Source-load interaction cooperative regulation and optimization method, system and device for high-proportion new energy access and medium

Abstract

The invention discloses a high-proportion new energy access-oriented source-load interaction cooperative regulation and control and optimization method, system, device and medium, and belongs to the field of power system operation and optimization control, and the method comprises the steps: collecting data, and carrying out the preprocessing of the collected data; constructing a source-load interaction cooperative regulation and control index system, and determining a core evaluation index; establishing a source-load interaction cooperative regulation and control model, and combining a multi-objective optimization algorithm to realize dynamic matching of new energy output and load demand; according to the method, links of data preprocessing, multi-dimensional index system construction, collaborative regulation and control model establishment, intelligent algorithm solution and effect verification are integrated, and through collaborative regulation and control of new energy power generation and load power utilization, the optimal regulation and control strategy is obtained through solution of the regulation and control model, and the effectiveness and practicability of the regulation and control optimization method are evaluated. The new energy consumption rate is effectively improved, the power grid operation risk is reduced, and scientific and effective technical support is provided for power grid operation management under high-proportion new energy access.

Description

Technical Field

[0001] This invention relates to the field of power system operation and optimization control, specifically to a source-load interactive coordinated regulation and optimization method, system, equipment, and medium for high-proportion renewable energy access. Background Technology

[0002] With the increasing proportion of renewable energy sources such as wind and solar power, the operating characteristics of the power system have changed significantly. The intermittent and random nature of renewable energy output poses challenges to system balancing and scheduling. The traditional "source follows load" model can no longer meet the flexibility requirements of high-proportion renewable energy systems.

[0003] In recent years, the participation of load-side resources has been continuously increasing, making "source-load interaction" an important means to improve system flexibility. Through coordinated source-load regulation, the system's ability to absorb new energy sources can be improved without relying on a large amount of reserve capacity.

[0004] Currently, existing research and applications still have the following shortcomings: existing regulation strategies are mostly focused on the power generation side, neglecting the spatiotemporal characteristics and comfort constraints of the load side response; there is a lack of a unified modeling and optimization framework for source-load interaction coordination, making it difficult to achieve system-level global optimization; there is a lack of systematic research on multi-timescale and multi-entity collaborative mechanisms; optimization objectives are often limited to economic efficiency or energy balance, lacking a comprehensive evaluation mechanism that considers flexibility and the capacity for renewable energy absorption.

[0005] To address the aforementioned issues, this invention proposes a source-load interaction coordinated regulation and optimization method for high-proportion renewable energy integration. This method is of great significance for improving renewable energy absorption capacity, enhancing grid operation stability, reducing the overall cost of the power system, and promoting the construction of new power systems. This invention aims to comprehensively and dynamically coordinate generation-load resources by constructing a full-process technical framework encompassing "data fusion, indicator construction, model optimization, algorithm solution, and effect verification." This provides grid dispatch with a coordinated regulation scheme that combines security, economy, and flexibility, thereby promoting the efficient implementation of the source-load interaction mechanism and the flexible and intelligent operation of the power system. Summary of the Invention

[0006] In view of the above-mentioned problems, the present invention is proposed.

[0007] Therefore, the purpose of this invention is to improve the limitations and deficiencies of current source-load interaction and collaborative regulation and optimization methods.

[0008] To address the aforementioned technical problems, this invention provides the following technical solution: a method for source-load interaction and coordinated regulation and optimization for high-proportion renewable energy access, comprising, High-proportion renewable energy operation data were collected and preprocessed; a source-load interaction and coordinated control index system was constructed, and core evaluation indicators were determined; a source-load interaction and coordinated control model was established to dynamically match renewable energy output with load demand; an improved particle swarm optimization algorithm was used to solve the control model to obtain the optimal control strategy; the effectiveness and practicality of the control optimization method were evaluated through simulation verification and real-world scenario testing.

[0009] As a preferred embodiment of the source-load interactive coordinated regulation and optimization method for high-proportion renewable energy access described in this invention, the collection of high-proportion renewable energy operation data includes: Data types and collection periods; Collect data on high proportions of new energy power generation, load-side electricity consumption data, and basic information on power grid operation.

[0010] As a preferred embodiment of the source-load interaction and coordinated regulation and optimization method for high-proportion new energy access described in this invention, the preprocessing includes: completing missing data; for missing data at any time, calculating the interpolated value and completing it. A hybrid approach is used to identify and correct outliers. Initial outliers are identified, further filtered, and final outliers are determined and corrected.

[0011] As a preferred embodiment of the source-load interaction and coordinated regulation and optimization method for high-proportion renewable energy access described in this invention, the determination of core evaluation indicators includes: Determine the dimensions of new energy consumption and calculate the new energy consumption rate and wind and solar curtailment rate; Determine the load regulation dimensions and calculate the load response rate and load fluctuation mitigation rate; Determine the power grid security dimensions and calculate the node voltage deviation rate and line power over-limit rate; By combining these three dimensions, the core evaluation indicators were determined.

