Integrated energy system management method based on day-ahead and intra-day double-layer optimization scheduling

Abstract

The invention discloses an integrated energy system management method based on day-ahead and intra-day double-layer optimization scheduling, and aims to improve the environmental protection property and energy efficiency of a system through a day-ahead and intra-day double-layer optimization strategy. Firstly, a system model including power supply, gas supply, heat supply and energy storage equipment is constructed; monte Carlo sampling is utilized to generate a new energy output scene, and a key scene is screened through a scene reduction technology. Thirdly, a day-ahead optimization model is established, and time-of-use electricity prices and heat prices are considered by taking cost minimization as a target; the intra-day model is adjusted in real time based on the day-ahead plan, and cost and scheduling adjustment are reduced by adopting multi-objective optimization. The fuzzy mathematics theory is introduced to process the load demand uncertainty, and the system adaptability is enhanced. The method can improve energy efficiency, reduce cost and reduce pollution, and has practical value and application potential.

Description

Technical Field

[0001] The present invention relates to the technical field of management of integrated energy systems, and in particular to an integrated energy system management method based on day-ahead and intra-day double-layer optimization scheduling. Background Art

[0002] In the context of the growing demand for energy and the increasing requirements for environmental protection, the Integrated Energy System (IES) has attracted extensive attention as a new type of energy supply and management. The IES integrates multiple energy sources such as electricity, thermal energy, and natural gas to achieve energy complementarity and efficient utilization, which is of great significance for improving energy efficiency, reducing energy costs, and reducing environmental pollution.

[0003] Traditional energy management systems often use a single energy supply method, which makes it difficult to cope with the fluctuations and uncertainties of energy supply and demand. Especially when the proportion of new energy, such as wind power and solar energy, is gradually increasing, the randomness and volatility of their output have brought new challenges to the stable operation and scheduling of energy systems. In addition, with the gradual opening of the electricity market and natural gas market, the fluctuation of energy prices has also put forward higher requirements on the economic efficiency of the energy system.

[0004] In order to solve the above problems, researchers have proposed a variety of optimization scheduling methods in order to achieve efficient operation of the integrated energy system. However, existing scheduling methods often focus on day-ahead scheduling, that is, making scheduling plans based on forecast data before the start of the day, but lack the ability to respond quickly to real-time changes during 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, which can formulate a preliminary scheduling plan on the day before and have the ability to adjust and optimize in real time during the day, so as to better cope with the uncertainty of new energy output and the fluctuation of load demand, and realize the efficient, economical and stable operation of the integrated energy system. Based on such a demand, the present invention proposes an integrated energy system management method based on dual-layer optimization scheduling of day-ahead and intraday. Summary of the invention

[0006] The purpose of the present invention is to solve the technical problem that the uncertainty of new energy output and the fluctuation of load demand in the existing integrated energy management technology easily lead to poor reliability of the integrated energy system, and to propose an integrated energy system management method based on day-ahead and intraday double-layer optimization scheduling.

[0007] In order to solve the above technical problems, the technical solution adopted by the present invention is:

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

[0009] Step s1: Establishing 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: Generate scenarios of renewable energy output using the Monte Carlo sampling method; including determining a probability distribution model for wind and solar energy, performing random sampling to generate possible output data, and performing statistical analysis to determine expected values ​​and variability of the output;

[0011] Step s3: Using the scene reduction technique, the generated scenes are screened based on the 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 prices and time-of-use heat prices, as well as equipment operating costs and maintenance costs, including supply and demand balance constraints for electricity, heat and natural gas, and equipment operating restrictions;

[0013] Step s5: construct an intraday optimization scheduling model with the goal of minimizing operating costs and scheduling adjustment costs, and adopt a multi-objective optimization method for real-time scheduling, including minimizing operating costs and 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 to achieve real-time data exchange and instruction issuance;

[0016] The power supply system includes power stations. As the core of the power supply system, power stations generate electricity and increase the voltage through step-up transformers to meet the needs of long-distance power transmission. Electric energy is transmitted to various substations through high-voltage transmission lines. Substations are responsible for reducing the voltage to a level suitable for the urban distribution network. The distribution network is connected to end users through smart meters. 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 adjusts the power generation plan of the power station and the operating status of the power grid based on real-time data analysis to ensure the stability of power supply.

[0017] The gas supply system includes natural gas wells, from which natural gas is pressurized through compression stations and then transmitted to storage facilities through high-pressure transmission pipelines; storage facilities store natural gas when demand is low and release it when demand peaks to balance supply and demand; distribution pipelines transport natural gas from storage facilities to distribution nodes and finally to commercial and residential users; central control systems monitor the entire gas supply process to ensure the continuity and safety of natural gas supply;

[0018] The heating system includes a heat source. The heating system starts with a heat source (such as a thermal power plant). The heat energy generated is transported to a heat exchange station through a heat pipe network. The heat exchange station is responsible for converting the heat energy into a form suitable for users. The heat pipe network directly transports the heat energy to the radiator or other heat energy-using equipment at the user end. The central control system adjusts the output of the heat source and the working state of the heat exchange station according to the user's needs and the external ambient temperature to achieve efficient and energy-saving heating.

[0019] Energy storage equipment includes electrical energy storage equipment, which is connected to the power supply system and can store electricity when there is an oversupply of electricity, or release electricity when demand peaks, thereby balancing the load on the power grid; thermal energy storage equipment is connected to the heating system and stores thermal energy for use when demand peaks, thereby improving the flexibility and reliability of the heating system; the central control system intelligently dispatches the charging and discharging or heat storage and release operations of the energy storage equipment according to the energy supply and demand conditions and price signals, so as 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 dispatching of the entire integrated energy system, improves energy utilization efficiency, reduces operating costs, and enhances system reliability and flexibility.

