Rural micro-grid maximum access capacity optimization method and system fusing demand response

Abstract

The invention discloses a rural micro-grid maximum access capacity optimization method fusing demand response, and belongs to the technical field of micro-grids, and the method comprises the following steps: obtaining a typical load normalized power curve, demand response unit price and a power distribution network flow topological graph of a rural micro-grid; determining active power and reactive power of each node load, extracting standard power flow calculation nodes by combining the power flow topological graph of the power distribution network, establishing an active power balance equation and a reactive power balance equation of the power distribution network, and determining system constraint conditions of the power distribution network; and obtaining a node new energy injection power matrix, taking a system constraint condition of the power distribution network as a boundary, taking maximization of the new energy capacity of each node of the power distribution network as a target, and carrying out iterative updating on the new energy capacity of each node by adopting a particle swarm optimization algorithm to obtain the maximum new energy access capacity of each node of the rural microgrid. According to the invention, the problem of insufficient maximum access capacity under the condition that high-proportion new energy accesses the rural microgrid can be solved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of micro-grid, and particularly relates to a maximum access capacity optimization method and system of a rural micro-grid integrated with demand response. BACKGROUND

[0002] As an independent or semi-independent energy supply system, the rural micro-grid provides a basic support for rural energy security supply through the preliminary integration of conventional power supply and distributed energy. With the continuous increase of new energy access proportion, the existing rural micro-grid technology system gradually shows significant deficiencies, which is difficult to adapt to the intermittent and fluctuating characteristics of high-proportion new energy. The new energy consumption capacity of the rural micro-grid under high-proportion new energy access is insufficient, and the carrying capacity is limited, which is difficult to support the optimal control strategy formulation and capacity configuration optimization of the micro-grid, resulting in low system operation efficiency. SUMMARY

[0003] The purpose of the application is to provide a maximum access capacity optimization method and system of a rural micro-grid integrated with demand response, so as to solve the problem of insufficient maximum access capacity under high-proportion new energy access in the rural micro-grid.

[0004] To achieve the above purpose, the application adopts the following technical scheme: In a first aspect, a maximum access capacity optimization method of a rural micro-grid integrated with demand response comprises the following steps: Obtain the typical load normalized power curve of the rural micro-grid, the demand response unit price, and the power distribution network power flow topology map, determine the active power and reactive power of each node load based on the typical load normalized power curve and the demand response unit price, and extract the standard power flow calculation node in combination with the power distribution network power flow topology map; Based on the standard power flow calculation node, establish the active power balance equation and the reactive power balance equation of the power distribution network, solve the power flow distribution of the original rural power distribution network before new energy penetration, obtain the voltage per unit value of each node, and determine the system constraint condition of the power distribution network based on the voltage per unit value of each node; Obtain the node new energy injection power matrix, take the system constraint condition of the power distribution network as the boundary, take the maximum new energy capacity of each node of the power distribution network as the target, and use the particle swarm optimization algorithm to iteratively update the new energy capacity of each node, to obtain the maximum access capacity of the new energy of each node of the rural micro-grid.

[0005] In some embodiments, the step of determining the active power and reactive power of each node load based on the typical load normalized power curve and the demand response unit price specifically comprises: extracting the normalized power proportion of each type of typical load in the rural micro-grid in the typical load normalized power curve in a quarter of a year, and combining the maximum power of each type of typical load in a quarter of a year, the active power basic value of each type of load at different time is calculated; According to the demand response unit price, the load adjustment priority is divided, and the active power of each node load is calculated through the active power basic value of each type of load at different time, and the reactive power of each node load is calculated in combination with the preset power factor.

[0006] In some embodiments, the step of extracting the standard power flow calculation node in combination with the power distribution network power flow topology graph specifically includes: Identify the physical connection nodes in the power distribution network power flow topology graph, and eliminate the redundant nodes without actual electrical function, the physical connection nodes include: active power load node, reactive power load node, new energy access node, power supply node and upper substation node; Divide the active power load node, the reactive power load node and the new energy access node into PQ nodes, the PQ node is a load node, divide the power supply node into a PV node, the PV node is a generator node, and divide the upper substation node into a balance node, to obtain the standard power flow calculation node.