[0012] As a preferred embodiment of the source-load interaction coordinated regulation and optimization method for high-proportion renewable energy access described in this invention, the establishment of the source-load interaction coordinated regulation model includes using the maximization of renewable energy absorption rate, the minimization of grid operation cost, and the minimization of load regulation cost as multi-objective functions, and determining the weights of each objective through the analytic hierarchy process (AHP); wherein, the objective function for maximizing the renewable energy absorption rate is: The objective function for minimizing the power grid operating cost is: in, For power grid transmission costs, For grid power loss costs, For the cost of curtailing renewable energy, For the first Average transmission power of the lines To calculate the period duration, For the first The unit power transmission cost of the line For the total power loss of the power grid, For the unit cost of electricity, For the abandoned electricity of new energy sources, The amount of subsidy for new energy units. For the actual electricity consumed by new energy sources, This represents the total power generation from new energy sources. M represents the renewable energy absorption rate, and M represents the total number of power lines. The objective function for minimizing load regulation costs is: in, To reduce industrial load adjustment costs, To adjust costs for commercial load, To adjust the cost of residential load, Regulate electricity for industrial loads. Adjusting costs per unit of industrial load, Regulating electricity consumption for commercial loads Adjusting costs per commercial load unit For residential load during time period Electricity consumption For time period Time-of-use electricity pricing The benchmark electricity price; Set constraints, including power balance constraints, renewable energy output constraints, traditional power output constraints, and grid security constraints.

[0013] The beneficial effects of the preferred technical solution in the embodiments of the present invention are as follows: through multi-objective optimization and constraints, dynamic matching between renewable energy output and load demand is achieved, thereby improving the renewable energy absorption rate and reducing the grid operating cost.

[0014] As a preferred embodiment of the source-load interaction and coordinated regulation and optimization method for high-proportion renewable energy access described in this invention, the optimal regulation strategy includes introducing adaptive inertia weights and a cross-mutation mechanism, wherein the adaptive inertia weights... The inertia weight is dynamically adjusted using a linear decreasing strategy, increasing the weight in the early stages of iteration and decreasing it in the later stages of iteration. in, and These are the maximum and minimum inertia weights, respectively. To preset the maximum number of iterations, This represents the current iteration number; The crossover and mutation mechanism draws inspiration from the gene recombination concept in genetic algorithms, where the crossover operation employs a single-point crossover strategy, randomly selecting two particles. , and 1 intersection position The gene fragments after exchanging the crossover positions of two particles are specifically: ,in, For cross-particles The Location and subsequent genes, For cross-particles The Location and subsequent genes; The mutation operation involves making small perturbations to random gene loci of particles, specifically: ,in, For particles The Allogeneic, for A random number within a given range; Initialize the particle swarm and set the number of particles. Each particle corresponds to a complete set of control strategies, defining a position vector; simultaneously, the particle velocity vector is initialized. The weighted summation method is used to transform the multi-objective function into a single-objective fitness function to calculate the particle fitness; Simultaneously record the individual optimal solution for each particle. and the global optimal solution Based on the individual optimal solution and the global optimal solution, and combined with adaptive inertia weights, the particle velocity and position are updated. After the update, the... Boundary checks are performed to ensure that all genes meet the constraints; crossover and mutation operations are performed on the updated particle population to generate a new population; if the maximum number of iterations is reached, the global optimal solution, i.e. the optimal control strategy, is output.

[0015] The preferred technical solution in the embodiments of the present invention has the following beneficial effects: the introduction of adaptive inertia weight and crossover mutation mechanism enhances the global search capability and convergence speed of the algorithm, and obtains a better control strategy.

[0016] As a preferred embodiment of the source-load interaction and coordinated regulation and optimization method for high-proportion renewable energy integration described in this invention, the evaluation of the effectiveness and practicality of the regulation and optimization method includes: building a power grid simulation model with a high proportion of renewable energy based on PSCAD / EMTDC, inputting preprocessed historical data, applying the optimal regulation strategy for simulation, comparing the changes in renewable energy absorption rate, power grid operating costs, and load fluctuations before and after regulation, and evaluating the effectiveness and improvement rate of the method by calculating the improvement rate of core indicators in simulation and actual testing. The calculation formula is: in, , These are the indicator values ​​before and after the regulation.

[0017] The beneficial effects of the preferred technical solution in the embodiments of the present invention are as follows: by comparing the changes in core indicators before and after regulation, the effectiveness and practicality of the evaluation method are quantified, ensuring the reliability of the regulation and optimization method in practical applications.

[0018] Another objective of this invention is to provide a source-load interactive coordinated regulation and optimization system for high-proportion renewable energy access.

