An integrated energy multi-scale regulation method based on event triggering and error self-compensation

Abstract

The application discloses a kind of based on event triggering and error self-compensation integrated energy multi-scale regulation method, it is related to intelligent power grid and integrated energy collaborative field, including the following steps: obtaining multi-energy flow day-ahead prediction data, under the constraint of multi-energy flow habit energy balance, with minimizing integrated operation cost containing equipment loss as target, generate day-ahead benchmark output trajectory;The error of current day-ahead prediction data and day-ahead prediction data is converted into operation cost deviation;Operation cost deviation is compared with preset dynamic tolerance threshold, and the residual value of actual measurement value and day-ahead prediction data is calculated, and the data in prediction time domain is corrected in combination with preset error compensation coefficient;According to the data after correction, with minimizing the deviation of system actual state and day-ahead benchmark output trajectory and equipment action penalty as target, solve optimal control instruction increment and issue execution.The application is used to solve the problem of mechanical loss intensification and higher calculation redundancy caused by frequent equipment action.

Description

Technical Field

[0001] This invention relates to the field of smart grid and integrated energy coordination technology, and more specifically, to an integrated energy multi-scale control method based on event triggering and error self-compensation. Background Technology

[0002] Existing optimization scheduling methods for integrated energy systems encompassing multiple energy flows such as electricity, heat, and water typically employ model predictive control based on fixed step sizes.

[0003] However, in actual operation scenarios, due to the high degree of randomness in industrial load and renewable energy output, traditional constant-step control strategies exhibit some problems: First, when faced with small predicted disturbances, existing strategies often maintain the real-time balance of multiple energy flows by frequently fine-tuning equipment actions. This can easily lead to mechanical fatigue and hardware wear of core equipment in practical engineering, thereby affecting the long-term operational safety and overall energy efficiency of the integrated energy system. Second, existing strategies cannot adapt to the dynamic changes in system operating conditions. When the system is running smoothly, its underlying controller still periodically triggers optimization algorithms, resulting in redundant consumption of computing resources, which in turn reduces the online computing efficiency and real-time response performance of the control system.

[0004] Therefore, there is an urgent need for a comprehensive multi-scale energy regulation method that takes into account online computing efficiency, real-time balance of multiple energy flows, and system operation safety. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a comprehensive energy multi-scale control method based on event triggering and error self-compensation. By using day-ahead baseline planning under inertial constraints, combined with event triggering activated by operating cost deviation and residual compensation correction logic, the method addresses the problems of increased mechanical wear and high computational redundancy caused by frequent equipment operations in the prior art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A comprehensive energy multi-scale regulation method based on event triggering and error self-compensation includes the following steps: Acquire day-ahead forecast data for multi-energy flows, and generate the day-ahead reference output trajectory of the equipment under the energy balance constraint considering the inertia of multi-energy flows, with the goal of minimizing the overall operating cost including equipment losses; Based on the energy conversion efficiency of the equipment, the error between the current intraday forecast data and the previous day's forecast data is converted into operating cost deviation; The deviation in operating costs is compared with the preset dynamic tolerance threshold, and the residual value between the actual measurement value and the intraday forecast data is calculated. Then, the data in the forecast time domain is corrected in combination with the preset error compensation coefficient. Based on the corrected data, with the goal of minimizing the deviation between the actual system state and the day-ahead reference output trajectory and the penalty for equipment action, the optimal control command increment is solved and issued for execution.

[0007] In a preferred embodiment, the energy balance constraint considering multi-energy flow inertia includes: establishing a multi-energy flow coupled energy conversion equation that includes electrical transient power balance constraint and thermal dynamic hysteresis power balance constraint; wherein the dynamic hysteresis power balance constraint is established based on a preset pipeline transmission delay constant and thermal inertia coefficient.

[0008] In a preferred embodiment, the comprehensive operating cost includes energy procurement cost and equipment wear penalty cost. Solving the day-ahead reference output trajectory of the equipment includes: establishing multi-dimensional constraints including multi-energy flow energy balance, equipment capacity and ramp rate limits; under the multi-dimensional constraints, using a linear programming algorithm to solve the objective function that minimizes the comprehensive operating cost, and obtaining the day-ahead reference output trajectory including grid interaction power, gas turbine output status and water pump flow rate.

