uL u Let f represent the velocity vector, fitness value, and position vector of particle u, respectively. G Let represent the objective function of the problem to be solved.

[0088] The optimal solution for this iteration was determined by calculating the particle fitness and comparing the solutions. This solution value was used to determine the optimal position of the individual and the overall population. The new parameter values ​​were then used to calculate the particle's V. u and L u To ensure that the algorithm searches for a better position in the next iteration, the iterative formulas for the position vector and velocity vector are expressed as:

[0089]

[0090]

[0091] Where w represents the inertia weight factor and k represents the number of iterations.

[0092] The selection of the above parameters has a significant impact on the performance of the particle swarm optimization algorithm. Among them, c1 represents the learning ability of a particle from an individual, which adjusts the iteration step size of the individual's optimal position, and its value has a significant impact on the iteration speed of the individual's optimal position; c2 represents the learning ability of a particle from the swarm, which adjusts the iteration step size of the swarm's optimal position, and its value has a significant impact on the iteration speed of the global optimal position; r1 and r2 are random numbers between [0,1], used to increase randomness.

[0093] The inertia factor *w* determines the trend of particle velocity changes, affecting the speed and accuracy of the algorithm. A larger *w* value results in particles flying farther, a larger search space, and a greater ease of finding the global optimum, leading to higher global search capability. Conversely, a smaller *w* value prevents particles from remembering historical velocities, causing them to tend to fly shorter distances and find local optima, resulting in better local optimization capabilities. The value of the inertia weight has a significant impact on the particle swarm optimization algorithm; the results of "local" and "global" particle searches depend on the weight level.

[0094] In the process of reactive power optimization in distribution networks, it is necessary to handle complex data from multiple aspects. Furthermore, due to the complex relationship between the objective function and constraint variables, it is also necessary to maintain a balance between solution set diversity and convergence. Therefore, this patent adopts an improved inertial decreasing weight, the formula of which is shown below:

[0095]

[0096] Among them, w min w is the minimum inertia weighting factor. max Let t be the maximum inertia weight factor, and t be the current iteration number. max w represents the maximum number of iterations.max =0.9, w min =0.4.

[0097] S3.4 Optimization algorithm solution.

[0098] The reactive power optimization model of a distribution network containing distributed photovoltaic power is solved using the PSO algorithm. The solution steps are as follows:

[0099] S3.4.1. Introduce power system operation data, set the basic parameters of the optimization algorithm, and encode control variables.

[0100] S3.4.2 Calculate the velocity and position of the first generation of particles, use the forward-backward substitution method to perform power flow calculations and obtain the corresponding active power loss and voltage deviation values;

[0101] S3.4.3. In the current population, compare the positions of global particles, determine the optimal position according to the global optimal particle selection principle, and define it as pbest. Find the dominance relationship between particles, put the solution set of non-dominance relationship into the external archive, and determine gbest according to the global optimal particle selection criterion.

[0102] S3.4.4. Iteratively update the formulas for calculating particle velocity and position, and perform power flow calculations to obtain a new solution set;

[0103] S3.4.5. Determine pbest through the individual optimal particle selection criterion. Place the solution set that is not in a dominance relationship into the external archive. Based on the principle of maintaining the external archive, keep the richness of the solution set and determine the full gbest again.

[0104] S3.4.6 If the algorithm reaches the maximum number of iterations or meets the convergence condition, output the optimal solution set; otherwise, return to S3.4.4 for iterative operation.

[0105] S4. Issuance and Response.

[0106] Data delivery: The intelligent terminal analyzes, processes, and calculates the data before sending out control signals.

[0107] Inverter response: After receiving the control signal, the inverter processes it accordingly and generates response data.

[0108] Data reception: The acquisition device receives the response data from the inverter.

[0109] Data processing: The acquisition device processes the received inverter response data and assembles it into a standard format response frame.

[0110] Response to the data acquisition terminal: The data acquisition device sends the processed data back to the intelligent measurement and control terminal device, which then parses the data according to the standard format.

[0111] Example 2

[0112] Reference Figure 4 This is the second embodiment of the present invention, which differs from the previous embodiment in that it provides a distributed grid-connected photovoltaic reactive power optimization system with an intelligent measurement and control terminal, including: a data acquisition module, a monitoring module and a reactive power optimization module.

