Integrated energy system management method based on day-ahead and intraday dual-level optimization scheduling

Abstract

This integrated energy system management approach, based on day-ahead and intraday dual-tier optimization scheduling, aims to improve the system's environmental friendliness and energy efficiency through this dual-tier optimization strategy. First, a system model is constructed, encompassing power supply, gas supply, heating, and energy storage equipment. Monte Carlo sampling is used to generate renewable energy output scenarios, and scenario reduction techniques are used to screen key scenarios. Next, a day-ahead optimization model is established, minimizing costs while taking into account time-of-use electricity and heating prices. The intraday model makes real-time adjustments based on the day-ahead plan, employing multi-objective optimization to reduce costs and scheduling adjustments. Fuzzy mathematics theory is introduced to address load demand uncertainty and enhance system adaptability. This method improves energy efficiency, reduces costs, and mitigates pollution, demonstrating its practical value and potential for application.

Description

Technical Field

[0001] This invention relates to the field of integrated energy system management technology, specifically to an integrated energy system management method based on day-ahead and intraday dual-layer optimized scheduling. Background Art

[0002] Against the backdrop of ever-increasing energy demand and ever-increasing environmental protection requirements, Integrated Energy Systems (IES) have attracted widespread attention as a new energy supply and management approach. By integrating multiple energy sources such as electricity, heat, and natural gas, IES achieves energy complementarity and efficient utilization, which is of great significance for improving energy efficiency, reducing energy costs, and minimizing environmental pollution.

[0003] Traditional energy management systems often rely on a single energy supply method, making it difficult to cope with fluctuations and uncertainties in energy supply and demand. Especially with the increasing integration of new energy sources such as wind and solar power, the randomness and volatility of their output pose new challenges to the stable operation and dispatch of energy systems. Furthermore, the gradual opening of the electricity and natural gas markets, coupled with fluctuations in energy prices, places higher demands on the economic efficiency of energy systems.

[0004] To address these issues, researchers have proposed various optimized scheduling methods to achieve efficient operation of integrated energy systems. However, existing scheduling methods often focus on day-ahead scheduling, i.e., formulating scheduling plans based on forecast data before the start of the day, lacking the ability to quickly respond to real-time changes within the day. This limits the system's adaptability to fluctuations in renewable energy output and changes in load demand, and also affects the system's economy and reliability.

[0005] Therefore, it is necessary to develop a new integrated energy system management method that can simultaneously formulate preliminary scheduling plans day-ahead and perform real-time intraday adjustments and optimizations to better address the uncertainties in renewable energy output and fluctuations in load demand, thereby achieving efficient, economical, and stable operation of the integrated energy system. This invention is based on this need and proposes an integrated energy system management method based on a two-tiered optimization scheduling approach combining day-ahead and intraday optimization. Summary of the Invention

[0006] The purpose of this invention is to solve the technical problem that existing integrated energy management technologies are prone to poor reliability of integrated energy systems due to the uncertainty of new energy output and the fluctuation of load demand. The invention proposes an integrated energy system management method based on day-ahead and intraday dual-layer optimization scheduling.

[0007] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0008] The integrated energy system management method based on day-ahead and intraday dual-level optimized scheduling includes the following steps:

[0009] Step s1: Establish a comprehensive energy system model, including at least one power supply system, at least one gas supply system, at least one heating system, and at least one energy storage device;

[0010] Step s2: Use Monte Carlo sampling to generate scenarios for new energy output; this includes determining the probability distribution models of wind and solar energy, performing random sampling to generate possible output data, and conducting statistical analysis to determine the expected value and variability of the output.

[0011] Step s3: Using scene reduction technology, the generated scenes are filtered based on Euclidean distance, the Euclidean distance between each scene and other scenes is calculated, cluster analysis is performed, and representative scenes are selected;

[0012] Step s4: Construct a day-ahead optimization scheduling model with the goal of minimizing operating costs, taking into account time-of-use electricity and heat prices, as well as the operating and maintenance costs of equipment, including supply and demand balance constraints for electricity, heat and natural gas, and operating limitations of equipment;

[0013] Step s5: Construct an intraday optimized scheduling model with the goal of minimizing operating costs and scheduling adjustment costs. Use a multi-objective optimization method for real-time scheduling, including minimizing operating costs and minimizing scheduling adjustment costs.

[0014] Step s6: Use fuzzy mathematics theory to model the uncertainty of load demand, define the fuzzy set of load demand, use fuzzy logic reasoning to quantify the uncertainty, and apply the analysis results to the intraday optimization scheduling model.

[0015] In step s1, each subsystem is equipped with corresponding input and output interfaces, as well as an interface for data communication with the central control system, in order to realize real-time data exchange and command issuance;

[0016] The power supply system includes power plants, which are the core of the system. Power plants generate electricity and boost voltage through step-up transformers to meet the demands of long-distance power transmission. Electricity is transmitted via high-voltage transmission lines to various substations, which reduce the voltage to a level suitable for the city's distribution network. The distribution network connects to end users through smart meters. These smart meters not only record users' electricity consumption but also receive signals from the central control system for demand-side management. The central control system analyzes real-time data to adjust the power plant's generation plans and the grid's operating status, ensuring the stability of the power supply.

[0017] The gas supply system includes natural gas wells, from which natural gas is pressurized through compression stations and then transported to storage facilities via high-pressure pipelines. The storage facilities store natural gas when demand is low and release it when demand is high to balance supply and demand. Distribution pipelines transport natural gas from storage facilities to distribution nodes and finally distribute it to commercial and residential users. A central control system monitors the entire gas supply process to ensure the continuity and security of natural gas supply.

[0018] A heating system includes a heat source (such as a thermal power plant), which generates heat energy that is transported to a heat exchange station via a heating network. The heat exchange station is responsible for converting the heat energy into a form suitable for user use. The heating network then delivers the heat energy directly to radiators or other heat-using equipment at the user end. The central control system adjusts the output of the heat source and the operating status of the heat exchange station according to user needs and external ambient temperature to achieve high efficiency and energy saving in heating.

[0019] Energy storage devices include electrical energy storage devices, which are connected to the power supply system and can store electrical energy when there is a power surplus or release electrical energy when demand is high, thereby balancing the grid load; thermal energy storage devices are connected to the heating system and store thermal energy for use when demand is high, improving the flexibility and reliability of the heating system; the central control system intelligently schedules the charging and discharging or thermal storage and release operations of energy storage devices based on energy supply and demand conditions and price signals, in order to optimize the operating efficiency of the entire system;

[0020] The central control system is connected to the data communication interfaces of all subsystems through a high-speed communication network. The control system receives data from the power supply system, gas supply system, heating system and energy storage equipment in real time, analyzes the energy supply and demand situation, and issues dispatch instructions accordingly. Through advanced data processing and optimization algorithms, the central control system realizes centralized monitoring and dispatch of the entire integrated energy system, improves energy utilization efficiency, reduces operating costs, and enhances the reliability and flexibility of the system.

[0021] In step s2, when generating a scenario for new energy output, the following steps are adopted:

[0022] S201: Data collection and analysis: Collect historical data on wind speed and solar radiation over the past year, analyze the data, and determine the distribution characteristics of wind speed and solar radiation;

[0023] s202: Establishing probability distribution models: Based on the analysis results, establish specific probability distribution models for wind energy and solar energy; for wind energy, use the Weiber distribution model, with parameters determined by the above analysis; for solar energy, use the normal distribution model, with parameters also determined by the analysis.

[0024] s203: The Monte Carlo method is used for random sampling: for wind energy, several samples are drawn from the Weiber distribution, each sample representing a possible wind speed value; for solar energy, several samples are drawn from the normal distribution, each sample representing a possible solar radiation value.

[0025] s204: Perform power output data conversion: Convert the sampled wind speed values ​​into wind energy output using the wind speed-power curve;

[0026] s205: Perform statistical analysis: Statistically analyze the generated wind and solar power output samples to calculate the expected average output and standard deviation of the output;

[0027] s206: Scenario generation and output: Integrate the results of the above statistical analysis to generate multiple most representative new energy output scenarios. Each scenario contains wind and solar power output data at several time points, covering various time periods within a day. These scenarios are generated, output, and scheduled through the central control system to provide accurate new energy output forecasts for day-ahead and intraday optimized scheduling.