[0007] In some embodiments, based on the standard power flow calculation node, the active power balance equation and the reactive power balance equation of the power distribution network are established to solve the power flow distribution of the original rural power distribution network before the penetration of new energy, and the step of obtaining the voltage per unit value of each node specifically includes: Collect the branch electrical parameters associated with the standard power flow calculation node, and establish the active power balance equation and the reactive power balance equation for the PQ node and the PV node, calculate the power deviation of each standard power flow calculation node by using the Newton-Raphson method, and output the voltage per unit value of each node after the power deviation meets the preset convergence accuracy.

[0008] In some embodiments, the system constraint condition of the power distribution network is that the voltage per unit value of each node is 0.95-1.05.

[0009] In some embodiments, the particle swarm optimization algorithm is used to iteratively update the new energy capacity of each node, and the step of obtaining the maximum access capacity of the new energy of each node of the rural micro-grid specifically includes: Randomly generate a particle swarm, the initial position population is the access capacity of the new energy of each node, the objective function is the total amount of new energy installed in the power distribution network, if the system constraint condition is met, the fitness value of the particle is calculated, otherwise the particle position is adjusted again; The position of each particle with the maximum fitness value in the iteration process and the position of the particle group with the maximum fitness value are recorded, and iteration updating is performed until the maximum iteration number is reached, so that the new energy capacity of each node corresponding to the global optimal position is obtained, that is, the maximum access capacity of the new energy of each node of the rural micro-grid.

[0010] In a second aspect, a rural micro-grid maximum access capacity optimization system integrated with demand response includes: A standard power flow calculation node extraction module is configured to obtain a typical load normalized power curve of the rural micro-grid, a demand response unit price, and a power distribution network power flow topology graph, determine active power and reactive power of each node load based on the typical load normalized power curve and the demand response unit price, and extract standard power flow calculation nodes in combination with the power distribution network power flow topology graph. A system constraint determination module is configured to establish active power balance equations and reactive power balance equations of the power distribution network based on the standard power flow calculation nodes, solve power flow distribution of the original rural power distribution network before new energy penetration, obtain voltage per unit values of each node, and determine system constraint conditions of the power distribution network based on the voltage per unit values of each node. A new energy access capacity optimization module is configured to obtain a node new energy injection power matrix, take the system constraint conditions of the power distribution network as boundaries, take maximization of new energy capacity of each node of the power distribution network as a target, perform iteration updating on the new energy capacity of each node by using a particle swarm optimization algorithm, and obtain the maximum access capacity of the new energy of each node of the rural micro-grid.

[0011] In a third aspect, an electronic device includes a memory, a processor, and a computer program stored in the memory and executable in the processor, and the processor implements steps of a rural micro-grid maximum access capacity optimization method integrated with demand response when executing the computer program.

[0012] In a fourth aspect, a computer readable storage medium stores a computer program, and the computer program implements steps of a rural micro-grid maximum access capacity optimization method integrated with demand response when executed by a processor.

[0013] In a fifth aspect, a computer program product includes a computer program, and the computer program implements steps of a rural micro-grid maximum access capacity optimization method integrated with demand response when executed by a processor.

