A method and apparatus for equipment scheduling in an integrated energy system

Abstract

This application discloses a method and apparatus for equipment scheduling in an integrated energy system. By acquiring operating status information, establishing a real equipment model, determining dynamic candidate energy flow directions, and generating global scheduling instructions, it is possible to achieve adaptive and coordinated scheduling of energy conversion equipment and multiple types of energy storage equipment based on actual physical characteristics and system status. This improves the utilization rate of surplus energy, reduces ineffective conversion and low-value energy storage calls, extends energy storage life, and enhances overall energy utilization efficiency.

Description

Technical Field

[0001] This invention relates to the field of equipment scheduling technology for integrated energy systems, and in particular to an equipment scheduling method for integrated energy systems, an equipment scheduling device for integrated energy systems, an electronic device, and a readable storage medium. Background Technology

[0002] In the field of integrated energy system operation optimization, accurate modeling and coordinated scheduling of energy conversion and storage devices are crucial for improving system efficiency. However, existing technologies often employ simplified linear models or energy hub frameworks at the modeling level, typically setting device conversion efficiency as a constant value and neglecting the lifespan degradation characteristics and charging / discharging losses of energy storage devices during actual operation. This lack of description of physical dynamic characteristics leads to scheduling commands often deviating from the actual operating conditions of the devices, making it difficult to balance system operational economy with the long-term health of the equipment. Furthermore, existing scheduling methods often lack a global perspective on energy flow, employing fixed priority strategies or loosely coupled hierarchical architectures, failing to fully explore the synergistic potential between energy conversion and storage devices under different time periods and load conditions. Because it is impossible to comprehensively and in real-time assess all possible flow paths of deployable energy within the system, the system struggles to generate globally optimal scheduling commands covering all energy flow paths when facing complex energy supply and demand changes, limiting the overall regulation capabilities of the integrated energy system. Summary of the Invention

[0003] The present invention provides a method, apparatus, electronic device, and readable storage medium for equipment scheduling in an integrated energy system, to overcome or at least partially solve the above-mentioned problems.

[0004] To solve the above-mentioned technical problems, this application is implemented as follows: In a first aspect, embodiments of this application provide a method for equipment scheduling in an integrated energy system, including: Obtain operational status information of the integrated energy system; Using the aforementioned operating status information, an operating model for energy conversion equipment is established to characterize the non-constant conversion efficiency characteristics under different load conditions, as well as an operating model for energy storage equipment to characterize energy storage capacity constraints, charging and discharging losses, and lifespan degradation characteristics. The energy conversion device operation model and the energy storage device operation model are used to determine one or more candidate energy flows to represent all possible destinations of the currently available energy in the integrated energy system; the integrated energy system includes energy conversion devices and energy storage devices. Global multi-energy flow collaborative scheduling instructions for the energy conversion device and the energy storage device are generated based on the candidate energy flow directions.

[0005] Optionally, the step of determining one or more candidate energy flows to represent all possible destinations of the currently available energy in the integrated energy system using the energy conversion device operation model and the energy storage device operation model includes: Using the energy conversion equipment operation model and the energy storage equipment operation model, the non-constant conversion efficiency characteristics and operating range constraints of the energy conversion equipment under different load conditions, as well as the capacity constraints, charging and discharging losses and lifespan degradation characteristics of the energy storage equipment, are extracted to generate a quantitative dataset of the actual operating characteristics of the equipment. The actual operating characteristics of the equipment are used to quantify the dataset, identify the available energy to be allocated in the integrated energy system during the current period, and generate available energy information for the current period. The actual operating characteristics of the equipment are quantified using the dataset and the available energy information for the current time period. All feasible energy conversion paths for surplus electrical energy are enumerated to generate a set of candidate destination paths for surplus electrical energy. The feasible energy conversion paths include paths that directly charge the battery energy storage device, paths that convert electrical energy into heat energy through a heat pump and store it in a thermal energy storage device, paths that convert electrical energy into hydrogen energy through an electrolyzer and store it in a hydrogen energy storage device, paths that directly supply the current adjustable electrical load, and paths that reduce purchased electricity or suppress the power generation output of the combined heat and power unit. The actual operating characteristics of the equipment are used to quantify the dataset and the available energy information for the current time period. All feasible energy replenishment paths for the energy supply gap are enumerated to generate a set of candidate energy replenishment paths for the energy supply gap. The feasible energy replenishment paths include paths that satisfy the electrical load by discharging battery energy storage, paths that satisfy the thermal load by releasing heat from thermal energy storage, paths that start fuel cells to convert hydrogen energy into electrical energy and thermal energy, paths that increase the output of cogeneration units, paths that start electric boilers or heat pumps to supplement the thermal load, and paths that purchase electricity from the external power grid or gas from the gas grid. The set of candidate destination paths for surplus electrical energy and the set of candidate replenishment paths for energy supply gaps are merged and deduplicated. The conversion efficiency, loss and lifespan impact parameters in the dataset are quantified in combination with the actual operating characteristics of the equipment. A comprehensive marginal value assessment index is calculated for each path to generate a set of candidate energy flow directions with assessment indexes. Using the aforementioned set of candidate energy flow directions with evaluation indicators, one or more candidate energy flow directions are determined to represent all possible destinations of the currently available energy in the integrated energy system.

[0006] Optionally, the step of generating global multi-energy flow collaborative scheduling instructions for the energy conversion device and the energy storage device based on the candidate energy flow directions includes: Determine the specific path combination, expected conversion efficiency, loss cost, lifetime reduction, and future demand matching degree corresponding to the candidate energy flow direction; Based on the candidate energy flow direction, a multi-objective optimization model is constructed that takes into account operating economy, carbon emission level, equipment health status and energy supply security requirements. Using the specific path combination, the expected conversion efficiency, the loss cost, the lifespan reduction, and the future demand matching degree as constraints, the energy conversion equipment and the energy storage equipment are used in a collaborative optimization solution under different candidate energy flow directions to obtain an initial target scheduling scheme; When the initial target scheduling scheme is determined to meet the preset conditions, a global multi-energy flow collaborative scheduling instruction corresponding to each energy conversion device and each energy storage device is generated according to the initial target scheduling scheme. The global multi-energy flow collaborative scheduling instruction is used to control each energy conversion device in the integrated energy system to perform corresponding energy conversion operations, and is also used to control each energy storage device in the integrated energy system to perform corresponding charging and discharging operations, and is also used to control the load units in the integrated energy system to perform corresponding load supply operations.

[0007] Optionally, it also includes: Based on the initial target scheduling scheme, a multi-timescale collaborative control mechanism combining daily planning, intraday rolling correction, and real-time execution is constructed to dynamically update and control the initial target scheduling scheme and generate multi-timescale collaborative control results. A set of random scenarios is established to address the fluctuations in renewable energy output and load forecasting errors. The system operating status under different scenarios is evaluated using the set of random scenarios. Based on the scenario evaluation results, the multi-time-scale collaborative control results are corrected to obtain the corrected target scheduling scheme. Based on the revised target scheduling scheme, a global multi-energy flow collaborative scheduling instruction corresponding to each energy conversion device and each energy storage device is generated.