[0009] In a preferred embodiment, the equipment wear penalty cost includes depreciation penalty cost and pump speed regulation wear cost, as shown in the following formula: , , in, Let this be the discrete time step at the current moment. For the equipment in The depreciation penalty cost per start-stop cycle. This is a preset mechanical loss conversion constant. To characterize the device A Boolean variable indicating the start / stop status at any given time, and ; for The wear and tear costs of constant water pump speed regulation The preset speed regulation loss coefficient, for Instantaneous flow rate of the water pump at any given moment This is the preset discrete time step.

[0010] In a preferred embodiment, the error between the current intraday forecast data and the previous day's forecast data is converted into an operating cost deviation using the following formula: , in, For operating cost deviation; This refers to the real-time time-of-use electricity price within the day. For natural gas prices; For the heat production efficiency of gas-fired boilers, For pump efficiency, , , These represent the prediction errors for electrical power, thermal load, and hydraulic load, respectively.

[0011] In a preferred embodiment, the dynamic tolerance threshold is negatively correlated with the current intraday real-time time-of-use electricity price, as shown in the following formula: , in, This is a dynamic tolerance threshold. This is the preset baseline tolerance threshold; for Real-time hourly electricity price at any given moment; This is the average electricity price for the whole day, which is the arithmetic average of the real-time time-of-use electricity prices within the day; This is the preset sensitivity adjustment coefficient.

[0012] In a preferred embodiment, the correction of the data in the prediction time domain includes: calculating the residual value between the actual measured value of the multi-energy flow at the current moment and the intraday prediction data, and recursively updating the historical error compensation coefficient in combination with a preset forgetting factor; and performing feedforward compensation correction on the original prediction data in the prediction time domain based on the updated error compensation coefficient and a preset error decay constant.

[0013] The technical effects and advantages of the integrated energy multi-scale control method based on event triggering and error self-compensation of this invention are as follows: 1. This invention introduces an event-triggered mechanism, which uses the comparison between the converted economic operating deviation and the dynamic tolerance threshold as the basis for solving subsequent control command increments. This reduces redundant calculations during the system's stable operation period or when the error is within an acceptable range, avoids the waste of computing power in the traditional equal-step triggering mode, and improves the real-time response performance of the control system.

[0014] 2. This invention introduces energy balance constraints and equipment losses that take into account the inertia of multiple energy flows when generating the day-ahead reference output trajectory of the equipment, so as to suppress frequent equipment actions when facing small prediction fluctuations. This fundamentally suppresses the mechanical fatigue and hardware losses of the equipment caused by high-frequency control commands, and improves the safety of long-term system operation while ensuring the real-time balance of multiple energy flows.

[0015] 3. This invention extracts the operational residual between actual measurement values ​​and predicted data, and performs feedforward correction on future time-domain data, making the corrected predicted data more consistent with the actual operating state of the system. This reduces the risk of frequent false triggering of the event triggering mechanism due to measurement noise, ensures the accuracy of control command issuance, and further enhances the robustness of multi-energy flow coordinated regulation. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the process of the integrated energy multi-scale regulation method based on event triggering and error self-compensation provided in the embodiments of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0018] Example 1, Figure 1 This invention presents a comprehensive multi-scale energy regulation method based on event triggering and error self-compensation, comprising the following steps: S1. Obtain day-ahead forecast data for multi-energy flow, and under the energy balance constraint considering the inertia of multi-energy flow, generate the day-ahead reference output trajectory of the equipment with the goal of minimizing the overall operating cost including equipment losses.