[0014] Compared with the prior art, the present application has the following beneficial effects: This invention provides a method for optimizing the maximum access capacity of rural microgrids by integrating demand response. It determines the active and reactive power of each node load based on the normalized power curve of a typical load and the demand response unit price, transforming load power from a fixed parameter into an optimization variable that dynamically changes according to the adjustment priority assigned by the demand response unit price. This guides flexible load-side adjustment, enabling the load curve to dynamically adapt to the fluctuating output of renewable energy. Then, by combining the distribution network power flow topology diagram, standard power flow calculation nodes are extracted, effectively alleviating the contradiction between the volatility of renewable energy output and load rigidity, and significantly expanding the space for renewable energy absorption. Based on the standard power flow calculation nodes, the active and reactive power balance equations of the distribution network are established to solve for the power flow distribution of the original rural distribution network before renewable energy penetration, obtaining the per-unit voltage values ​​of each node. Based on this, the system constraints of the distribution network are determined, clarifying the safety boundary centered on the per-unit voltage values ​​of the nodes. Any scheme that leads to node voltage exceeding the limit will be automatically eliminated, thus avoiding voltage exceeding the limit and power flow disturbance caused by a high proportion of renewable energy access, ensuring that the grid remains in a safe operating state during capacity expansion. The power matrix of renewable energy injection at each node is obtained. Taking the system constraints of the distribution network as the boundary and the goal of maximizing the renewable energy capacity of each node in the distribution network, the renewable energy capacity of each node is iteratively updated using the particle swarm optimization algorithm to obtain the maximum renewable energy access capacity of each node in the rural microgrid. The use of the particle swarm optimization algorithm to perform global optimization of the renewable energy capacity of multiple nodes can avoid the limitations of local optimal solutions and achieve the maximization of renewable energy access capacity of each node while maintaining a balance with the rural microgrid system.

[0015] Furthermore, the normalized power ratio of various typical loads in the rural microgrid for 24 hours during the quarter is extracted from the normalized power curve of typical loads. Combined with the maximum power of various typical loads within 24 hours during the quarter, the basic active power values ​​of various loads at different times are calculated. This can fully adapt to the seasonal and temporal fluctuation characteristics of agricultural production and residential loads in the rural microgrid, significantly reducing the calculation error of the basic active power value. Load regulation priority is divided according to the demand response unit price, and the active power of each node load is calculated through the basic active power values ​​of various loads at different times. Combined with the preset power factor, the reactive power of each node load is calculated. The market incentive signal can be transformed into a calculable load regulation potential through the demand response unit price, making the demand response a quantitative parameter. The reactive power is calculated synchronously with the preset power factor, which is consistent with the actual scenario of a high proportion of inductive loads such as motors and water pumps in rural microgrids. Attached Figure Description

[0016] Figure 1 A flowchart of a method for optimizing the maximum access capacity of a rural microgrid with integrated demand response, provided in an embodiment of the present invention. Figure 2The diagram shows the structure of the rural microgrid maximum access capacity optimization system with integrated demand response provided in this embodiment of the invention. Detailed Implementation

[0017] To enable those skilled in the art to better understand the present invention, the technical solution of the present invention will be further described in detail below with reference to the accompanying drawings. The content is for explanation rather than limitation of the present invention.

[0018] It should be noted that the terms "comprising" and "having" and any variations thereof in the specification and claims of this invention are intended to cover a non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such processes, methods, systems, products, or devices.

[0019] Currently, with the increasing proportion of renewable energy integration, rural microgrids face the following problems: First, the development of rural microgrids suffers from significant spatiotemporal heterogeneity of rural loads and insufficient exploitation of their adjustability potential: Rural load scale and operating characteristics vary considerably across different regions and seasons, and the adjustability of the load has not been fully explored. Demand response mechanisms have not been effectively implemented, making it difficult to achieve time-series matching with renewable energy output. This leads to a mismatch between peak renewable energy output and off-peak loads, exacerbating the problem of power curtailment. Second, the hierarchical division of rural distribution networks is unclear, and microgrid configuration lacks specificity: Rural distribution networks are not clearly divided according to voltage levels (e.g., 10kV, 35kV, 110kV, 220kV). The network structure and power supply capacity of distribution networks at different voltage levels do not match the renewable energy integration needs. There is a lack of microgrid system configuration schemes for different levels of distribution networks, making it impossible to achieve efficient local consumption of renewable energy. Third, the planning of renewable energy access capacity lacks scientific basis and cost-benefit linkage: traditional power flow calculation methods do not fully consider the constraints of distribution network parameters, which can easily lead to problems such as transformer overload and line current exceeding limits; at the same time, a cost-benefit linkage analysis mechanism between renewable energy access and distribution network transformation has not been established, making it impossible to balance the economics of renewable energy access scale and distribution network transformation investment. Fourth, there is a lack of renewable energy consumption assessment and optimization tools: existing technologies cannot accurately calculate the renewable energy curtailment rate, local consumption level, and external power transmission in high-penetration scenarios, making it difficult to support the formulation of optimal microgrid control strategies and capacity configuration optimization, resulting in low microgrid operating efficiency and insufficient renewable energy utilization efficiency.

