Wind, light, fire and nuclear storage integrated scheduling architecture based on complex and uncertain environment of cold region power grid

Abstract

The invention provides a wind, light, fire and nuclear storage integrated scheduling architecture based on a complex and uncertain environment of a cold region power grid, and relates to the technical field of power grid system processing, the architecture comprises a data acquisition and monitoring layer, an analysis and decision layer, a control execution layer and an evaluation and optimization layer, the anti-freezing and deicing technology of the power transmission line effectively improves the reliability of equipment in severe environments such as low temperature and ice and snow, guarantees the stability of power transmission of a power grid, reduces equipment faults and power failure accidents caused by cold environment factors, enhances the anti-risk capability of the whole power grid, and improves the safety of the whole power grid. According to the method, characteristics and complementary relations of multiple energy sources of wind, light, fire and nuclear storage are comprehensively considered, peak regulation and frequency modulation analysis and decision making and integrated regulation and control analysis and decision making are carried out, and by constructing a reasonable operation scene and a scheduling model, optimal configuration and cooperative operation of power generation resources can be achieved under different operation conditions.

Description

Technical Field

[0001] The present invention relates to the field of power grid system processing technology, and specifically to an integrated wind, solar, thermal, and nuclear storage dispatching architecture based on the complex and uncertain environment of cold-region power grids. Background Art

[0002] According to the cold-region integrated energy optimization dispatching method based on wind energy and solar energy disclosed in China's publication number "CN118117668A", the method includes the following steps: the intelligent control system predicts the energy supply and demand situation in the next 1-3 days according to weather forecast data, and realizes the intelligent dispatching of wind power generation system and solar photovoltaic power generation system through fuzzy control algorithm; the intelligent control system establishes a neural network model to predict the energy consumption in the next 1-3 days through the analysis of historical energy data, and realizes the accurate dispatching of wind power generation system and solar photovoltaic power generation system to improve energy utilization efficiency; the intelligent control system monitors and predicts the load of the power grid, and uses the weather forecast data and historical energy data for prediction and analysis through the intelligent control system, thereby improving the accuracy and precision of future energy supply and demand and energy consumption, which helps to better dispatch wind power generation system and solar photovoltaic power generation system to improve energy utilization efficiency.

[0003] According to the capacity optimization configuration method of wind, solar, thermal and energy storage in the power grid planning disclosed in China's publication number "CN112564183B", computer equipment and programs are used for operation, and the following steps are followed: Step A: Collect historical annual power transmission data, energy output data, and energy storage device data and pre-process them; Step B: Predict the future total power load curve; Step C: Formulate an annual maintenance plan for thermal power units, determine the daily unit peak-shaving space, and fit the coal consumption curve of each unit; Step D: Perform traversal calculations on the capacity of each power source and energy storage combination; Step E: Select a group of power capacity combinations for daily balance calculations and count the operating data of wind power, photovoltaic power, thermal power, and energy storage; Step F: Perform economic calculations on the operating data; Step G: Determine whether all combinations have been traversed; Step H: Compare the results of all capacity combinations and determine the optimal capacity configuration plan; and achieve the optimization of system investment and operating economy.

[0004] The above patent documents and prior art have the following technical problems when used:

[0005] Problem 1: In the above documents and existing technologies, the low temperature, ice and snow and other severe climatic conditions in cold areas have a serious impact on power grid equipment. Traditional equipment lacks effective insulation, heating and cold-proof measures, which makes the equipment prone to failure in cold environments, and the reliability and stability of the equipment are low;

[0006] Problem 2: In the above documents and existing technologies, the dispatching architecture often fails to fully consider the characteristics and complementary relationships of multiple energy sources such as wind, solar, thermal, nuclear and storage. The randomness and volatility of wind and solar energy make it difficult to coordinate with traditional energy sources, and it is impossible to achieve effective energy optimization configuration. The uncertainty of wind and solar energy is not well understood and handled, and it is impossible to achieve effective matching with control resources on a time scale. This leads to a low absorption rate of new energy in the power grid and a large amount of wind and solar energy abandonment.

[0007] Question three: In the above-mentioned documents and existing technologies, the scheduling strategies were not comprehensively considered when formulating the system operating performance and economic cost factors. In the process of peak-shaving and frequency regulation, multiple factors such as fuel cost, equipment investment cost, and operation and maintenance cost were not fully considered, resulting in unreasonable resource allocation and high operating costs. There was insufficient analysis of the peak-shaving and frequency regulation performance under different operating scenarios, and it was impossible to formulate accurate scheduling strategies based on actual conditions. Summary of the invention

[0008] In view of the shortcomings of the prior art, the present invention provides an integrated dispatching architecture of wind, solar, thermal, nuclear and storage based on the complex and uncertain environment of the cold-region power grid, which solves the following problems:

[0009] 1. The poor adaptability of equipment in cold regions leads to insufficient reliability and stability of the power grid;

[0010] 2. The grid dispatching architecture has poor matching and coordination with multiple energy sources and insufficient energy consumption;

[0011] 3. Address the issues of increased costs and lack of refined management caused by unreasonable grid dispatching strategies.

[0012] To achieve the above objectives, the present invention is implemented through the following technical solutions: an integrated dispatching architecture of wind, solar, thermal, nuclear and storage based on the complex and uncertain environment of cold-region power grids, the architecture includes a data collection and monitoring layer, an analysis and decision-making layer, a control execution layer and an evaluation and optimization layer, wherein:

[0013] The data collection and monitoring layer is used to collect real-time power grid operation data, meteorological data, equipment status data, etc.

[0014] The analysis and decision-making layer analyzes and calculates the data collected by the data collection and monitoring layer, and formulates an optimized dispatching strategy, including peak-shaving analysis and decision-making of multiple power generation resources, frequency regulation analysis and decision-making of multiple power generation resources, and integrated regulation analysis and decision-making, and respectively uses particle swarm optimization algorithm, genetic algorithm, etc. to solve the corresponding models;

[0015] The control execution layer is responsible for sending dispatch instructions to each power generation equipment and energy storage device, and for real-time control of the power grid;

[0016] The evaluation and optimization layer conducts a comprehensive evaluation on the control effect of the control strategy, adopts the hierarchical analysis method to determine the weight of each evaluation index, uses the fuzzy comprehensive evaluation method to conduct fuzzy evaluation, and performs optimization and adjustment according to the evaluation results.

[0017] Preferably, the data acquisition and monitoring layer includes a power grid operation data acquisition module, a meteorological data acquisition module and an equipment status data acquisition module, wherein:

[0018] The power grid operation data acquisition module acquires the output of each power generation equipment, the voltage, current, power parameters of the power grid and the real-time change data of load demand in real time;

[0019] The meteorological data collection module collects information such as temperature, wind speed, light intensity, precipitation, etc. in cold regions;

[0020] The equipment status data acquisition module collects the operating status, health status, and fault information of wind turbines, photovoltaic panels, thermal power units, nuclear power units, energy storage equipment, etc.

[0021] Preferably, the peak load analysis and decision-making of the multiple power generation resources of the analysis and decision-making layer includes the following steps:

[0022] Sp1: Analyze the operating conditions of wind and photovoltaic grid-connected scenarios with random uncertainty, determine the system power distribution and output change trend of each resource after wind and photovoltaic are connected to the system, and clarify the main influencing factors of the randomness of wind and photovoltaic resources;

[0023] Sp2: Introduce power generation systems such as energy storage and nuclear energy, take into account the flexibility of wind, solar and grid, comprehensively consider the operation and seasonal characteristics of cold-region power grids, construct typical operation and disturbance scenarios of cold-region power grid systems, and study the peak-shaving methods of diversified resources such as wind, solar, thermal, nuclear and storage;

[0024] Sp3: Taking the optimal system operation performance and regulation economic cost as the objective function, and power balance, new energy resources, network topology, branch constraints, etc. as constraints, the particle swarm optimization algorithm is used to construct an integrated wind, solar, thermal and nuclear storage peak-shaving model and method to evaluate the peak-shaving capacity.

[0025] Preferably, the frequency modulation analysis and decision-making of the multiple power generation resources of the analysis and decision-making layer includes the following steps:

[0026] Sp1: Investigate the operation data of multiple power generation systems such as wind, solar, thermal, and nuclear storage in the power grid, analyze the frequency regulation characteristics and methods of diversified resources such as wind, solar, thermal, and nuclear storage, and study the matching relationship between the uncertainty of wind and solar new energy and various regulation resources on different time scales;

[0027] Sp2: Analyze the constraints of the frequency regulation capabilities of diversified resources, study the active frequency support technology for the grid-connected power electronic converters such as wind, solar and storage, and the frequency regulation technology for nuclear power participating in the power grid that takes into account both performance and safety, and study the frequency regulation costs and benefits of various resources;

[0028] Sp3: Genetic algorithm is used to construct an integrated frequency regulation dispatching model for cold-region power grids containing multiple power generation resources such as wind, solar, thermal, and nuclear storage. The cost, wind and solar curtailment rate, and system dynamic performance are used as objective functions. The dispatching model is constructed based on constraints such as power, state variables, unit output, thermal power unit ramping, and grid structure. Frequency regulation technology is proposed and frequency regulation performance analysis is conducted in multiple scenarios.