[0008] Optionally, the step of constructing a multi-objective optimization model based on the candidate energy flow direction that takes into account operating economy, carbon emission level, equipment health status, and energy supply security requirements includes: Using the candidate energy flow direction, combined with the non-constant conversion efficiency characteristics, operating range constraints, capacity constraints, charging and discharging losses, and lifespan decay characteristics in the energy conversion equipment operation model and the energy storage equipment operation model, as well as the renewable energy predicted output information, load prediction information, external energy price information, and environmental parameter information for the current period, the operating cost, conversion efficiency, energy storage status, equipment lifespan impact parameters, carbon emission factors, and future demand value corresponding to each candidate path are extracted to generate a path comprehensive evaluation parameter set; Using the path comprehensive evaluation parameter set and the external energy price information, an economic objective function is constructed with minimizing the total system operating cost as the first optimization objective, thus generating an operational economic objective function; wherein the total operating cost includes electricity purchase cost, natural gas cost, and equipment operation and maintenance cost; By utilizing the comprehensive evaluation parameter set of the aforementioned path and the carbon emission factors of each energy conversion path, a carbon emission objective function is constructed with minimizing carbon emission costs as the second optimization objective, thereby generating a carbon emission level objective function; By utilizing the path comprehensive evaluation parameter set and the lifetime degradation characteristics and equipment start-stop frequency parameters in the energy storage device operation model, an equipment health objective function is constructed with minimizing the energy storage lifetime loss cost and the equipment frequent start-stop depreciation cost as the third optimization objective, and an equipment health status objective function is generated. Using the path comprehensive evaluation parameter set, the load forecast information, and the energy supply security margin requirements, an energy supply security objective function is constructed with minimizing the risk of insufficient energy supply or wind and solar curtailment losses as the fourth optimization objective, and an energy supply security requirement objective function is generated. The objective functions of operational economy, carbon emission level, equipment health status, and energy supply safety requirements are combined into a set of optimization objectives for a multi-objective optimization model, thereby generating a multi-objective optimization objective set. Using the energy conversion equipment operation model, the energy storage equipment operation model, the candidate energy flow direction, and the system power balance requirements of the integrated energy system, a set of constraints for a multi-objective optimization model is constructed. The set of constraints for the multi-objective optimization model includes the operating range constraints of each energy conversion equipment, the capacity boundary constraints and state of charge constraints of each energy storage equipment, and the supply and demand balance constraints of various energy forms. Using the set of multi-objective optimization objectives and the set of constraints of the multi-objective optimization model, a multi-objective optimization model that takes into account the requirements of operational economy, carbon emission level, equipment health status and energy supply security is constructed, and a multi-objective optimization model is generated.

[0009] Optionally, the step of constructing a multi-timescale collaborative control mechanism combining daily planning, intraday rolling correction, and real-time execution based on the initial target scheduling scheme, and dynamically updating and controlling the initial target scheduling scheme in a closed loop to generate multi-timescale collaborative control results includes: Based on the initial target scheduling scheme, obtain the renewable energy forecast output information, load forecast information, external energy price information, and status information of energy conversion equipment and energy storage equipment for the next day, and generate the day-ahead layer input dataset; The day-ahead baseline scheduling plan is generated using the day-ahead layer input dataset. The day-ahead baseline scheduling plan includes the planned output of cogeneration units for each time period, the operation plans of heat pumps, electric boilers, electrolyzers and fuel cells for each time period, the charging and discharging plans of battery energy storage, thermal energy storage and hydrogen energy storage, and the optimal allocation schemes of various energy sources for different time periods. Based on the day-ahead baseline scheduling plan and the real-time updated renewable energy forecast output information and load forecast information, the latest forecast data is obtained according to the first preset time period, and the day-ahead baseline scheduling plan is rolled over and revised to generate an intraday rolling revised scheduling plan. Based on the intraday rolling adjustment scheduling plan and the latest measured real-time operating status information, rapid adjustment actions are executed at a second preset time period, and multi-time-scale collaborative control results are generated; the rapid adjustment actions include increasing the heat release power of thermal energy storage, starting heat pumps or electric boilers to supplement the heat load, and coordinating battery energy storage and purchased electricity to ensure synchronous balance of electric and heat loads.

[0010] Optionally, the steps of establishing a random scenario set for renewable energy output fluctuations and load forecasting errors, using the random scenario set to evaluate the system operating status under different scenarios, and correcting the multi-timescale coordinated control results based on the scenario evaluation results to obtain the corrected target scheduling scheme include: To address the fluctuations in renewable energy output and load forecasting errors, a random scenario set containing multiple target operating scenarios is constructed. The random scenario set includes at least the following scenarios: normal high photovoltaic load, normal low photovoltaic load, normal photovoltaic heat load increase, low wind and solar power load with sudden increase, and simultaneous increase of multiple energy loads. For the photovoltaic high-load normal scenario, the photovoltaic low-load normal scenario, the photovoltaic normal heat load rising scenario, the wind and solar low power load sudden increase scenario, and the multi-energy load simultaneous increase scenario, the energy conversion equipment output plan and energy storage charging and discharging plan under each scenario are recalculated using the multi-time scale collaborative control results, and a set of operation schemes for each scenario is generated. Based on the set of operation schemes for each scenario, and the probability or risk weight of occurrence for different scenarios, the operation schemes under different scenarios are weighted and fused for evaluation to generate robust scheduling results. The robust scheduling results are used to correct the deviations in the multi-timescale collaborative control results, thereby obtaining the corrected target scheduling scheme.

[0011] Secondly, embodiments of this application provide an equipment scheduling device for an integrated energy system, comprising: The operation status information acquisition module is used to acquire the operation status information of the integrated energy system; The model building module is used to establish an energy conversion equipment operation model that characterizes the non-constant conversion efficiency characteristics under different load conditions using the operation status information, and an energy storage equipment operation model that characterizes the energy storage capacity constraints, charging and discharging losses and lifespan degradation characteristics. The candidate energy flow direction determination module is used to determine one or more candidate energy flow directions that express all possible destinations of the currently available energy in the integrated energy system, using the energy conversion equipment operation model and the energy storage equipment operation model; the integrated energy system includes energy conversion equipment and energy storage equipment; A global multi-energy flow collaborative scheduling instruction generation module is used to generate global multi-energy flow collaborative scheduling instructions for the energy conversion device and the energy storage device based on the candidate energy flow direction.

[0012] Thirdly, embodiments of this application provide an electronic device including a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method described in the first aspect.

[0013] Fourthly, embodiments of this application provide a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect.

[0014] Fifthly, embodiments of this application provide a chip, the chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the method as described in the first aspect.