[0028] In step s3, the following steps are performed during scene reduction:

[0029] s301: Scenario generation: Using the Monte Carlo sampling method, several possible new energy output scenarios are generated. Each scenario contains wind and solar power output data at multiple time points to simulate the energy output of each hour of the day.

[0030] s302: Calculate the Euclidean distance: For each pair of scenes, calculate the Euclidean distance between them;

[0031] s303: Perform clustering analysis: Use clustered k-means to group all scenarios to identify similar power output patterns;

[0032] s304: Select a representative scenario: Select a representative scenario from each cluster that best represents the average characteristics of all scenarios in that cluster;

[0033] s305: Retention of key information: The retained representative scenarios will be used as input for day-ahead and intraday scheduling models. These scenarios reflect the possible range and probability distribution of renewable energy output.

[0034] S306: Execution Output and Reception: The central control system performs scenario reduction and outputs the final representative scenario set; the central control system receives the above representative scenario set and uses the above representative scenarios to formulate the day-ahead and intraday scheduling strategies.

[0035] In step s4, the specific steps for constructing the day-ahead optimization scheduling model are as follows:

[0036] s401: Determine the objective function: The objective function of the model is to minimize the total operating cost, which includes the cost of purchasing electricity, heat, and natural gas, as well as the cost of equipment operation and maintenance.

[0037] S402: Set cost parameters: Set time-of-use electricity price; Set time-of-use heat price; Calculate equipment operating costs; Consider equipment maintenance costs;

[0038] S403: Obtaining supply and demand balance constraints: ensuring power supply and demand balance, that is, the power generation of the power supply system plus the discharge of the energy storage device equals the total power demand of the user; ensuring heat supply and demand balance, that is, the heat supply of the heating system plus the heat release of the thermal energy storage device equals the total heat demand of the user; ensuring natural gas supply and demand balance, that is, the gas supply of the gas supply system equals the total natural gas demand of the user.

[0039] S404: Set device operation limits: Set device start-up and shutdown time limits; Set minimum stable operating time for the device;

[0040] s405: Solve the model: Use a linear programming or mixed-integer linear programming solver to solve the model and obtain the optimal operating plan for each device;

[0041] s406. Output and Receive: Input the operating plan of each device for each hour of the day, and then schedule each device according to the operating plan output by the model.

[0042] In step s5, the specific steps for constructing the intraday optimized scheduling model are as follows:

[0043] s501: Construct the objective function:

[0044] This includes minimizing operating costs. An objective function is defined that calculates the total cost of meeting predicted load demand given real-time electricity, heat, and natural gas prices. The real-time electricity price function is set as C. e (t)=α e ·P e (t), where C e (t) is the electricity cost at time t, α e It is the unit electricity price, P e (t) represents the electricity consumption at time t;

[0045] It also includes minimizing scheduling adjustment costs: defining a second objective function that evaluates the costs incurred when adjusting day-ahead plans due to fluctuations in renewable energy output or changes in load demand, specifically expressed as C. a (t)=β.|P pred (t)-P real(t)|, where C a (t) is the adjustment cost, β is the unit adjustment cost, and P pred (t) represents the predicted output of new energy sources, P real (t) represents the actual output of new energy sources;

[0046] S502: Real-time data integration and processing: Integrates real-time wind speed and sunshine data from weather stations, as well as real-time load data from smart meters and heat meters; uses data preprocessing algorithms, including moving average or Kalman filtering, to smooth the data and reduce noise;

[0047] S503: Application of multi-objective optimization algorithms: Applying genetic algorithms, where each "individual" represents a set of possible equipment operation strategies and energy storage operations, and exploring the solution space through simulated annealing or particle swarm optimization algorithms to find the optimal solution that simultaneously satisfies two objective functions;

[0048] s504: Configure the specific model parameters: Set the prediction error range of new energy output and define the minimum start-up and shutdown time for each device to achieve a response to rapidly changing market conditions;

[0049] s505: Execution of real-time scheduling decisions: Utilizing the optimal solution obtained from the optimization algorithm, specific scheduling instructions are generated, such as adjusting the output power of the wind turbine or changing the charging and discharging level of the energy storage device; the scheduling instructions include specific operating parameters, including the new target power setpoint of the wind turbine and the charging and discharging rate of the energy storage device;

[0050] S506: Specific implementation of output and reception: The output is the central control system, which generates and sends dispatch instructions; the receiving is the control system of each device in the integrated energy system, including the control system of wind turbines and the management system of energy storage devices, which execute corresponding operations according to the received instructions.

[0051] S507: Monitoring and feedback of execution results: After the equipment control system executes the scheduling command, it monitors the operation results in real time and feeds back the execution status to the central control system; the central control system evaluates the scheduling effect based on the feedback results and makes adjustments when necessary to ensure that the system always runs along the optimal path.

[0052] In step s6, fuzzy mathematics theory is used to model the uncertainty of load demand. The specific steps are as follows:

[0053] s601: Define the fuzzy set: First, define the fuzzy set of load demand; for power load, establish a fuzzy set containing three fuzzy variables: "low", "medium", and "high", each variable corresponding to a different load level;

[0054] s602: Constructing membership functions: Construct membership functions for each fuzzy variable; for “low” load, this includes defining a trapezoidal membership function, whose membership increases from 0 to 1 at 400 to 600 kW, and then decreases to 0 at 600 to 800 kW.

[0055] s603: Formulate fuzzy logic reasoning rules: Formulate fuzzy logic reasoning rules to handle uncertain information in fuzzy sets; if the actual load is close to the boundary between "low" and "medium", the rules will determine how to adjust the operation strategy of power generation and energy storage equipment;

[0056] s604: Implementing a fuzzy inference system: This system uses fuzzy logic inference rules to analyze real-time load data and converts the results into clear action instructions; if the real-time load data indicates that the current load is in the range of "medium" to "high", the fuzzy inference system will output an instruction to increase power generation;

[0057] s605: Defuzzify the fuzzy results: Defuzzify the results of fuzzy inference to obtain specific numerical outputs; including using the centroid method (also known as the centroid method) to convert the fuzzy output into a specific scheduling decision, including increasing the power generation capacity by several kilowatts;

[0058] s606: Integration of intraday optimized scheduling model: Integrating the defuzzified output into the intraday optimized scheduling model; including: using the increased power generation as a constraint condition of the intraday scheduling model to adjust the equipment's operating plan;

[0059] s607: Specific implementation of output and reception: Output scheduling suggestions based on fuzzy logic reasoning, and then adjust the device's operating strategy according to the output of the fuzzy mathematical model;

[0060] s608: Implementation of dynamic response: The central control system implements dynamic response based on the output of the fuzzy mathematical model to adapt to load changes.

[0061] Compared with the prior art, the present invention has the following technical effects:

[0062] 1) Improve the economics of the energy system: Through the day-ahead optimization scheduling model, the integrated energy system can formulate the lowest-cost operating plan before the start of the day based on the predicted renewable energy output and load demand. The intraday optimization scheduling model can then adjust the day-ahead plan based on real-time data to cope with fluctuations in renewable energy output and changes in load demand. This two-tiered optimization strategy enables the system to minimize operating costs, including energy purchase costs, equipment operation and maintenance costs, and additional costs incurred due to scheduling adjustments, while ensuring supply and demand balance.

[0063] 2) Enhancing System Reliability and Stability: This invention models the uncertainty of load demand using fuzzy mathematics theory, enabling the system to respond more flexibly to random load changes. In intraday optimized scheduling, a rolling optimization strategy is employed to monitor and respond to real-time changes in renewable energy output and load demand, allowing for rapid adjustments and ensuring stable system operation. Furthermore, considerations regarding equipment start-up and shutdown time limits and minimum stable operating time help avoid equipment damage and operational risks caused by frequent start-ups and shutdowns.