[0019] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a source-load interaction and coordinated regulation and optimization system for high-proportion renewable energy access, comprising: a data processing module for collecting high-proportion renewable energy operation data and preprocessing the collected data; The indicator determination module constructs an indicator system for source-load interaction and coordinated regulation, and determines the core evaluation indicators. The matching module establishes a source-load interaction and coordinated control model to dynamically match the output of new energy sources with the demand for load. The solution module uses an improved particle swarm optimization algorithm to solve the control model and obtain the optimal control strategy. The evaluation module assesses the effectiveness and practicality of the control and optimization methods through simulation verification and real-world scenario testing.

[0020] The present invention provides a computer device, including 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 source-load interaction and coordinated regulation and optimization method for high proportion of new energy access.

[0021] The present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of the source-load interaction and coordinated regulation and optimization method for high-proportion renewable energy access.

[0022] The beneficial effects of this invention are as follows: This invention constructs a source-load interactive and coordinated regulation model, using the renewable energy absorption rate and wind / solar curtailment rate as core evaluation indicators, and combines a multi-objective optimization algorithm to achieve dynamic matching between renewable energy output and load demand. It optimizes the output allocation logic of renewable energy sources such as photovoltaics and wind power, and guides industrial, commercial, and residential loads to proactively adapt to the fluctuation patterns of renewable energy. This "source-load two-way coordination" model breaks through the limitations of traditional technologies that "only optimize the renewable energy side or only adjust the load side," enabling more efficient absorption of intermittent renewable energy, significantly reducing clean energy waste, and significantly improving the overall efficiency and stability of renewable energy absorption.

[0023] This invention achieves a cost advantage through a three-pronged approach to reduce costs. First, it reduces economic losses from renewable energy curtailment. Second, it replaces frequent ramp-up and start-up / shutdown operations of traditional power sources with load-side regulation, lowering the operation and maintenance costs and energy consumption of traditional power sources. Third, it guides load regulation through economic means such as time-of-use pricing, precisely controlling additional costs during the load regulation process. Compared to the high costs caused by excessive reliance on traditional power source regulation in existing technologies, this invention effectively reduces the overall cost of power grid operation while avoiding the risk of cost runaway in traditional load regulation methods, thus improving the economic operation of the power system.

[0024] This invention establishes a dynamic control mechanism with node voltage deviation rate and line power over-limit rate as core safety indicators. When renewable energy output fluctuates drastically or load changes suddenly, the allocation ratio of renewable energy output and the adjustment of various loads can be adjusted in real time to ensure that the grid node voltage remains within a safe range and that the line transmission power does not exceed the rated capacity. Compared with existing technologies that are difficult to respond quickly to fluctuations and prone to voltage anomalies or line overloads, this invention effectively avoids safety risks such as equipment damage and grid tripping, significantly enhancing the safety and reliability of power system operation.

[0025] This invention designs differentiated regulation strategies based on the characteristics of different types of loads and uses load response rate and load fluctuation mitigation rate as evaluation criteria to accurately tap the regulation potential of various load types. This "classification-guided + precise control" model solves the problems of insufficient utilization of load-side resources and limited regulation effects in existing technologies. It can significantly improve the overall regulation capacity on the load side and fully activate the flexible regulation value of the load side by ensuring the balance between regulation effects and user experience while guaranteeing the basic electricity needs of users, thereby alleviating the pressure on power grid supply and demand balance. Attached Figure Description

[0026] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0027] Figure 1 The above is a flowchart of a source-load interaction and coordinated regulation and optimization method for high-proportion renewable energy access, provided as an embodiment of the present invention.

[0028] Figure 2 This is a data preprocessing flowchart of a source-load interaction and coordinated regulation and optimization method for high-proportion renewable energy access, provided as an embodiment of the present invention.

[0029] Figure 3 The flowchart of an improved particle swarm optimization algorithm for source-load interaction and coordinated regulation and optimization method for high-proportion renewable energy access is provided in one embodiment of the present invention. Detailed Implementation

[0030] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0031] Example 1, referring to Figures 1-3 This is one embodiment of the present invention, which provides a source-load interaction and coordinated regulation and optimization method for high-proportion renewable energy access, including: S100: Collect high-proportion new energy operation data and preprocess the collected data; S200, Construct a source-load interaction and coordinated regulation indicator system and determine core evaluation indicators; S300: Establish a source-load interaction and coordinated control model to dynamically match new energy output with load demand; S400. An improved particle swarm optimization algorithm is used to solve the control model to obtain the optimal control strategy. S500 evaluates the effectiveness and practicality of the control and optimization methods through simulation verification and real-world scenario testing. It should be noted that, in the context of high-proportion renewable energy integration, existing technologies often suffer from problems such as weak data foundation, single evaluation dimensions, rigid control models, optimization algorithms that are prone to getting trapped in local optima, and a lack of sufficient simulation and practical verification. These problems lead to difficulties in renewable energy consumption, poor grid operation economy, and low source-load interaction efficiency.