[0015] The embodiments of the present invention have the following advantages: This invention, through acquiring operational status information, establishing a realistic equipment model, determining dynamic candidate energy flow directions, and generating global scheduling instructions, enables adaptive and coordinated scheduling of energy conversion devices and multiple types of energy storage devices based on actual physical characteristics and system status. This improves the utilization rate of surplus energy, reduces ineffective conversion and low-value energy storage calls, extends energy storage lifespan, and enhances overall energy utilization efficiency. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the steps of an equipment scheduling method for an integrated energy system provided in this embodiment of the invention. Figure 2 This is a flowchart illustrating a method for scheduling equipment in an integrated energy system, as provided in an embodiment of the present invention. Figure 3 This is a structural block diagram of an equipment scheduling device for an integrated energy system provided in an embodiment of the present invention; Figure 4 This is a hardware structure block diagram of an electronic device provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of a computer-readable medium provided in an embodiment of the present invention. Detailed Implementation

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

[0018] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] Reference Figure 1 The diagram illustrates a flowchart of a device scheduling method for an integrated energy system provided in an embodiment of the present invention, which may specifically include the following steps: Step 101: Obtain the operating status information of the integrated energy system; Step 102: Using the operating status information, establish an energy conversion equipment operation model to characterize the non-constant conversion efficiency characteristics under different load conditions, and an energy storage equipment operation model to characterize the energy storage capacity constraints, charging and discharging losses, and lifespan degradation characteristics. Step 103: Using the energy conversion device operation model and the energy storage device operation model, determine one or more candidate energy flows to represent all possible destinations of the currently available energy in the integrated energy system; the integrated energy system includes energy conversion devices and energy storage devices; Step 104: Generate a global multi-energy flow collaborative scheduling command for the energy conversion device and the energy storage device based on the candidate energy flow direction.

[0021] The embodiments of the present invention can obtain the operating status information of a comprehensive energy system, so as to provide accurate real-time and predictive basic data for subsequent model building and decision-making.

[0022] Operational status information refers to the total number of operational parameters that are currently available in the integrated energy system and are available in the short term. This includes renewable energy output forecast information, load forecast information, external energy price information, operational status information of energy conversion equipment, status information of energy storage equipment, and environmental parameter information.

[0023] Renewable energy output forecast information refers to the predicted power generation data of photovoltaic and wind power generation devices during the future dispatch period. Load forecast information refers to the predicted demand values ​​of the park's electrical and thermal loads during the future dispatch period.

[0024] External energy price information refers to market price data such as time-of-use electricity prices and natural gas prices.

[0025] Energy conversion equipment operation status information refers to the current output level and operating status of combined heat and power units, heat pumps, electric boilers, electrolyzers, and fuel cells.

[0026] Energy storage device status information refers to the current storage level, such as the state of charge of battery energy storage, the remaining capacity of thermal energy storage, and the storage capacity of hydrogen energy storage tanks.

[0027] Environmental parameters refer to external conditions that affect equipment performance, such as outdoor temperature, solar irradiance, and wind speed.

[0028] This invention can utilize the aforementioned operating status information to establish operating models for energy conversion equipment, characterizing non-constant conversion efficiency under different load conditions, and operating models for energy storage equipment, characterizing energy storage capacity constraints, charge / discharge losses, and lifetime degradation characteristics. This replaces the idealized constant efficiency assumption with a dynamic model reflecting actual engineering characteristics, providing a real physical basis for subsequent candidate path decisions. An energy conversion equipment operating model is a mathematical model describing the input-output conversion relationship of equipment such as cogeneration units, heat pumps, electric boilers, electrolyzers, and fuel cells under different load rates. Non-constant conversion efficiency characteristics refer to the characteristic that equipment efficiency varies with factors such as load rate and ambient temperature, rather than remaining constant. An energy storage equipment operating model is a mathematical model describing the capacity changes, energy losses, and lifetime impacts of devices such as battery energy storage, thermal energy storage, and hydrogen energy storage. Energy storage capacity constraints refer to the maximum and minimum allowable storage capacity limits of energy storage devices. Charge / discharge losses refer to the energy losses generated by energy storage devices during charging and discharging due to conversion and self-discharge. Lifetime degradation characteristics refer to the reduction in the remaining lifespan of energy storage devices caused by factors such as battery cycle count, charge / discharge depth, and rate of return.

[0029] The embodiments of the present invention can use the energy conversion equipment operation model and the energy storage equipment operation model to determine one or more candidate energy flows that express all possible destinations of the currently available energy in the integrated energy system, so as to break through the fixed priority strategy, establish a dynamic energy allocation decision space, and provide clear alternative paths for optimization.

[0030] Candidate energy flow direction refers to the discrete set of all possible destinations of the available energy in the current time period.

[0031] The available energy at the current time refers to the surplus electrical energy or energy shortage that occurs in the system.

[0032] An integrated energy system refers to an integrated system consisting of an electric power input unit, a natural gas input unit, a renewable energy power generation unit, an energy conversion unit, an energy storage unit, and a load unit.

[0033] Energy conversion equipment refers to devices such as combined heat and power units, heat pumps, electric boilers, electrolyzers, and fuel cells that enable the conversion of different energy forms.

[0034] Energy storage devices refer to devices such as battery energy storage devices, thermal energy storage devices, and hydrogen energy storage devices that enable energy transfer over time.

[0035] In this embodiment of the invention, global multi-energy flow collaborative scheduling instructions for the energy conversion device and the energy storage device can be generated through the candidate energy flow direction, so as to transform the candidate path into directly executable device control signals through optimization decision-making, thereby realizing global collaborative scheduling of multi-energy flows.

[0036] Global multi-energy flow collaborative scheduling instructions refer to the final output set of instructions used to control the specific power settings, start / stop status, and energy allocation ratio of each device.

[0037] The embodiments of the present invention, by acquiring operating status information, establishing a real equipment model, determining dynamic candidate energy flow directions and generating global scheduling instructions, can enable energy conversion equipment and multiple types of energy storage equipment to perform adaptive and coordinated scheduling based on actual physical characteristics and system status, thereby improving the utilization rate of surplus energy, reducing ineffective conversion and low-value energy storage calls, extending energy storage life and improving overall energy utilization efficiency.

[0038] Based on the above embodiments, modified embodiments of the above embodiments are proposed. It should be noted that, in order to keep the description brief, only the differences from the above embodiments are described in the modified embodiments.

[0039] In an optional embodiment of the present invention, the step of determining one or more candidate energy flows to represent all possible destinations of the currently available energy in the integrated energy system using the energy conversion device operation model and the energy storage device operation model includes: Using the energy conversion equipment operation model and the energy storage equipment operation model, the non-constant conversion efficiency characteristics and operating range constraints of the energy conversion equipment under different load conditions, as well as the capacity constraints, charging and discharging losses and lifespan degradation characteristics of the energy storage equipment, are extracted to generate a quantitative dataset of the actual operating characteristics of the equipment. The actual operating characteristics of the equipment are used to quantify the dataset, identify the available energy to be allocated in the integrated energy system during the current period, and generate available energy information for the current period. The actual operating characteristics of the equipment are quantified using the dataset and the available energy information for the current time period. All feasible energy conversion paths for surplus electrical energy are enumerated to generate a set of candidate destination paths for surplus electrical energy. The feasible energy conversion paths include paths that directly charge the battery energy storage device, paths that convert electrical energy into heat energy through a heat pump and store it in a thermal energy storage device, paths that convert electrical energy into hydrogen energy through an electrolyzer and store it in a hydrogen energy storage device, paths that directly supply the current adjustable electrical load, and paths that reduce purchased electricity or suppress the power generation output of the combined heat and power unit. The actual operating characteristics of the equipment are used to quantify the dataset and the available energy information for the current time period. All feasible energy replenishment paths for the energy supply gap are enumerated to generate a set of candidate energy replenishment paths for the energy supply gap. The feasible energy replenishment paths include paths that satisfy the electrical load by discharging battery energy storage, paths that satisfy the thermal load by releasing heat from thermal energy storage, paths that start fuel cells to convert hydrogen energy into electrical energy and thermal energy, paths that increase the output of cogeneration units, paths that start electric boilers or heat pumps to supplement the thermal load, and paths that purchase electricity from the external power grid or gas from the gas grid. The set of candidate destination paths for surplus electrical energy and the set of candidate replenishment paths for energy supply gaps are merged and deduplicated. The conversion efficiency, loss and lifespan impact parameters in the dataset are quantified in combination with the actual operating characteristics of the equipment. A comprehensive marginal value assessment index is calculated for each path to generate a set of candidate energy flow directions with assessment indexes. Using the aforementioned set of candidate energy flow directions with evaluation indicators, one or more candidate energy flow directions are determined to represent all possible destinations of the currently available energy in the integrated energy system.