[0019] In this embodiment, the energy balance constraint considering multi-energy flow inertia in step S1 is as follows: S101. Define the discrete time step. At the current discrete time step Under these conditions, the multi-energy flow input-output balance relationship of the system can be expressed as: (1) (2) (3) in, For the system in The output multi-energy flow demand load matrix at any given time. , , The system in the current day forecast data is respectively Predicted values ​​of electrical load, thermal load, and hydraulic load at any given time; For the system in The energy matrix is ​​supplied with input at any time. For the system in The amount of electrical energy input from the outside at any given time (usually referring to the power purchased by the main power grid). For the system in The power of natural gas input from the outside at any given time; This is an energy conversion matrix, representing the mapping relationship between the energy supplied at the input end and the demand load at the output end; S102. To address the limitation of traditional energy hub (EH) models that often neglect the dynamic delay of physical pipe networks (heat and water networks), a multi-energy flow coupled energy conversion equation that distinguishes time scales is constructed. The energy conversion equation includes transient power balance constraints for electrical energy and dynamic hysteresis power balance constraints for thermal energy. The specific formula is as follows: (4) (5) in, For power grid transmission efficiency, equipment constants; The electrical power purchased from the external main grid; The power generation efficiency of the gas turbine; The natural gas power output of the gas turbine; To contribute to the renewable energy sources in the recent forecast data; This represents the total power consumption of the water pump unit. , These are the transmission delay constant and thermal inertia coefficient of the heat network pipeline, respectively. Let be the time hysteresis decay function of the thermal inertia coefficient, and satisfy . ; , These are the heat production efficiencies of the gas turbine and the gas boiler, respectively. The natural gas power of the gas boiler; in this step, formula (4) characterizes the transient balance of electrical energy without inertia, and formula (5) establishes a dynamic thermal energy balance with memory effect through the time hysteresis decay function. By considering the inertial difference of multiple energy flows and establishing the energy conversion equation, a physical basis is provided for implementing asynchronous triggering of multiple time scales during subsequent intraday regulation.

[0020] In this embodiment, solving the day-ahead reference output trajectory of the equipment in S1 includes: S103. Establish multi-dimensional constraints including multi-energy flow energy balance, equipment capacity, and ramp rate limitations, as follows: 1) The system at each discrete time step Within this framework, the multi-energy flow coupling energy conversion equation constructed in step S102 must be satisfied; furthermore, for water flow networks with energy storage characteristics, the total pumping flow rate within the preset scheduling cycle must meet the daily total water demand of the industrial park or water system, as shown in the following formula: (6) in, Preset scheduling period (e.g., all day). For variable frequency water pump sets in Instantaneous flow rate at any given moment This is the total pumping flow rate. To determine the total daily water demand; 2) Due to the thermal stress limitation of the gas turbine, the power adjustment in adjacent time steps must meet the ramp rate constraint to prevent over-adjustment. The formula is as follows: (7) in, These are the maximum uphill and downhill ramp rates of the equipment, respectively. 3) For the key adjustable variable frequency pump set in the system, its power consumption is nonlinearly coupled with the water head and instantaneous flow rate of the pipeline network; therefore, a dynamic power consumption equation for the pump is established as a nonlinear physical boundary constraint for the variable frequency pump set, as follows: (8) in, This represents the total power consumption of the water pump set. For the density of water, It is the acceleration due to gravity. For Yang Cheng, The dynamic pump efficiency varies with flow rate; 4) To ensure the operational safety of the underlying hardware, upper and lower limits of equipment operating capacity are established. That is, the output power of all equipment must be between the upper and lower limits of its nameplate rated capacity, as shown in the following formula: (9) in, for The power exchange between the time system and the external main grid (usually referring to the power purchased by the main grid). , These are the lower and upper limits of the power grid interaction constraint value, respectively. To characterize the gas turbine in A Boolean variable indicating the start / stop status at any given time, and ; , These are the gas turbines in the startup and operation state (i.e. Minimum and maximum allowable values ​​for natural gas consumption power (per hour); , These are the minimum allowable lower limit and the maximum allowable upper limit of the instantaneous flow rate of the variable frequency water pump set, respectively.

[0021] S104. Construct an objective function that minimizes the overall operating cost, which includes energy procurement costs and equipment depreciation penalty costs; specifically as follows: (10) (11) (12) (13) (14) in, To account for overall operating costs, For energy procurement costs, Penalty costs for dynamic equipment wear and tear; These are preset normalized weighting coefficients used to balance economic costs and equipment lifespan. This is the time-of-use electricity price forecast series from the day-ahead forecast data, containing Forecast of intraday time-of-use electricity prices at specific times; A constant natural gas price; For the equipment in The depreciation penalty cost per start-stop cycle. This is a preset mechanical loss conversion constant; for The wear and tear costs of constant water pump speed regulation This is the preset speed regulation loss coefficient.