[0029] Preferably, the integrated control analysis and decision-making of the analysis and decision-making layer includes the following contents:

[0030] Sp1: Based on the peak-shaving and frequency-regulating capabilities of wind, solar, thermal, nuclear and other resources in the cold-region power grid, quantitatively characterize the differences and complementary relationships between the operating status and power response capabilities of various resources in the system at different time scales, and study the balance mechanism at multiple time and space scales, including coordinated control strategies in terms of energy balance, power balance, and frequency balance;

[0031] Sp2: Based on the typical operation and disturbance scenarios of wind, solar, thermal, nuclear and other integrated power grids in cold regions, a wind, solar, thermal, nuclear and other integrated dispatching architecture and system adapted to complex and uncertain environments is constructed, the control objectives and constraints under different scenarios are clarified, and a multi-time scale wind, solar, thermal, nuclear and nuclear integrated power grid control model is constructed;

[0032] Sp3: From the aspects of response speed, regulation capability, operating cost, etc., multi-temporal and integrated control strategies are proposed from different problem levels, such as unit-level control strategy, coordination of station-level control strategy and system-level integrated dispatch strategy.

[0033] Preferably, the control execution layer includes an instruction sending module and a monitoring feedback module, and further includes the following contents:

[0034] The command sending module sends power generation control instructions to wind turbines and photovoltaic power stations according to the dispatching strategy formulated by the analysis and decision-making layer, issues output adjustment instructions to thermal power units, controls the power output of nuclear power units, and issues charging and discharging instructions to the energy storage system;

[0035] The monitoring feedback module monitors the operating status of each device and system in real time to ensure the accurate execution of the scheduling instructions, and feeds back the operating status to the analysis and decision-making layer.

[0036] Preferably, the evaluation index system of the evaluation and optimization layer includes power supply reliability index, new energy absorption rate index, system operation cost index, frequency stability index, voltage stability index and environmental protection index. The power supply reliability index is used to measure the system's ability to meet load demand, the new energy absorption rate index reflects the efficiency of new energy utilization, the system operation cost index covers various aspects of cost, the frequency stability index evaluates the frequency stability of the power grid, the voltage stability index ensures the voltage stability of the power grid, and the environmental protection index reflects the environmental protection benefits of the system.

[0037] Preferably, the evaluation and optimization layer adopts the hierarchical analysis method to construct a hierarchical model, compares the relative importance of factors at each level through expert scoring or data analysis, calculates the weight of each evaluation index, and uses the fuzzy comprehensive evaluation method to determine the evaluation level of each index and the corresponding fuzzy membership function. The control strategy is evaluated according to actual data to obtain the membership of each index for different evaluation levels, and the comprehensive evaluation result is obtained by combining the weights through fuzzy synthesis operation.

[0038] Preferably, the architecture forms a closed-loop operation process during the operation of the system. The data acquisition and monitoring layer transmits the collected data to the analysis and decision-making layer. The analysis and decision-making layer formulates a scheduling strategy which is executed by the control execution layer. The evaluation and optimization layer evaluates the regulation effect and feeds back the result to the analysis and decision-making layer for optimization and adjustment, thereby continuously optimizing the system's operating performance and regulation effect to adapt to the changes and needs of the complex and uncertain environment of the cold-region power grid.

[0039] The present invention provides an integrated dispatching architecture of wind, solar, thermal, nuclear and storage based on the complex and uncertain environment of cold-region power grids. It has the following beneficial effects:

[0040] 1. The present invention effectively improves the reliability of equipment in harsh environments such as low temperature, ice and snow through equipment insulation, heating and cold-proof technologies for special climatic conditions in cold regions, as well as anti-freezing and de-icing technologies for transmission lines, thereby ensuring the stability of power grid transmission, reducing equipment failures and power outages caused by cold environmental factors, enhancing the overall risk resistance of the power grid, and providing a solid foundation for continuous and stable power supply in cold regions. The present invention comprehensively considers the characteristics and complementary relationships of multiple energy sources such as wind, solar, fire, and nuclear storage, conducts peak-shaving and frequency regulation analysis and decision-making, as well as integrated regulation analysis and decision-making. By constructing reasonable operating scenarios and scheduling models, the present invention can achieve optimal configuration and coordinated operation of various power generation resources under different operating conditions. For example, when wind and solar power generation is sufficient, excess electric energy can be reasonably stored, and when load peaks or wind and solar power generation is insufficient, other power sources can be quickly called to supplement, effectively smoothing power fluctuations, ensuring system power balance and frequency stability, and improving the reliability and stability of power grid operation.

[0041] 2. The present invention adopts in-depth research on the uncertainty of wind and solar energy and its matching relationship with other regulatory resources on different time scales, realizes the optimal allocation of energy in time and space, matches the short-term fluctuations of wind and solar power generation with the rapid response of the energy storage system, and matches the long-term power changes with the regulation capabilities of thermal power and nuclear power, fully gives play to the advantages of various energy sources, improves the comprehensive utilization efficiency of energy, and clarifies the control strategy aimed at improving the new energy consumption rate by constructing a scheduling architecture and system that adapts to complex and uncertain environments. In the scheduling process, priority is given to the power generation and access to the grid of new energy such as wind and solar energy. Reasonable peak-shaving and frequency regulation technologies and strategies are adopted to reduce the phenomenon of wind and solar power abandonment, increase the proportion of new energy consumption in the power grid, promote the effective utilization of renewable energy, and meet the requirements of energy sustainable development.

[0042] 3. The present invention adopts a system to formulate a dispatching strategy with the system operating performance and the optimal economic cost of regulation as the objective function. In the process of peak shaving, frequency regulation and integrated regulation, multiple factors such as fuel cost, equipment investment cost, operation and maintenance cost are comprehensively considered. By reasonably arranging the output of each power generation resource and optimizing the charging and discharging strategy of the energy storage system, the overall operating cost of the system is reduced. For example, on the premise of meeting the load demand, the deep peak shaving of thermal power is reasonably arranged to reduce fuel consumption; the operation of the energy storage system is optimized to reduce the loss cost of equipment. By analyzing the peak shaving and frequency regulation performance under different operating scenarios, targeted dispatching strategies can be formulated according to actual conditions. This refined management method avoids waste of resources and unnecessary cost expenditure, improves the economic operation efficiency of the system, and maximizes economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a structural diagram of the scheduling architecture of the present invention;

[0044] Figure 2 It is a diagram of the steps of the architecture operation of the present invention;

[0045] Figure 3 It is a data transmission trend diagram of the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Specific embodiment one:

[0048] like Figure 1-Figure 3As shown in the figure, the wind, solar, thermal, nuclear and storage integrated dispatching architecture based on the complex and uncertain environment of the cold-region power grid includes data collection and monitoring layer, analysis and decision layer, control execution layer and evaluation and optimization layer, among which:

[0049] The data collection and monitoring layer is used to collect real-time grid operation data, meteorological data, equipment status data, etc.

[0050] The analysis and decision-making layer analyzes and calculates the data collected by the data collection and monitoring layer, and formulates an optimized dispatching strategy, including peak-shaving analysis and decision-making of multiple power generation resources, frequency regulation analysis and decision-making of multiple power generation resources, and integrated regulation analysis and decision-making, and uses particle swarm optimization algorithm, genetic algorithm, etc. to solve the corresponding models;

[0051] The control execution layer is responsible for sending dispatch instructions to each power generation equipment and energy storage device, and for real-time control of the power grid;

[0052] The evaluation and optimization layer conducts a comprehensive evaluation on the control effect of the control strategy, uses the hierarchical analysis method to determine the weight of each evaluation index, uses the fuzzy comprehensive evaluation method to perform fuzzy evaluation, and makes optimization adjustments based on the evaluation results.

[0053] The operation process of the entire architecture is as follows:

[0054] Sp1: The data collection and monitoring layer collects various types of data in real time and transmits them to the analysis and decision-making layer;

[0055] Sp2: The analysis and decision-making layer analyzes and processes the data, performs peak load regulation, frequency regulation analysis and decision-making, and integrated regulation analysis and decision-making, and formulates corresponding dispatching strategies and control instructions;

[0056] Sp3: The control execution layer receives instructions from the analysis and decision-making layer and performs real-time control on each power generation equipment and energy storage device;

[0057] Sp4: During the operation of the system, the evaluation and optimization layer continuously evaluates the effect of the control strategy, and feeds back the evaluation results to the analysis and decision-making layer for optimization and adjustment, forming a closed-loop system operation process, continuously optimizing the system's operating performance and control effect to adapt to the changes and needs of the complex and uncertain environment of the cold-region power grid. The architecture forms a closed-loop operation process during the system operation. The data acquisition and monitoring layer transmits the collected data to the analysis and decision-making layer. The analysis and decision-making layer formulates the scheduling strategy and then executes it. The evaluation and optimization layer evaluates the control effect and feeds back the results to the analysis and decision-making layer for optimization and adjustment, continuously optimizing the system's operating performance and control effect to adapt to the changes and needs of the complex and uncertain environment of the cold-region power grid.