[0020] In response to the above problems, such as Figure 1 As shown, this embodiment provides a method for optimizing the maximum access capacity of a rural microgrid that integrates demand response, including the following steps: S1. Obtain the normalized power curve of typical loads, demand response unit price and distribution network power flow topology of rural microgrids. Based on the normalized power curve of typical loads and demand response unit price, determine the active power and reactive power of each node load, and extract the standard power flow calculation nodes in combination with the distribution network power flow topology. Specifically, the analysis involves obtaining the operational characteristics and scale distribution trends of rural loads. Operational characteristics include identifying prevalent electricity consumption patterns based on user habits, analyzing these patterns as typical days, and determining the quarterly power curves for each load. Scale distribution trends include the active and reactive power of each load area at each node in the distribution network. The analysis also includes the load's adjustability, hourly electricity volume available for demand response, and the corresponding demand response unit price. Load adjustability refers to the load's potential to actively adjust its power upwards or downwards without affecting its own production targets, which can then be used for subsequent peak-shaving control. This adjustment process is called demand response. During demand response, because the load's self-regulation can alleviate the grid's peak-shaving pressure, a certain price compensation mechanism is provided, based on the demand response unit price, i.e., the price per kilowatt-hour. A deep survey of typical rural loads was conducted. Typical loads are defined as various sub-loads that account for more than 30% of the annual electricity consumption of industrial, agricultural, residential, and charging pile loads in rural load composition. The normalized power operation curves of typical rural loads in different seasons are used to standardize load curves by dividing the typical load power curve of each type of typical load by the maximum power curve value.

[0021] The system acquires characteristic curves for 15 typical rural loads, including agricultural irrigation and drainage, aluminum industry, cement production, and heavy calcium carbonate plants, forming a typical rural load database with various normalized curves. It also supports expanding the database through an Excel input interface, which allows defining typical load types and quarterly power normalized curves. The system then synthesizes rural loads using the curves in the database.

[0022] For scenarios where only electricity price lists are available, load characteristic curves for different seasons are generated using key information from these lists. The synthesis approach involves obtaining the electricity consumption or price for loads in peak, flat, valley, peak-peak, and deep valley conditions, along with the time and time period for each condition. This allows for the statistical calculation of daily electricity consumption duration for each condition, followed by dividing the electricity consumption by the duration to obtain the average operating power. Finally, matching the average operating power for each condition to the corresponding time period yields the quarterly 24-hour load curve for that load enterprise. For loads in specific scenarios, hourly output data is input to obtain the operating curve for that specific load. Finally, by combining the maximum power of various loads with their respective normalized output curves, the overall rural load curve is synthesized, enabling the export of standardized output files.

[0023] S2, based on standard power flow calculation nodes, establish the active power balance equation and reactive power balance equation of the distribution network to solve the power flow distribution of the original rural distribution network before the penetration of new energy, obtain the per-unit voltage value of each node, and determine the system constraints of the distribution network. Specifically, by performing parameter equivalence through the rural power distribution network topology, the branch electrical parameters associated with the standard power flow calculation nodes are extracted. Active power balance equations and reactive power balance equations are established for PQ nodes and PV nodes. The power deviation of each standard power flow calculation node is calculated using the Newton-Raphson method until the power deviation meets the preset convergence accuracy, and then the per-unit voltage value of each node is output.