[0040] The embodiments of the present invention can use the operating model of the energy conversion equipment and the operating model of the energy storage equipment to extract the non-constant conversion efficiency characteristics and operating range constraints of the energy conversion equipment under different load conditions, as well as the capacity constraints, charging and discharging losses and lifespan decay characteristics of the energy storage equipment, so as to generate a quantitative dataset of the actual operating characteristics of the equipment, providing an accurate physical parameter basis for subsequent path enumeration and value assessment.

[0041] The quantitative dataset of actual operating characteristics of equipment refers to the set of quantitative characteristic parameters of each device under the current operating conditions, calculated by the operating models of energy conversion equipment and energy storage equipment.

[0042] Non-constant conversion efficiency characteristics refer to the dynamic change of the actual conversion efficiency of energy conversion equipment under different load rates with factors such as load rate and ambient temperature.

[0043] Operating range constraints refer to the safe operating power range and load rate range allowed for each energy conversion device.

[0044] Capacity constraints refer to the maximum and minimum storage capacity limits that energy storage devices must not exceed during charging and discharging.

[0045] Energy loss during charging and discharging refers to the amount of energy lost by an energy storage device during the charging and discharging process due to conversion efficiency and self-discharge.

[0046] Lifetime degradation characteristics refer to the laws governing the capacity decay and lifespan reduction of battery energy storage due to charge / discharge depth, rate of change, and number of cycles.

[0047] The embodiments of the present invention can use the actual operating characteristics of the equipment to quantify the dataset and identify the available energy to be allocated in the integrated energy system during the current period, so as to generate available energy information for the current period and provide clear energy supply and demand gap data for path enumeration.

[0048] The available energy information for the current period refers to the surplus electricity generated when the predicted output of photovoltaic or wind power in the system exceeds the load, as well as the energy gap caused by the predicted load exceeding the current supply.

[0049] Surplus electricity refers to the available electricity remaining after renewable energy output exceeds the electricity load and the demand that must be converted during the current period.

[0050] Energy supply gap refers to the energy shortfall that needs to be made up when the current demand for electricity or heat exceeds the existing supply.

[0051] The embodiments of the present invention can use the actual operating characteristics of the equipment to quantify the dataset and the available energy information for the current time period to enumerate all feasible energy conversion paths for surplus power, so as to generate a set of candidate destination paths for surplus power and provide specific alternative directions for dynamic diversion decisions.

[0052] The set of candidate destinations for surplus electricity refers to the set of all feasible destinations for surplus electricity.

[0053] Feasible energy conversion pathways include direct charging of battery energy storage devices, conversion of electrical energy into heat energy via heat pumps and storage in thermal energy storage devices, conversion of electrical energy into hydrogen energy via electrolyzers and storage in hydrogen energy storage devices, direct supply to current adjustable electrical loads, and reduction of external power purchases or suppression of combined heat and power (CHP) unit power generation output.

[0054] The path of directly charging the battery energy storage device refers to the energy flow of directly inputting surplus electrical energy into the battery energy storage device for charging.

[0055] The path of converting electrical energy into heat energy and storing it in a thermal energy storage device through a heat pump refers to the energy flow direction in which the heat pump consumes electrical energy to generate heat and stores it in the thermal energy storage device.

[0056] The path of converting electrical energy into hydrogen energy through an electrolyzer and storing it in a hydrogen energy storage device refers to the energy flow direction of using an electrolyzer to consume electrical energy to produce hydrogen and storing it in a hydrogen energy storage device.

[0057] The path of directly supplying current adjustable electrical loads refers to the energy flow that directly uses surplus electrical energy to meet the demand of adjustable electrical loads.

[0058] The path to reduce purchased electricity or suppress the power output of combined heat and power (CHP) units refers to the energy flow that absorbs surplus electricity by reducing the amount of electricity purchased from the external power grid or reducing the power output of CHP units.

[0059] The embodiments of the present invention can use the actual operating characteristics of the equipment to quantify the dataset and the available energy information for the current time period to enumerate all feasible energy replenishment paths for the energy supply gap, so as to generate a set of candidate energy replenishment paths for the energy supply gap, and provide specific alternative solutions for energy replenishment in the gap scenario.

[0060] The set of candidate energy supply gap replenishment paths refers to the set of all feasible energy supply gap replenishment paths.

[0061] Feasible energy replenishment pathways include those that meet electrical loads by discharging energy from battery storage, those that meet thermal loads by releasing heat from thermal storage, those that convert hydrogen energy into electrical and thermal energy by starting fuel cells, those that increase the output of combined heat and power units, those that supplement thermal loads by starting electric boilers or heat pumps, and those that purchase electricity from external power grids or gas from gas grids.

[0062] The path of satisfying electrical load through battery energy storage discharge refers to the energy flow that uses battery energy storage devices to release electrical energy to meet electrical load requirements.

[0063] The path of satisfying heat load by releasing heat from thermal energy storage refers to the energy flow that uses thermal energy storage devices to release heat energy to meet heat load requirements.

[0064] The path of starting a fuel cell to convert hydrogen energy into electrical and thermal energy refers to the energy flow direction in which the fuel cell consumes hydrogen from a hydrogen storage device to generate electrical and thermal energy.

[0065] The path to increase the output of cogeneration units refers to the energy flow that increases the power generation and heat output of cogeneration units to make up for the energy supply gap.

[0066] The path of starting an electric boiler or heat pump to supplement the heat load refers to the energy flow of starting an electric boiler or heat pump to consume electrical energy and generate heat energy to supplement the heat load.

[0067] The path of purchasing electricity from an external power grid or gas from a gas grid refers to the energy flow that uses electricity from an external power grid or natural gas from a gas grid to supplement the energy supply gap.

[0068] In this embodiment of the invention, the set of candidate destination paths for surplus electrical energy and the set of candidate replenishment paths for energy supply gaps can be merged and deduplicated. The conversion efficiency, loss and lifespan impact parameters in the dataset are quantified in combination with the actual operating characteristics of the equipment. A comprehensive marginal value assessment index is calculated for each path to generate a set of candidate energy flow directions with assessment indexes, providing decision branches with quantitative value for the optimization model.

[0069] The candidate energy flow set with evaluation indicators refers to the set of all candidate paths after merging, deduplication, and assigning a comprehensive marginal value evaluation indicator.