[0058] Through the above-mentioned architecture, the special climate and geographical environment of cold regions are fully taken into consideration. Through targeted technical research and strategy formulation, the system's operating reliability and stability under adverse conditions such as low temperature, ice and snow are improved, and the integrated dispatch of multiple energy sources such as wind, solar, thermal, and nuclear storage is realized. The advantages of each energy source are fully utilized, and the deficiencies are complemented. The energy utilization efficiency and the overall performance of the system are improved. Advanced intelligent algorithms such as particle swarm optimization algorithm and genetic algorithm are used for model solving and strategy optimization, which improves the quality and solution efficiency of the dispatching plan. A systematic evaluation index system is established, and the hierarchical analysis method and fuzzy comprehensive evaluation method are used to comprehensively evaluate the control strategy. Problems can be discovered in time and optimized and adjusted, and the operation level of the system can be continuously improved. The system can effectively respond to various challenges faced by cold-region power grids, realize the reasonable allocation and efficient utilization of energy, ensure the safe and stable operation of the power grid, and provide strong support for energy development and power supply in cold regions. Specific embodiment 2:

[0060] like Figure 1-Figure 3 As shown, according to the contents in the above specific embodiments, the following contents are further disclosed:

[0061] The data collection and monitoring layer includes the power grid operation data collection module, the meteorological data collection module and the equipment status data collection module, among which:

[0062] The power grid operation data acquisition module obtains the output of each power generation equipment, the voltage, current, power parameters of the power grid and the real-time change data of load demand in real time;

[0063] The meteorological data collection module collects information such as temperature, wind speed, light intensity, precipitation, etc. in cold regions;

[0064] The equipment status data acquisition module collects the operating status, health status, and fault information of wind turbines, photovoltaic panels, thermal power units, nuclear power units, energy storage equipment, etc.

[0065] The data collection and monitoring layer provides a detailed basis for the precise scheduling of the system by collecting a variety of data in real time. For example, according to the real-time grid load demand and the output of power generation equipment, the output power of each power generation resource can be better arranged to avoid insufficient or excessive power supply. The collection of equipment status data helps to discover hidden dangers of equipment failure in advance. By monitoring the health of the equipment, maintenance and repair can be carried out before the equipment fails seriously, reducing the impact of sudden equipment failures on grid operation and improving system reliability. The meteorological conditions in cold areas are complex and changeable. The collection of meteorological data enables the system to promptly understand the impact of weather changes on wind and solar power generation. For example, sudden changes in wind speed will affect the output of wind turbines, and changes in light intensity will affect photovoltaic power generation. Through these data, the system can make adjustments in advance to better adapt to environmental changes.

[0066] The peak load analysis and decision-making of multiple power generation resources at the analytical decision-making level includes the following steps:

[0067] Sp1: Analyze the operating conditions of wind and photovoltaic grid-connected scenarios with random uncertainty, determine the system power distribution and output change trend of each resource after wind and photovoltaic are connected to the system, and clarify the main influencing factors of the randomness of wind and photovoltaic resources;

[0068] Sp2: Introduce power generation systems such as energy storage and nuclear energy, take into account the flexibility of wind, solar and grid, comprehensively consider the operation and seasonal characteristics of cold-region power grids, construct typical operation and disturbance scenarios of cold-region power grid systems, and study the peak-shaving methods of diversified resources such as wind, solar, thermal, nuclear and storage;

[0069] Sp3: Taking the optimal system operation performance and regulation economic cost as the objective function, and power balance, new energy resources, network topology, branch constraints, etc. as constraints, the particle swarm optimization algorithm is used to construct an integrated wind, solar, thermal and nuclear storage peak-shaving model and method to evaluate the peak-shaving capacity.

[0070] The frequency regulation analysis and decision-making of multiple power generation resources at the analytical decision-making level includes the following steps:

[0071] Sp1: Investigate the operation data of multiple power generation systems such as wind, solar, thermal, and nuclear storage in the power grid, analyze the frequency regulation characteristics and methods of diversified resources such as wind, solar, thermal, and nuclear storage, and study the matching relationship between the uncertainty of wind and solar new energy and various regulation resources on different time scales;

[0072] Sp2: Analyze the constraints of the frequency regulation capabilities of diversified resources, study the active frequency support technology for the grid-connected power electronic converters such as wind, solar and storage, and the frequency regulation technology for nuclear power participating in the power grid that takes into account both performance and safety, and study the frequency regulation costs and benefits of various resources;

[0073] Sp3: Genetic algorithm is used to construct an integrated frequency regulation dispatching model for cold-region power grids containing multiple power generation resources such as wind, solar, thermal, and nuclear storage. The cost, wind and solar curtailment rate, and system dynamic performance are used as objective functions. The dispatching model is constructed based on constraints such as power, state variables, unit output, thermal power unit ramping, and grid structure. Frequency regulation technology is proposed and frequency regulation performance analysis is conducted in multiple scenarios.

[0074] The integrated control analysis and decision-making of the analysis and decision-making layer includes the following contents:

[0075] Sp1: Based on the peak-shaving and frequency-regulating capabilities of wind, solar, thermal, nuclear and other resources in the cold-region power grid, quantitatively characterize the differences and complementary relationships between the operating status and power response capabilities of various resources in the system at different time scales, and study the balance mechanism at multiple time and space scales, including coordinated control strategies in terms of energy balance, power balance, and frequency balance;

[0076] Sp2: Based on the typical operation and disturbance scenarios of wind, solar, thermal, nuclear and other integrated power grids in cold regions, a wind, solar, thermal, nuclear and other integrated dispatching architecture and system adapted to complex and uncertain environments is constructed, the control objectives and constraints under different scenarios are clarified, and a multi-time scale wind, solar, thermal, nuclear and nuclear integrated power grid control model is constructed;

[0077] Sp3: From the aspects of response speed, regulation capability, operating cost, etc., multi-temporal and integrated control strategies are proposed from different problem levels, such as unit-level control strategy, coordination of station-level control strategy and system-level integrated dispatch strategy.

[0078] Through peak load and frequency regulation analysis and decision-making, the analysis and decision-making layer can reasonably arrange the output of various power generation resources such as wind, solar, thermal, nuclear and storage, and realize the optimal allocation of resources. For example, during peak electricity consumption, the output of thermal power units, nuclear power units and energy storage systems can be reasonably dispatched, and wind and solar resources can be fully utilized to meet electricity demand. In the frequency regulation process, according to the frequency regulation characteristics of different resources, the most suitable resources are selected for frequency regulation to improve the frequency stability of the system. Integrated regulation analysis and decision-making can study the balance mechanism of multiple time and space scales, and ensure the balance of energy, power and frequency through coordinated control strategies. This will help improve the stability of the entire cold-region power grid system and reduce the risk of power grid failure caused by power fluctuations or imbalances. By constructing various optimization models, while ensuring the safe and stable operation of the system, economic cost factors are considered. For example, in the peak load and frequency regulation decision-making process, the optimal economic cost is used as the objective function to reasonably arrange resources, reduce the operating cost of the system, and improve the economic benefits of the system.

[0079] The control execution layer includes the instruction sending module and the monitoring feedback module, and further includes the following contents:

[0080] The command sending module sends power generation control commands to wind turbines and photovoltaic power stations according to the dispatching strategy formulated by the analysis and decision-making layer, issues output adjustment commands to thermal power units, controls the power output of nuclear power units, and issues charging and discharging commands to the energy storage system;

[0081] The monitoring and feedback module monitors the operating status of each device and system in real time to ensure the accurate execution of scheduling instructions and feeds back the operating status to the analysis and decision-making layer.

[0082] The control execution layer can accurately convert the complex scheduling strategies formulated by the analysis and decision-making layer into control instructions for each power generation equipment and energy storage device, thereby realizing precise control of the power grid. For example, the power generation of wind turbines can be accurately adjusted according to the scheduling strategy so that the actual power generation situation meets the needs of the system. Through real-time monitoring and feedback, once the equipment operating status is abnormal or the instruction execution deviates, the analysis and decision-making layer can be notified in time to make adjustments. This real-time adjustment mechanism ensures the stable operation of the system and improves the system's ability to respond to emergencies.

[0083] The evaluation index system of the evaluation and optimization layer includes power supply reliability index, new energy consumption rate index, system operation cost index, frequency stability index, voltage stability index and environmental protection index. The power supply reliability index is used to measure the system's ability to meet load demand. The new energy consumption rate index reflects the efficiency of new energy utilization. The system operation cost index covers all aspects of cost. The frequency stability index evaluates the frequency stability of the power grid. The voltage stability index ensures the voltage stability of the power grid. The environmental protection index reflects the environmental protection benefits of the system.