[0021] In step s2, when generating a scenario of renewable 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, and analyze these data to determine the distribution characteristics of wind speed and solar radiation;

[0023] s202: Establish a probability distribution model: Based on the analysis results, establish specific probability distribution models for wind energy and solar energy; for wind energy, use the Weibull distribution model, and the parameters are determined by the above analysis; for solar energy, use the normal distribution model, and the parameters are also determined by the analysis;

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

[0025] s204: Convert output data: convert the sampled wind speed value into wind energy output using the wind speed-power curve;

[0026] s205: Perform statistical analysis: Perform statistical analysis on several 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 renewable energy output scenarios. Each scenario contains wind and solar energy output data at several time points, covering all time periods of the day. These scenarios are generated, output and dispatched through the central control system to provide accurate renewable energy output forecasts for optimal dispatching before and during the day.

[0028] In step s3, when performing scene reduction, the following steps are adopted:

[0029] s301: Generate scenarios: Use the Monte Carlo sampling method to generate several possible renewable energy output scenarios. Each scenario contains wind and solar output data at multiple time points to simulate energy output every hour of a day.

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

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

[0032] s304: Select representative scenes: Select a representative scene from each cluster, which best represents the average characteristics of all scenes in the cluster;

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

[0034] s306: Execution output and reception: The central control system executes scenario reduction and outputs the final representative scenario set; the central control system receives the representative scenario set and uses the representative scenarios to formulate day-ahead and day-intraday scheduling strategies.

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

[0036] s401: Determine the objective function: Determine the objective function of the model to minimize the total operating cost, which includes the electricity purchase cost, heat purchase cost, natural gas purchase cost, and equipment operation and maintenance cost;

[0037] s402: Set cost parameters: set time-of-use electricity price; set time-of-use heat price; calculate equipment operating cost; consider equipment maintenance cost;

[0038] s403: Obtain supply and demand balance constraints: Ensure the balance of electricity supply and demand, that is, the power generation of the power supply system plus the discharge of the energy storage device is equal to the total power demand at the user end; ensure the balance of heat supply and demand, that is, the heat supply of the heating system plus the heat release of the thermal energy storage device is equal to the total heat demand at the user end; ensure the balance of natural gas supply and demand, that is, the gas supply of the gas supply system is equal to the total natural gas demand at the user end;

[0039] s404: Set equipment operation restrictions: set equipment start and stop time limits; set the minimum stable operation time of the equipment;

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

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

[0042] In step s5, the steps of constructing the intraday optimization scheduling model are as follows:

[0043] s501: Construct the objective function:

[0044] Including minimizing the operating cost, defining an objective function that calculates the total cost of meeting the predicted load demand under given real-time electricity prices, heat prices, and natural gas prices; setting the real-time electricity price function to C e (t) = α e ·P e (t), where C e (t) is the electricity cost at time t, α e is the unit electricity price, P e (t) is the power consumption at time t;

[0045] It also includes minimizing the scheduling adjustment cost: defining a second objective function, which evaluates the cost of adjusting the day-ahead plan due to fluctuations in renewable energy output or changes in load demand, which can be 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, P pred (t) is the predicted new energy output, P real (t) is the actual new energy output;

[0046] s502: Integrate and process real-time data: Integrate real-time wind speed and sunshine data from weather stations, as well as real-time load data from smart electricity meters and heat meters; use data preprocessing algorithms, including moving average or Kalman filtering, to smooth data and reduce noise;

[0047] s503: Apply multi-objective optimization algorithms: Apply genetic algorithms, where each "individual" represents a set of possible equipment operation strategies and energy storage operations, and use simulated annealing or particle swarm optimization algorithms to explore the solution space and find the optimal solution that satisfies both objective functions;

[0048] s504: Specific configuration of model parameters: set the forecast error range of renewable energy output and define the minimum start and stop time for each device to respond to rapidly changing market conditions;

[0049] s505: Execution of real-time dispatch decisions: Generate specific dispatch instructions using the optimal solution obtained by the optimization algorithm, such as adjusting the output power of the wind turbine or changing the charge and discharge level of the energy storage device; the dispatch instructions include specific operating parameters, including the new target power set point of the wind turbine and the charge and discharge rate of the energy storage device;

[0050] s506: Specific implementation of output and reception: The output side is the central control system, which generates and sends dispatch instructions; the receiver is the control system of each device in the integrated energy system, including the control system of the wind turbine and the management system of the energy storage device, which perform corresponding operations according to the received instructions;

[0051] s507: Monitoring and feedback of execution results: After the equipment control system executes the scheduling instruction, it monitors its 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, the uncertainty of load demand is modeled using fuzzy mathematics theory. The specific steps are as follows:

[0053] s601: Define fuzzy sets: 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 a membership function: constructing a membership function for each fuzzy variable; for "low" load, including defining a trapezoidal membership function, whose membership gradually increases from 0 to 1 between 400 and 600 kWh, and then gradually decreases to 0 between 600 and 800 kWh;

[0055] s603: Formulate fuzzy logic reasoning rules: Formulate fuzzy logic reasoning rules to handle uncertainty 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 the fuzzy reasoning system: This system uses fuzzy logic reasoning rules to analyze real-time load data and convert the results into clear action instructions; if the real-time load data indicates that the current load belongs to the range of "medium" to "high", the fuzzy reasoning system will output an instruction to increase the power generation;

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

[0058] s606: Integration of intraday optimization scheduling model: Integrate the defuzzified output into the intraday optimization scheduling model; including: taking the increased power generation as a constraint condition of the intraday scheduling model to adjust the operation plan of the equipment;

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

[0060] s608: Implementation of dynamic response: The central control system implements dynamic response according to 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 economic efficiency of the energy system: Through the day-ahead optimization scheduling model, the integrated energy system can formulate the lowest-cost operation plan based on the predicted renewable energy output and load demand before the start of the day. The intraday optimization scheduling model can 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-layer optimization strategy enables the system to minimize operating costs while ensuring supply and demand balance, including energy purchase costs, equipment operation and maintenance costs, and additional costs caused by scheduling adjustments.

[0063] 2) Enhance the reliability and stability of the system: This invention models the uncertainty of load demand through fuzzy mathematical theory, so that the system can respond to random changes in load more flexibly. In the intraday optimization scheduling, the rolling optimization strategy can monitor and respond to the real-time changes of new energy output and load demand in real time, so as to make adjustments quickly to ensure the stable operation of the system. In addition, the consideration of equipment start and stop time limits and minimum stable operation time can also help avoid equipment losses and operation risks caused by frequent start and stop.