[0064] 3) Enhancing the absorption capacity of new energy sources and the system's energy efficiency: Through day-ahead and intraday dual-layer optimized scheduling, the system can more effectively utilize new energy sources such as wind and solar power. The day-ahead scheduling model considers the probability distribution of new energy output, while the intraday scheduling model can adjust based on real-time new energy output data, thereby maximizing the utilization rate of new energy sources. Simultaneously, by optimizing the charging and discharging strategies of energy storage devices, the system can better balance supply and demand, reduce energy waste, and improve overall energy efficiency.

[0065] In summary, the technical solution of this invention, through refined scheduling strategies and advanced optimization algorithms, achieves significant improvements in the economy, reliability, and renewable energy absorption capacity of integrated energy systems, providing strong technical support for the efficient management and sustainable development of integrated energy systems. Attached Figure Description

[0066] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0067] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0068] like Figure 1 As shown, a comprehensive energy system management method based on day-ahead and intraday dual-layer optimization scheduling includes the following steps:

[0069] Step s1: Establish a comprehensive energy system model, including at least one power supply system, at least one gas supply system, at least one heating system, and at least one energy storage device;

[0070] Step s2: Use Monte Carlo sampling to generate scenarios for new energy output; this includes determining the probability distribution models of wind and solar energy, performing random sampling to generate possible output data, and conducting statistical analysis to determine the expected value and variability of the output.

[0071] Step s3: Using scene reduction technology, the generated scenes are filtered based on Euclidean distance, the Euclidean distance between each scene and other scenes is calculated, cluster analysis is performed, and representative scenes are selected;

[0072] Step s4: Construct a day-ahead optimization scheduling model with the goal of minimizing operating costs, taking into account time-of-use electricity and heat prices, as well as the operating and maintenance costs of equipment, including supply and demand balance constraints for electricity, heat and natural gas, and operating limitations of equipment;

[0073] Step s5: Construct an intraday optimized scheduling model with the goal of minimizing operating costs and scheduling adjustment costs. Use a multi-objective optimization method for real-time scheduling, including minimizing operating costs and minimizing scheduling adjustment costs.

[0074] Step s6: Use fuzzy mathematics theory to model the uncertainty of load demand, define the fuzzy set of load demand, use fuzzy logic reasoning to quantify the uncertainty, and apply the analysis results to the intraday optimization scheduling model.

[0075] In step s1, each subsystem is equipped with corresponding input and output interfaces, as well as an interface for data communication with the central control system, in order to realize real-time data exchange and command issuance;

[0076] The power supply system includes power plants, which are the core of the system. Power plants generate electricity and boost voltage through step-up transformers to meet the demands of long-distance power transmission. Electricity is transmitted via high-voltage transmission lines to various substations, which reduce the voltage to a level suitable for the city's distribution network. The distribution network connects to end users through smart meters. These smart meters not only record users' electricity consumption but also receive signals from the central control system for demand-side management. The central control system analyzes real-time data to adjust the power plant's generation plans and the grid's operating status, ensuring the stability of the power supply.

[0077] The gas supply system includes natural gas wells, from which natural gas is pressurized through compression stations and then transported to storage facilities via high-pressure pipelines. The storage facilities store natural gas when demand is low and release it when demand is high to balance supply and demand. Distribution pipelines transport natural gas from storage facilities to distribution nodes and finally distribute it to commercial and residential users. A central control system monitors the entire gas supply process to ensure the continuity and security of natural gas supply.

[0078] The heating system includes a heat source. Starting from the heat source, the generated heat energy is transported to the heat exchange station through the heat pipe network. The heat exchange station is responsible for converting the heat energy into a form suitable for user use. The heat pipe network directly delivers the heat energy to the radiators or other heat energy-using equipment at the user end. The central control system adjusts the output of the heat source and the working status of the heat exchange station according to the user's needs and the external ambient temperature to achieve high efficiency and energy saving in heating.

[0079] Energy storage devices include electrical energy storage devices, which are connected to the power supply system and can store electrical energy when there is a power surplus or release electrical energy when demand is high, thereby balancing the grid load; thermal energy storage devices are connected to the heating system and store thermal energy for use when demand is high, improving the flexibility and reliability of the heating system; the central control system intelligently schedules the charging and discharging or thermal storage and release operations of energy storage devices based on energy supply and demand conditions and price signals, in order to optimize the operating efficiency of the entire system;

[0080] The central control system is connected to the data communication interfaces of all subsystems through a high-speed communication network. The control system receives data from the power supply system, gas supply system, heating system and energy storage equipment in real time, analyzes the energy supply and demand situation, and issues dispatch instructions accordingly. Through advanced data processing and optimization algorithms, the central control system realizes centralized monitoring and dispatch of the entire integrated energy system, improves energy utilization efficiency, reduces operating costs, and enhances the reliability and flexibility of the system.

[0081] In step s2, when generating a scenario for new energy output, the following steps are adopted:

[0082] S201: Data collection and analysis: Collect historical data on wind speed and solar radiation over the past year, analyze the data, and determine the distribution characteristics of wind speed and solar radiation;

[0083] s202: Establishing probability distribution models: Based on the analysis results, establish specific probability distribution models for wind energy and solar energy; for wind energy, use the Weiber distribution model, with parameters determined by the above analysis; for solar energy, use the normal distribution model, with parameters also determined by the analysis.

[0084] s203: The Monte Carlo method is used for random sampling: for wind energy, several samples are drawn from the Weiber distribution, each sample representing a possible wind speed value; for solar energy, several samples are drawn from the normal distribution, each sample representing a possible solar radiation value.

[0085] s204: Perform power output data conversion: Convert the sampled wind speed values ​​into wind energy output using the wind speed-power curve;

[0086] s205: Perform statistical analysis: Statistically analyze the generated wind and solar power output samples to calculate the expected average output and standard deviation of the output;

[0087] s206: Scenario generation and output: Integrate the results of the above statistical analysis to generate multiple most representative new energy output scenarios. Each scenario contains wind and solar power output data at several time points, covering various time periods within a day. These scenarios are generated, output, and scheduled through the central control system to provide accurate new energy output forecasts for day-ahead and intraday optimized scheduling.

[0088] When reducing a scene, follow these steps:

[0089] s301: Scenario generation: Using the Monte Carlo sampling method, several possible new energy output scenarios are generated. Each scenario contains wind and solar power output data at multiple time points to simulate the energy output of each hour of the day.

[0090] s302: Calculate the Euclidean distance: For each pair of scenes, calculate the Euclidean distance between them;

[0091] s303: Perform clustering analysis: Use clustered k-means to group all scenarios to identify similar power output patterns;

[0092] s304: Select a representative scenario: Select a representative scenario from each cluster that best represents the average characteristics of all scenarios in that cluster;

[0093] s305: Retention of key information: The retained representative scenarios will be used as input for day-ahead and intraday scheduling models. These scenarios reflect the possible range and probability distribution of renewable energy output.

[0094] S306: Execution Output and Reception: The central control system performs scenario reduction and outputs the final representative scenario set; the central control system receives the above representative scenario set and uses the above representative scenarios to formulate the day-ahead and intraday scheduling strategies.

[0095] In step s4, the specific steps for constructing the day-ahead optimization scheduling model are as follows:

[0096] s401: Determine the objective function: The objective function of the model is to minimize the total operating cost, which includes the cost of purchasing electricity, heat, and natural gas, as well as the cost of equipment operation and maintenance.

[0097] S402: Set cost parameters: Set time-of-use electricity price; Set time-of-use heat price; Calculate equipment operating costs; Consider equipment maintenance costs;

[0098] S403: Obtaining supply and demand balance constraints: ensuring power supply and demand balance, that is, the power generation of the power supply system plus the discharge of the energy storage device equals the total power demand of the user; ensuring heat supply and demand balance, that is, the heat supply of the heating system plus the heat release of the thermal energy storage device equals the total heat demand of the user; ensuring natural gas supply and demand balance, that is, the gas supply of the gas supply system equals the total natural gas demand of the user.