[0084] The evaluation and optimization layer uses the hierarchical analysis method to construct a hierarchical model, compares the relative importance of factors at each level through expert scoring or data analysis, calculates the weight of each evaluation indicator, and uses the fuzzy comprehensive evaluation method to determine the evaluation level of each indicator and the corresponding fuzzy membership function. The control strategy is evaluated based on actual data, and the membership of each indicator to different evaluation levels is obtained. The comprehensive evaluation result is obtained by combining the weights through fuzzy synthesis operation.

[0085] The evaluation and optimization layer can comprehensively evaluate the system's operating performance through a multi-dimensional evaluation index system. For example, it not only considers the reliability and stability of power supply, but also the absorption of new energy and environmental benefits, so as to have a comprehensive and objective evaluation of the system, and conducts feedback optimization based on the evaluation results so that the system can continuously adapt to environmental changes and demands. For example, if it is found that the absorption rate of new energy is low, the scheduling strategy can be adjusted, such as increasing the configuration of the energy storage system or optimizing the access method of wind and solar power generation, to increase the absorption rate of new energy and thus improve the overall performance of the system. Specific embodiment three:

[0087] like Figure 1-Figure 3 As shown, according to the contents in the above specific embodiments, the following contents are further disclosed:

[0088] The algorithm contents in each layer of the system architecture are as follows:

[0089] Application of Particle Swarm Optimization (PSO) in the Integrated Peak Loading Model of Wind, Solar, Thermal and Nuclear Energy Storage

[0090] Speed ​​update formula:

[0091]

[0092] in:

[0093] represents the velocity of particle i at the k+1th iteration in the d-dimensional space;

[0094] w is the inertia weight, which is used to balance the global search and local search capabilities of particles. A larger value is beneficial to global search, while a smaller value is beneficial to local search.

[0095] is the velocity of particle i at the kth iteration in the dth dimension space. The velocity determines the moving direction and distance of the particle in the solution space;

[0096] c1 and c2 are learning factors, c1 represents the step length of a particle learning from its own historical optimal position, and c2 represents the step length of a particle learning from the group's historical optimal position;

[0097] r1 and r2 are random numbers uniformly distributed in the interval [0, 1], which are used to increase the randomness of the search and prevent particles from falling into the local optimum;

[0098] represents the individual optimal position of particle i at the kth iteration in the d-dimensional space, that is, the optimal solution found by the particle at the kth iteration during the search process;

[0099] is the current position of particle i at the kth iteration in the dth dimension, and the position represents a possible solution to the problem;

[0100] It represents the global optimal position of the entire particle swarm at the kth iteration in the d-dimensional space, that is, the optimal solution found by the entire particle swarm at the kth iteration during the search process;

[0101] Position update formula:

[0102]

[0103] in, represents the position of particle i at the k+1th iteration in the d-dimensional space;

[0104] The implementation steps are as follows:

[0105] Sp1: Initialization: Randomly generate a group of particles, and initialize the position and velocity of each particle in the solution space. At the same time, set the size of the particle group, the number of iterations, the inertia weight, the learning factor, and other parameters, and calculate the initial fitness value of each particle, that is, the objective function value. The objective function here is the optimal system operation performance and regulation economic cost. For example, it can be expressed as, where it may be a function of the system operation performance index, is a function of the regulation economic cost index, is the position vector of the particle, and represents the output allocation plan of each power generation resource and other decision variables;

[0106] Sp2: Individual optimal and global optimal update: For each particle, compare its current fitness value with its individual historical optimal fitness value. If the current fitness value is better, update the individual optimal position to the current position, and update the individual optimal fitness value at the same time. In the entire particle group, compare the individual optimal fitness values ​​of all particles, find the particle position corresponding to the global optimal fitness value, and use it as the global optimal position;

[0107] Sp3: Speed ​​and position update: Calculate the speed of each particle in each dimension according to the speed update formula, and update the position of each particle in each dimension using the position update formula;

[0108] Sp4: Iteration judgment: judge whether the preset number of iterations is reached or other stop conditions are met (such as the objective function value reaches a certain accuracy, etc. If not, return to Sp2: continue to iterate; if reached, output the global optimal position and its corresponding fitness value, that is, the optimal peak-shaving solution and the optimal value of the objective function.

[0109] The operating logic is as follows:

[0110] The particle swarm algorithm searches for the optimal solution by simulating the foraging behavior of bird flocks. Each particle flies in the solution space, and its speed and position are adjusted according to its own historical optimal position, the historical optimal position of the group, and the current random factors. During the iteration process, the particles continue to approach the individual optimal and global optimal positions, thereby searching for better areas in the solution space. Through multiple iterations, the particle swarm gradually converges to the vicinity of the optimal solution, and finally finds the peak-shaving solution that optimizes the objective function, that is, the optimal output allocation plan for each power generation resource, so as to achieve the optimal balance between system operation performance and regulation economic cost.

[0111] Application of Genetic Algorithm (GA) in Frequency Regulation Integrated Dispatching Model of Cold Region Power Grid

[0112] Fitness function: Let the objective function be Z = f(x), where x is a decision variable vector representing various parameters in the frequency regulation scheme, such as the output adjustment of each power generation resource. The fitness function Fitness(x) is usually defined as a transformation of the objective function. For example, if the objective function is to be minimized, the fitness function can be defined as Fitness(x) = 1 / Z (assuming Z>0), or other appropriate transformations can be performed to ensure that the fitness value is positive and the higher the fitness, the better the solution.

[0113] The probability formula for the selection operation is based on the roulette wheel selection method: The probability of individual i being selected is P i :

[0114]

[0115] Among them, N is the population size, and Fitness(i) is the fitness value of individual i;

[0116] For the crossover operation, single-point crossover is adopted: Assume two parent individuals x1 = (x 11 , x 12 , …, x 1n ) and x2 = (x 21 , x 22 , …, x 2n ). Randomly select a crossover point position k (1 < k < n), then the two offspring individuals obtained after crossover are y1 = (x 11 , x 12 , …, x 1k , x 2(k+1) , x 2(k+2) , …, x 2n ) and

[0117] y2 = (x 21 , x 22 , …, x 2k , x 1(k+1) , x 1(k+2) , …, x 1n );

[0118] For the mutation operation, basic bit mutation is adopted: For individual x = (x1, x2, …, x n ), randomly select a mutation bit m (1 ≤ m ≤ n), and then mutate this bit. If x m = 0, then after mutation x m = 1; if x m = 1, then after mutation x m = 0. The mutation probability is generally set to a relatively small value P m .

[0119] Among them:

[0120] Z: Represents the objective function value, reflecting the quality of the frequency modulation scheme. Here, the objective function is a comprehensive objective function considering cost, curtailment rate of wind and solar power, system dynamic performance, etc.;

[0121] x: Represents the decision variable vector, including various parameters related to frequency modulation, such as the output adjustment of each power generation resource, the charge and discharge power of the energy storage system, etc.;

[0122] Fitness(x): Represents the fitness function value, used to measure the quality of an individual in the population. The higher the fitness value, the more likely the individual is to be inherited to the next generation;

[0123] N: Represents the population size, that is, the total number of individuals participating in the genetic algorithm search;

[0124] P i: It indicates the probability of an individual being selected for genetic operation, which is proportional to the fitness value of the individual;

[0125] k: represents the crossover point, which is used to determine the position of gene exchange between parent individuals in the crossover operation;

[0126] y1 and y2: represent the offspring individuals obtained after the crossover operation;

[0127] m: represents the mutation position, which determines the position where the individual gene mutates in the mutation operation;

[0128] P m : Indicates the probability of mutation, which controls the possibility of individual gene mutation;

[0129] The implementation steps are as follows:

[0130] Sp1: Encoding: Encode the decision variables of the frequency modulation problem to form chromosome individuals. Commonly used encoding methods include binary encoding and real number encoding. For example, if the decision variable is the output adjustment of each power generation resource, real number encoding can be used to directly use the output value as the gene value on the chromosome;

[0131] Sp2: Initialize the population: randomly generate an initial population of a certain size, each individual represents a frequency modulation scheme, and the population size is determined according to factors such as the complexity of the problem and computing resources;

[0132] Sp3: Calculate fitness: For each individual in the population, calculate its fitness value according to the fitness function. The fitness function is defined according to the frequency regulation objectives, such as minimizing the cost, wind and solar power abandonment rate, and optimizing the system dynamic performance.

[0133] Sp4: Selection operation: Use selection methods (such as roulette selection method, tournament selection method, etc.) to select a part of individuals from the current population as parents for breeding the next generation of individuals. The selection probability is determined according to the fitness value of the individual. Individuals with high fitness have a high probability of being selected.

[0134] Sp5: Crossover operation: Perform a crossover operation on the selected parent individuals to generate new offspring individuals. The crossover probability is generally set to a moderate value, such as 0.6-0.9. According to the crossover operation formula, the crossover point position is randomly selected, and some genes of the parent individuals are exchanged to generate new individuals.