[0064] 3) Improve the absorption capacity of new energy and the energy efficiency of the system: Through the dual-layer optimization scheduling of day-ahead and intraday, the system can more effectively utilize new energy, such as wind and solar energy. The day-ahead scheduling model takes into account the probability distribution of new energy output, while the intraday scheduling model can be adjusted according to the real-time new energy output data to maximize the utilization of new energy. At the same time, by optimizing the charging and discharging strategy of energy storage equipment, the system can better balance supply and demand, reduce energy waste, and improve overall energy efficiency.

[0065] In summary, the technical solution of the present invention achieves a significant improvement in the economy, reliability and new energy absorption capacity of the integrated energy system through refined scheduling strategies and advanced optimization algorithms, and provides strong technical support for the efficient management and sustainable development of the integrated energy system. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0067] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[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: Establishing 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: Generate scenarios of renewable energy output using the Monte Carlo sampling method; including determining a probability distribution model for wind and solar energy, performing random sampling to generate possible output data, and performing statistical analysis to determine expected values ​​and variability of the output;

[0071] Step s3: Using the scene reduction technique, the generated scenes are screened based on the 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 prices and time-of-use heat prices, as well as equipment operating costs and maintenance costs, including supply and demand balance constraints for electricity, heat and natural gas, and equipment operating restrictions;

[0073] Step s5: construct an intraday optimization scheduling model with the goal of minimizing operating costs and scheduling adjustment costs, and adopt a multi-objective optimization method for real-time scheduling, including minimizing operating costs and 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 to achieve real-time data exchange and instruction issuance;

[0076] The power supply system includes power stations. As the core of the power supply system, power stations generate electricity and increase the voltage through step-up transformers to meet the needs of long-distance power transmission. Electric energy is transmitted to various substations through high-voltage transmission lines. Substations are responsible for reducing the voltage to a level suitable for the urban distribution network. The distribution network is connected to end users through smart meters. 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 adjusts the power generation plan of the power station and the operating status of the power grid based on real-time data analysis to ensure the stability of power supply.

[0077] The gas supply system includes natural gas wells, from which natural gas is pressurized through compression stations and then transmitted to storage facilities through high-pressure transmission pipelines; storage facilities store natural gas when demand is low and release it when demand peaks to balance supply and demand; distribution pipelines transport natural gas from storage facilities to distribution nodes and finally to commercial and residential users; central control systems monitor the entire gas supply process to ensure the continuity and safety of natural gas supply;

[0078] The heating system includes a heat source. The heating system starts with the heat source. The heat energy generated 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 users. The heat pipe network directly transports the heat energy to the radiator or other heat energy-using equipment at the user end. The central control system adjusts the output of the heat source and the working state of the heat exchange station according to the user's needs and the external ambient temperature to achieve efficient and energy-saving heating.

[0079] Energy storage equipment includes electrical energy storage equipment, which is connected to the power supply system and can store electricity when there is an oversupply of electricity, or release electricity when demand peaks, thereby balancing the load on the power grid; thermal energy storage equipment is connected to the heating system and stores thermal energy for use when demand peaks, thereby improving the flexibility and reliability of the heating system; the central control system intelligently dispatches the charging and discharging or heat storage and release operations of the energy storage equipment according to the energy supply and demand conditions and price signals, so as 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 dispatching of the entire integrated energy system, improves energy utilization efficiency, reduces operating costs, and enhances system reliability and flexibility.

[0081] In step s2, when generating a scenario of renewable 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, and analyze these data to determine the distribution characteristics of wind speed and solar radiation;

[0083] s202: Establish a probability distribution model: Based on the analysis results, establish specific probability distribution models for wind energy and solar energy; for wind energy, use the Weibull distribution model, and the parameters are determined by the above analysis; for solar energy, use the normal distribution model, and the parameters are also determined by the analysis;

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

[0085] s204: Convert output data: convert the sampled wind speed value into wind energy output using the wind speed-power curve;

[0086] s205: Perform statistical analysis: Perform statistical analysis on several 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 renewable energy output scenarios. Each scenario contains wind and solar energy output data at several time points, covering all time periods of the day. These scenarios are generated, output and dispatched through the central control system to provide accurate renewable energy output forecasts for optimal dispatching before and during the day.

[0088] When doing scene reduction, use the following steps:

[0089] s301: Generate scenarios: Use the Monte Carlo sampling method to generate several possible renewable energy output scenarios. Each scenario contains wind and solar output data at multiple time points to simulate energy output every hour of a day.

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

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

[0092] s304: Select representative scenes: Select a representative scene from each cluster, which best represents the average characteristics of all scenes in the cluster;

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

[0094] s306: Execution output and reception: The central control system executes scenario reduction and outputs the final representative scenario set; the central control system receives the representative scenario set and uses the representative scenarios to formulate day-ahead and day-intraday scheduling strategies.

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

[0096] s401: Determine the objective function: Determine the objective function of the model to minimize the total operating cost, which includes the electricity purchase cost, heat purchase cost, natural gas purchase cost, and equipment operation and maintenance cost;

[0097] s402: Set cost parameters: set time-of-use electricity price; set time-of-use heat price; calculate equipment operating cost; consider equipment maintenance cost;

[0098] s403: Obtain supply and demand balance constraints: Ensure the balance of electricity supply and demand, that is, the power generation of the power supply system plus the discharge of the energy storage device is equal to the total power demand at the user end; ensure the balance of heat supply and demand, that is, the heat supply of the heating system plus the heat release of the thermal energy storage device is equal to the total heat demand at the user end; ensure the balance of natural gas supply and demand, that is, the gas supply of the gas supply system is equal to the total natural gas demand at the user end;

[0099] s404: Set equipment operation restrictions: set equipment start and stop time limits; set the minimum stable operation time of the equipment;

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

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

[0102] In step s5, the steps of constructing the intraday optimization scheduling model are as follows:

[0103] s501: Construct the objective function:

[0104] Including minimizing the operating cost, defining an objective function that calculates the total cost of meeting the predicted load demand under given real-time electricity prices, heat prices, and natural gas prices; setting the real-time electricity price function to C e (t) = α e ·P e (t), where C e (t) is the electricity cost at time t, α e is the unit electricity price, P e (t) is the power consumption at time t;

[0105] It also includes minimizing the scheduling adjustment cost: defining a second objective function, which evaluates the cost of adjusting the day-ahead plan due to fluctuations in renewable energy output or changes in load demand, which can be 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, P pred (t) is the predicted new energy output, P real (t) is the actual new energy output;

[0106] s502: Integrate and process real-time data: Integrate real-time wind speed and sunshine data from weather stations, as well as real-time load data from smart electricity meters and heat meters; use data preprocessing algorithms, including moving average or Kalman filtering, to smooth data and reduce noise;

[0107] s503: Apply multi-objective optimization algorithms: Apply genetic algorithms, where each "individual" represents a set of possible equipment operation strategies and energy storage operations, and use simulated annealing or particle swarm optimization algorithms to explore the solution space and find the optimal solution that satisfies both objective functions;

[0108] s504: Specific configuration of model parameters: set the forecast error range of renewable energy output and define the minimum start and stop time for each device to respond to rapidly changing market conditions;

[0109] s505: Execution of real-time dispatch decisions: Generate specific dispatch instructions using the optimal solution obtained by the optimization algorithm, such as adjusting the output power of the wind turbine or changing the charge and discharge level of the energy storage device; the dispatch instructions include specific operating parameters, including the new target power set point of the wind turbine and the charge and discharge rate of the energy storage device;

[0110] s506: Specific implementation of output and reception: The output side is the central control system, which generates and sends dispatch instructions; the receiver is the control system of each device in the integrated energy system, including the control system of the wind turbine and the management system of the energy storage device, which perform corresponding operations according to the received instructions;

[0111] s507: Monitoring and feedback of execution results: After the equipment control system executes the scheduling instruction, it monitors its 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, the uncertainty of load demand is modeled using fuzzy mathematics theory. The specific steps are as follows:

[0113] s601: Define fuzzy sets: 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 a membership function: constructing a membership function for each fuzzy variable; for "low" load, including defining a trapezoidal membership function, whose membership gradually increases from 0 to 1 between 400 and 600 kWh, and then gradually decreases to 0 between 600 and 800 kWh;

[0115] s603: Formulate fuzzy logic reasoning rules: Formulate fuzzy logic reasoning rules to handle uncertainty 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 the fuzzy reasoning system: This system uses fuzzy logic reasoning rules to analyze real-time load data and convert the results into clear action instructions; if the real-time load data indicates that the current load belongs to the range of "medium" to "high", the fuzzy reasoning system will output an instruction to increase the power generation;

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

[0118] s606: Integration of intraday optimization scheduling model: Integrate the defuzzified output into the intraday optimization scheduling model; including: taking the increased power generation as a constraint condition of the intraday scheduling model to adjust the operation plan of the equipment;

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

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

[0121] Example:

[0122] The 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 electric 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 instruction issuance. In the comprehensive energy system optimization scheduling method, the construction of the system model and the connection between subsystems are the key to achieving efficient energy management. The following are detailed connection and operation steps:

[0123] Connection and operation of the power supply system: As the core of the power supply system, the power station generates electricity and increases the voltage through a step-up transformer to meet the needs of long-distance power transmission. The electricity is transmitted to various substations through high-voltage transmission lines, and the substations are responsible for reducing the voltage to a level suitable for the urban distribution network. The distribution network is connected to end users through smart meters. 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 adjusts the power generation plan of the power station and the operating status of the power grid based on real-time data analysis to ensure the stability and economy of power supply.

[0124] Connection and operation of gas supply system: The gas supply system starts from the gas well, pressurizes the natural gas through the compression station, and then transmits it to the storage facility through the high-pressure transmission pipeline. The storage facility stores natural gas when demand is low and releases it when demand peaks to balance supply and demand. The distribution pipeline transports natural gas from the storage facility to the distribution node, and finally distributes it to commercial and residential users. The central control system monitors the entire gas supply process to ensure the continuity and safety of natural gas supply.

[0125] Connection and operation of heating system: The heating system starts with a heat source (such as a thermal power plant). The heat energy generated is transported to the heat exchange station through the thermal network. The heat exchange station is responsible for converting the heat energy into a form suitable for users. The thermal network directly transports the heat energy to the radiator or other heat energy-using equipment at the user end. The central control system adjusts the output of the heat source and the working state of the heat exchange station according to the user's needs and the external ambient temperature to achieve efficient and energy-saving heating.

[0126] Connection and operation of energy storage equipment: Electric energy storage equipment is connected to the power supply system, which can store electricity when there is an oversupply of electricity, or release electricity when demand peaks, thereby balancing the load on the power grid. Thermal energy storage equipment is connected to the heating system, storing thermal energy for use during peak demand, improving the flexibility and reliability of the heating system. The central control system intelligently dispatches the charging and discharging or heat storage and release operations of energy storage equipment according to energy supply and demand conditions and price signals to optimize the operating efficiency of the entire system.

[0127] Connection and operation of the central control system: The central control system is the core of the entire integrated energy system. It 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 dispatching of the entire integrated energy system, improves energy utilization efficiency, reduces operating costs, and enhances system reliability and flexibility.

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

[0129] s201. Data collection and analysis: Collect historical data on wind speed and solar radiation over the past year. These data are recorded every 15 minutes, totaling 2880 data points per day. Use statistical software to analyze these data to determine the distribution characteristics of wind speed and solar radiation. For example, wind speed data may show a Weibull distribution with parameters k = 2.5 and c = 15 m / s; solar radiation data may show a normal distribution with a mean μ = 200 W / m 2 , standard deviation σ=30W / m 2 .

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

[0131] s203. Monte Carlo sampling: Monte Carlo method is used for random sampling. For wind energy, 10,000 samples are drawn from Weibull distribution, each sample represents a possible wind speed value; for solar energy, 10,000 samples are also drawn from normal distribution, each sample represents a possible solar radiation value.