[0099] S404: Set device operation limits: Set device start-up and shutdown time limits; Set minimum stable operating time for the device;

[0100] s405: Solve the model: Use a linear programming or mixed-integer linear programming solver to solve the model and obtain the optimal operating plan for each device;

[0101] s406. Output and Receive: Input the operating plan of each device for each hour of the day, and then schedule each device according to the operating plan output by the model.

[0102] In step s5, the specific steps for constructing the intraday optimized scheduling model are as follows:

[0103] s501: Construct the objective function:

[0104] This includes minimizing operating costs. An objective function is defined that calculates the total cost of meeting predicted load demand given real-time electricity, heat, and natural gas prices. The real-time electricity price function is set as C. e (t)=α e ·P e (t), where C e (t) is the electricity cost at time t, α e It is the unit electricity price, P e (t) represents the electricity consumption at time t;

[0105] It also includes minimizing scheduling adjustment costs: defining a second objective function that evaluates the costs incurred when adjusting day-ahead plans due to fluctuations in renewable energy output or changes in load demand, specifically expressed as C. a (t)=β.|P pred (t)-P real (t)|, where C a (t) is the adjustment cost, β is the unit adjustment cost, and P pred (t) represents the predicted output of new energy sources, P real (t) represents the actual output of new energy sources;

[0106] S502: Real-time data integration and processing: Integrates real-time wind speed and sunshine data from weather stations, as well as real-time load data from smart meters and heat meters; uses data preprocessing algorithms, including moving average or Kalman filtering, to smooth the data and reduce noise;

[0107] S503: Application of multi-objective optimization algorithms: Applying genetic algorithms, where each "individual" represents a set of possible equipment operation strategies and energy storage operations, and exploring the solution space through simulated annealing or particle swarm optimization algorithms to find the optimal solution that simultaneously satisfies two objective functions;

[0108] s504: Configure the specific model parameters: Set the prediction error range of new energy output and define the minimum start-up and shutdown time for each device to achieve a response to rapidly changing market conditions;

[0109] s505: Execution of real-time scheduling decisions: Utilizing the optimal solution obtained from the optimization algorithm, specific scheduling instructions are generated, such as adjusting the output power of the wind turbine or changing the charging and discharging level of the energy storage device; the scheduling instructions include specific operating parameters, including the new target power setpoint of the wind turbine and the charging and discharging rate of the energy storage device;

[0110] S506: Specific implementation of output and reception: The output is the central control system, which generates and sends dispatch instructions; the receiving is the control system of each device in the integrated energy system, including the control system of wind turbines and the management system of energy storage devices, which execute corresponding operations according to the received instructions.

[0111] S507: Monitoring and feedback of execution results: After the equipment control system executes the scheduling command, it monitors the operation results in real time and feeds back the execution status to the central control system; the central control system evaluates the scheduling effect based on the feedback results and makes adjustments when necessary to ensure that the system always runs along the optimal path.

[0112] In step s6, fuzzy mathematics theory is used to model the uncertainty of load demand. The specific steps are as follows:

[0113] s601: Define the fuzzy set: First, define the fuzzy set of load demand; for power load, establish a fuzzy set containing three fuzzy variables: "low", "medium", and "high", each variable corresponding to a different load level;

[0114] s602: Constructing membership functions: Construct membership functions for each fuzzy variable; for “low” load, this includes defining a trapezoidal membership function, whose membership increases from 0 to 1 at 400 to 600 kW, and then decreases to 0 at 600 to 800 kW.

[0115] s603: Formulate fuzzy logic reasoning rules: Formulate fuzzy logic reasoning rules to handle uncertain information in fuzzy sets; if the actual load is close to the boundary between "low" and "medium", the rules will determine how to adjust the operation strategy of power generation and energy storage equipment;

[0116] s604: Implementing a fuzzy inference system: This system uses fuzzy logic inference rules to analyze real-time load data and converts the results into clear action instructions; if the real-time load data indicates that the current load is in the range of "medium" to "high", the fuzzy inference system will output an instruction to increase power generation;

[0117] s605: Defuzzify the fuzzy results: Defuzzify the results of fuzzy inference to obtain specific numerical outputs; including using the centroid method to convert the fuzzy output into a specific scheduling decision, such as increasing the power generation capacity by several kilowatts.

[0118] s606: Integration of intraday optimized scheduling model: Integrating the defuzzified output into the intraday optimized scheduling model; including: using the increased power generation as a constraint condition of the intraday scheduling model to adjust the equipment's operating plan;

[0119] s607: Specific implementation of output and reception: Output scheduling suggestions based on fuzzy logic reasoning, and then adjust the device's operating strategy according to the output of the fuzzy mathematical model;

[0120] s608: Implementation of dynamic response: The central control system implements dynamic response based on the output of the fuzzy mathematical model to adapt to load changes.

[0121] Example:

[0122] This method includes: First, establishing a comprehensive energy system model, which includes at least one power supply system, at least one gas supply system, at least one heating system, and at least one energy storage device (such as electrical energy storage and thermal energy storage). Each subsystem is equipped with corresponding input and output interfaces, as well as an interface for data communication with the central control system to achieve real-time data exchange and command issuance. In the comprehensive energy system optimization scheduling method, the construction of the system model and the connection between subsystems are key to achieving efficient energy management. The following are the detailed connection and operation steps:

[0123] Power Supply System Connection and Operation: Power plants, as the core of the power supply system, generate electricity and boost voltage through step-up transformers to meet the demands of long-distance power transmission. Electricity is transmitted via high-voltage transmission lines to various substations, which then reduce the voltage to a level suitable for the city's distribution network. The distribution network connects to end users through smart meters. These smart meters not only record users' electricity consumption but also receive signals from the central control system for demand-side management. Based on real-time data analysis, the central control system adjusts the power plant's generation plan and the grid's operating status to ensure the stability and economy of the power supply.

[0124] Gas supply system connection and operation: The gas supply system starts from the natural gas well, pressurizes the natural gas through a compression station, and then transmits it to storage facilities via high-pressure pipelines. Storage facilities store natural gas when demand is low and release it when demand is high to balance supply and demand. Distribution pipelines transport natural gas from storage facilities to distribution nodes, and finally distribute it to commercial and residential users. A central control system monitors the entire gas supply process to ensure the continuity and security of the natural gas supply.

[0125] Connection and operation of the heating system: The heating system begins with a heat source (such as a thermal power plant). The generated heat energy is transported through a heating network to a heat exchange station, which is responsible for converting the heat energy into a form suitable for user consumption. The heating network then delivers the heat energy directly to radiators or other heat-using equipment at the user end. The central control system adjusts the output of the heat source and the operating status of the heat exchange station according to user needs and external ambient temperature to achieve high efficiency and energy saving in heating.

[0126] Connection and operation of energy storage devices: Electrical energy storage devices are connected to the power supply system, storing electrical energy when there is a power surplus or releasing it during peak demand periods, thereby balancing the grid load. Thermal energy storage devices are connected to the heating system, storing thermal energy for use during peak demand periods, improving the flexibility and reliability of the heating system. The central control system intelligently schedules the charging and discharging or thermal energy release operations of energy storage devices based on energy supply and demand conditions and price signals to optimize the overall system operating efficiency.

[0127] Central Control System Connection and Operation: The central control system is the core of the entire integrated energy system. It is connected to all subsystems via a high-speed communication network and data communication interfaces. The control system receives data in real time from the power supply system, gas supply system, heating system, and energy storage equipment, analyzes the energy supply and demand situation, and issues dispatch instructions accordingly. Through advanced data processing and optimization algorithms, the central control system achieves centralized monitoring and dispatching of the entire integrated energy system, improving energy utilization efficiency, reducing operating costs, and enhancing system reliability and flexibility.

[0128] In the integrated energy system optimization scheduling method of the present invention, the generation of new energy output scenarios is achieved through the following specific steps:

[0129] S201. Data Collection and Analysis: Collect historical wind speed and solar radiation data for the past year. These data are recorded every 15 minutes, totaling 2880 data points per day. Statistical software is used to analyze this data to determine the distribution characteristics of wind speed and solar radiation. For example, wind speed data may follow a Weibull distribution with parameters k = 2.5 and c = 15 m / s; solar radiation data may follow a normal distribution with a mean μ = 200 W / m². 2 Standard deviation σ = 30 W / m 2 .