[0135] Sp6: Mutation operation: Perform mutation operation on the newly generated offspring individuals to increase the diversity of the population. The mutation probability is usually set to a small value, such as 0.01-0.1. According to the mutation operation formula, the mutation position is randomly selected and the gene value of the position is mutated;

[0136] Sp7: Iteration judgment: Replace some individuals in the original population with the newly generated individuals to form a new population, and then judge whether the preset number of iterations is reached or other stopping conditions are met (such as the fitness value reaches a certain accuracy, etc.). If not, return to step 3 to continue iterating; if reached, output the individual with the highest fitness as the optimal solution, that is, the best frequency modulation scheme.

[0137] The operating logic is as follows: the genetic algorithm searches for the optimal solution by simulating the biological evolution process. The individuals in the initial population represent different frequency modulation schemes. The pros and cons of each scheme are evaluated by calculating the fitness value. In the selection operation, excellent individuals have a greater chance of being selected to participate in reproduction, thereby passing on excellent genes to the next generation. The crossover operation allows the genes of different individuals to be exchanged, resulting in new combinations and exploring more areas of the solution space. The mutation operation introduces new genes to a certain extent to prevent the algorithm from falling into a local optimum. After many iterations, the population gradually evolves and the fitness value continues to increase, and eventually the optimal frequency modulation scheme that meets the objective function and constraints is found, achieving an optimal balance of multiple objectives such as cost, wind and solar power abandonment rate and system dynamic performance.

[0138] Application of AHP in Determining the Weight of Evaluation Index

[0139] Construct a judgment matrix: Assume that there are n evaluation indicators, construct a judgment matrix A = (a ij ) n×n , where a ij Indicates the importance of indicator i relative to the indicator, satisfying a ij >0, a ii =1, usually a scale of 1-9 is used to determine a ij The value of, for example, a ij =1 means that indicator i and indicator j are equally important, a ij =3 means that indicator i is slightly more important than indicator j, a ij =5 means that indicator i is obviously more important than indicator j, etc.;

[0140] Calculate eigenvectors and eigenvalues: The largest eigenvalue λ of matrix A max and its corresponding eigenvector w, which is usually solved by numerical methods such as the power method, and the eigenvector is normalized to obtain the normalized eigenvector in, That is the weight of indicator i;

[0141] Consistency check: Calculate consistency index Find the average random consistency index RI (obtained from the standard table according to the order of the judgment matrix) and calculate the consistency ratio When CR < 0.1, the consistency of the judgment matrix is ​​considered acceptable, otherwise the judgment matrix needs to be adjusted or reconstructed.

[0142] in:

[0143] A: represents the judgment matrix, which is used to represent the relative importance of each evaluation index;

[0144] a ij : represents the elements in the judgment matrix, reflecting the importance of the indicator relative to the indicator;

[0145] n: represents the number of evaluation indicators;

[0146] λ max : represents the maximum eigenvalue of the judgment matrix;

[0147] w: represents the eigenvector corresponding to the maximum eigenvalue;

[0148] Represents the normalized feature vector, that is, the weight vector of each evaluation index;

[0149] CI: represents the consistency index, which is used to measure the consistency of the judgment matrix;

[0150] RI: represents the average random consistency index, which is a constant related to the order of the judgment matrix and is used to compare consistency indicators;

[0151] CR: represents the consistency ratio, which is used to determine whether the consistency of the judgment matrix is ​​acceptable.

[0152] The implementation steps are as follows:

[0153] Sp1: Establish a hierarchical model: decompose the evaluation problem into a hierarchical structure including the target layer, criterion layer and indicator layer. For example, the target layer is a comprehensive evaluation of the control strategy, the criterion layer may include aspects such as power supply reliability, new energy consumption rate, system operation cost, etc., and the indicator layer is a specific evaluation indicator, such as power outage time, new energy consumption rate index, fuel cost, etc.;

[0154] Sp2: Constructing a judgment matrix: By means of expert scoring or data analysis, the importance of each factor at the same level is compared with that of a factor at the previous level, and a judgment matrix is ​​constructed. For example, for the comparison of the importance of the two factors of power supply reliability and new energy consumption rate at the criterion level with respect to the target level, experts give corresponding values ​​based on experience and construct a judgment matrix.

[0155] Sp3: Calculate weight vector: Calculate the maximum eigenvalue of the judgment matrix and its corresponding eigenvector, then normalize the eigenvector to obtain the weight vector of each evaluation index relative to the previous level factor. For example, calculate the weight vector of the criterion level factors such as power supply reliability and new energy consumption rate relative to the target level.

[0156] Sp4: Consistency test: calculate the consistency index, find the average random consistency index, and calculate the consistency ratio. If , the consistency of the judgment matrix is ​​considered acceptable and the weight calculation result is valid; otherwise, it is necessary to readjust the judgment matrix or re-judge.

[0157] The operating logic is as follows: The hierarchical analysis method decomposes complex evaluation problems into multiple levels, compares the factors at each level pairwise, and constructs a judgment matrix to quantify the relative importance of each factor. The weight of each evaluation indicator is obtained by calculating the eigenvector and eigenvalue of the judgment matrix to provide a basis for comprehensive evaluation. The consistency test is to ensure the rationality and reliability of the judgment matrix and avoid the deviation of the weight result due to the inconsistency of subjective judgment. If the consistency test passes, it means that the expert's judgment is reasonable to a certain extent, and the calculated weight can be used for subsequent evaluation; if it fails, it is necessary to review and adjust the judgment to ensure the scientificity and accuracy of the evaluation results.

[0158] Application of fuzzy comprehensive evaluation method in the evaluation of control strategy

[0159] Determine the evaluation factor set and evaluation level set: Let the evaluation factor set U = {u1, u2, ..., u m}, where u i represents the i-th evaluation index, such as power supply reliability index, new energy consumption rate index, etc.; let the evaluation level set V = {v1, v2, ..., v n}, for example, V = {excellent, good, average, poor, very poor}, etc.;

[0160] Construct the fuzzy relationship matrix: For each evaluation index u i , evaluate it according to the actual data and determine its membership degree r for each evaluation level ij , where r ij Represents the evaluation index u i Evaluation level v j The degree of membership, 0≤r ij ≤1, thus constructing the fuzzy relationship matrix R=(r ij ) m×n ;

[0161] Determine the weight vector: Use the analytic hierarchy process and other methods to determine the weight vector A = (a1, a2, ..., am ), where a i Represents the evaluation index u i The weight of , and ∑ i =1 m a i =1;

[0162] Fuzzy comprehensive evaluation operation: Calculate the fuzzy comprehensive evaluation result vector in Represents fuzzy synthesis operation. Commonly used fuzzy synthesis operators include weighted average type, main factor prominent type, etc. Taking weighted average type as an example, Here j It indicates the comprehensive membership of the evaluated object to the evaluation level;

[0163] in:

[0164] U: represents the evaluation factor set, which includes various indicators used to evaluate the control strategy;

[0165] u i : represents the i evaluation index in the evaluation factor set;

[0166] V: represents the evaluation level set, which defines the different levels of evaluation results;

[0167] v j : represents the j evaluation levels in the evaluation level set;

[0168] R: represents the fuzzy relationship matrix, which describes the degree of membership of each evaluation index to different evaluation levels;

[0169] r ij : represents the elements in the fuzzy relationship matrix, representing the evaluation index u i Evaluation level v j The degree of membership;

[0170] A: represents the weight vector, which is determined by the hierarchical analysis method and reflects the importance of each evaluation index;

[0171] a i : The i-th element in the weight vector, i.e., the evaluation index u i The weight of

[0172] B: represents the fuzzy comprehensive evaluation result vector, whose element b j It indicates the comprehensive membership of the evaluated object to the evaluation level.

[0173] The implementation steps are as follows:

[0174] Sp1: Determine the evaluation factor set and evaluation level set: According to the characteristics of the control strategy and the evaluation objectives, determine the evaluation factor set, such as power supply reliability index, new energy consumption rate index, system operation cost index and other specific indicators, define the evaluation level set, such as excellent, good, medium, poor, very poor and other different levels, so as to classify and evaluate the effect of the control strategy;

[0175] Sp2: Constructing a fuzzy relationship matrix: For each evaluation indicator, according to the actual operation data or simulation results, use appropriate methods to determine its membership to each evaluation level. For example, you can use historical data statistical analysis or set thresholds to determine the degree to which the indicator value belongs to which evaluation level, and then construct a fuzzy relationship matrix;

[0176] Sp3: Determine the weight vector: Use methods such as hierarchical analysis to calculate the weight of each evaluation indicator and obtain the weight vector to ensure that the sum of the weights is 1 and the weight value reflects the relative importance of each indicator in the evaluation;

[0177] Sp4: Fuzzy comprehensive evaluation operation: Use the selected fuzzy synthesis operator to perform the operation. Taking the weighted average type as an example, calculate each element of the fuzzy comprehensive evaluation result vector according to the formula;

[0178] Sp5: Analysis of evaluation results: According to the fuzzy comprehensive evaluation result vector, the comprehensive evaluation level of the evaluated control strategy is determined. Usually, the maximum membership principle can be selected, that is, the evaluation level corresponding to the maximum value in is selected as the final evaluation result. For example, if is the largest and represents "medium", then the comprehensive evaluation level of the control strategy is considered to be medium. At the same time, the elements in can also be analyzed to understand the distribution of the evaluated objects at different evaluation levels, so as to more comprehensively evaluate the advantages and disadvantages of the control strategy.