[0132] s204. Output data conversion: The sampled wind speed value is converted into wind power output using the wind speed-power curve. For example, if the wind speed sample is 8m / s, the corresponding wind power output is 200kW by looking up the table or calculating. The sampled solar radiation value is converted into solar power output using the photovoltaic efficiency curve. For example, if the solar radiation sample is 250W / m 2 , then the corresponding solar energy output is calculated to be 50kW.

[0133] s205. Statistical analysis: Perform statistical analysis on the 10,000 wind and solar output samples generated to calculate the expected average output and standard deviation of the output. For example, the average output of wind power may be 150kW with a standard deviation of 30kW; the average output of solar power may 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 most representative renewable energy output scenarios, each of which contains wind and solar energy output data at 24 time points, covering all periods of the day. These scenarios are generated by the data analysis module of the central control system and output to the dispatch module, providing accurate renewable energy output forecasts for day-ahead and intraday optimization dispatch.

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

[0136] s301. Scenario generation: Using the Monte Carlo sampling method, 10,000 possible renewable energy output scenarios were generated. Each scenario contains wind and solar output data at 24 time points, simulating energy output every hour of a 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 outputs of scenario A at time t, PtB\ and QtB tB They represent the wind and solar power outputs of scenario B at time t respectively.

[0138] s303. Cluster analysis: Use k-means to group all scenarios to identify similar output patterns. For example, 10,000 scenarios are divided into 100 clusters, each containing scenarios with similar output curves.

[0139] s304. Representative scene selection: Select a representative scene from each cluster that best represents the average characteristics of all scenes in the 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 the day-ahead and intraday dispatch models. These scenarios reflect the possible range and probability distribution of renewable energy output.

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

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

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

[0144] s402. Cost parameter setting: Set the time-of-use electricity price, for example, the electricity price during peak hours is $0.15 / kWh and the price during off-peak hours is $0.05 / kWh. Set the time-of-use heat price, for example, the heat price during peak hours is $0.07 / kcal and the price during off-peak hours is $0.03 / kcal. Calculate the operating cost of the equipment, for example, the operating cost of the gas turbine is $5,000 / hour and the operating cost of the electric boiler is $1,000 / hour. Consider the maintenance cost of the equipment, for example, the annual maintenance cost of each equipment is 2% of the total cost.

[0145] s403. Supply and demand balance constraint: Ensure the balance of electricity supply and demand, that is, the power generation of the power supply system plus the discharge of the energy storage device is equal to the total power demand at the user end. Ensure the balance of thermal supply and demand, that is, the heat supply of the heating system plus the heat release of the thermal energy storage device is equal to the total thermal demand at the user end. Ensure the balance of natural gas supply and demand, that is, the gas supply of the gas supply system is equal to the total natural gas demand at the user end.

[0146] s404. Equipment operation limit: Set the start and stop time limit of the equipment, for example, the minimum start and stop interval of the gas turbine is 4 hours. Set the minimum stable operation time of the equipment, for example, the minimum stable operation time of the 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 operation plan for each device.

[0148] s406. Output and reception: The output side is the day-ahead optimization scheduling model, which provides the operation plan of 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 operation plan 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: Minimize the operating cost. Define an objective function that calculates the total cost of meeting the forecasted load demand given the real-time electricity price, heat price, and natural gas price. Set the real-time electricity price function to C e (t) = α e ·P e (t), where C e (t) is the electricity cost at time t, α e is the unit electricity price, P e(t) is the electricity consumption at time t. Similarly, corresponding cost functions are defined for heat and natural gas. Minimization of scheduling adjustment cost: Define a second objective function that evaluates the cost of adjusting the day-ahead plan due to fluctuations in renewable energy output or changes in load demand. For example, if the actual output of wind power generation is 10\% lower than predicted, the cost of purchasing additional electricity or adjusting the charging and discharging strategy of energy storage equipment is calculated. 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, P pred (t) is the predicted new energy output, P real (t) is the actual new energy output.

[0151] s502. Integration and processing of real-time data: Integrate real-time wind speed and sunshine data from weather stations, as well as real-time load data from smart electricity and heat meters. Use data preprocessing algorithms, such as moving average or Kalman filtering, to smooth the data and reduce noise.

[0152] s503.Application of multi-objective optimization algorithms: Apply genetic algorithms, where each "individual" represents a set of possible equipment operation strategies and energy storage operations. Use simulated annealing or particle swarm optimization algorithms to explore the solution space and find the optimal solution that satisfies both objective functions simultaneously.

[0153] s504. Specific configuration of model parameters: Set the forecast error range of renewable energy output to ±5%, and define the minimum start and stop time for each device, such as the minimum start and stop time of wind turbines is 1 hour. Define the time interval for scheduling adjustment to be every 15 minutes to respond to rapidly changing market conditions.

[0154] s505. Execution of real-time dispatch decisions: Using the optimal solution derived from the optimization algorithm, specific dispatch instructions are generated, such as adjusting the output power of wind turbines or changing the charge and discharge level of energy storage devices. Dispatch instructions include specific operating parameters, such as the new target power set point of wind turbines and the charge and discharge rate of energy storage devices.

[0155] s506. Specific implementation of output and reception: The output side is the intraday optimization scheduling module of the central control system, which generates and sends scheduling instructions. The receiving side is 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 equipment, which perform corresponding operations according to the received instructions.

[0156] s507. Monitoring and feedback of execution results: After the equipment control system executes the dispatch instruction, it monitors its operation results in real time and feeds back the execution status to the central control system. The central control system evaluates the dispatch effect based on the feedback results and makes adjustments when necessary to ensure that the system always runs according to the optimal path.

[0157] Using fuzzy mathematics theory to model the uncertainty of load demand is a crucial step. The specific steps are as follows:

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

[0159] s602. Construction of membership function: A membership function is constructed for each fuzzy variable. For example, for "low" load, a trapezoidal membership function is defined, whose membership gradually increases from 0 to 1 between 400 and 600 kWh, and then gradually decreases to 0 between 600 and 800 kWh.

[0160] s603. Development of fuzzy logic reasoning rules: Development of fuzzy logic reasoning rules to handle uncertainty 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 operation strategy of power generation and energy storage equipment.