[0130] s202. Establishment of Probability Distribution Models: Based on the analysis results, specific probability distribution models are established for wind and solar energy. For wind energy, the Weiber distribution model is used, with parameters determined by the above analysis; for solar energy, the normal distribution model is used, with parameters also determined by the analysis.

[0131] s203. Monte Carlo Sampling: Random sampling is performed using the Monte Carlo method. For wind energy, 10,000 samples are drawn from a Weiber distribution, each representing a possible wind speed value; for solar energy, 10,000 samples are also drawn from a normal distribution, each representing a possible solar radiation value.

[0132] s204. Output Data Conversion: The sampled wind speed values ​​are converted into wind power output using a wind speed-power curve. For example, if the wind speed sample is 8 m / s, the corresponding wind power output is 200 kW by looking up a table or calculation. The sampled solar radiation values ​​are converted into solar power output using a photovoltaic efficiency curve. For example, if the solar radiation sample is 250 W / m... 2 Therefore, the corresponding solar power output is calculated to be 50kW.

[0133] s205. Statistical Analysis: Perform statistical analysis on the 10,000 generated wind and solar power output samples to calculate the expected average output and standard deviation. For example, the average wind power output might be 150kW with a standard deviation of 30kW; the average solar power output might be 45kW with a standard deviation of 10kW.

[0134] s206. Scenario Generation and Output: The results of the above statistical analysis are integrated to generate 100 of the most representative renewable energy output scenarios. Each scenario contains wind and solar power output data at 24 time points, covering all time periods within a day. These scenarios are generated by the data analysis module of the central control system and output to the scheduling module, providing accurate renewable energy output forecasts for day-ahead and intraday optimized scheduling.

[0135] In the integrated energy system optimization scheduling method, the scenario reduction step is implemented through the following specific operations:

[0136] s301. Scenario Generation: Using the Monte Carlo sampling method, 10,000 possible new energy output scenarios were generated. Each scenario contains wind and solar power output data at 24 time points, simulating energy output every hour of the day.

[0137] s302. Euclidean Distance Calculation: For each pair of scenes, calculate the Euclidean distance between them. For example, for scene A and scene B, the Euclidean distance calculation formula is: Where PtA and QtA represent the wind and solar power output of scenario A at time t, respectively, and PtB and QtA represent the wind and solar power output of scenario A at time t. tB These represent the wind and solar power output of scenario B at time t, respectively.

[0138] s303. Cluster Analysis: Use k-means clustering to group all scenarios to identify similar power output patterns. For example, divide 10,000 scenarios into 100 clusters, with each cluster containing scenarios with similar power output curves.

[0139] s304. Representative Scene Selection: Select a representative scene from each cluster that best represents the average characteristics of all scenes in that cluster. For example, select the scene that minimizes the mean Euclidean distance in each cluster as the representative.

[0140] s305. Key Information Retention: The retained representative scenarios will be used as input for day-ahead and intraday scheduling models. These scenarios reflect the possible range and probability distribution of renewable energy output.

[0141] s306. Output and Receive: The output is the data analysis module in the central control system, which is responsible for executing the scene reduction algorithm and outputting the final representative scene set. The receiver is the scheduling module in the central control system, which will use these representative scenes to formulate day-ahead and intraday scheduling strategies.

[0142] In the integrated energy system optimization scheduling method, the specific steps for constructing the day-ahead optimization scheduling model are as follows:

[0143] s401. Objective function definition: The objective function of the model is to minimize the total operating cost, which includes the cost of purchasing electricity, heat, natural gas, and equipment operation and maintenance.

[0144] S402. Cost Parameter Settings: Set time-of-use electricity prices, for example, $0.15 / kWh during peak hours and $0.05 / kWh during off-peak hours. Set time-of-use heat prices, for example, $0.07 / kcal during peak hours and $0.03 / kcal during off-peak hours. Calculate equipment operating costs, for example, $5000 / hour for a gas turbine and $1000 / hour for an electric boiler. Consider equipment maintenance costs, for example, 2% of the total cost per unit per year.

[0145] S403. Supply and Demand Balance Constraints: Ensure a balance between electricity supply and demand, meaning the sum of the power generation from the power supply system and the discharge from energy storage devices equals the total electricity demand at the user end. Ensure a balance between heat supply and demand, meaning the sum of the heat supplied by the heating system and the heat released by thermal energy storage devices equals the total heat demand at the user end. Ensure a balance between natural gas supply and demand, meaning the gas supply from the gas supply system equals the total natural gas demand at the user end.

[0146] S404. Equipment Operation Limitations: Set start-up and shutdown time limits for the equipment. For example, the minimum start-up and shutdown interval for a gas turbine is 4 hours. Set the minimum stable operating time for the equipment. For example, the minimum stable operating time for a heat exchange station is 2 hours.

[0147] s405. Model Solving: Use a linear programming or mixed-integer linear programming solver to solve the model and obtain the optimal operating plan for each device.

[0148] S406. Output and Receive: The output is the day-ahead optimized scheduling model, which provides the operating plans for each device every hour of the day. The receiver is the execution module of the central control system, which schedules each device according to the operating plans output by the model.

[0149] In the integrated energy system optimization scheduling method, the construction of the intraday optimization scheduling model is achieved through the following detailed steps:

[0150] s501. Detailed Construction of the Objective Function: Minimizing Operating Costs. An objective function is defined that calculates the total cost of meeting predicted load demand given real-time electricity, heat, and natural gas prices. The real-time electricity price function is set as C. e (t)=α e ·P e (t), where C e (t) is the electricity cost at time t, α e It is the unit electricity price, P e(t) represents the electricity consumption at time t. Similarly, corresponding cost functions are defined for heat and natural gas. Minimizing dispatch adjustment costs: A second objective function is defined, which evaluates the costs incurred when adjustments to day-ahead plans are needed due to fluctuations in renewable energy output or changes in load demand. For example, if the actual output of wind power is 10% lower than predicted, the cost of purchasing additional electricity or adjusting the charging and discharging strategy of energy storage devices is calculated, and the formula can be expressed as C. a (t)=β.|P pred (t)-P real (t)|, where C a (t) is the adjustment cost, β is the unit adjustment cost, and P pred (t) represents the predicted output of new energy sources, P real (t) represents the actual output of new energy sources.

[0151] S502. Real-time Data Integration and Processing: Integrates real-time wind speed and sunshine data from weather stations, as well as real-time load data from smart meters and heat meters. Data preprocessing algorithms, such as moving averages or Kalman filtering, are used to smooth the data and reduce noise.

[0152] S503. Application of Multi-Objective Optimization Algorithms: Genetic algorithms are applied, where each "individual" represents a set of possible equipment operation strategies and energy storage operations. Simulated annealing or particle swarm optimization algorithms are used to explore the solution space and find the optimal solution that simultaneously satisfies two objective functions.

[0153] s504. Specific configuration of model parameters: Set the prediction error range of new energy output to ±5%, and define the minimum start-up and shutdown time for each device, for example, the minimum start-up and shutdown time for wind turbines is 1 hour. Define the time interval for scheduling adjustments as once every 15 minutes to achieve responsiveness to rapidly changing market conditions.

[0154] S505. Execution of Real-Time Scheduling Decisions: Utilizing the optimal solution derived from optimization algorithms, specific scheduling instructions are generated, such as adjusting the output power of wind turbines or changing the charging and discharging levels of energy storage devices. These scheduling instructions include specific operating parameters, such as the new target power setpoint for the wind turbines and the charging and discharging rates of the energy storage devices.

[0155] s506. Specific implementation of output and reception: The output is the intraday optimization scheduling module of the central control system, which generates and sends scheduling instructions. The receivers are the control systems of various devices in the integrated energy system, such as the control system of wind turbines and the management system of energy storage devices, which execute corresponding operations according to the received instructions.