[0179] The operation logic is: the fuzzy comprehensive evaluation method quantifies the fuzziness and uncertainty in the evaluation process, comprehensively considers multiple evaluation indicators and multiple evaluation levels, first determines the evaluation factor set and the evaluation level set, and constructs a fuzzy relationship matrix to describe the fuzzy relationship between each indicator and the evaluation level, and then combines the weight vector determined by the hierarchical analysis method to perform fuzzy synthesis operations to obtain a comprehensive evaluation result vector. This result vector reflects the comprehensive degree of membership of the evaluated control strategy at each evaluation level. Through the maximum membership principle or a detailed analysis of the result vector, a comprehensive and objective evaluation can be made on the effect of the control strategy, providing a basis for decision-making, so as to optimize and improve the control strategy and improve the system's operating performance and effect. Specific embodiment four:

[0181] like Figure 1-Figure 3 As shown, according to the contents in the above specific embodiments, the following contents are further disclosed:

[0182] The operating steps of the system architecture are as follows:

[0183] Sp1: Data collection and monitoring: The power grid operation data collection module obtains the output of each power generation equipment, the voltage, current, power parameters of the power grid and the real-time change data of load demand in real time. The meteorological data collection module collects information such as temperature, wind speed, light intensity, precipitation, etc. in cold regions. The equipment status data collection module collects the operation status, health status and fault information of wind turbines, photovoltaic panels, thermal power units, nuclear power units, energy storage equipment, etc.

[0184] Sp2: Analytical decision:

[0185] Sp2.1: Peak load analysis and decision-making of multiple power generation resources:

[0186] Sp2.1.1: Analyze the operating conditions of wind and photovoltaic grid-connected scenarios, determine the system power distribution and the output change trend of each resource, and clarify the main influencing factors of the randomness of wind and photovoltaic resources;

[0187] Sp2.1.2: Introduce power generation systems such as energy storage and nuclear energy, construct typical operation and disturbance scenarios of cold-region power grid systems, and study peak-shaving methods of diversified resources;

[0188] Sp2.1.3: Taking the optimal system operation performance and regulation economic cost as the objective function, construct an integrated wind, solar, thermal and nuclear peak-shaving model and method to evaluate the peak-shaving capacity;

[0189] Sp2.2: Frequency regulation analysis and decision-making of multiple power generation resources:

[0190] Sp2.2.1: Investigate the operating data of multiple power generation systems, analyze frequency regulation characteristics and methods, and study the matching relationship between new energy uncertainty and regulation resources;

[0191] Sp2.2.2: Analyze the constraints on frequency regulation capability, study active frequency support technology and frequency regulation technology for nuclear power to participate in the power grid, and study the cost and benefits of frequency regulation;

[0192] Sp2.2.3: Construct an integrated frequency regulation dispatching model for cold-region power grids with multiple power generation resources such as wind, solar, thermal, and nuclear power, propose frequency regulation technology, and conduct performance analysis;

[0193] Sp2.3: Integrated control analysis and decision-making:

[0194] Sp2.3.1: Quantitatively characterize the differences and complementary relationships in the operating status and power response capabilities of various resources in the system, and study the balance mechanism at multiple spatiotemporal scales;

[0195] Sp2.3.2: Build an integrated dispatching framework and system for wind, solar, thermal, nuclear and storage, clarify control objectives and constraints, and build a control model;

[0196] Sp2.3.3: Propose a multi-temporal and integrated control strategy;

[0197] Sp3: Control execution: The command sending module sends commands to various power generation equipment and energy storage devices according to the dispatching strategy, including sending power control commands to wind turbines and photovoltaic power stations, issuing output adjustment commands to thermal power units, controlling the power output of nuclear power units, and issuing charging and discharging commands to energy storage systems. The monitoring feedback module monitors the operating status of various devices and systems in real time to ensure the accurate execution of dispatching commands, and feeds back the operating status to the analysis and decision-making layer;

[0198] Sp4: Evaluation and Optimization:

[0199] Sp4.1: Determine the evaluation index system, including power supply reliability index, new energy consumption rate index, system operation cost index, frequency stability index, voltage stability index and environmental protection index;

[0200] Sp4.2: Use the hierarchical analysis method to construct a hierarchical model and calculate the weight of each evaluation index;

[0201] Sp4.3: Use the fuzzy comprehensive evaluation method to determine the evaluation level and fuzzy membership function of each indicator, evaluate the control strategy based on actual data, and obtain the comprehensive evaluation results;

[0202] Sp4.4: Feedback the evaluation results to the analysis and decision-making layer for optimization and adjustment.

[0203] The data transmission trends between each layer are as follows:

[0204] In the data collection stage, the power grid operation data collection module, meteorological data collection module and equipment status data collection module of the data collection and monitoring layer collect real-time data from the power grid, meteorological department and various power generation equipment and energy storage devices respectively. These data include power grid operation data (output of each power generation equipment, voltage, current, power parameters, load demand changes), meteorological data (temperature, wind speed, light intensity, precipitation, etc.) and equipment status data (operation status, health status, fault information of wind turbines, photovoltaic panels, thermal power units, nuclear power units and energy storage equipment);

[0205] During the data analysis phase, the collected data is transmitted to the analysis and decision-making layer, where it is processed according to different analysis tasks:

[0206] In the peak-shaving analysis and decision-making of diversified power generation resources, we first analyze the operating conditions of wind and photovoltaic grid-connected scenarios, and use the collected grid operation data and meteorological data to determine the system power distribution and output change trend of each resource after wind and photovoltaic are connected to the system, and clarify the main influencing factors of the randomness of wind and light resources. Then, we introduce power generation systems such as energy storage and nuclear energy. Combined with the operation and seasonal characteristics of cold-region power grids, we use grid operation data and meteorological data to construct typical operation and disturbance scenarios, and study the peak-shaving methods of diversified resources. Finally, with the system operation performance and the optimal economic cost of regulation as the objective function, and power balance, new energy resources, network topology, branch constraints, etc. as constraints, we use the particle swarm optimization algorithm to construct an integrated wind, solar, thermal, nuclear and storage peak-shaving model and method to evaluate the peak-shaving capability.

[0207] In the frequency regulation analysis and decision-making of diversified power generation resources, the operating data of diversified power generation systems such as wind, solar, thermal, and nuclear storage in the power grid are investigated, the frequency regulation characteristics and methods of diversified resources such as wind, solar, thermal, and nuclear storage are analyzed, the matching relationship between the uncertainty of wind and solar new energy and various regulation resources on different time scales is studied, and the constraints on the frequency regulation capacity of diversified resources are analyzed. The active frequency support technology for the grid connection of power electronic converters such as wind, solar, and nuclear storage and the frequency regulation technology of nuclear power participating in the power grid that takes into account performance and safety are studied. The frequency regulation costs and benefits of various resources are studied, and a genetic algorithm is used to construct an integrated frequency regulation dispatching model for cold-region power grids containing diversified power generation resources such as wind, solar, thermal, and nuclear storage. The cost, wind and solar abandonment rate, system dynamic performance, etc. are used as objective functions, and the dispatching model is constructed with power, state variables, unit output, thermal power unit climbing, grid and other constraints. Frequency regulation technology is proposed and frequency regulation performance analysis is conducted in multiple scenarios.

[0208] In the integrated control analysis and decision-making, according to the peak-shaving and frequency regulation capabilities of various resources such as wind, solar, fire, and nuclear storage in the cold-region power grid, the differences and complementary relationships in the operating status and power response capabilities of various resources in the system at different time scales are quantitatively characterized, and the grid operation data and equipment status data are used to study the balance mechanism at multiple time and space scales. Based on the typical operation and disturbance scenarios of the integrated wind, solar, fire, and nuclear storage in the cold-region power grid, an integrated dispatching architecture and system of wind, solar, fire, and nuclear storage that is adapted to complex and uncertain environments is constructed. The control objectives and constraints under different scenarios are clarified, and a multi-time-scale integrated system control model of wind, solar, fire, and nuclear storage in the cold-region power grid is constructed. From the aspects of response speed, regulation capability, and operating cost, multi-time and space, integrated control strategies are proposed from different problem levels such as unit-level control strategies, coordination of station-level control strategies, and system-level integrated dispatching strategies.

[0209] In the control execution stage, the scheduling strategy formulated by the analysis and decision-making layer is transmitted to the control execution layer. The instruction sending module sends power generation control instructions to wind turbines and photovoltaic power stations according to the scheduling strategy, issues output adjustment instructions to thermal power units, controls the power output of nuclear power units, and issues charging and discharging instructions to the energy storage system. The monitoring feedback module monitors the operating status of each device and system in real time, and feeds back the operating status to the analysis and decision-making layer to form a closed-loop control.