[0070] The comprehensive marginal value assessment index refers to the comprehensive quantitative evaluation result of each path in the current system state, including energy conversion efficiency, loss cost, equipment life depreciation, future demand matching degree, and carbon emission cost.

[0071] In this embodiment of the invention, the candidate energy flow direction set with evaluation indicators can be used to determine one or more candidate energy flow directions that express all possible destinations of the currently available energy in the integrated energy system, so as to establish a unified decision space and provide clear alternative path combinations for subsequent multi-objective optimization.

[0072] Candidate energy flow refers to the discrete set of all possible destinations of available energy in the current period. Each candidate energy flow includes a specific path combination, expected conversion efficiency, loss cost, lifetime reduction, and future demand matching degree.

[0073] This invention, through the use of energy conversion equipment operation models and energy storage equipment operation models to extract real characteristics, identify adjustable energy, enumerate surplus and deficit paths, calculate comprehensive marginal value assessment indicators, and finally determine candidate energy flow directions, can break through the traditional fixed priority or simple energy storage call logic, realize the dynamic diversion of surplus energy according to comprehensive marginal benefits, avoid ineffective conversion and low-value energy storage call, thereby improving the renewable energy absorption rate and the overall energy utilization efficiency of the system.

[0074] In an optional embodiment of the present invention, the step of generating a global multi-energy flow coordinated scheduling command for the energy conversion device and the energy storage device through the candidate energy flow direction includes: Determine the specific path combination, expected conversion efficiency, loss cost, lifetime reduction, and future demand matching degree corresponding to the candidate energy flow direction; Based on the candidate energy flow direction, a multi-objective optimization model is constructed that takes into account operating economy, carbon emission level, equipment health status and energy supply security requirements. Using the specific path combination, the expected conversion efficiency, the loss cost, the lifespan reduction, and the future demand matching degree as constraints, the energy conversion equipment and the energy storage equipment are used in a collaborative optimization solution under different candidate energy flow directions to obtain an initial target scheduling scheme; When the initial target scheduling scheme is determined to meet the preset conditions, a global multi-energy flow collaborative scheduling instruction corresponding to each energy conversion device and each energy storage device is generated according to the initial target scheduling scheme. The global multi-energy flow collaborative scheduling instruction is used to control each energy conversion device in the integrated energy system to perform corresponding energy conversion operations, and is also used to control each energy storage device in the integrated energy system to perform corresponding charging and discharging operations, and is also used to control the load units in the integrated energy system to perform corresponding load supply operations.

[0075] The embodiments of the present invention can determine the specific path combination, expected conversion efficiency, loss cost, lifetime depletion and future demand matching degree corresponding to the candidate energy flow direction, so as to provide quantitative input parameters for a multi-objective optimization model.

[0076] Specific path combinations refer to the specific combination scheme of one or more energy flows selected from the candidate energy flows.

[0077] Expected conversion efficiency refers to the actual energy conversion efficiency value of the energy conversion equipment under the current operating conditions for each candidate path.

[0078] Loss cost refers to the economic loss incurred during the conversion and storage of energy.

[0079] Lifespan loss refers to the amount of lifespan consumed by energy storage and energy conversion equipment due to this operation.

[0080] Future demand matching degree refers to the degree to which the current energy allocation plan matches the load demand and energy prices in the future over a certain period of time.

[0081] The embodiments of the present invention can construct a multi-objective optimization model based on the candidate energy flow direction, taking into account the operational economy, carbon emission level, equipment health status and energy supply security requirements, so as to unify multiple objectives such as economy, low carbon, equipment life and system safety into the optimization process.

[0082] Operational economy refers to the degree to which the total operating cost of a system is minimized.

[0083] Carbon emission level refers to the total amount of greenhouse gas emissions such as carbon dioxide generated during the operation of a system.

[0084] Equipment health status refers to the lifespan and health status of energy conversion and storage equipment.

[0085] Energy supply security requirements refer to the level of risk that ensures there is no insufficient energy supply for electrical and thermal loads.

[0086] A multi-objective optimization model is a mathematical model that considers multiple conflicting objective functions simultaneously and seeks the optimal solution under constraints.

[0087] The embodiments of the present invention can use the specific path combination, the expected conversion efficiency, the loss cost, the lifespan reduction, and the future demand matching degree as constraints to perform collaborative optimization solutions for using the energy conversion equipment and the energy storage equipment under different candidate energy flow directions, so as to obtain an initial target scheduling scheme.

[0088] The specific path combination, expected conversion efficiency, loss cost, lifespan reduction, and future demand matching degree as constraints refer to the conversion of the above parameters into equations or inequalities that must be satisfied in the optimization model.

[0089] Collaborative optimization refers to the process of simultaneously calculating different candidate energy flow directions under a multi-objective optimization model to find the optimal energy allocation scheme that satisfies multiple objectives at the same time.

[0090] The initial target scheduling scheme refers to the preliminary optimal scheduling plan obtained through multi-objective optimization, including the output plan and energy allocation scheme of each energy conversion device and energy storage device.

[0091] In this embodiment of the invention, when the initial target scheduling scheme is determined to meet preset conditions, a global multi-energy flow collaborative scheduling instruction corresponding to each energy conversion device and each energy storage device is generated according to the initial target scheduling scheme, so as to convert the optimization result into a control signal that can be directly executed.

[0092] Preset conditions refer to pre-defined judgment criteria such as system safety, economy, equipment health, and power supply reliability.

[0093] Global multi-energy flow collaborative scheduling instructions refer to the final output set of instructions used to control the specific power settings, start / stop status, and energy allocation ratio of each device.

[0094] Energy conversion equipment refers to devices such as combined heat and power units, heat pumps, electric boilers, electrolyzers, and fuel cells that enable the conversion of different energy forms.

[0095] Energy storage devices refer to devices such as battery energy storage devices, thermal energy storage devices, and hydrogen energy storage devices that enable energy transfer over time.

[0096] Energy conversion operation refers to the specific operational actions of energy conversion equipment in converting one form of energy into another.

[0097] Energy storage operation refers to the specific operational actions of energy storage equipment in charging, discharging, storing heat, releasing heat, producing hydrogen, or using hydrogen.

[0098] A load unit refers to an electrical load, thermal load, or adjustable load that consumes electrical or thermal energy.

[0099] Load supply operation refers to the specific supply action of providing the required electrical or thermal energy to the load unit.

[0100] This invention, through determining specific parameters of candidate energy flow directions, constructing a multi-objective optimization model, performing collaborative optimization solutions, and generating global multi-energy flow collaborative scheduling instructions when preset conditions are met, can achieve precise collaborative scheduling of energy conversion equipment and multiple types of energy storage equipment based on comprehensive consideration of operational economy, carbon emission levels, equipment health status, and energy supply security requirements. This improves the overall energy utilization efficiency of the system, reduces operating costs, and extends equipment lifespan.

[0101] Optionally, it also includes: Based on the initial target scheduling scheme, a multi-timescale collaborative control mechanism combining daily planning, intraday rolling correction, and real-time execution is constructed to dynamically update and control the initial target scheduling scheme and generate multi-timescale collaborative control results. A set of random scenarios is established to address the fluctuations in renewable energy output and load forecasting errors. The system operating status under different scenarios is evaluated using the set of random scenarios. Based on the scenario evaluation results, the multi-time-scale collaborative control results are corrected to obtain the corrected target scheduling scheme. Based on the revised target scheduling scheme, a global multi-energy flow collaborative scheduling instruction corresponding to each energy conversion device and each energy storage device is generated.