[0156] S507. Monitoring and Feedback of Execution Results: After executing scheduling instructions, the equipment control system monitors the operation results in real time and feeds back the execution status to the central control system. The central control system evaluates the scheduling effect based on the feedback results and makes adjustments as necessary to ensure that the system always operates along the optimal path.

[0157] Modeling the uncertainty of load demand using fuzzy mathematics theory is a crucial step, and the specific steps are as follows:

[0158] s601. Definition of Fuzzy Sets: First, define a fuzzy set for load demand. For example, for electricity load, establish a fuzzy set containing three fuzzy variables: "low," "medium," and "high," each corresponding to a different load level.

[0159] s602. Construction of Membership Functions: Construct a membership function for each fuzzy variable. For example, for "low" load, define a trapezoidal membership function, whose membership increases from 0 to 1 between 400 and 600 kW, and then decreases to 0 between 600 and 800 kW.

[0160] s603. Formulation of Fuzzy Logic Inference Rules: Formulate fuzzy logic inference rules to handle uncertain information in fuzzy sets. For example, if the actual load is close to the boundary between "low" and "medium", the rules will determine how to adjust the operating strategies of power generation and energy storage equipment.

[0161] s604. Implementation of the Fuzzy Inference System: Implement a fuzzy inference system that uses fuzzy logic inference rules to analyze real-time load data and convert the results into clear action instructions. For example, if real-time load data indicates that the current load is in the "medium" to "high" range, the fuzzy inference system will output an instruction to increase power generation.

[0162] s605. Defuzzification of Fuzzy Results: The results of fuzzy inference are defuzzified to obtain specific numerical outputs. For example, the centroid method (also known as the center of gravity method) is used to convert the fuzzy output into a specific scheduling decision, such as increasing the power generation capacity by 100 kilowatts.

[0163] s606. Integration of Intraday Optimized Scheduling Model: Integrate the defuzzified output into the intraday optimized scheduling model. For example, use the increased power generation as a constraint in the intraday scheduling model to adjust the equipment's operating schedule.

[0164] s607. Specific implementation of output and reception: The output side is the fuzzy mathematics theory modeling module, which provides scheduling suggestions based on fuzzy logic reasoning. The receiver is the intraday optimization scheduling module of the central control system, which adjusts the equipment's operating strategy according to the output of the fuzzy mathematical model.

[0165] s608. Implementation of dynamic response: The central control system implements dynamic response based on the output of the fuzzy mathematical model to adapt to load changes. For example, if the fuzzy model predicts that the load will increase in the next hour, the control system will adjust the charging and discharging schedule of the energy storage equipment in advance.

[0166] To further facilitate understanding by those skilled in the art, the present invention is further explained and described as follows:

[0167] like Figure 1 As shown, the integrated energy system optimization and scheduling method constructs an integrated model encompassing power supply, gas supply, heating, and energy storage equipment, achieving efficient energy management through precise parameter settings and real-time monitoring. In the power supply system, the power plant generates electricity at a rated power of 950 MW, which is then stepped up to 345 kV via a transformer to reduce losses during long-distance transmission. Electricity is connected to users via smart meters with an accuracy of 0.5%, enabling real-time recording and adjustment of user power consumption. The gas supply system starts from natural gas wells, pressurizing the gas to 70 bar via a compression station, and then transmitting it through a 1-meter diameter pipeline to storage facilities with a total capacity of 5 million cubic meters to meet peak-hour gas demand. The heating system is provided by a thermal power plant, outputting hot water at 120 degrees Celsius, which is transported to a heat exchange station via a 500 mm diameter heat pipe network. The heat exchange station achieves a conversion efficiency of 95%, ensuring that the heat received by users meets their needs. The energy storage system includes a 100 MWh battery energy storage system and a 50,000 cubic meter hot water storage tank, capable of storing energy during periods of excess electricity and heat supply and releasing it during peak demand to balance supply and demand. The central control system is connected to each subsystem via a fiber optic communication network with a transmission rate of 1 gigabits per second, ensuring real-time and accurate data transmission. Through precise parameter settings and real-time monitoring, the system of this invention can achieve optimal energy allocation and scheduling, improve energy utilization efficiency, reduce operating costs, and enhance system reliability and flexibility.

[0168] The generation of new energy power output scenarios is achieved through the following specific steps:

[0169] S201. Data Collection and Analysis: Collect historical wind speed and solar radiation data for the past year. These data are recorded every 15 minutes, totaling 2880 data points per day. Statistical software is used to analyze this data to determine the distribution characteristics of wind speed and solar radiation. For example, wind speed data may follow a Weibull distribution with parameters k = 2.5 and c = 15 m / s; solar radiation data may follow a normal distribution with a mean μ = 200 W / m². 2 Standard deviation σ = 30 W / m 2 .

[0170] s202. Establishment of Probability Distribution Models: Based on the analysis results, specific probability distribution models are established for wind and solar energy. For wind energy, the Weiber distribution model is used, with parameters determined by the above analysis; for solar energy, the normal distribution model is used, with parameters also determined by the analysis.

[0171] s203. Monte Carlo Sampling: Random sampling is performed using the Monte Carlo method. For wind energy, 10,000 samples are drawn from a Weiber distribution, each representing a possible wind speed value; for solar energy, 10,000 samples are also drawn from a normal distribution, each representing a possible solar radiation value.

[0172] s204. Output Data Conversion: The sampled wind speed values ​​are converted into wind power output using a wind speed-power curve. For example, if the wind speed sample is 8 m / s, the corresponding wind power output is 200 kW by looking up a table or calculation. The sampled solar radiation values ​​are converted into solar power output using a photovoltaic efficiency curve. For example, if the solar radiation sample is 250 W / m... 2 Therefore, the corresponding solar power output is calculated to be 50kW.

[0173] s205. Statistical Analysis: Perform statistical analysis on the 10,000 generated wind and solar power output samples to calculate the expected average output and standard deviation. For example, the average wind power output might be 150kW with a standard deviation of 30kW; the average solar power output might be 45kW with a standard deviation of 10kW.

[0174] s206. Scenario Generation and Output: The results of the above statistical analysis are integrated to generate 100 of the most representative renewable energy output scenarios. Each scenario contains wind and solar power output data at 24 time points, covering all time periods within a day. These scenarios are generated by the data analysis module of the central control system and output to the scheduling module, providing accurate renewable energy output forecasts for day-ahead and intraday optimized scheduling.

[0175] The scenario reduction process was achieved through precise calculations and cluster analysis. First, from 10,000 renewable energy output scenarios generated via Monte Carlo sampling, each scenario detailed hourly wind and solar power output data for a single day. Next, the Euclidean distance between each pair of scenarios was calculated; for example, for scenario A and scenario B, the formula was used... To calculate, where P tA and Q tA Let P represent the wind and solar power output of scenario A in hour t, respectively. tB and Q tBThe corresponding power output of scenario B is represented. Then, the k-means clustering algorithm is used to divide these scenarios into 100 clusters, each containing scenarios with similar power output characteristics. Within each cluster, the scenario whose power output characteristics are closest to the cluster center is selected as a representative scenario, for example, a scenario whose mean Euclidean distance is less than a threshold of 5%. Finally, 100 representative scenarios are selected from the 100 clusters. These scenarios comprehensively consider the possible range and probability distribution of new energy power output, providing accurate input for day-ahead and intraday scheduling models. This process is executed by the data analysis module of the central control system, and the results are output to the scheduling module to formulate the optimal energy scheduling strategy. Through this scenario reduction method based on parameter values, this invention significantly improves the accuracy and efficiency of system scheduling.