[0210] In the evaluation and optimization stage, the evaluation and optimization layer conducts a comprehensive evaluation on the control effect of the control strategy. First, the evaluation index system is determined, including power supply reliability index, new energy consumption rate index, system operation cost index, frequency stability index, voltage stability index and environmental protection index. The hierarchical structure model is constructed by using the hierarchical analysis method. The relative importance of factors at each level is compared through expert scoring or data analysis, and the weight of each evaluation index is calculated. The fuzzy comprehensive evaluation method is used to determine the evaluation level of each indicator and the corresponding fuzzy membership function. The control strategy is evaluated according to the actual data, and the membership of each indicator for different evaluation levels is obtained. The comprehensive evaluation results are obtained by combining the weights through fuzzy synthesis operations. According to the evaluation results, they are fed back to the analysis and decision-making layer for optimization and adjustment, and the system's operating performance and control effect are continuously optimized to adapt to the changes and needs of the complex and uncertain environment of the cold-region power grid. Specific embodiment five:

[0212] like Figure 1-Figure 3 As shown, according to the contents in the above specific embodiments, the following contents are further disclosed:

[0213] The hardware structure of the system architecture in actual use is as follows:

[0214] The data acquisition and monitoring layer hardware includes the power grid operation data acquisition module hardware, the meteorological data acquisition module hardware and the equipment status data acquisition module hardware;

[0215] The hardware of the power grid operation data acquisition module includes smart meters, data acquisition terminals and sensor networks. The specific structure is as follows:

[0216] Smart meters: installed at various power generation equipment and grid access points, used to accurately measure the output of power generation equipment (such as power generation) and grid voltage, current, power and other parameters; smart meters can record data in real time and transmit data to the data acquisition center through communication interfaces (such as RS-485, power line carrier communication, etc.);

[0217] Data acquisition terminal (DTU): It is a data transmission device used to collect data from multiple smart meters and perform preliminary processing and packaging of these data. DTU has multiple communication interfaces, such as Ethernet, GPRS / 4G / 5G, etc., and can send the collected data to the host system (such as the monitoring server);

[0218] Sensor network: including voltage sensors, current sensors, etc. These sensors are distributed at key nodes of the power grid and are used to sense changes in voltage and current in real time. They have the characteristics of high precision and high reliability, can work accurately in complex electromagnetic environments, and can adapt to harsh climatic conditions in cold areas, such as normal operation in low temperature environments.

[0219] The hardware of the meteorological data acquisition module includes a weather station and a meteorological data collector. The specific contents are as follows:

[0220] Meteorological station: contains a variety of meteorological sensors, for example, wind speed sensor (such as three-cup anemometer or ultrasonic anemometer) is used to measure wind speed. Its working principle is to generate electrical signals by sensing the flow of wind, so as to determine the wind speed; light intensity sensor (such as photodiode sensor) can generate current or voltage signals of different intensities according to the light intensity to measure the light conditions; temperature sensor (such as platinum resistance temperature sensor) is used to measure air temperature. It uses the characteristic of platinum resistance changing with temperature to accurately measure temperature; rainfall sensor (such as tipping bucket rain gauge) records rainfall by collecting precipitation and converting it into electrical signals. These meteorological sensors transmit the collected data to the meteorological data collector;

[0221] Meteorological data collector: It is one of the core components of the weather station. It is responsible for receiving data from various meteorological sensors and performing preliminary processing and storage. The meteorological data collector usually has a data cache function to prevent data loss, and can send data to the data center or host computer system via wired (such as RS-232, RS-485) or wireless (such as Zigbee, GPRS / 4G / 5G) communication methods for subsequent analysis and processing.

[0222] The hardware of the equipment status data acquisition module includes status monitoring sensors and equipment data collectors. The specific contents are as follows:

[0223] Condition monitoring sensors: There are different types of condition monitoring sensors for different power generation equipment and energy storage devices. On wind turbines, there are vibration sensors used to monitor the vibration of the unit. By detecting the vibration frequency and amplitude, it is determined whether the mechanical parts of the unit are operating normally. On photovoltaic panels, there are current-voltage (IV) characteristic monitoring sensors used to monitor the power generation performance of photovoltaic panels in real time. In thermal power units and nuclear power units, there are temperature, pressure, flow and other sensors used to monitor steam temperature, pressure, coolant flow and other parameters to ensure the safety and normal operation of the unit. These sensors convert the collected equipment status signals into electrical signals and transmit them to the equipment status data collector.

[0224] Equipment status data collector: Similar to a meteorological data collector, it receives data from various status monitoring sensors, performs data preprocessing and storage, and can send equipment status data to the data center via industrial Ethernet, fieldbus (such as Profibus, CAN bus) and other communication methods, providing data support for equipment fault diagnosis and health management.

[0225] Analyze the decision-making layer hardware (usually servers or high-performance computers, including server hardware. The specific content of server hardware is as follows:

[0226] Processor (CPU): Use high-performance multi-core processors, such as Intel Xeon series or AMD EPYC series. These processors have powerful computing capabilities and can quickly process large amounts of data transmitted from the data collection and monitoring layer. Multiple cores can process different analysis tasks in parallel. For example, one core is responsible for peak analysis, and another core is responsible for frequency analysis, etc., to improve analysis efficiency.

[0227] Memory (RAM): Equipped with large-capacity high-speed memory to meet the needs of data storage and processing. When performing complex model calculations (such as particle swarm optimization algorithm, genetic algorithm, etc.) and data processing, a large amount of data and intermediate results need to be stored in the memory. Sufficient memory can reduce data reading and writing time and improve the overall performance of the system.

[0228] Storage device (hard disk or solid-state drive): A storage device with a large capacity, used to store operating systems, analysis software, historical data, and various model parameters. Solid-state drives (SSDs) are usually used to store frequently accessed data and programs due to their fast read and write speeds, while large-capacity mechanical hard disks (HDDs) are used for long-term storage of historical data and backup data.

[0229] Network Interface Card (NIC): Equipped with a high-speed network interface card to ensure data transmission between the data acquisition and monitoring layer and the control execution layer. For example, a 10 Gigabit Ethernet interface card can quickly receive data sent by the data acquisition layer and send the scheduling instructions after analysis and decision-making to the control execution layer.

[0230] The control execution layer hardware includes the instruction sending module hardware and the monitoring feedback module hardware. The instruction sending module hardware includes the programmable logic controller (PLC), distributed control system (DCS) and communication equipment. The monitoring feedback module hardware includes sensors and monitoring equipment and data acquisition and monitoring system (SCADA), and further includes the following contents:

[0231] Programmable Logic Controller (PLC): PLC is a digital computing and operating electronic system widely used in the field of industrial control. It can receive dispatch instructions from the analysis and decision-making layer and convert these instructions into specific control signals for power generation equipment and energy storage devices. PLC has the characteristics of high reliability and strong anti-interference ability. It can work stably in complex industrial environments and harsh climatic conditions. For example, when controlling the output adjustment of thermal power units, PLC can accurately control the operation of fuel supply systems, steam valves and other equipment according to the received instructions.

[0232] Distributed Control System (DCS): For large-scale power generation equipment (such as nuclear power units), the DCS system plays an important role. It is a distributed network system composed of process control stations, data acquisition stations, operator stations, etc. DCS can realize decentralized control and centralized management of complex production processes, and distribute the instructions of the analysis and decision-making layer to various control subsystems, such as the power control system and cooling system of nuclear reactors, to ensure the safe and stable operation of the equipment.

[0233] Communication equipment: including industrial Ethernet switches, wireless communication modules, etc. Industrial Ethernet switches are used to build a stable wired communication network to ensure the reliable transmission of instructions within the control execution layer and between other layers. Wireless communication modules (such as Zigbee, GPRS / 4G / 5G, etc.) are used for communication in some mobile devices or scenarios where wiring is difficult, such as remote control of distributed energy storage systems.

[0234] Sensors and monitoring equipment: Similar to the equipment status monitoring at the data acquisition and monitoring layer, various sensors are also needed at the control execution layer to monitor the operating status of the equipment. For example, during the control execution of the wind turbine, vibration sensors, speed sensors, etc. continue to be used to monitor the operating status of the unit in real time. These sensors feed back the actual operating data of the equipment to the monitoring system.

[0235] Supervisory Control and Data Acquisition System (SCADA): The SCADA system is a system used for industrial process control and data acquisition. It can collect data from various monitoring sensors in real time and display them visually. Through the SCADA system, operators can intuitively see the operating parameters, status, and alarm information of the equipment. At the same time, the SCADA system can feed back this data to the analysis and decision-making layer to provide a basis for further decision-making.

[0236] Evaluation and optimization layer hardware (usually also servers or high-performance computers), including server hardware, further includes the following:

[0237] Processor (CPU): Similar to the analysis and decision-making layer, a high-performance multi-core processor is required to handle complex evaluation algorithms. When calculating the weights of each evaluation indicator (such as hierarchical analysis method) and performing fuzzy comprehensive evaluation, the processor needs to quickly perform a large number of mathematical operations and logical judgments.