[0022] S105. Since the comprehensive operating cost objective function includes Boolean state variables, nonlinear terms of pump power consumption, and absolute value functions, it is a typical mixed-integer nonlinear programming problem (MINLP). The Big M method and piecewise linearization techniques are used to transform the nonlinear terms into a mixed-integer linear programming (MILP) form, and a solver is used for global optimization. The solution yields a reference vector containing the day-ahead reference output trajectory of each device. The form of the reference vector is as follows: (15) in, This is a reference vector that includes the day-ahead baseline output trajectory of each device. The solution obtained earlier The optimal grid interaction power between the time-based system and the external main grid. The gas turbine obtained from the previous solution is in Optimal natural gas consumption power at any given time. The characterization of the gas turbine obtained from the previous solution is as follows: A Boolean variable representing the optimal start / stop state at any given time. The variable frequency water pump set obtained from the previous solution is in The optimal instantaneous flow rate at any given time; the day-ahead reference output trajectory of each device serves as the reference input baseline for subsequent intraday phase event triggering mechanisms.

[0023] This step effectively characterizes the physical response differences between electrical transients and thermal hysteresis by constructing multi-energy flow coupling equations that differentiate time scales, laying the physical foundation for intraday multi-time-scale asynchronous triggering mechanisms. In addition, by introducing equipment start-up and shutdown depreciation and speed regulation wear targets into the day-ahead baseline planning, the overall system operating efficiency and the physical lifespan of the underlying hardware are synergistically optimized.

[0024] S2. Based on equipment energy conversion efficiency, the error between the current intraday forecast data and the previous day's forecast data is converted into operating cost deviation, including: During the intraday operation and control phase (e.g., with a rolling time step of 15 minutes), a conventional SCADA data acquisition system in this field is used in conjunction with a standard time series forecasting model to continuously acquire short-term intraday multi-energy flow forecast data in the future time domain as intraday real-time forecast data. Of the intraday real-time forecast data and the previous day's forecast data, The predicted values ​​of electrical load, thermal load, and hydraulic load at any given time are subtracted to obtain the real-time prediction error vector of the multi-energy flow at the current time. Based on the energy conversion efficiency of the corresponding equipment, the real-time prediction error vector of the multi-energy flow is equivalently converted into the operating cost deviation, as shown in the following formula: (16) (17) in, For the current moment The real-time prediction error vector of multi-energy flow; , , These are the absolute deviations of the day-ahead forecast and the intraday real-time forecast for electrical power, heat load, and hydraulic load, respectively. For operating cost deviation; For time-of-use electricity price forecast series Real-time time-of-use electricity price at any given moment. For water pump efficiency; For the density of water, It is the acceleration due to gravity. For the journey.

[0025] This step, by using the equipment's energy conversion efficiency, unifies the prediction errors of electrical energy, thermal energy, and hydraulic load, which have different physical dimensions, into operating cost deviations. This eliminates the dimensional differences between multiple energy flows and provides a standard quantitative criterion for subsequent intraday control triggered by a single condition.

[0026] S3. Compare the operating cost deviation with the preset dynamic tolerance threshold, calculate the residual value between the actual measurement value and the intraday forecast data, and then correct the data in the forecast time domain by combining it with the preset error compensation coefficient; including: The dynamic tolerance threshold is negatively correlated with the current intraday real-time time-of-use electricity price. Specifically, during peak electricity price periods, the dynamic tolerance threshold decays exponentially, thereby improving the system's responsiveness to subsequent control deviations and triggering timely control command adjustments to avoid excessive equipment wear and operating costs. During off-peak electricity price periods, the dynamic tolerance threshold rises accordingly, allowing the system to absorb minor disturbances through the natural physical inertia of the heating and water networks, thus suppressing redundant equipment adjustments. The calculation formula for the dynamic tolerance threshold is as follows: (18) in, This is a dynamic tolerance threshold. This is the preset baseline tolerance threshold; This is the average electricity price for the whole day, which is the arithmetic average of the real-time time-of-use electricity prices within the day; This is the preset sensitivity adjustment coefficient; At each intraday control time step, the system's underlying controller compares the operating cost deviation with the dynamic tolerance threshold in real time. Only when the operating cost deviation is greater than or equal to the dynamic tolerance threshold is the subsequent intraday optimization and control operation activated and executed. Otherwise, the control command from the previous moment is maintained (i.e., zero-order hold), thereby reducing unnecessary hardware wear and computational resource consumption.