[0024] Distinguishing between PQ nodes, PV nodes, and slack nodes in the power flow system, system equations are formulated for PQ and PV nodes, and the power flow is solved iteratively using the Newton-Raphson method. In PQ nodes, voltage and phase angle are system variables; in PV nodes, reactive power and voltage phase angle are system variables. Therefore, calculating the changes in the corresponding system variables using the Newton-Raphson method, superimposing the initial values ​​for iteration, and calculating the function values ​​of the variables after iteration is the key process in power flow calculation. To solve for the changes in system variables, it is necessary to take the partial reciprocal of the function of the system variables with respect to the corresponding variables, and then use matrix multiplication to obtain the changes in all variables. The matrix containing the partial reciprocals is the Jacobian matrix, which can be calculated by combining system power, voltage, and phase angle.

[0025] A power flow-based distribution network function named `powerflow` is constructed using Matlab. The input parameters include: `aa` (active and reactive power matrices injected into each node), `Node_Data_value` (active, reactive, voltage amplitude, and voltage phase matrices of each node), `Plant_Data` (branch inductance, reactance, susceptance, transformer ratio, branch start node, and branch end node matrices), `Data,gnd` (ground susceptance and ground resistance matrices of each node), and `alternative_rule` (node ​​numbering substitution rule matrix). The output is a Boolean variable indicating whether the per-unit voltage of a node is within the range of 0.95 to 1.05. If the node voltage is within the normal range, `flag_manzu` is 1; otherwise, `flag_manzu` is 0. The specific distribution network function is shown below: flag_manzu=powerflow(aa,Node_Data_value,Plant_Data,gnd,alternative_rule) S3. Obtain the node renewable energy injection power matrix. Using the system constraints of the distribution network as the boundary and maximizing the renewable energy capacity of each node in the distribution network as the objective, the particle swarm optimization algorithm is used to iteratively update the renewable energy capacity of each node to obtain the maximum renewable energy access capacity of each node in the rural microgrid. The node renewable energy injection power matrix is ​​the renewable energy power generation matrix data at the node and is one of the contents of the standardized data collection template.

[0026] Using the particle swarm optimization algorithm, the renewable energy capacity of each node is increased at different nodes. The aa matrix is ​​adjusted to convert the active and reactive power of each node, with the optimization objective of maximizing the total renewable energy access capacity of each node. The system constraint is that the per-unit voltage value of the nodes is in the range of 0.95 to 1.05 during the power flow calculation, and the maximum renewable energy access capacity of each node is determined.

[0027] Particle Swarm Optimization (PSO) originates from the simulation of bird flock foraging behavior. It seeks the optimal solution through cooperation and information sharing among individuals in the group. Its core principle is that the solution to each optimization problem is a particle in the search space. The particle iteratively updates its position and velocity, moving closer to the best particle in the group and its own historical best position.

[0028] Initialize a randomly generated particle swarm (including position and velocity). The initial position population is the power corresponding to the renewable energy access capacity of each node. Set the particle swarm size to 100 individuals, the maximum number of iterations to 150, the learning factor (usually 2), and the inertia weight (to control the influence of historical velocity). For fitness evaluation, the objective function is set to the total installed capacity of renewable energy at each node of the distribution network, and the objective function value for each particle is calculated. If the voltage exceeds the limit (flag_manzu is 0), iteratively update the over-limit individuals until the voltage requirement is met. Update the extreme values ​​according to the optimal fitness (the larger the installed capacity, the better the fitness), and record the individual's optimal position (the particle's own historical best) and the global optimal position (the swarm's best). Iteratively optimize and update the particle velocity, then update the new position population, repeating the evaluation and update until the maximum number of iterations or convergence accuracy is reached, obtaining the renewable energy capacity of each node corresponding to the global optimal position, i.e., the maximum renewable energy access capacity of each node in the rural microgrid.

[0029] The following are specific examples illustrating the above embodiments: Taking a rural multi-point grid connection upgrade scheme for a certain project as an example, following the above implementation method, data such as the typical daily electricity purchase and sale price, demand response price, hourly photovoltaic power generation, hourly load power consumption, permitted power exchange with the grid, and hourly wind power generation in the village are first collected to form a database of basic data such as the typical daily basic load. The resulting data is shown in Table 1 below: Table 1. Basic data such as typical daily basic load.