[0102] The embodiments of the present invention can construct a multi-timescale collaborative control mechanism that combines daily planning, intraday rolling correction and real-time execution based on the initial target scheduling scheme, and dynamically update and control the initial target scheduling scheme to generate multi-timescale collaborative control results.

[0103] The initial target scheduling scheme refers to the preliminary optimal scheduling plan obtained through multi-objective optimization, including the output plan and energy allocation scheme of each energy conversion device and energy storage device.

[0104] A day-ahead plan is a baseline dispatch plan generated on a 24-hour scale based on the next day's renewable energy forecasts, load forecasts, energy prices, and equipment status.

[0105] Intraday rolling revision refers to the process of updating the day-ahead plan using the latest forecast data on a 1-hour or 30-minute cycle.

[0106] Real-time execution refers to an execution layer that adjusts rapidly based on actual measurement values ​​in 5-minute or 15-minute cycles.

[0107] The multi-timescale coordinated control mechanism refers to a closed-loop control mechanism that organically combines three timescales: daily planning, intraday rolling correction, and real-time execution.

[0108] Multi-timescale coordinated control results refer to the final control scheme formed after three-level linkage updates and closed-loop control, including day-ahead, intraday, and real-time updates.

[0109] The embodiments of the present invention can establish a set of random scenarios for the fluctuation of renewable energy output and load forecasting errors, use the set of random scenarios to evaluate the system operating status under different scenarios, and correct the multi-time-scale collaborative control results based on the scenario evaluation results to obtain a corrected target scheduling scheme.

[0110] Renewable energy output fluctuations refer to the phenomenon where the actual output of photovoltaic and wind power deviates from the predicted value.

[0111] Load forecasting error refers to the deviation between actual load demand and the forecast value.

[0112] A random scenario set refers to a set of scenarios that include various typical operating conditions, including a normal scenario with high photovoltaic load, a normal scenario with low photovoltaic load, a scenario with normal photovoltaic heat load increasing, a scenario with low wind and solar power load and sudden increase in power load, and a scenario with simultaneous increase in multiple energy loads.

[0113] System operating status refers to the comprehensive performance of energy conversion equipment output, energy storage charge status, and load satisfaction in various scenarios.

[0114] The scenario evaluation results refer to the quantitative evaluation results of the economic efficiency, reliability and risk level of the operation plan under each scenario.

[0115] The revised target scheduling scheme refers to the more robust final scheduling scheme obtained after evaluation and revision of random scenarios.

[0116] In this embodiment of the invention, global multi-energy flow collaborative scheduling instructions corresponding to each energy conversion device and each energy storage device can be generated according to the modified target scheduling scheme.

[0117] The revised target scheduling scheme refers to the final optimal scheduling plan obtained after revisions based on multiple time scales and random scenarios.

[0118] Global multi-energy flow collaborative scheduling instructions refer to the final output set of instructions used to control the specific power settings, start / stop status, and energy allocation ratio of each device.

[0119] Energy conversion equipment refers to devices such as combined heat and power units, heat pumps, electric boilers, electrolyzers, and fuel cells that enable the conversion of different energy forms.

[0120] Energy storage devices refer to devices such as battery energy storage devices, thermal energy storage devices, and hydrogen energy storage devices that enable energy transfer over time.

[0121] The embodiments of the present invention construct a multi-timescale collaborative control mechanism, dynamically update and control the initial scheme, establish a random scenario set for evaluation and correction, and finally generate a global multi-energy flow collaborative scheduling instruction. This can effectively cope with the fluctuation of renewable energy output and load forecasting errors, improve the system's operational reliability and adaptability under uncertain conditions, thereby reducing operating costs, reducing excessive equipment wear and tear, and improving overall energy supply security.

[0122] Optionally, the step of constructing a multi-objective optimization model based on the candidate energy flow direction that takes into account operating economy, carbon emission level, equipment health status, and energy supply security requirements includes: Using the candidate energy flow direction, combined with the non-constant conversion efficiency characteristics, operating range constraints, capacity constraints, charging and discharging losses, and lifespan decay characteristics in the energy conversion equipment operation model and the energy storage equipment operation model, as well as the renewable energy predicted output information, load prediction information, external energy price information, and environmental parameter information for the current period, the operating cost, conversion efficiency, energy storage status, equipment lifespan impact parameters, carbon emission factors, and future demand value corresponding to each candidate path are extracted to generate a path comprehensive evaluation parameter set; Using the path comprehensive evaluation parameter set and the external energy price information, an economic objective function is constructed with minimizing the total system operating cost as the first optimization objective, thus generating an operational economic objective function; wherein the total operating cost includes electricity purchase cost, natural gas cost, and equipment operation and maintenance cost; By utilizing the comprehensive evaluation parameter set of the aforementioned path and the carbon emission factors of each energy conversion path, a carbon emission objective function is constructed with minimizing carbon emission costs as the second optimization objective, thereby generating a carbon emission level objective function; By utilizing the path comprehensive evaluation parameter set and the lifetime degradation characteristics and equipment start-stop frequency parameters in the energy storage device operation model, an equipment health objective function is constructed with minimizing the energy storage lifetime loss cost and the equipment frequent start-stop depreciation cost as the third optimization objective, and an equipment health status objective function is generated. Using the path comprehensive evaluation parameter set, the load forecast information, and the energy supply security margin requirements, an energy supply security objective function is constructed with minimizing the risk of insufficient energy supply or wind and solar curtailment losses as the fourth optimization objective, and an energy supply security requirement objective function is generated. The objective functions of operational economy, carbon emission level, equipment health status, and energy supply safety requirements are combined into a set of optimization objectives for a multi-objective optimization model, thereby generating a multi-objective optimization objective set. Using the energy conversion equipment operation model, the energy storage equipment operation model, the candidate energy flow direction, and the system power balance requirements of the integrated energy system, a set of constraints for a multi-objective optimization model is constructed. The set of constraints for the multi-objective optimization model includes the operating range constraints of each energy conversion equipment, the capacity boundary constraints and state of charge constraints of each energy storage equipment, and the supply and demand balance constraints of various energy forms. Using the set of multi-objective optimization objectives and the set of constraints of the multi-objective optimization model, a multi-objective optimization model that takes into account the requirements of operational economy, carbon emission level, equipment health status and energy supply security is constructed, and a multi-objective optimization model is generated.

[0123] The embodiments of the present invention can use the candidate energy flow direction, combined with the non-constant conversion efficiency characteristics, operating range constraints, capacity constraints, charging and discharging losses and lifespan decay characteristics in the energy conversion equipment operation model and the energy storage equipment operation model, as well as the renewable energy predicted output information, load prediction information, external energy price information and environmental parameter information for the current period, to extract the operating cost, conversion efficiency, energy storage status, equipment lifespan impact parameters, carbon emission factors and future demand value corresponding to each candidate path, so as to generate a path comprehensive evaluation parameter set.

[0124] The path comprehensive evaluation parameter set refers to the set of quantitative parameters extracted from each candidate energy flow direction for optimization decision-making.