[0176] In the integrated energy system optimization scheduling method of this invention, a day-ahead optimization scheduling model is constructed. This model precisely aims to minimize operating costs and meticulously considers the fluctuations in time-of-use electricity and heat prices. For example, the peak-hour electricity price is set at $0.15 / kWh, while the off-peak price is $0.05 / kWh; the off-peak heat price is $0.07 / kcal during peak hours and $0.03 / kcal during off-peak hours. Simultaneously, the operating costs of equipment are calculated, such as $5000 per hour for a gas turbine and $1000 per hour for an electric boiler, and annual maintenance costs, typically 2% of the total cost, are considered. The model also includes constraints on the supply and demand balance of electricity, heat, and natural gas to ensure that the power supply, heating, and gas supply systems can meet the total demand of users. Furthermore, operating limitations are set for equipment, such as a minimum start-stop interval of 4 hours for gas turbines and a minimum stable operating time of 2 hours for heat exchange stations. The linear programming solver obtains the optimal operating plan for each device every hour of the day. These plans are output by the day-ahead optimization scheduling model and received by the execution module of the central control system to schedule each device to operate according to the plan, thereby minimizing operating costs while ensuring the reliability of energy supply.

[0177] The intraday optimized scheduling model was constructed through the following detailed steps: First, two objective functions were defined. The first focused on minimizing real-time operating costs, including the purchase costs of electricity, heat, and natural gas calculated based on the latest market data, as well as the real-time operating costs of the equipment. For example, setting the real-time electricity price at $0.12 / kWh and the natural gas price at $0.06 / cubic meter, the model calculated the minimum cost to meet the predicted load demand in different time periods. The second objective function aimed to minimize scheduling adjustment costs, which involved the costs incurred when adjustments to the day-ahead schedule were needed due to fluctuations in renewable energy output or changes in load demand. For example, if the actual output of wind power was 10% lower than the forecast, the model calculated the cost of adjusting the charging and discharging strategies of energy storage equipment or purchasing additional energy. Next, real-time wind speed and sunshine data from weather stations, as well as real-time load data from smart meters and heat meters, were integrated, and data preprocessing algorithms, such as moving average or Kalman filtering, were used to smooth the data and reduce noise. Then, a genetic algorithm is applied, where each "individual" represents a set of possible equipment operation strategies and energy storage operations. The solution space is explored using simulated annealing or particle swarm optimization algorithms to find the optimal solution that simultaneously satisfies two objective functions. Specific configurations of model parameters include setting the prediction error range for new energy output to ±5% and defining minimum start-up and shutdown times for each device, for example, a minimum start-up and shutdown time of 1 hour for wind turbines. The scheduling adjustment interval is defined as once every 15 minutes to respond to rapidly changing market conditions. Using the optimal solution obtained from the optimization algorithm, specific scheduling instructions are generated, such as adjusting the output power of wind turbines or changing the charging and discharging levels of energy storage devices. These instructions include specific operating parameters, such as the new target power setpoint for wind turbines and the charging and discharging rates of energy storage devices. The output is the intraday optimization scheduling module of the central control system, which generates and sends scheduling instructions. The receivers are the control systems of various devices in the integrated energy system, such as the control systems of wind turbines and the management systems of energy storage devices, which execute corresponding operations based on the received instructions. After the equipment control system executes the scheduling command, it monitors the operation results in real time and feeds back the execution status to the central control system. The central control system evaluates the scheduling effect based on the feedback results and makes adjustments when necessary to ensure that the system always runs along the optimal path.

[0178] The application of fuzzy mathematics theory aims to precisely handle the uncertainty of load demand. First, a fuzzy set of power load is defined, including three fuzzy variables: "low," "medium," and "high," each corresponding to a different load level. For example, for "low" load, a trapezoidal membership function is constructed, which gradually increases from 0 to 1 when the load is between 400 and 600 kW, and then gradually decreases to 0 when the load is between 600 and 800 kW. Next, fuzzy logic inference rules are formulated. These rules can handle the uncertainty information in the fuzzy set; for example, when the actual load approaches the boundary between "low" and "medium," the rules will guide how to adjust the operating strategies of power generation and energy storage equipment. The fuzzy inference system uses these rules to analyze real-time load data and converts the results into clear action instructions. For example, if real-time load data indicates that the current load falls within the "medium" to "high" range, the fuzzy inference system will output an instruction to increase power generation. Then, the centroid method is used to convert the fuzzy output into a specific scheduling decision, such as deciding to increase power generation by 100 kW in the next hour. The outputs of these fuzzy mathematical models are integrated into the intraday optimized scheduling model, serving as the basis for adjusting equipment operation plans. The central control system implements dynamic responses based on the outputs of the fuzzy mathematical models to adapt to load changes. For example, if the fuzzy model predicts an increase in load in the next hour, the control system will adjust the charging and discharging plans of the energy storage equipment in advance to ensure that the system can meet actual load demands, thereby improving the system's adaptability and reliability. Through this modeling method based on fuzzy mathematics theory, this invention can ensure that the integrated energy system can make a rapid and accurate response to uncertainties in load demand.

Claims

1. A comprehensive energy system management method based on day-ahead and intraday dual-layer optimized scheduling, characterized in that, Includes the following steps: Step s1: Establish a comprehensive energy system model, including at least one power supply system, at least one gas supply system, at least one heating system, and at least one energy storage device; Step s2: Use Monte Carlo sampling to generate scenarios for new energy output; this includes determining the probability distribution models of wind and solar energy, performing random sampling to generate possible output data, and conducting statistical analysis to determine the expected value and variability of the output. Step s3: Using scene reduction technology, the generated scenes are filtered based on Euclidean distance, the Euclidean distance between each scene and other scenes is calculated, cluster analysis is performed, and representative scenes are selected; Step s4: Construct a day-ahead optimization scheduling model with the goal of minimizing operating costs, taking into account time-of-use electricity and heat prices, as well as the operating and maintenance costs of equipment, including supply and demand balance constraints for electricity, heat and natural gas, and operating limitations of equipment; Step s5: Construct an intraday optimized scheduling model with the goal of minimizing operating costs and scheduling adjustment costs. Use a multi-objective optimization method for real-time scheduling, including minimizing operating costs and minimizing scheduling adjustment costs. Step s6: Use fuzzy mathematics theory to model the uncertainty of load demand, define the fuzzy set of load demand, use fuzzy logic reasoning to quantify the uncertainty, and apply the analysis results to the intraday optimization scheduling model; In step s6, fuzzy mathematics theory is used to model the uncertainty of load demand. The specific steps are as follows: s601: Define the fuzzy set: First, define the fuzzy set of load demand; for power load, establish a fuzzy set containing three fuzzy variables: "low", "medium", and "high", each variable corresponding to a different load level; s602: Constructing membership functions: Construct membership functions for each fuzzy variable; for "low" load, this includes defining a trapezoidal membership function, whose membership increases from 0 to 1 at 400 to 600 kW, and then decreases to 0 at 600 to 800 kW. s603: Formulate fuzzy logic reasoning rules: Formulate fuzzy logic reasoning rules to handle uncertain information in fuzzy sets; If the actual load is close to the boundary between "low" and "medium", the rules will determine how to adjust the operating strategies of power generation and energy storage equipment; s604: Implementing a fuzzy inference system: This system uses fuzzy logic inference rules to analyze real-time load data and converts the results into clear action instructions; if the real-time load data indicates that the current load is in the "medium" to "high" range, the fuzzy inference system will output an instruction to increase power generation; s605: Defuzzify the fuzzy results: Defuzzify the results of fuzzy inference to obtain specific numerical outputs; including using the centroid method to convert the fuzzy output into a specific scheduling decision, such as increasing the power generation capacity by several kilowatts. s606: Integration of intraday optimized scheduling model: Integrating the defuzzified output into the intraday optimized scheduling model; including: using the increased power generation as a constraint condition of the intraday scheduling model to adjust the equipment's operating plan; s607: Specific implementation of output and reception: Output scheduling suggestions based on fuzzy logic reasoning, and then adjust the device's operating strategy according to the output of the fuzzy mathematical model; s608: Implementation of dynamic response: The central control system implements dynamic response based on the output of the fuzzy mathematical model to adapt to load changes.