[0032] Therefore, in response to the above-mentioned problems, a systematic, adaptive, and multi-objective optimized source-load interaction and coordinated control method was realized through the steps of S100-S500. This method effectively solved the supply and demand matching problem under the high proportion of renewable energy access, significantly improved the renewable energy consumption level, and ensured the safe, stable, and economical operation of the power grid.

[0033] Example 2, refer to Figures 1-3 This is one embodiment of the present invention, which provides a source-load interaction and coordinated regulation and optimization method for high-proportion renewable energy access, including: In this embodiment of the invention, S100 involves collecting high-proportion renewable energy operation data and preprocessing the collected data, including the following steps S101-S102: S101. Data type and cycle of data collection: The data collection cycle is 5 minutes per interval; The high proportion of new energy power generation data includes real-time output data of photovoltaic power plants and wind farms over the past month; Load-side electricity consumption data includes historical power consumption data for industrial load, commercial load, and residential load; Basic information on power grid operation includes power line transmission capacity, node voltage limits, transformer rated capacity, time-of-use pricing policy, and new energy subsidy standards.

[0034] S102. Preprocess the collected data; In an embodiment of the present invention, missing value completion includes the following steps A1-A2: A1. Use cubic spline interpolation (CSI) to complete the missing data; A2. For a certain moment For the missing data, let the times of the nearest valid data points to the left and right be respectively. and The corresponding values ​​are respectively and The interpolated value is calculated using the following formula. : in, , , , The coefficients are the cubic spline interpolation coefficients, which satisfy... and The function value at time t is obtained by solving the continuity condition of the first derivative, ensuring that the interpolation curve is smooth and closely matches the actual data change trend.

[0035] In an optional implementation, missing value completion in S102 can be performed by linear interpolation. For missing data at a certain moment, the two nearest valid data points on the left and right are located first, and the corresponding time and value are recorded. These two valid data points are connected by a straight line, and the interpolated value is directly calculated based on the linear proportional relationship of the missing time on the straight line to ensure that the interpolation result is continuous with the adjacent data points. However, it cannot effectively capture the non-linear change trend of the data.

[0036] In another optional implementation, missing value completion in S102 can also be achieved by moving average. For missing data at a certain moment, all valid data points within the time window before and after it are taken, and the arithmetic mean of these valid data points is calculated. This average is used as the completion value for the missing data. However, this may over-smooth the data and ignore short-term fluctuations.

[0037] In an embodiment of the present invention, outlier correction includes the following steps B1-B3: B1. Adopting a 3D-based approach A hybrid approach combining principles and the Isolation Forest algorithm is used to identify and correct outliers, specifically by calculating the mean of the data sequence. and standard deviation This will exceed [ , Data within a certain range are marked as preliminary outliers; B2. Construct an isolated forest model to further filter out preliminary outliers and determine the final outliers; B3. Correct the outlier using the historical average data for the same period in the time frame where the outlier occurred, using the following formula: in, These are the corrected values. This refers to the number of valid data points within the same time period over the past three months. For the first Historical data for the same period.

[0038] In an optional implementation, outlier correction in S102 can be based on a hybrid of IQR and LOF. I calculates the first quartile (Q1) and the third quartile (Q3) of the data sequence to obtain the IQR (i.e., Q3-Q1). Data below Q1-1.5×IQR or above Q3+1.5×IQR are marked as preliminary outliers. An LOF model is constructed for the preliminary outliers, and an anomaly score is calculated based on local density. Points with scores higher than a threshold are determined as final outliers. For the final outliers, the average of valid data from the same period in the past 3 months is used for replacement. However, this method is inefficient and unstable in large-scale data scenarios.

[0039] In another optional implementation, outlier correction in S102 can also be based on a sliding window and Z-score. Taking the current data point as the center, a time window is taken, the mean and standard deviation of the data within the window are calculated, the Z-score of each data point relative to the window is calculated, and data with an absolute value exceeding 3 are marked as outliers. For outliers, the median of the data within the window or the historical average data of the same period is used to replace them; however, it is poorly adapted to periodic or abrupt changes in the data.

[0040] In this embodiment of the invention, S200 involves constructing a source-load interaction and coordinated regulation index system and determining core evaluation indicators, including the following steps S201-S203: S201. Construct a source-load interaction and coordinated regulation indicator system, and determine the core evaluation indicators from three dimensions: new energy consumption, load regulation, and power grid security. The specific methods are as follows: Selecting the renewable energy consumption rate from the renewable energy consumption dimension and wind and solar curtailment rates As a core indicator; among them, the renewable energy consumption rate and wind and solar curtailment rates The calculation formulas are as follows: in, For the actual electricity consumed by new energy sources, This represents the total power generation from new energy sources. This refers to the amount of electricity wasted due to renewable energy sources.