[0030] Considering the output of multiple power sources such as existing distributed photovoltaic, decentralized wind power, and energy storage, and under the capacity constraints of the upstream core transformer, the overall system performance is as follows: the total annual power generation of new energy is 498,332,400 kWh, the curtailment rate of new energy is 23.29%, the total annual electricity consumption of rural load is 253,312,900 kWh, the power transmission volume is 179,766,300 kWh, and the penetration rate of new energy is 88.27%.

[0031] Power flow calculations were performed on the system, distinguishing between PQ nodes, PV nodes, and slack nodes. System equations were formulated for PQ and PV nodes, and the Newton-Raphson method was used iteratively to solve the power flow problem. Calculations showed that the total renewable energy capacity that can be integrated into the entire power flow system is 10.232 per-unit values. After the renewable energy capacity is maximized, the voltage distribution of each node changes. With the introduction of renewable energy, the voltage of each power flow node can be maintained between 0.95 and 1.05 pu, and no node voltage exceeding the limit has been observed.

[0032] Finally, based on hourly power absorption analysis and the limiting conditions for the maximum accessibility of renewable energy sources for power flow, the calculation results are as follows: Before the integration of renewable energy, the existing renewable energy projects in Fuping County inevitably resulted in power curtailment within the county due to the mismatch between rural load and renewable energy output timing. Before the integration, the system's existing renewable energy total annual power generation was 498,332,400 kWh, with a renewable energy curtailment rate of 23.29%. The total annual electricity consumption of rural loads was 253,312,900 kWh, and the power transmission volume to other regions was 179,766,300 kWh, with a renewable energy penetration rate of 88.27%.

[0033] Given the insufficient carrying capacity within existing county-level areas, the maximum access capacity optimization method for rural microgrids provided in the above embodiments is used for calculation. With a maximum photovoltaic capacity of 300MWp, a maximum wind power capacity of 300MW, a maximum energy storage capacity of 300MW, and a maximum operating hours of 2 hours, and a cost environment where the cost per MWp for photovoltaic power is 2.7 yuan / Wp, the cost per MWp for wind power is 4.1 yuan / W, and the cost per Wh for energy storage is 0.8 yuan, the optimal configuration can access 30MW of wind power capacity, 0MWp of photovoltaic power, and 20MW / 20MWh of energy storage capacity. After the optimal renewable energy capacity configuration, the total annual renewable energy generation is 588,010,400 kWh, the renewable energy curtailment rate is 20.88%, the total annual rural load electricity consumption is 253,312,900 kWh, the power transmission volume is 221,872,000 kWh, and the renewable energy penetration rate is 97.91%. The renewable energy curtailment rate can be reduced by approximately 2.41%, and the renewable energy penetration rate can be increased by approximately 9.64%, showing a significant improvement in renewable energy penetration.

[0034] like Figure 2 As shown, this embodiment also provides a rural microgrid maximum access capacity optimization system integrating demand response, including: The standard power flow calculation node extraction module is used to obtain the normalized power curve of typical loads, demand response unit price and distribution network power flow topology of rural microgrids. Based on the normalized power curve of typical loads and demand response unit price, the active power and reactive power of each node load are determined, and the standard power flow calculation node is extracted in combination with the distribution network power flow topology. The system constraint determination module is used to establish the active power balance equation and reactive power balance equation of the distribution network based on the standard power flow calculation node, solve the power flow distribution of the original rural distribution network before the penetration of new energy, obtain the per-unit voltage value of each node, and determine the system constraint conditions of the distribution network. The renewable energy access capacity optimization module is used to obtain the renewable energy injection power matrix of the nodes. Taking the system constraints of the distribution network as the boundary and the goal of maximizing the renewable energy capacity of each node of the distribution network, the particle swarm optimization algorithm is used to iteratively update the renewable energy capacity of each node to obtain the maximum renewable energy access capacity of each node of the rural microgrid.