2. The method according to claim 1, characterized in that, In step s1, each subsystem is equipped with corresponding input and output interfaces, as well as an interface for data communication with the central control system, in order to realize real-time data exchange and command issuance; The power supply system includes power plants, which are the core of the power supply system. They generate electricity and boost the voltage through step-up transformers to meet the needs of long-distance power transmission. Electricity is transmitted to various substations via high-voltage transmission lines. The substations are responsible for reducing the voltage to a level suitable for the city's power distribution network. The power distribution network is connected to end users through smart meters. The smart meters not only record users' electricity consumption but also receive signals from the central control system to achieve demand-side management. The central control system analyzes real-time data to adjust the power generation plans of power plants and the operating status of the power grid to ensure the stability of the power supply. The gas supply system includes natural gas wells, from which natural gas is pressurized through compression stations and then transported to storage facilities via high-pressure pipelines; Storage facilities store natural gas when demand is low and release it when demand is high to balance supply and demand; distribution pipelines transport natural gas from storage facilities to distribution nodes, and finally distribute it to commercial and residential users. The central control system monitors the entire gas supply process to ensure the continuity and security of natural gas supply. The heating system includes a heat source. Starting from the heat source, the generated heat energy is transported to the heat exchange station through the heat pipe network. The heat exchange station is responsible for converting the heat energy into a form suitable for user use. The heat pipe network directly delivers the heat energy to the radiators or other heat energy-using equipment at the user end. The central control system adjusts the output of the heat source and the working status of the heat exchange station according to the user's needs and the external ambient temperature to achieve high efficiency and energy saving in heating. Energy storage devices include electrical energy storage devices, which are connected to the power supply system and can store electrical energy when there is a surplus of electricity or release electrical energy when there is a peak in demand, thereby balancing the grid load. Thermal energy storage equipment is connected to the heating system to store thermal energy for use during peak demand periods, improving the flexibility and reliability of the heating system. The central control system intelligently schedules the charging and discharging or thermal energy release operations of the energy storage equipment based on energy supply and demand conditions and price signals to optimize the overall system operating efficiency. The central control system is connected to the data communication interfaces of all subsystems through a high-speed communication network. The control system receives data from the power supply system, gas supply system, heating system and energy storage equipment in real time, analyzes the energy supply and demand situation, and issues dispatch instructions accordingly. Through advanced data processing and optimization algorithms, the central control system realizes centralized monitoring and dispatch of the entire integrated energy system, improves energy utilization efficiency, reduces operating costs, and enhances the reliability and flexibility of the system.

3. The method according to claim 1, characterized in that, In step s2, when generating a scenario for new energy output, the following steps are adopted: S201: Data collection and analysis: Collect historical data on wind speed and solar radiation over the past year, analyze the data, and determine the distribution characteristics of wind speed and solar radiation; s202: Establishing probability distribution models: Based on the analysis results, establish specific probability distribution models for wind energy and solar energy; for wind energy, use the Weiber distribution model, with parameters determined by the above analysis; for solar energy, use the normal distribution model, with parameters also determined by the analysis. s203: The Monte Carlo method is used for random sampling: for wind energy, several samples are drawn from the Weiber distribution, each sample representing a possible wind speed value; for solar energy, several samples are drawn from the normal distribution, each sample representing a possible solar radiation value. s204: Perform power output data conversion: Convert the sampled wind speed values ​​into wind energy output using the wind speed-power curve; s205: Perform statistical analysis: Statistically analyze the generated wind and solar power output samples to calculate the expected average output and standard deviation of the output; s206: Scenario generation and output: Integrate the results of the above statistical analysis to generate multiple most representative new energy output scenarios. Each scenario contains wind and solar power output data at several time points, covering various time periods throughout the day. These scenarios are generated, output, and scheduled through a central control system, providing accurate forecasts of new energy output for day-ahead and intraday optimized scheduling.

4. The method according to claim 1, characterized in that, When reducing a scene, follow these steps: s301: Scenario generation: Using the Monte Carlo sampling method, several possible new energy output scenarios are generated. Each scenario contains wind and solar power output data at multiple time points to simulate the energy output of each hour of the day. s302: Calculate the Euclidean distance: For each pair of scenes, calculate the Euclidean distance between them; s303: Perform clustering analysis: Use clustered k-means to group all scenarios to identify similar power output patterns; s304: Select a representative scenario: Select a representative scenario from each cluster that best represents the average characteristics of all scenarios in that cluster; s305: Retention of key information: The retained representative scenarios will be used as input for day-ahead and intraday scheduling models. These scenarios reflect the possible range and probability distribution of renewable energy output. S306: Execution Output and Reception: The central control system performs scenario reduction and outputs the final representative scenario set; the central control system receives the above representative scenario set and uses the above representative scenarios to formulate the day-ahead and intraday scheduling strategies.

5. The method according to claim 1, characterized in that, In step s4, the specific steps for constructing the day-ahead optimization scheduling model are as follows: s401: Determine the objective function: The objective function of the model is to minimize the total operating cost, which includes the cost of purchasing electricity, heat, and natural gas, as well as the cost of equipment operation and maintenance. S402: Set cost parameters: Set time-of-use electricity price; Set time-of-use heat price; Calculate equipment operating costs; Consider equipment maintenance costs; S403: Obtaining supply and demand balance constraints: ensuring power supply and demand balance, that is, the power generation of the power supply system plus the discharge of the energy storage device equals the total power demand of the user; ensuring heat supply and demand balance, that is, the heat supply of the heating system plus the heat release of the thermal energy storage device equals the total heat demand of the user; ensuring natural gas supply and demand balance, that is, the gas supply of the gas supply system equals the total natural gas demand of the user. S404: Set device operation limits: Set device start-up and shutdown time limits; Set minimum stable operating time for the device; s405: Solve the model: Use a linear programming or mixed-integer linear programming solver to solve the model and obtain the optimal operating plan for each device; s406: Output and Receive: Input the operating plan of each device for each hour of the day, and then schedule each device according to the operating plan output by the model.

6. The method according to claim 1, characterized in that, In step s5, the specific steps for constructing the intraday optimized scheduling model are as follows: s501: Construct the objective function: This includes minimizing operating costs by defining an objective function that calculates the total cost of meeting forecasted load demand given real-time electricity, heat, and natural gas prices. Set the real-time electricity price function as follows , in It is in time electricity costs, This is the unit electricity price. It is in time Electricity consumption; It also includes minimizing scheduling adjustment costs: defining a second objective function that evaluates the costs incurred when day-ahead schedules need to be adjusted due to fluctuations in renewable energy output or changes in load demand, specifically expressed as follows: . , in It's about adjusting costs. It is the unit adjustment cost. It is the predicted output of new energy sources. It is the actual output of new energy; S502: Real-time data integration and processing: Integrates real-time wind speed and sunshine data from weather stations, as well as real-time load data from smart meters and heat meters; uses data preprocessing algorithms, including moving average or Kalman filtering, to smooth the data and reduce noise; S503: Application of multi-objective optimization algorithms: Applying genetic algorithms, where each "individual" represents a set of possible equipment operation strategies and energy storage operations, and exploring the solution space through simulated annealing or particle swarm optimization algorithms to find the optimal solution that simultaneously satisfies two objective functions; s504: Configure the specific model parameters: Set the prediction error range of new energy output and define the minimum start-up and shutdown time for each device to achieve a response to rapidly changing market conditions; s505: Execution of real-time scheduling decisions: Utilizing the optimal solution obtained from the optimization algorithm, specific scheduling instructions are generated, such as adjusting the output power of the wind turbine or changing the charging and discharging level of the energy storage device; the scheduling instructions include specific operating parameters, including the new target power setpoint of the wind turbine and the charging and discharging rate of the energy storage device; S506: Specific implementation of output and reception: The output is the central control system, which generates and sends dispatch instructions; the receiving is the control system of each device in the integrated energy system, including the control system of wind turbines and the management system of energy storage devices, which execute corresponding operations according to the received instructions. S507: Monitoring and feedback of execution results: After the equipment control system executes the scheduling command, it monitors the operation results in real time and feeds back the execution status to the central control system; the central control system evaluates the scheduling effect based on the feedback results and makes adjustments when necessary to ensure that the system always runs along the optimal path.

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