[0113] The data acquisition module uses acquisition equipment to collect and upload the power data of the photovoltaic inverter and each user.

[0114] The monitoring module uses an intelligent measurement and control terminal to receive and process data from the acquisition equipment, thereby monitoring the operating status of the photovoltaic inverter.

[0115] The reactive power optimization module uses an intelligent measurement and control terminal to enable the reactive power optimization scheme and sends control commands to the photovoltaic inverter.

[0116] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0117] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0118] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0119] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0120] Example 3

[0121] Reference Figure 5 and Figure 6 This is the third embodiment of the present invention, which differs from the previous two embodiments in that it is used to verify and explain the technical effects adopted in the present invention, so as to verify the real effect of the method.

[0122] The simulation model adopts the IEEE 33-node standard model, such as Figure 5 As shown, take U 基准 =12.66kV, S 基准 =1MVA, distributed photovoltaic grid-connected capacity 1MW, maximum active power output 900KW. Power factor range is [-0.95, 0.95], voltage acceptable range is [0.95pu, 1.05pu]. Based on this, 4 PVs are added to nodes 10, 15, 20, and 31. In the figure, distributed photovoltaic is classified as a negative load in the PQ node category.

[0123] The final result is as follows Figure 6 As shown, after optimization by the algorithm, the voltage of each node is within the normal voltage range. Therefore, this reactive power optimization scheme can achieve controllability of the power grid network in the distribution area.

[0124] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for optimizing the reactive power of distributed grid-connected photovoltaic systems using an intelligent monitoring and control terminal, characterized in that: include, The data acquisition equipment collects and uploads the power data from the photovoltaic inverters and each user. The intelligent monitoring and control terminal receives data from the acquisition equipment, processes and analyzes it, and monitors the operating status of the photovoltaic inverter; The intelligent monitoring and control terminal activates the reactive power optimization scheme and sends control commands to the photovoltaic inverter.

2. The distributed grid-connected photovoltaic reactive power optimization method for intelligent monitoring and control terminals as described in claim 1, characterized in that: The power data of the photovoltaic inverter and each user includes active power, reactive power, power factor and grid connection point voltage; The data upload process includes: the intelligent monitoring and control terminal sending a request message; the acquisition device receiving the request message from the intelligent monitoring and control terminal; the acquisition device parsing the received request message and identifying the request content; and after identifying the request content, the acquisition device uploading the data to be acquired to the intelligent monitoring and control terminal via a communication serial port.

3. The distributed grid-connected photovoltaic reactive power optimization method for intelligent monitoring and control terminals as described in claim 2, characterized in that: The operational status includes the intelligent monitoring and control terminal processing the received information, monitoring the operational status of the power grid network in the distribution area, and determining whether the voltage of each node exceeds the limit.

4. The distributed grid-connected photovoltaic reactive power optimization method for intelligent monitoring and control terminals as described in claim 3, characterized in that: The reactive power optimization scheme includes adjusting the operating status and output power of the photovoltaic inverter by an intelligent monitoring and control terminal based on the monitored operating status of the photovoltaic inverter. The reactive power optimization scheme also includes establishing the objective function, optimization constraints, optimization algorithm, and solving the optimization algorithm. The objective function includes establishing a function that minimizes network loss and voltage deviation, expressed as follows: Where i and j are node numbers, P loss For the system's active power loss, G ij Let θ be the conductance between nodes i and j. ij Let N be the phase difference between nodes i and j. b U represents the total number of network branches. i Let U be the voltage value at node i. j U is the voltage value at node j, U0 is the rated voltage at node j, and U max U is the maximum node voltage. min This represents the minimum node voltage. The optimization constraints include power flow balance constraints, node voltage constraints, and photovoltaic output constraints. The power flow balance constraint is expressed as follows: Among them, P i Let Q be the active power at node i. i Let G be the reactive power of node i. ij Let B be the real part of the admittance between nodes i and j. ij Let N be the imaginary part of the admittance between nodes i and j, and N represent the total number of nodes. The node voltage constraint is expressed as follows: IN imin ≤U i ≤U imax Among them, U imin U is the lower limit of the voltage at node i. imax This represents the upper limit of the voltage at node i; The photovoltaic output constraint is expressed as follows: Among them, P t Contributing to photovoltaic power, Q t These represent the reactive power output of photovoltaic systems, and S represents the photovoltaic capacity.