[0035] Based on the rural microgrid maximum access capacity optimization system with integrated demand response provided in the above embodiments, the following example illustrates the configuration of a project's original system: 2kW residential photovoltaic (40 units, totaling 80kW), 200kW centralized photovoltaic array, 50kW small wind power, 150kWh energy storage module, and 15 5.5kW irrigation centrifugal water pumps.

[0036] First, the scale and characteristics of typical local loads are collected, mainly including the usage time and real-time power of various loads, such as household lighting, refrigerators, microwave ovens, and irrigation equipment. Known loads include 15 centrifugal irrigation pumps of 5.5kW each; household / public loads: 80 households (peak load approximately 120kW), and the village committee's 3kW public load. Inputting the names and corresponding parameters of each load generates typical load curves. Relevant data for the region is collected, including electricity purchase and sale prices, renewable energy price discounts, existing renewable energy installed capacity, energy storage costs, wind power costs, photovoltaic costs, renewable energy hourly power generation, and load hourly power consumption. The project's grid structure is also identified. The maximum renewable energy capacity that can be connected to the project is calculated using power flow calculation. Simulation is performed by inputting the increased photovoltaic power, increased energy storage power, increased wind power, and increased energy storage hours. Comparisons of power generation, curtailment rate, and renewable energy penetration rate before and after optimization are obtained. In this embodiment, the project can add 20kW of residential photovoltaic power, 10kW of small wind power, and 150kWh of energy storage.

[0037] The module division in this embodiment of the invention is illustrative and represents only one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in the various embodiments of the invention can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0038] This embodiment also provides a computer device, which includes a processor and a memory. The memory is used to store a computer program (in this embodiment, the computer program includes a computing component and an iterative component, capable of model calculation and model updating). The computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and it is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to realize the corresponding method flow or corresponding function. The processor of this embodiment can be used to operate a method for optimizing the maximum access capacity of a rural microgrid that integrates demand response.

[0039] This embodiment also provides a storage medium, specifically a computer-readable storage medium (Memory). A computer-readable storage medium is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the rural microgrid maximum access capacity optimization method with integrated demand response described in the above embodiment.

[0040] This embodiment also provides a computer program product, which includes a computer program that, when executed by a processor, implements the corresponding steps of the rural microgrid maximum access capacity optimization method with integrated demand response described in the above embodiment.

[0041] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0042] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0043] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0044] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0045] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing the maximum access capacity of rural microgrids that integrates demand response, characterized in that, Includes the following steps: Obtain the normalized power curve of typical loads, demand response unit price, and distribution network power flow topology of rural microgrids. Based on the normalized power curve of typical loads and demand response unit price, determine the active and reactive power of each node load, and extract the standard power flow calculation nodes in combination with the distribution network power flow topology. Based on the standard power flow calculation nodes, the active power balance equation and reactive power balance equation of the distribution network are established, the power flow distribution of the original rural distribution network before the penetration of new energy is solved, the per-unit voltage value of each node is obtained, and the system constraints of the distribution network are determined based on the per-unit voltage value of each node. The renewable energy injection power matrix of each node is obtained. Taking the system constraints of the distribution network as the boundary and the goal of maximizing the renewable energy capacity of each node of the distribution network, the renewable energy capacity of each node is iteratively updated using the particle swarm optimization algorithm to obtain the maximum renewable energy access capacity of each node of the rural microgrid.

2. The method for optimizing the maximum access capacity of a rural microgrid with integrated demand response as described in claim 1, characterized in that, The steps for determining the active and reactive power of each node load based on the normalized power curve of the typical load and the demand response unit price specifically include: Extract the quarterly 24-hour normalized power ratio of various typical loads in the rural microgrid from the normalized power curve of the typical load, and combine it with the maximum power of various typical loads in the quarterly 24-hour period to calculate the basic active power value of various loads at different times. Load adjustment priorities are assigned based on the demand response unit price, and the active power of each node load is calculated using the active power base values ​​of the various types of loads at different times. The reactive power of each node load is then calculated in conjunction with the preset power factor.