[0238] Memory (RAM): Sufficient memory is required to store data such as the evaluation index system, intermediate results in the weight calculation process, and membership functions of fuzzy evaluation. Large-capacity memory can improve the efficiency of the evaluation process and reduce the time for data exchange.

[0239] Storage device (hard disk or solid-state drive): used to store evaluation algorithm programs, historical evaluation data, optimization strategies, etc. Solid-state drives are used to quickly read and store frequently used data, while large-capacity mechanical hard drives are used to store evaluation records and backup data for a long time.

[0240] Network Interface Card (NIC): It exchanges data with other layers through a high-speed network interface. Reliable network communication is required when receiving the scheduling strategy execution result data from the analysis and decision-making layer and feeding back the optimization and adjustment suggestions to the analysis and decision-making layer. The network interface card ensures the efficiency of data transmission.

[0241] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprising a reference structure" do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0242] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. The wind, solar, thermal, nuclear and storage integrated dispatching architecture based on the complex and uncertain environment of the cold-region power grid is characterized by: The architecture includes a data collection and monitoring layer, an analysis and decision-making layer, a control execution layer, and an evaluation and optimization layer, among which: The data acquisition and monitoring layer is used to collect power grid operation data, meteorological data, and equipment status data in real time; The analysis and decision-making layer analyzes and calculates the data collected by the data collection and monitoring layer, and formulates an optimized dispatching strategy, including peak-shaving analysis and decision-making of multiple power generation resources, frequency regulation analysis and decision-making of multiple power generation resources, and integrated regulation analysis and decision-making, and respectively uses particle swarm optimization algorithm, genetic algorithm, etc. to solve the corresponding models; The control execution layer is responsible for sending dispatch instructions to each power generation equipment and energy storage device, and for real-time control of the power grid; The evaluation and optimization layer conducts a comprehensive evaluation on the control effect of the control strategy, adopts the hierarchical analysis method to determine the weight of each evaluation index, uses the fuzzy comprehensive evaluation method to conduct fuzzy evaluation, and performs optimization and adjustment according to the evaluation results.

2. The wind, solar, thermal, nuclear and storage integrated dispatching architecture based on the complex and uncertain environment of the cold-region power grid according to claim 1 is characterized by: The data acquisition and monitoring layer includes a power grid operation data acquisition module, a meteorological data acquisition module and an equipment status data acquisition module, wherein: The power grid operation data acquisition module acquires the output of each power generation equipment, the voltage, current, power parameters of the power grid and the real-time change data of load demand in real time; The meteorological data collection module collects temperature, wind speed, light intensity, and precipitation information in cold regions; The equipment status data acquisition module collects the operating status, health status, and fault information of wind turbines, photovoltaic panels, thermal power units, nuclear power units, and energy storage equipment.

3. The wind, solar, thermal, nuclear and storage integrated dispatching architecture based on the complex and uncertain environment of the cold-region power grid according to claim 1 is characterized by: The peak load analysis and decision-making of the multiple power generation resources at the analysis and decision-making layer includes the following steps: Sp1: Analyze the operating conditions of wind and photovoltaic grid-connected scenarios with random uncertainty, determine the system power distribution and output change trend of each resource after wind and photovoltaic are connected to the system, and clarify the main influencing factors of the randomness of wind and photovoltaic resources; Sp2: Introduce power generation systems such as energy storage and nuclear energy, take into account the flexibility of wind, solar and grid, comprehensively consider the operation and seasonal characteristics of cold-region power grids, construct typical operation and disturbance scenarios of cold-region power grid systems, and study the peak-shaving methods of diversified resources such as wind, solar, thermal, nuclear and storage; Sp3: Taking the optimal system operation performance and regulation economic cost as the objective function, and power balance, new energy resources, network topology, branch constraints, etc. as constraints, the particle swarm optimization algorithm is used to construct an integrated wind, solar, thermal and nuclear storage peak-shaving model and method to evaluate the peak-shaving capacity.

4. The wind, solar, thermal, nuclear and storage integrated dispatching architecture based on the complex and uncertain environment of the cold-region power grid according to claim 1 is characterized by: The frequency modulation analysis and decision-making of the multiple power generation resources at the analysis and decision-making layer includes the following steps: Sp1: Investigate the operation data of multiple power generation systems such as wind, solar, thermal, and nuclear storage in the power grid, analyze the frequency regulation characteristics and methods of diversified resources such as wind, solar, thermal, and nuclear storage, and study the matching relationship between the uncertainty of wind and solar new energy and various regulation resources on different time scales; Sp2: Analyze the constraints of the frequency regulation capability of diversified resources, study the active frequency support technology of wind, solar and storage power electronic converters connected to the grid and the frequency regulation technology of nuclear power participating in the power grid that takes into account both performance and safety, and study the frequency regulation costs and benefits of various resources; Sp3: Genetic algorithm is used to construct an integrated frequency regulation dispatching model for cold-region power grids containing multiple power generation resources of wind, solar, thermal and nuclear energy. The cost, wind and solar power abandonment rate and system dynamic performance are used as objective functions. The dispatching model is constructed based on power, state variables, unit output, thermal power unit ramping and grid condition constraints. Frequency regulation technology is proposed and frequency regulation performance analysis is conducted in multiple scenarios.

5. The wind, solar, thermal, nuclear and storage integrated dispatching architecture based on the complex and uncertain environment of the cold-region power grid according to claim 1 is characterized by: The integrated control analysis and decision-making of the analysis and decision-making layer includes the following contents: Sp1: Based on the peak-shaving and frequency-regulating capabilities of wind, solar, thermal, and nuclear power resources in cold-region power grids, quantitatively characterize the differences and complementary relationships between the operating states and power response capabilities of various resources in the system at different time scales, and study the balancing mechanism at multiple time and space scales, including coordinated control strategies in terms of energy balance, power balance, and frequency balance. Sp2: Based on the typical operation and disturbance scenarios of wind, solar, thermal, nuclear and other integrated power grids in cold regions, a wind, solar, thermal, nuclear and other integrated dispatching architecture and system adapted to complex and uncertain environments is constructed, the control objectives and constraints under different scenarios are clarified, and a multi-time scale wind, solar, thermal, nuclear and nuclear integrated power grid control model is constructed; Sp3: From the aspects of response speed, regulation capability, operating cost, etc., multi-temporal and integrated control strategies are proposed from different problem levels, such as unit-level control strategy, coordination of station-level control strategy and system-level integrated dispatch strategy.

6. The wind, solar, thermal, nuclear and storage integrated dispatching architecture based on the complex and uncertain environment of the cold-region power grid according to claim 1 is characterized by: The control execution layer includes an instruction sending module and a monitoring feedback module, and further includes the following contents: The command sending module sends power generation control instructions to wind turbines and photovoltaic power stations according to the dispatching strategy formulated by the analysis and decision-making layer, issues output adjustment instructions to thermal power units, controls the power output of nuclear power units, and issues charging and discharging instructions to the energy storage system; The monitoring feedback module monitors the operating status of each device and system in real time to ensure the accurate execution of the scheduling instructions, and feeds back the operating status to the analysis and decision-making layer.

7. The wind, solar, thermal, nuclear and storage integrated dispatching architecture based on the complex and uncertain environment of the cold-region power grid according to claim 1 is characterized by: The evaluation index system of the evaluation and optimization layer includes power supply reliability index, new energy absorption rate index, system operation cost index, frequency stability index, voltage stability index and environmental protection index. The power supply reliability index is used to measure the system's ability to meet load demand, the new energy absorption rate index reflects the efficiency of new energy utilization, the system operation cost index covers various aspects of cost, the frequency stability index evaluates the frequency stability of the power grid, the voltage stability index ensures the stability of the power grid voltage, and the environmental protection index reflects the environmental protection benefits of the system.

8. The wind, solar, thermal, nuclear and storage integrated dispatching architecture based on the complex and uncertain environment of the cold-region power grid according to claim 1 is characterized by: The evaluation and optimization layer adopts the hierarchical analysis method to construct a hierarchical model, compares the relative importance of factors at each level through expert scoring or data analysis, calculates the weight of each evaluation index, and uses the fuzzy comprehensive evaluation method to determine the evaluation level of each index and the corresponding fuzzy membership function. The control strategy is evaluated according to actual data to obtain the membership of each index to different evaluation levels, and the comprehensive evaluation result is obtained by combining the weights through fuzzy synthesis operation.

9. The wind, solar, thermal, nuclear and storage integrated dispatching architecture based on the complex and uncertain environment of the cold-region power grid according to claim 1 is characterized by: The architecture forms a closed-loop operation process during the operation of the system. The data acquisition and monitoring layer transmits the collected data to the analysis and decision-making layer. The analysis and decision-making layer formulates a scheduling strategy, which is then executed by the control execution layer. The evaluation and optimization layer evaluates the regulation effect and feeds back the results to the analysis and decision-making layer for optimization and adjustment, continuously optimizing the system's operating performance and regulation effect to adapt to the changes and needs of the complex and uncertain environment of the cold-region power grid.

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