[0041] S202. Select the load response rate in the load adjustment dimension. and load fluctuation mitigation rate As a core indicator; among them, load response rate and load fluctuation mitigation rate The calculation formulas are as follows: in, The actual power consumption adjusted by the load. To regulate the target power of the load, To regulate the standard deviation of the front load power, To regulate the standard deviation of the afterload power.

[0042] S203. Select node voltage deviation rate in the power grid security dimension. and line power over-limit rate As a core indicator; among them, the node voltage deviation rate and line power over-limit rate The calculation formulas are as follows: in, This represents the actual voltage at the node. The node's rated voltage, The duration during which the line power exceeds its rated capacity. To calculate the total duration.

[0043] In an embodiment of the present invention, a source-load interaction and coordinated control model is established in S300 to dynamically match the output of new energy sources with the load demand, including the following steps S301-S302: S301. Establish a source-load interaction and coordinated control model, and combine it with a multi-objective optimization algorithm to achieve dynamic matching between new energy output and load demand; With the goal of maximizing the renewable energy absorption rate, minimizing the power grid operation cost, and minimizing the load regulation cost as multiple objective functions, the weights of each objective are determined by the analytic hierarchy process. Wherein, the objective function for maximizing the renewable energy absorption rate is: The objective function for minimizing the power grid operating cost is: in, For power grid transmission costs, For grid power loss costs, For the cost of curtailing renewable energy, For the first Average transmission power of the lines To calculate the period duration, For the first The unit power transmission cost of the line For the total power loss of the power grid, For the unit cost of electricity, For the abandoned electricity of new energy sources, The amount of subsidy for new energy units. For the actual electricity consumed by new energy sources, This represents the total power generation from new energy sources. M represents the renewable energy absorption rate, and M represents the total number of power lines. The objective function for minimizing load regulation costs is: in, To reduce industrial load adjustment costs, To adjust costs for commercial load, To adjust the cost of residential load, Regulate electricity for industrial loads. Adjusting costs per unit of industrial load, Regulating electricity consumption for commercial loads Adjusting costs per commercial load unit For residential load during time period Electricity consumption For time period Time-of-use electricity pricing The benchmark electricity price; S302. Set constraints, including power balance constraints, new energy output constraints, traditional power output constraints, and grid security constraints. Among them, ① power balance constraint: in, for New energy sources are always contributing their power. for Traditional power supply for Adjust the afterload power in a timely manner. for Power loss of the power grid at any time.

[0044] ② New energy output constraints: Considering the intermittency of new energy sources, the actual output must be between 0 and the maximum available power. Specifically: in, for The maximum power output of new energy sources at all times for Maximum power output of photovoltaic power at any given time; for Maximum generating capacity of wind power at any given time.

[0045] The formula for the maximum power output of photovoltaic power is: in, Rated power of photovoltaic, for Actual irradiance at any given time This is the standard irradiance.

[0046] The formula for the maximum renewable power output of wind power is: in, for Real-time wind speed To cut into wind speed, Rated wind speed, To cut off the wind speed, This refers to the rated power of the wind power.

[0047] ③ Output constraints of traditional power sources: Traditional power sources are subject to minimum technical output and ramp-up rate limitations, specifically: in, and These represent the minimum and maximum output of a conventional power supply, respectively. and These are the maximum gradeability and the maximum gradient, respectively. This refers to the previous data collection time.

[0048] ④ Load regulation constraints: The regulation range of different types of loads must meet the basic electricity needs of users. Specifically: in, The minimum allowable power for loads of type "type" (industrial, commercial, residential). This represents the maximum allowable power for this type of load.

[0049] ⑤ Power grid safety constraints, ensuring that node voltage and line power are within safe limits, specifically include: in, for Actual voltage at time node and These are the minimum and maximum allowable voltages of the node, respectively, and are each multiplied by 0.95. and 1.05 times ; for Actual transmission power at any given time node This refers to the rated transmission power of the line.

[0050] In this embodiment of the invention, an improved particle swarm optimization algorithm is used in step S400 to solve the control model and obtain the optimal control strategy, including the following steps S401-S402: S401. An improved particle swarm optimization algorithm is used to solve the control model to obtain the optimal control strategy, as detailed below: First, the algorithm is improved. To address the problems of traditional particle swarm optimization algorithms being prone to getting trapped in local optima and having slow convergence speed, an adaptive inertia weight and crossover mutation mechanism are introduced to balance global search and local search capabilities. ① Adaptive Inertia Weights The inertia weights are dynamically adjusted using a linear decreasing strategy. In the early stages of iteration, the weights are increased to enhance the global search, while in the later stages, the weights are decreased to improve the accuracy of the local search. in, and These are the maximum and minimum inertia weights, respectively. To preset the maximum number of iterations, This represents the current iteration number.