[0027] At the current scheduling time t, the system acquires the actual measurement values ​​of each physical node within the system through the underlying Supervisory Control and Data Acquisition (SCADA) system; The vector containing the actual measurements of multi-energy flow at any given time is defined as follows: The intraday real-time forecast vector at the corresponding time is defined as Calculate the residual between the actual measured value vector and the intraday real-time predicted value vector to obtain the multi-energy flow operation residual vector at the current moment. : (19) in, , respectively, represent the absolute differences between the actual values ​​of electrical load, thermal load and hydraulic load and the real-time forecast values ​​within the day; Considering that the load fluctuations of the integrated energy system are time-dependent, and that the residual at a single moment often contains high-frequency measurement noise, this embodiment introduces an exponential smoothing algorithm with a forgetting factor to recursively update the historical error compensation coefficients, as shown in the following formula: (20) in, This represents the error compensation coefficient at the current moment. This is the historical error compensation coefficient from the previous moment; It is a forgetting factor, and The forgetting factor is used to adjust the algorithm's memory length of historical data: when the system is running smoothly, a larger forgetting factor is selected. The value (e.g., 0.95) is used to filter out measurement noise; when the system experiences a sudden disturbance (such as a sudden drop in photovoltaic output), the system will automatically adjust the value downwards. Value (e.g., 0.80); Finally, using the updated error compensation coefficients and the preset error attenuation constant, the future time domain is predicted. The original prediction input data within Error feedforward correction is performed; the error decay constant is used to reflect the objective physical law that the prediction error gradually decays over time; the specific formula for the error feedforward correction is as follows: (twenty one) in, To predict the step size, The predicted input data is after feedforward compensation correction. The original prediction input data in the prediction time domain; Let be the error attenuation constant, and .

[0028] This step introduces a dynamic tolerance threshold to construct an event triggering mechanism, effectively filtering out computational redundancy during the system's stable period, and utilizing the physical inertia of the pipeline network to absorb minor disturbances and suppress ineffective regulation. At the same time, it is combined with a feedforward compensation strategy based on adaptive tuning of the forgetting factor according to the residual change rate to achieve a performance balance between smoothing high-frequency measurement noise and quickly tracking sudden disturbances, thereby enhancing the system's ability to resist disturbances in the input data.

[0029] S4. Based on the corrected data, with the objective of minimizing the deviation between the actual system state and the day-ahead reference output trajectory, as well as the equipment action penalty, solve for the optimal control command increment and issue it for execution; including: The corrected predicted input data output from step S3 is used as the real-time boundary condition for intraday rolling optimization and substituted into the multi-energy flow energy balance constraint. Under the premise of satisfying the corrected real-time energy balance constraint, a quadratic objective function is constructed with the goal of minimizing the deviation between the actual system state and the day-ahead reference power output trajectory, as well as the penalty for equipment control actions. The control command increment sequence is obtained by quadratic programming, as shown in the following formula: (twenty two) in, It is a quadratic objective function. This represents the actual operating state predicted by the system. The reference vector containing the day-ahead reference output trajectory of each device; W represents the increment of control commands (such as water pump speed setpoint and gas turbine output command) in adjacent time steps; W and R are the state tracking weight matrix and control increment penalty matrix, respectively. The preset control increment penalty weight; After optimization, the first element in the control command increment sequence (i.e. the optimal control command increment at the current moment) is extracted and sent to the underlying physical device to perform the control action. Then, the prediction and control time domain is rolled forward to enter the next scheduling time step.

[0030] This step, through intraday rolling optimization, uses the feedforward-corrected data as the physical boundary to construct a quadratic objective function that balances baseline trajectory tracking and equipment action penalties. This guides the controller to output a sequence of commands with a smooth amplitude while maintaining the balance between multi-energy flow supply and demand. From an algorithmic perspective, this suppresses mechanical fatigue and hardware wear caused by high-frequency adjustment of core equipment, ensuring the long-term operational stability of the system.