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

[0162] s605. Defuzzification of fuzzy results: Defuzzification of the results of fuzzy reasoning to obtain specific numerical outputs. For example, using the centroid method (also known as the center of gravity method) to convert the fuzzy output into a specific scheduling decision, such as increasing the power generation by 100 kilowatts.

[0163] s606. Integration of intraday optimization scheduling model: Integrate the defuzzified output into the intraday optimization scheduling model. For example, use the increased power generation as a constraint in the intraday scheduling model to adjust the operation plan of the equipment.

[0164] s607. Specific implementation of output and reception: The output side is the fuzzy mathematical 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 operation strategy of the equipment 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 plan of the energy storage device in advance.

[0166] In order to further facilitate the understanding of those skilled in the art, the present invention is further explained and described as follows:

[0167] like Figure 1 As shown in the figure, in the integrated energy system optimization scheduling method, an integrated model including power supply, gas supply, heat supply and energy storage equipment is constructed, and efficient energy management is achieved through precise parameter setting and real-time monitoring. In the power supply system, the power station produces electricity with a rated power of 950 MW, which is increased to 345 kV through a step-up transformer to reduce losses during long-distance transmission; the electricity is connected to the user end through a smart meter, and the meter has an accuracy of 0.5%, which can record and adjust the user's electricity consumption in real time. The gas supply system starts from the natural gas well, increases the pressure to 70 bar through a compression station, and then transmits it to the storage facility through a transmission pipeline with a diameter of 1 meter. The total capacity of the storage facility is 5 million cubic meters to meet the gas supply demand during peak hours. The heating system is provided by the thermal power plant, and the output temperature of hot water is 120 degrees Celsius, which is transported to the heat exchange station through a thermal pipe network with a diameter of 500 mm. The conversion efficiency of the heat exchange station reaches 95%, ensuring that the heat received by the user end meets the demand. The energy storage equipment includes a battery energy storage system with a capacity of 100 megawatt hours and a hot water energy storage tank with a capacity of 50,000 cubic meters, which can store energy when there is an oversupply of electricity and heat energy, and release it when demand peaks to balance supply and demand. The central control system is connected to each subsystem through an optical fiber communication network, with a transmission rate of 1 gigabit per second to ensure the real-time and accuracy of data transmission. Through these precise parameter settings and real-time monitoring, the system of the present invention can achieve optimal allocation and scheduling of energy, improve energy utilization efficiency, reduce operating costs, and enhance the reliability and flexibility of the system.

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

[0169] s201. Data collection and analysis: Collect historical data on wind speed and solar radiation over the past year. These data are recorded every 15 minutes, totaling 2880 data points per day. Use statistical software to analyze these data to determine the distribution characteristics of wind speed and solar radiation. For example, wind speed data may show a Weibull distribution with parameters k = 2.5 and c = 15 m / s; solar radiation data may show a normal distribution with a mean μ = 200 W / m 2 , standard deviation σ=30W / m 2 .

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

[0171] s203. Monte Carlo sampling: Monte Carlo method is used for random sampling. For wind energy, 10,000 samples are drawn from Weibull distribution, each sample represents a possible wind speed value; for solar energy, 10,000 samples are also drawn from normal distribution, each sample represents a possible solar radiation value.

[0172] s204. Output data conversion: The sampled wind speed value is converted into wind power output using the wind speed-power curve. For example, if the wind speed sample is 8m / s, the corresponding wind power output is 200kW by looking up the table or calculating. The sampled solar radiation value is converted into solar power output using the photovoltaic efficiency curve. For example, if the solar radiation sample is 250W / m 2 , then the corresponding solar energy output is calculated to be 50kW.

[0173] s205. Statistical analysis: Perform statistical analysis on the 10,000 wind and solar output samples generated to calculate the expected average output and standard deviation of the output. For example, the average output of wind power may be 150kW with a standard deviation of 30kW; the average output of solar power may 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 most representative renewable energy output scenarios, each of which contains wind and solar energy output data at 24 time points, covering all periods of the day. These scenarios are generated by the data analysis module of the central control system and output to the dispatch module, providing accurate renewable energy output forecasts for day-ahead and intraday optimization dispatch.