5. The distributed grid-connected photovoltaic reactive power optimization method for intelligent monitoring and control terminals as described in claim 4, characterized in that: The optimization algorithm is represented by the population G in the particle swarm optimization algorithm (PSO). Among them, P u Let N(D) be the particle in the population, and let N(D) be the search dimension of the population space. The position of a particle in the population represents the solution to the objective problem, and the particle velocity represents the particle's direction in the next iteration. The particle fitness is determined by the objective function to determine whether the iteration process meets the requirements, and is expressed as follows: Z u =f G (L u ) Among them, V u Z is the velocity vector of particle u. u L represents the fitness value of particle u. u Let f be the position vector of particle u. G Denotes the objective function of the problem to be solved; The optimal solution for this iteration is determined by calculating the particle fitness and comparing the solutions. The optimal solution is used to determine the optimal position of the individual and the population as a whole. The particle's V is then calculated using the new parameter values. u and L u , is represented as , Where w is the inertia weight factor, k is the number of iterations, c1 is the particle's learning ability from an individual, c2 is the particle's learning ability from a group, and r1 and r2 are random numbers between [0,1] to increase randomness. best For the particle's own historical optimal position, g best This is the best position in the global history. An improved inertia weighting factor is adopted using an improved inertia decreasing weight, expressed as follows: Among them, w min w is the minimum inertia weighting factor. max Let t be the maximum inertia weight factor, and t be the current iteration number. max This represents the maximum number of iterations.

6. The distributed grid-connected photovoltaic reactive power optimization method for intelligent monitoring and control terminals as described in claim 5, characterized in that: The optimization algorithm solution includes: introducing power system operating data, setting the basic parameters of the optimization algorithm, and encoding control variables; calculating the velocity and position of the initial particles, using the forward-backward substitution method to perform power flow calculations and obtain the corresponding active power loss and voltage deviation values; comparing the positions of global particles in the current population, determining the optimal position according to the global optimal particle selection principle, and defining it as p. best It then searches for dominance relationships between particles, placing the solution set of non-dominance relationships into an external archive, and determines g based on the global optimal particle selection criterion. best The formulas for calculating particle velocity and position are iteratively updated to obtain a new solution set through power flow calculations; p is determined using the individual optimal particle selection criterion. best Solution sets that are not related to dominance are placed in an external archive. Based on the principles of maintaining the external archive, the richness of the solution sets is preserved, and the total g is redefined. best If the algorithm reaches the maximum number of iterations or meets the convergence condition, it outputs the optimal solution set; otherwise, it returns to power flow calculation for iterative operation.

7. The distributed grid-connected photovoltaic reactive power optimization method for intelligent monitoring and control terminals as described in claim 6, characterized in that: The control command issuance includes: the intelligent terminal analyzes, processes, and calculates the data before issuing a control signal; the inverter receives the control signal, processes it accordingly, and generates response data; the acquisition device receives the inverter's response data, processes the received inverter response data, assembles a standard format reply frame, and sends the processed data back to the intelligent measurement and control terminal device; the intelligent measurement and control terminal device parses the data according to the standard format.

8. A system employing a distributed grid-connected photovoltaic reactive power optimization method using an intelligent monitoring and control terminal as described in any one of claims 1 to 7, characterized in that: It includes a data acquisition module, a monitoring module, and a reactive power optimization module; The data acquisition module uses acquisition equipment to collect and upload the power data of the photovoltaic inverter and each user. The monitoring module uses an intelligent measurement and control terminal to receive data from the acquisition equipment and process and analyze it to monitor the operating status of the photovoltaic inverter. The reactive power optimization module uses an intelligent measurement and control terminal to enable the reactive power optimization scheme and sends control commands to the photovoltaic inverter.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the distributed grid-connected photovoltaic reactive power optimization method of the intelligent measurement and control terminal as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the distributed grid-connected photovoltaic reactive power optimization method of the intelligent measurement and control terminal as described in any one of claims 1 to 7.

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