[0051] ② Crossover and mutation mechanism: Borrowing the gene recombination idea from genetic algorithms, this mechanism increases population diversity to prevent premature convergence of the algorithm. Crossover operation: A single-point crossover strategy is adopted, randomly selecting 2 particles ( , ) and 1 intersection position The formula for the gene fragments after exchanging the crossover positions of two particles is: in, For cross-particles The Location and subsequent genes, For cross-particles The Location and subsequent genes; Mutation operation: A small perturbation is applied to the random gene loci of the particle, using the following formula: in, For particles The Allogeneic, for A random number within a given range.

[0052] S402. After improving the algorithm, initialize the particle swarm and set the number of particles. Each particle corresponds to a complete set of control strategies, and its position vector is defined as: in, and These represent the proportions of photovoltaic and wind power output in the total output of new energy sources, respectively. For industrial load regulation, For commercial load regulation, For residential load regulation, This is the output adjustment value for traditional power supplies; Simultaneously initialize the particle velocity vector Its value range is , This represents the maximum allowable value for each gene.

[0053] The multi-objective function is transformed into a single-objective fitness function using a weighted summation method, and the particle fitness is calculated: in, , , The weights of the objective function satisfy... ; , These are the theoretical maximum value of renewable energy absorption rate, the historical maximum value of power grid operation cost, and the historical maximum value of load regulation cost, respectively. , , These are the actual values ​​of renewable energy absorption rate, power grid operation cost, and load regulation cost, respectively. Simultaneously record the individual optimal solution for each particle. and the global optimal solution Based on the individual optimal solution and the global optimal solution, and combined with adaptive inertia weight updates, the particle velocity is updated. With position : in, This represents the current iteration number. For adaptive inertia weights, , The first The particle in the first Position vector and velocity vector for each iteration; , These are the learning factors that guide particles to search towards their individual optimal and global optimal directions, respectively. , They are respectively A random number within a given range; After the update, it is necessary to... Perform boundary checks to ensure that all genes meet the constraints.

[0054] ④ Crossover and mutation operations: Perform crossover and mutation operations on the updated particle population to generate a new population, thus avoiding the algorithm from getting trapped in local optima.

[0055] ⑤ Determine the termination condition of the iteration: If the maximum number of iterations is reached, output the global optimal solution, i.e. the optimal control strategy; otherwise, recalculate the particle fitness and continue iterating.

[0056] In an embodiment of the present invention, step S500 evaluates the effectiveness and practicality of the regulation and optimization method through simulation verification and real-world scenario testing, including the following steps S501: In an embodiment of the present invention, S501, simulation verification and real-world scenario testing, includes the following steps C1-C2: C1. Based on PSCAD / EMTDC, build a power grid simulation model with a high proportion of new energy sources, input preprocessed historical data, apply the optimal control strategy for simulation, and compare the changes in new energy absorption rate, power grid operating cost, and load fluctuation before and after control. C2. Select a provincial power grid pilot area, apply the control optimization method to the actual power grid operation, monitor continuously for 1 month, and record daily new energy consumption data, power grid safety indicators and load regulation effects; In an optional implementation, the simulation verification in S501 can be based on MATLAB / Simulink simulation verification. The MATLAB / Simulink software is used to build a power grid simulation model with a high proportion of new energy sources. The model includes photovoltaic, wind power, load and grid components. The preprocessed historical data is input, and the optimal control strategy is applied to perform dynamic simulation. The changes in key indicators before and after control are compared, including the new energy absorption rate, grid operating cost and load fluctuation smoothing rate. A comparison report is generated through Simulink's data analysis tools.

[0057] In another optional implementation, the simulation verification in S501 can also be based on historical data backtesting simulation verification. This involves collecting historical power grid operation data over a period of time, including renewable energy output, load demand, and power grid security parameters; constructing a simplified power grid model in the computing environment; applying the optimal control strategy to backtest the historical data; calculating the changes in indicators after control; analyzing the backtesting results; evaluating the degree of improvement in renewable energy absorption rate, power grid operating costs, and load fluctuations; and statistically comparing the results with the data before control.

[0058] In embodiments of the present invention, evaluating the effectiveness and practicality of the regulation and optimization method includes the following steps D1-D2: D1. Evaluate the effectiveness of the method by calculating the improvement rate of each core indicator in simulation and actual testing. The calculation formula is: in, , These are the indicator values ​​before and after the regulation.

[0059] D2, if If the result is positive (for positive indicators such as renewable energy absorption rate) or negative (for negative indicators such as wind and solar curtailment rate and cost), it indicates that the method is effective.