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

[0176] In the integrated energy system optimization scheduling method of the present invention, a day-ahead optimization scheduling model is constructed, which accurately takes minimizing the operating cost as the goal and carefully considers the fluctuations of time-of-use electricity price and time-of-use heat price. For example, the electricity price during peak hours is set to 0.15 US dollars / kWh, while it is 0.05 US dollars / kWh during off-peak hours; the heat price is 0.07 US dollars / kcal during peak hours and 0.03 US dollars / kcal during off-peak hours. At the same time, the operating cost of the equipment is calculated, such as the hourly operating cost of the gas turbine is 5,000 US dollars and the electric boiler is 1,000 US dollars, and the annual maintenance cost of the equipment is considered, which is usually 2% of its total cost. The model also includes constraints on the balance of supply and demand of electricity, heat and natural gas to ensure that the power supply system, heating system and gas supply system can meet the total demand of the user end. In addition, the operating restrictions of the equipment are set, such as the minimum start-stop interval of the gas turbine is 4 hours, and the minimum stable operation time of the heat exchange station is 2 hours. Through the linear programming solver, the optimal operation plan for each device every hour of the day is obtained. 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 construction of the intraday optimization scheduling model is achieved through the following detailed steps: First, two objective functions are defined. The first one focuses on minimizing the real-time operating costs, including the purchase costs of electricity, heat and natural gas calculated based on the latest market data, and the real-time operating costs of the equipment. For example, setting the real-time electricity price to $0.12 / kWh and the natural gas price to $0.06 / m3, the model will calculate the minimum cost to meet the predicted load demand in different time periods. The second objective function aims to minimize the scheduling adjustment cost, which involves the cost incurred when the day-ahead plan needs to be adjusted due to fluctuations in renewable energy output or changes in load demand. For example, when the actual output of wind power generation is 10% lower than the forecast, the model will calculate the cost of adjusting the charging and discharging strategy of energy storage equipment or purchasing additional energy. Next, the real-time wind speed and sunshine data from the weather station and the real-time load data of smart meters and heat meters are integrated, and data preprocessing algorithms such as moving average or Kalman filtering are used to smooth the data and reduce noise. Then, a genetic algorithm is applied, in which each "individual" represents a set of possible equipment operation strategies and energy storage operations, and the solution space is explored through simulated annealing or particle swarm optimization algorithms to find the optimal solution that satisfies both objective functions. The specific configuration of the model parameters includes setting the forecast error range of renewable energy output to ±5% and defining the minimum start and stop time for each device, such as the minimum start and stop time of wind turbines to 1 hour. The time interval for scheduling adjustment is defined as once every 15 minutes to achieve response to rapidly changing market conditions. Using the optimal solution obtained by the optimization algorithm, specific scheduling instructions are generated, such as adjusting the output power of wind turbines or changing the charging and discharging level of energy storage devices. These scheduling instructions include specific operating parameters, such as the new target power set point of wind turbines and the charging and discharging rate of energy storage devices. The output side is the intraday optimization scheduling module of the central control system, which generates and sends scheduling instructions, and the receiver is the various equipment control systems in the integrated energy system, such as the control system of wind turbines and the management system of energy storage devices, which perform corresponding operations according to the received instructions. After the equipment control system executes the scheduling instructions, it monitors its 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 is to accurately handle the uncertainty of load demand. First, the fuzzy set of power load is defined, including three fuzzy variables of "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 400 to 600 kWh, and then gradually decreases to 0 when the load is 600 to 800 kWh. Next, fuzzy logic reasoning rules are formulated, which can handle the uncertainty information in the fuzzy set, such as when the actual load is close to the boundary of "low" and "medium", the rules will guide how to adjust the operation strategy of power generation and energy storage equipment. The fuzzy reasoning system uses these rules to analyze the real-time load data and convert the results into clear action instructions. For example, if the real-time load data indicates that the current load belongs to the range of "medium" to "high", the fuzzy reasoning system will output an instruction to increase the power generation. Then, the centroid method is used to convert the fuzzy output into a specific scheduling decision, such as deciding to increase the power generation power by 100 kilowatts in the next hour. The outputs of these fuzzy mathematical models are integrated into the intraday optimization scheduling model as the basis for adjusting the equipment operation plan. The central control system implements dynamic response according to 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 plan of the energy storage device in advance to ensure that the system can meet the actual load demand, thereby improving the adaptability and reliability of the system. Through this modeling method based on fuzzy mathematical theory, the present invention can ensure that the integrated energy system responds quickly and accurately when faced with the uncertainty of load demand.

Claims

1. A comprehensive energy system management method based on day-ahead and intraday dual-layer optimization scheduling, characterized in that: The following steps are involved: Step s1: Establishing 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: Generate scenarios of renewable energy output using the Monte Carlo sampling method; including determining a probability distribution model for wind and solar energy, performing random sampling to generate possible output data, and performing statistical analysis to determine expected values ​​and variability of the output; Step s3: Using the scene reduction technique, the generated scenes are screened based on the 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 prices and time-of-use heat prices, as well as equipment operating costs and maintenance costs, including supply and demand balance constraints for electricity, heat and natural gas, and equipment operating restrictions; Step s5: construct an intraday optimization scheduling model with the goal of minimizing operating costs and scheduling adjustment costs, and adopt a multi-objective optimization method for real-time scheduling, including minimizing operating costs and 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.

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 to achieve real-time data exchange and instruction issuance; The power supply system includes power stations. As the core of the power supply system, power stations generate electricity and increase the voltage through step-up transformers to meet the needs of long-distance power transmission. Electric energy is transmitted to various substations through high-voltage transmission lines. Substations are responsible for reducing the voltage to a level suitable for the city's distribution network. The distribution network is connected to end users through smart meters. 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 adjusts the power generation plan of the power station and the operating status of the power grid based on real-time data analysis to ensure the stability of power supply. The gas supply system includes natural gas wells, from which the natural gas is pressurized through compression stations and then transmitted to storage facilities through high-pressure transmission pipelines; Storage facilities store natural gas when demand is low and release it when demand peaks to balance supply and demand; distribution pipelines transport natural gas from storage facilities to distribution nodes and finally to commercial and residential users; The central control system monitors the entire gas supply process to ensure the continuity and safety of natural gas supply; The heating system includes a heat source. The heating system starts with the heat source. The heat energy generated 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 users. The heat pipe network directly transports the heat energy to the radiator or other heat energy-using equipment at the user end. The central control system adjusts the output of the heat source and the working state of the heat exchange station according to the user's needs and the external ambient temperature to achieve efficient and energy-saving heating. Energy storage devices include electrical energy storage devices, which are connected to the power supply system and can store electricity when there is excess supply of electricity, or release electricity when demand peaks, thereby balancing the load on the power grid; The thermal energy storage device is connected to the heating system to store heat for use during peak demand, improving the flexibility and reliability of the heating system. The central control system intelligently dispatches the charging and discharging or heat storage and release operations of the energy storage device according to the energy supply and demand conditions and price signals to optimize the operating efficiency of the entire system. 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 dispatching of the entire integrated energy system, improves energy utilization efficiency, reduces operating costs, and enhances system reliability and flexibility.

3. The method according to claim 1, characterized in that In step s2, when generating a scenario of renewable 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, and analyze these data to determine the distribution characteristics of wind speed and solar radiation; s202: Establish a probability distribution model: Based on the analysis results, establish specific probability distribution models for wind energy and solar energy; for wind energy, use the Weibull distribution model, and the parameters are determined by the above analysis; for solar energy, use the normal distribution model, and the parameters are also determined by the analysis; s203: Monte Carlo method is used for random sampling: for wind energy, several samples are drawn from Weibull distribution, each sample represents a possible wind speed value; for solar energy, several samples are also drawn from normal distribution, each sample represents a possible solar radiation value; s204: Convert output data: convert the sampled wind speed value into wind energy output using the wind speed-power curve; s205: Perform statistical analysis: Perform statistical analysis on several 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 a number of most representative renewable energy output scenarios. Each scenario contains wind and solar output data at several time points, covering all periods of the day. These scenarios are generated, output and dispatched through the central control system, providing accurate forecasts of renewable energy output for day-ahead and day-intraday optimization scheduling.