[0125] Non-constant conversion efficiency characteristics refer to the dynamic change of the actual conversion efficiency of energy conversion equipment under different load rates with factors such as load rate and ambient temperature.

[0126] Operating range constraints refer to the safe operating power range and load rate range allowed for each energy conversion device.

[0127] Capacity constraints refer to the maximum and minimum storage capacity limits that energy storage devices must not exceed during charging and discharging.

[0128] Energy loss during charging and discharging refers to the amount of energy lost by an energy storage device during the charging and discharging process due to conversion efficiency and self-discharge.

[0129] Lifetime degradation characteristics refer to the laws governing the capacity decay and lifespan reduction of battery energy storage due to charge / discharge depth, rate of change, and number of cycles.

[0130] Renewable energy forecast output information refers to the predicted power generation data of photovoltaic power generation devices and wind power generation devices during future scheduling periods.

[0131] Load forecasting information refers to the predicted demand values ​​of the park's electrical and thermal loads during future scheduling periods.

[0132] External energy price information refers to market price data such as time-of-use electricity prices and natural gas prices.

[0133] Environmental parameters refer to external conditions that affect equipment performance, such as outdoor temperature, solar irradiance, and wind speed.

[0134] Operating costs refer to the economic expenditures incurred during the operation of each path.

[0135] Conversion efficiency refers to the effective proportion of energy actually converted in different pathways.

[0136] Energy storage status refers to the current remaining capacity and charge level of each energy storage device.

[0137] Equipment lifespan impact parameters refer to the quantitative impact of path operation on equipment lifespan.

[0138] The carbon emission factor refers to the amount of carbon emissions generated per unit of energy conversion or consumption.

[0139] Future demand value refers to a quantitative assessment of how well current energy allocation meets demand in future periods.

[0140] The embodiments of the present invention can utilize the path comprehensive evaluation parameter set and the external energy price information to construct an economic objective function with minimizing the total system operating cost as the first optimization objective, so as to generate an operating economic objective function.

[0141] The path comprehensive evaluation parameter set refers to the set of quantitative parameters extracted from each candidate energy flow direction for optimization decision-making.

[0142] External energy price information refers to market price data such as time-of-use electricity prices and natural gas prices.

[0143] Total operating cost of a system refers to all economic expenditures incurred during the operation of an integrated energy system.

[0144] The economic objective function is a mathematical expression that aims to minimize the total operating cost of the system.

[0145] Total operating costs include electricity purchase costs, natural gas costs, and equipment operation and maintenance costs.

[0146] Electricity purchase cost refers to the cost of purchasing electricity from an external power grid.

[0147] Natural gas cost refers to the fee paid to purchase natural gas from a gas network.

[0148] Equipment operation and maintenance costs refer to the expenses incurred in operating and maintaining energy conversion equipment and energy storage equipment.

[0149] The embodiments of the present invention can utilize the comprehensive evaluation parameter set of the path and the carbon emission factors of each energy conversion path to construct a carbon emission objective function with minimizing carbon emission costs as the second optimization objective, so as to generate a carbon emission level objective function.

[0150] The path comprehensive evaluation parameter set refers to the set of quantitative parameters extracted from each candidate energy flow direction for optimization decision-making.

[0151] The carbon emission factor refers to the amount of carbon emissions generated per unit of energy conversion or consumption.

[0152] The carbon emission objective function is a mathematical expression that aims to minimize the cost of carbon emissions.

[0153] The carbon emission level objective function is a mathematical expression that takes minimizing carbon emission costs as the second optimization objective.

[0154] The embodiments of the present invention can utilize the path comprehensive evaluation parameter set and the lifetime degradation characteristics and equipment start-stop frequency parameters in the energy storage device operation model to construct an equipment health objective function with minimizing the energy storage lifetime loss cost and the equipment frequent start-stop depreciation cost as the third optimization objective, so as to generate the equipment health status objective function.

[0155] The path comprehensive evaluation parameter set refers to the set of quantitative parameters extracted from each candidate energy flow direction for optimization decision-making.

[0156] Lifetime degradation characteristics refer to the laws governing the capacity decay and lifespan reduction of battery energy storage due to charge / discharge depth, rate of change, and number of cycles.

[0157] Equipment start-stop frequency parameters refer to the parameters that affect the lifespan of energy conversion and energy storage equipment based on the number of start-stop cycles within a scheduling period.

[0158] The equipment health objective function is a mathematical expression that aims to minimize the cost of energy storage lifespan loss and the cost of frequent start-stop damage.

[0159] The objective function for equipment health status is a mathematical expression that takes minimizing the energy storage life loss cost and the equipment frequent start-up and shutdown depreciation cost as the third optimization objective.

[0160] The embodiments of the present invention can utilize the path comprehensive evaluation parameter set, the load forecast information, and the energy supply security margin requirements to construct an energy supply security objective function with minimizing the risk of insufficient energy supply or wind and solar curtailment losses as the fourth optimization objective, so as to generate an energy supply security requirement objective function.

[0161] The path comprehensive evaluation parameter set refers to the set of quantitative parameters extracted from each candidate energy flow direction for optimization decision-making.

[0162] Load forecasting information refers to the predicted demand values ​​of the park's electrical and thermal loads during future scheduling periods.

[0163] The power supply safety margin requirement refers to the minimum safety reserve level set by the system to ensure the reliability of power supply.

[0164] The energy supply security objective function is a mathematical expression that aims to minimize the risk of insufficient energy supply or the loss from wind and solar power curtailment.

[0165] The objective function for energy supply security requirements is a mathematical expression that takes minimizing the risk of insufficient energy supply or the loss from wind and solar curtailment as the fourth optimization objective.

[0166] In this embodiment of the invention, the objective function of operational economy, the objective function of carbon emission level, the objective function of equipment health status, and the objective function of energy supply security requirements can be combined into a set of optimization objectives for a multi-objective optimization model to generate a set of multi-objective optimization objectives.

[0167] The economic objective function is a mathematical expression that aims to minimize the total operating cost of the system.

[0168] The carbon emission level objective function is a mathematical expression that takes minimizing carbon emission costs as the second optimization objective.

[0169] The objective function for equipment health status is a mathematical expression that takes minimizing the energy storage life loss cost and the equipment frequent start-up and shutdown depreciation cost as the third optimization objective.

[0170] The objective function for energy supply security requirements is a mathematical expression that takes minimizing the risk of insufficient energy supply or the loss from wind and solar curtailment as the fourth optimization objective.

[0171] A multi-objective optimization objective set refers to an optimization objective system composed of multiple objective functions.

[0172] The embodiments of the present invention can use the operating model of the energy conversion equipment, the operating model of the energy storage equipment, the candidate energy flow direction, and the system power balance requirements of the integrated energy system to construct a set of constraints for a multi-objective optimization model. The set of constraints for the multi-objective optimization model includes the operating range constraints of each energy conversion equipment, the capacity boundary constraints and state of charge constraints of each energy storage equipment, and the supply and demand balance constraints of various energy forms.

[0173] An energy conversion equipment operation model is a mathematical model that describes the input and output conversion relationship of equipment such as combined heat and power units, heat pumps, electric boilers, electrolyzers, and fuel cells under different load rates.