3. The method for optimizing the maximum access capacity of a rural microgrid with integrated demand response as described in claim 2, characterized in that, The steps for extracting standard power flow calculation nodes based on the power flow topology diagram of the distribution network specifically include: Identify the physical connection nodes in the power flow topology diagram of the distribution network and remove redundant nodes without actual electrical functions. The physical connection nodes include: active power load nodes, reactive power load nodes, nodes to be connected to new energy sources, power supply nodes and upstream substation nodes. The active power load nodes, reactive power load nodes, and nodes to be connected to new energy sources are classified as load nodes, the power supply nodes are classified as generator nodes, and the upstream substation nodes are classified as balancing nodes. The load nodes, generator nodes, and balancing nodes are used as standard power flow calculation nodes.

4. The method for optimizing the maximum access capacity of a rural microgrid with integrated demand response as described in claim 3, characterized in that, Based on the aforementioned standard power flow calculation nodes, the steps of establishing the active power balance equation and reactive power balance equation of the distribution network, solving the power flow distribution of the original rural distribution network before the penetration of new energy sources, and obtaining the per-unit voltage values ​​of each node specifically include: The branch electrical parameters associated with the standard power flow calculation nodes are collected, and active power balance equations and reactive power balance equations are established for the load nodes and generator nodes. The power deviation of each standard power flow calculation node is calculated using the Newton-Raphson method until the power deviation meets the preset convergence accuracy, and then the per-unit voltage value of each node is output.

5. The method for optimizing the maximum access capacity of a rural microgrid with integrated demand response as described in claim 4, characterized in that, The system constraints of the distribution network are: the per-unit voltage of each node is 0.95~1.

05.

6. The method for optimizing the maximum access capacity of a rural microgrid with integrated demand response as described in claim 4, characterized in that, The steps of iteratively updating the renewable energy capacity of each node using the particle swarm optimization algorithm to obtain the maximum renewable energy access capacity of each node in the rural microgrid specifically include: A particle swarm is randomly generated. The initial position population is the power corresponding to the access capacity of new energy sources at each node. The objective function is the total installed capacity of new energy sources at each node of the distribution network. If the system constraints are met, the fitness value of the particle is calculated; otherwise, the particle position is readjusted. Record the position with the highest fitness value for each particle during the iteration process and the position with the highest fitness value for the entire particle swarm. Simultaneously, perform iterative updates until the maximum number of iterations is reached to obtain the renewable energy capacity of each node corresponding to the globally optimal position, which is the maximum renewable energy access capacity of each node in the rural microgrid.

7. A rural microgrid maximum access capacity optimization system integrating demand response, characterized in that, include: The standard power flow calculation node extraction module is used to obtain the normalized power curve of typical loads, demand response unit price and distribution network power flow topology of rural microgrids. Based on the normalized power curve of typical loads and demand response unit price, the active power and reactive power of each node load are determined, and the standard power flow calculation node is extracted in combination with the distribution network power flow topology. The system constraint determination module is used to establish the active power balance equation and reactive power balance equation of the distribution network based on the standard power flow calculation node, solve the power flow distribution of the original rural distribution network before the penetration of new energy, obtain the per-unit voltage value of each node, and determine the system constraint conditions of the distribution network based on the per-unit voltage value of each node. The renewable energy access capacity optimization module is used to obtain the renewable energy injection power matrix of the nodes. Taking the system constraints of the distribution network as the boundary and the goal of maximizing the renewable energy capacity of each node of the distribution network, the particle swarm optimization algorithm is used to iteratively update the renewable energy capacity of each node to obtain the maximum renewable energy access capacity of each node of the rural microgrid.

8. An electronic device, characterized in that, The device includes a memory, a processor, and a computer program stored in the memory and executable in the processor, wherein the processor executes the computer program to implement the steps of the method for optimizing the maximum access capacity of a rural microgrid with integrated demand response as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method for optimizing the maximum access capacity of a rural microgrid with integrated demand response as described in any one of claims 1 to 6.

10. A computer program product, the computer program product comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for optimizing the maximum access capacity of a rural microgrid with integrated demand response as described in any one of claims 1 to 6.

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