4. The method according to claim 1, characterized in that: When doing scene reduction, use the following steps: s301: Generate scenarios: Use the Monte Carlo sampling method to generate several possible renewable energy output scenarios. Each scenario contains wind and solar output data at multiple time points to simulate energy output every hour of a day. s302: Calculate the Euclidean distance: for each pair of scenes, calculate the Euclidean distance between them; s303: Perform cluster analysis: Use k-means to group all scenarios to identify similar output patterns; s304: Select representative scenes: Select a representative scene from each cluster, which best represents the average characteristics of all scenes in the cluster; s305: Retention of key information: The retained representative scenarios will be used as inputs for the day-ahead and intraday dispatch models. These scenarios reflect the possible range and probability distribution of renewable energy output; s306: Execution output and reception: The central control system executes scenario reduction and outputs the final representative scenario set; the central control system receives the representative scenario set and uses the representative scenarios to formulate day-ahead and day-intraday scheduling strategies.

5. The method according to claim 1, characterized in that In step s4, the steps of constructing the day-ahead optimization scheduling model are as follows: s401: Determine the objective function: Determine the objective function of the model to minimize the total operating cost, which includes the electricity purchase cost, heat purchase cost, natural gas purchase cost, and equipment operation and maintenance cost; s402: Set cost parameters: set time-of-use electricity price; set time-of-use heat price; calculate equipment operating cost; consider equipment maintenance cost; s403: Obtain supply and demand balance constraints: Ensure the balance of electricity supply and demand, that is, the power generation of the power supply system plus the discharge of the energy storage device is equal to the total power demand at the user end; ensure the balance of heat supply and demand, that is, the heat supply of the heating system plus the heat release of the thermal energy storage device is equal to the total heat demand at the user end; ensure the balance of natural gas supply and demand, that is, the gas supply of the gas supply system is equal to the total natural gas demand at the user end; s404: Set equipment operation restrictions: set equipment start and stop time limits; set the minimum stable operation time of the equipment; s405: Solve the model: Use a linear programming or mixed integer linear programming solver to solve the model and obtain the optimal operation plan for each device; s406. Output and reception: Input the operation plan of each device every hour of the day, and then schedule each device according to the operation plan output by the model.

6. The method according to claim 1, characterized in that In step s5, the steps of constructing the intraday optimization scheduling model are as follows: s501: Construct the objective function: Including minimizing the operating cost, defining an objective function that calculates the total cost of meeting the forecasted load demand under given real-time electricity prices, heat prices, and natural gas prices; setting the real-time electricity price function to Ce(t) = αe·Pe(t), where Ce(t) is the electricity cost at time t, αe is the unit electricity price, and Pe(t) is the electricity consumption at time t; It also includes minimizing the dispatch adjustment cost: defining the second objective function, which evaluates the cost incurred when the day-ahead plan needs to be adjusted due to fluctuations in renewable energy output or changes in load demand. Specifically, it can be expressed as Ca(t) = β.|Ppred(t)-Preal(t)|, where Ca(t) is the adjustment cost, β is the unit adjustment cost, Ppred(t) is the predicted renewable energy output, and Preal(t) is the actual renewable energy output; s502: Integrate and process real-time data: Integrate real-time wind speed and sunshine data from weather stations, as well as real-time load data from smart electricity meters and heat meters; use data preprocessing algorithms, including moving average or Kalman filtering, to smooth data and reduce noise; s503: Application of multi-objective optimization algorithms: Apply genetic algorithms, where each "individual" represents a set of possible equipment operation strategies and energy storage operations, and use simulated annealing or particle swarm optimization algorithms to explore the solution space and find the optimal solution that satisfies both objective functions at the same time; s504: Specific configuration of model parameters: set the forecast error range of renewable energy output and define the minimum start and stop time for each device to respond to rapidly changing market conditions; s505: Execution of real-time dispatch decisions: Generate specific dispatch instructions using the optimal solution obtained by the optimization algorithm, such as adjusting the output power of the wind turbine or changing the charge and discharge level of the energy storage device; the dispatch instructions include specific operating parameters, including the new target power set point of the wind turbine and the charge and discharge rate of the energy storage device; s506: Specific implementation of output and reception: The output side is the central control system, which generates and sends dispatch instructions; the receiver is the control system of each device in the integrated energy system, including the control system of the wind turbine and the management system of the energy storage device, which perform corresponding operations according to the received instructions; s507: Monitoring and feedback of execution results: After the equipment control system executes the scheduling instruction, it monitors its 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.

7. The method according to claim 1, characterized in that In step s6, the uncertainty of load demand is modeled using fuzzy mathematics theory. The specific steps are as follows: s601: Define fuzzy sets: 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 a membership function: constructing a membership function for each fuzzy variable; for "low" load, including defining a trapezoidal membership function, whose membership gradually increases from 0 to 1 between 400 and 600 kWh, and then gradually decreases to 0 between 600 and 800 kWh; s603: Formulate fuzzy logic reasoning rules: Formulate fuzzy logic reasoning rules to handle uncertainty 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; s604: Implementing the fuzzy reasoning system: This system uses fuzzy logic reasoning rules to analyze real-time load data and convert the results into clear action instructions; if the real-time load data indicates that the current load belongs to the range of "medium" to "high", the fuzzy reasoning system will output an instruction to increase the power generation; s605: Defuzzify the fuzzy results: Defuzzify the results of fuzzy reasoning to obtain specific numerical outputs; including using the centroid method to convert the fuzzy output into a specific scheduling decision, including increasing the power generation by several kilowatts; s606: Integration of intraday optimization scheduling model: Integrate the defuzzified output into the intraday optimization scheduling model; including: taking the increased power generation as a constraint condition of the intraday scheduling model to adjust the operation plan of the equipment; s607: Specific implementation of output and reception: output scheduling suggestions based on fuzzy logic reasoning, and then adjust the operation strategy of the equipment according to the output of the fuzzy mathematical model; s608: Implementation of dynamic response: The central control system implements dynamic response according to the output of the fuzzy mathematical model to adapt to load changes.

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