[0060] In an optional implementation, the effectiveness and practicality of the control optimization method in S501 can be evaluated based on simplified simulation and user feedback. A simplified power grid model can be built using MATLAB / Simulink, preprocessed historical data can be input, and the optimal control strategy can be applied for rapid simulation, focusing on monitoring key indicators such as renewable energy absorption rate and load fluctuation. During the simulation, typical daily scenarios (such as sunny days and rainy days) are introduced for comparative analysis, and the changing trends of indicators before and after control are recorded. A small-scale pilot area (such as an industrial park or community power grid) is selected to actually deploy the control strategy. After one week of operation, user (such as industrial users and residents) feedback on their electricity consumption experience is collected, focusing on the acceptance and satisfaction of load regulation. The practicality and acceptability of the control method are qualitatively evaluated by combining the simulation results and user feedback. However, this implementation method cannot fully reflect the dynamics of a complex power grid, and subjective user feedback is prone to introducing bias, resulting in an insufficiently objective evaluation result.

[0061] In another optional implementation, the effectiveness and practicality of the control optimization method in S501 can also be evaluated based on historical data playback and cost-benefit analysis, but this may ignore the impact of real-time interaction; directly using preprocessed historical operating data, the optimal control strategy is applied through script playback to calculate indicators such as the renewable energy absorption rate and grid operating costs after control; the playback results are compared with the original historical data to analyze the improvement of indicators and calculate cost savings; combined with the actual grid operation log, it is checked whether the control strategy leads to safety events such as voltage overruns or line overloads, and the frequency of event occurrence is recorded; the effectiveness of the method is evaluated through simple ratio analysis; however, the lack of real-time interactive verification makes it impossible to capture sudden situations, and the cost-benefit analysis underestimates the actual risks.

[0062] Example 3 is an embodiment of the present invention. The above is an illustrative scheme of a source-load interaction coordinated regulation and optimization method for high-proportion renewable energy access. It should be noted that the technical solution of a source-load interaction coordinated regulation and optimization system for high-proportion renewable energy access and the technical solution of the above-described source-load interaction coordinated regulation and optimization method for high-proportion renewable energy access belong to the same concept. Details not described in detail in the technical solution of the source-load interaction coordinated regulation and optimization system for high-proportion renewable energy access in this embodiment can be found in the description of the above-described source-load interaction coordinated regulation and optimization method for high-proportion renewable energy access.

[0063] This embodiment provides a source-load interaction and coordinated regulation and optimization system for high-proportion renewable energy access, including: a data processing module, which collects high-proportion renewable energy operation data and preprocesses the collected data; The indicator determination module constructs an indicator system for source-load interaction and coordinated regulation, and determines the core evaluation indicators. The matching module establishes a source-load interaction and coordinated control model to dynamically match the output of new energy sources with the demand for load. The solution module uses an improved particle swarm optimization algorithm to solve the control model and obtain the optimal control strategy. The evaluation module assesses the effectiveness and practicality of the control and optimization methods through simulation verification and real-world scenario testing.

[0064] This embodiment also provides an electronic device applicable to a source-load interaction and coordinated regulation and optimization method for high-proportion renewable energy access, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the source-load interaction and coordinated regulation and optimization method for high-proportion renewable energy access proposed in the above embodiment.

[0065] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a source-load interaction and coordinated regulation and optimization method for high-proportion renewable energy access as proposed in the above embodiment.

[0066] The storage medium proposed in this embodiment and the source-load interaction and coordinated regulation and optimization method proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0067] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0068] 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 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 source-load interaction and coordinated regulation and optimization method for high-proportion renewable energy integration, characterized in that: include, Collect high-proportion renewable energy operation data and preprocess the collected data; Construct a source-load interaction and coordinated regulation indicator system and determine core evaluation indicators; Establish a source-load interaction and coordinated regulation model to dynamically match the output of new energy sources with the demand for loads; An improved particle swarm optimization algorithm was used to solve the control model, and the optimal control strategy was obtained. The effectiveness and practicality of the regulation and optimization methods are evaluated through simulation verification and real-world scenario testing.

2. The source-load interaction and coordinated regulation and optimization method for high-proportion renewable energy access as described in claim 1, characterized in that: The collection of high-proportion new energy operation data includes Data types and collection periods; Collect data on high proportions of new energy power generation, load-side electricity consumption data, and basic information on power grid operation.

3. The source-load interaction and coordinated regulation and optimization method for high-proportion renewable energy access as described in claim 2, characterized in that: The preprocessing includes filling in missing data; for missing data at any given time, calculating the interpolated value and filling it in. A hybrid approach is used to identify and correct outliers. Initial outliers are identified, further filtered, and final outliers are determined and corrected.

4. The source-load interaction and coordinated regulation and optimization method for high-proportion renewable energy access as described in claim 3, characterized in that: The determination of core evaluation indicators includes, Determine the dimensions of new energy consumption and calculate the new energy consumption rate and wind and solar curtailment rate; Determine the load regulation dimensions and calculate the load response rate and load fluctuation mitigation rate; Determine the power grid security dimensions and calculate the node voltage deviation rate and line power over-limit rate; By combining these three dimensions, the core evaluation indicators were determined.