[0031] The above formulas are all dimensionless calculations. The formulas are derived from software simulations using a large amount of collected data, and are the closest to the real situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0032] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0033] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0034] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0035] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0036] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A comprehensive energy multi-scale control method based on event triggering and error self-compensation, characterized in that, Includes the following steps: Acquire day-ahead forecast data for multi-energy flows, and generate the day-ahead reference output trajectory of the equipment under the energy balance constraint considering the inertia of multi-energy flows, with the goal of minimizing the overall operating cost including equipment losses; Based on the energy conversion efficiency of the equipment, the error between the current intraday forecast data and the previous day's forecast data is converted into operating cost deviation; The deviation in operating costs is compared with the preset dynamic tolerance threshold, and the residual value between the actual measurement value and the intraday forecast data is calculated. Then, the data in the forecast time domain is corrected in combination with the preset error compensation coefficient. Based on the corrected data, with the goal of minimizing the deviation between the actual system state and the day-ahead reference output trajectory and the penalty for equipment action, the optimal control command increment is solved and issued for execution.

2. The integrated energy multi-scale control method based on event triggering and error self-compensation according to claim 1, characterized in that, The energy balance constraint considering multi-energy flow inertia includes: Establish a multi-energy flow coupled energy conversion equation that includes both transient power balance constraints of electrical energy and dynamic hysteresis power balance constraints of thermal energy; The dynamic hysteresis power balance constraint is established based on a preset pipeline transmission delay constant and thermal inertia coefficient.

3. The integrated energy multi-scale control method based on event triggering and error self-compensation according to claim 1, characterized in that, The comprehensive operating cost includes energy procurement costs and equipment wear and tear penalty costs. The method for determining the day-ahead baseline output trajectory of the equipment includes: Establish multi-dimensional constraints that include multi-energy flow energy balance, equipment capacity, and ramp rate limitations; Under the aforementioned multidimensional constraints, a linear programming algorithm is used to solve the objective function that minimizes the overall operating cost, resulting in a day-ahead reference output trajectory that includes grid interaction power, gas turbine output status, and water pump flow rate.

4. The integrated energy multi-scale control method based on event triggering and error self-compensation according to claim 3, characterized in that, The equipment wear and tear penalty cost includes depreciation penalty cost and pump speed regulation wear cost, as shown in the following formula: ， ， in, Let this be the discrete time step at the current moment. For the equipment in The depreciation penalty cost per start-stop cycle. This is a preset mechanical loss conversion constant. To characterize the device A Boolean variable indicating the start / stop status at any given time, and ; for The wear and tear costs of constant water pump speed regulation This is the preset speed regulation loss coefficient. for Instantaneous flow rate of the water pump at any given moment This is the preset discrete time step.

5. The integrated energy multi-scale control method based on event triggering and error self-compensation according to claim 1, characterized in that, The formula for converting the error between the current intraday forecast data and the previous day's forecast data into operating cost deviation is as follows: ， in, For operating cost deviation; This refers to the real-time time-of-use electricity price within the day. For natural gas prices; For the heat production efficiency of gas-fired boilers, For pump efficiency, , , These represent the prediction errors for electrical power, thermal load, and hydraulic load, respectively.

6. The integrated energy multi-scale control method based on event triggering and error self-compensation according to claim 1, characterized in that, The dynamic tolerance threshold is negatively correlated with the current intraday real-time time-of-use electricity price, as shown in the following formula: ， in, This is a dynamic tolerance threshold. This is the preset baseline tolerance threshold; for Real-time hourly electricity price at any given moment; This is the average electricity price for the whole day, which is the arithmetic average of the real-time time-of-use electricity prices within the day; This is the preset sensitivity adjustment coefficient.

7. The integrated energy multi-scale control method based on event triggering and error self-compensation according to claim 1, characterized in that, The data in the corrected prediction time domain includes: Calculate the residual between the current actual measurement value of multi-energy flow and the intraday prediction data, and recursively update the historical error compensation coefficient by combining it with a preset forgetting factor; Based on the updated error compensation coefficient and the preset error attenuation constant, the original prediction data in the prediction time domain is corrected by feedforward compensation.

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