5. The source-load interaction and coordinated regulation and optimization method for high-proportion renewable energy access as described in claim 4, characterized in that: The establishment of the source-load interaction and coordinated regulation model includes using multiple objective functions—maximizing the renewable energy absorption rate, minimizing grid operation costs, and minimizing load regulation costs—and determining the weights of each objective through the analytic hierarchy process (AHP); wherein, the objective function for maximizing the renewable energy absorption rate is: The objective function for minimizing the power grid operating cost is: in, For power grid transmission costs, For grid power loss costs, For the cost of curtailing renewable energy, For the first Average transmission power of the lines To calculate the period duration, For the first The unit power transmission cost of the line For the total power loss of the power grid, For the unit cost of electricity, For the abandoned electricity of new energy sources, The amount of subsidy for new energy units. For the actual electricity consumed by new energy sources, This represents the total power generation from new energy sources. M represents the renewable energy absorption rate, and M represents the total number of power lines. The objective function for minimizing load regulation costs is: in, To reduce industrial load adjustment costs, To adjust costs for commercial load, To adjust the cost of residential load, Regulate electricity for industrial loads. Adjusting costs per unit of industrial load, Regulating electricity consumption for commercial loads Adjusting costs per commercial load unit For residential load during time period Electricity consumption For time period Time-of-use electricity pricing The benchmark electricity price; Set constraints, including power balance constraints, renewable energy output constraints, traditional power output constraints, and grid security constraints.

6. The source-load interaction and coordinated regulation and optimization method for high-proportion renewable energy access as described in claim 5, characterized in that: The optimal control strategy includes introducing adaptive inertia weights and a crossover mutation mechanism, wherein the adaptive inertia weights... The inertia weight is dynamically adjusted using a linear decreasing strategy, increasing the weight in the early stages of iteration and decreasing it in the later stages of iteration. in, and These are the maximum and minimum inertia weights, respectively. To preset the maximum number of iterations, This represents the current iteration number; The crossover and mutation mechanism draws inspiration from the gene recombination concept in genetic algorithms, where the crossover operation employs a single-point crossover strategy, randomly selecting two particles. , and 1 intersection position The gene fragments after exchanging the crossover positions of two particles are specifically: ,in, For cross-particles The Location and subsequent genes, For cross-particles The Location and subsequent genes; The mutation operation involves making small perturbations to random gene loci of particles, specifically: ,in, For particles The Allogeneic, for A random number within a given range; Initialize the particle swarm and set the number of particles. Each particle corresponds to a complete set of control strategies, defining a position vector; simultaneously, the particle velocity vector is initialized. The weighted summation method is used to transform the multi-objective function into a single-objective fitness function to calculate the particle fitness; Simultaneously record the individual optimal solution for each particle. and the global optimal solution Based on the individual optimal solution and the global optimal solution, and combined with adaptive inertia weights, the particle velocity and position are updated. After the update, the... Boundary checks are performed to ensure that all genes meet the constraints; crossover and mutation operations are performed on the updated particle population to generate a new population; if the maximum number of iterations is reached, the global optimal solution, i.e. the optimal control strategy, is output.

7. The source-load interaction and coordinated regulation and optimization method for high-proportion renewable energy access as described in claim 6, characterized in that: The effectiveness and practicality of the proposed control optimization method are evaluated by: building a power grid simulation model with a high proportion of renewable energy sources based on PSCAD / EMTDC; inputting preprocessed historical data; applying the optimal control strategy for simulation; comparing the changes in renewable energy absorption rate, power grid operating costs, and load fluctuations before and after control; and assessing the effectiveness and improvement rate of the method by calculating the improvement rate of core indicators in simulation and actual testing. The calculation formula is: in, , These are the indicator values ​​before and after the regulation.

8. A source-load interactive coordinated regulation and optimization system for high-proportion renewable energy access, employing the source-load interactive coordinated regulation and optimization method for high-proportion renewable energy access as described in any one of claims 1 to 7, characterized in that, include: The data processing module collects high-proportion new energy operation data and preprocesses the collected data. The indicator determination module constructs an indicator system for source-load interaction and coordinated regulation, and determines the core evaluation indicators. The matching module establishes a source-load interaction and coordinated control model to dynamically match the output of new energy sources with the demand for load. The solution module uses an improved particle swarm optimization algorithm to solve the control model and obtain the optimal control strategy. The evaluation module assesses the effectiveness and practicality of the control and optimization methods through simulation verification and real-world scenario testing.

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 source-load interaction and coordinated regulation and optimization method for high-proportion new energy access 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 source-load interaction and coordinated regulation and optimization method for high-proportion new energy access as described in any one of claims 1 to 7.

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