[0174] An energy storage device operation model is a mathematical model that describes the impact of capacity changes, energy losses, and lifespan on devices such as battery energy storage, thermal energy storage, and hydrogen energy storage.

[0175] Candidate energy flow direction refers to the discrete set of all possible destinations of the available energy in the current time period.

[0176] System power balance requirements refer to the conditions under which various forms of energy, such as electrical energy and thermal energy, must maintain a balance between supply and demand at any given moment.

[0177] The constraint set of a multi-objective optimization model refers to the set of all equality and inequality restrictions that must be satisfied during the optimization process.

[0178] Operating range constraints refer to the safe operating power range and load rate range allowed for each energy conversion device.

[0179] Capacity boundary constraints refer to the maximum and minimum storage capacity limits that energy storage devices are allowed to store.

[0180] State of charge (SCC) constraints refer to the limitations that a battery's energy storage state of charge must be maintained within a safe range.

[0181] The supply and demand balance constraint refers to the equilibrium condition where the supply of various forms of energy equals the demand at any given moment.

[0182] The embodiments of the present invention can use the set of multi-objective optimization objectives and the set of constraints of the multi-objective optimization model to construct a multi-objective optimization model that takes into account the requirements of operating economy, carbon emission level, equipment health status and energy supply security, so as to generate a multi-objective optimization model.

[0183] A multi-objective optimization objective set refers to an optimization objective system composed of multiple objective functions.

[0184] The constraint set of a multi-objective optimization model refers to the set of all equality and inequality restrictions that must be satisfied during the optimization process.

[0185] A multi-objective optimization model is a mathematical model that simultaneously considers multiple objectives such as operational economy, carbon emission level, equipment health status and energy supply security requirements, and solves for the optimal solution under constraints.

[0186] This invention extracts path comprehensive evaluation parameters, constructs objective functions for economy, carbon emissions, equipment health, and energy supply security, and finally builds a multi-objective optimization model. This model can simultaneously balance multiple objectives such as operating costs, carbon emissions, equipment lifespan, and energy supply security within a unified framework, achieving scientific collaborative optimization of energy conversion equipment and various types of energy storage equipment, thereby improving the long-term economic efficiency and operational reliability of the system.

[0187] Optionally, the step of constructing a multi-timescale collaborative control mechanism combining daily planning, intraday rolling correction, and real-time execution based on the initial target scheduling scheme, and dynamically updating and controlling the initial target scheduling scheme in a closed loop to generate multi-timescale collaborative control results includes: Based on the initial target scheduling scheme, obtain the renewable energy forecast output information, load forecast information, external energy price information, and status information of energy conversion equipment and energy storage equipment for the next day, and generate the day-ahead layer input dataset; The day-ahead baseline scheduling plan is generated using the day-ahead layer input dataset. The day-ahead baseline scheduling plan includes the planned output of cogeneration units for each time period, the operation plans of heat pumps, electric boilers, electrolyzers and fuel cells for each time period, the charging and discharging plans of battery energy storage, thermal energy storage and hydrogen energy storage, and the optimal allocation schemes of various energy sources for different time periods. Based on the day-ahead baseline scheduling plan and the real-time updated renewable energy forecast output information and load forecast information, the latest forecast data is obtained according to the first preset time period, and the day-ahead baseline scheduling plan is rolled over and revised to generate an intraday rolling revised scheduling plan. Based on the intraday rolling adjustment scheduling plan and the latest measured real-time operating status information, rapid adjustment actions are executed at a second preset time period, and multi-time-scale collaborative control results are generated; the rapid adjustment actions include increasing the heat release power of thermal energy storage, starting heat pumps or electric boilers to supplement the heat load, and coordinating battery energy storage and purchased electricity to ensure synchronous balance of electric and heat loads.

[0188] According to the initial target scheduling scheme, the embodiments of the present invention can obtain the next day's renewable energy forecast output information, load forecast information, external energy price information, and status information of energy conversion equipment and energy storage equipment to generate the day-ahead input dataset.

[0189] The initial target scheduling scheme refers to the preliminary optimal scheduling plan obtained through multi-objective optimization, including the output plan and energy allocation scheme of each energy conversion device and energy storage device.

[0190] Renewable energy forecast output information refers to the predicted power generation data of photovoltaic power generation devices and wind power generation devices during future scheduling periods.

[0191] Load forecasting information refers to the predicted demand values ​​of the park's electrical and thermal loads during future scheduling periods.

[0192] External energy price information refers to market price data such as time-of-use electricity prices and natural gas prices.

[0193] Energy conversion equipment status information refers to the current output level and operating status of combined heat and power units, heat pumps, electric boilers, electrolyzers, and fuel cells.

[0194] Energy storage device status information refers to the current storage level, such as the state of charge of battery energy storage, the remaining capacity of thermal energy storage, and the storage capacity of hydrogen energy storage tanks.

[0195] The day-ahead input dataset refers to the set of all basic data used to generate the next day's baseline scheduling plan.

[0196] The embodiments of the present invention can use the day-ahead layer input dataset to generate a day-ahead baseline scheduling plan.

[0197] The day-ahead input dataset refers to the set of all basic data used to generate the next day's baseline scheduling plan.

[0198] The day-ahead baseline scheduling plan refers to the overall scheduling baseline scheme for the next day generated on a 24-hour scale.

[0199] The planned output of a combined heat and power (CHP) unit refers to the pre-set power generation and heat production values ​​of the CHP unit for each time period.

[0200] The operation plan for heat pumps, electric boilers, electrolyzers, and fuel cells refers to the start-up and shutdown status and output settings of the above-mentioned equipment at different times.

[0201] The charging and discharging plans for battery energy storage, thermal energy storage, and hydrogen energy storage refer to the charging and discharging power of battery energy storage, the heat storage and release power of thermal energy storage, and the hydrogen production and utilization plans for hydrogen energy storage at different times.

[0202] The optimal allocation scheme for various energy sources at different times refers to the optimal flow direction and allocation ratio of electrical energy, thermal energy, and hydrogen energy at each time period.

[0203] According to the embodiments of the present invention, the latest forecast data can be obtained according to the day-ahead baseline scheduling plan and the real-time updated renewable energy forecast output information and load forecast information, and the day-ahead baseline scheduling plan can be rolled over and corrected to generate an intraday rolling correction scheduling plan.

[0204] The day-ahead baseline scheduling plan refers to the overall scheduling baseline scheme for the next day generated on a 24-hour scale.

[0205] Renewable energy forecast output information refers to the predicted power generation data of photovoltaic power generation devices and wind power generation devices during future scheduling periods.

[0206] Load forecasting information refers to the predicted demand values ​​of the park's electrical and thermal loads during future scheduling periods.

[0207] The first preset time period refers to a rolling update cycle of 1 hour or 30 minutes.

[0208] The intraday rolling revision scheduling plan refers to the intraday optimized scheduling scheme obtained after rolling updates.

[0209] According to the embodiments of the present invention, the scheduling plan can be revised intraday and the latest measured real-time operating status information can be used to perform rapid adjustment actions in a second preset time period and generate multi-time-scale collaborative control results.

[0210] The intraday rolling revision scheduling plan refers to the intraday optimized scheduling scheme obtained after rolling updates.

[0211] Real-time operational status information refers to the actual measured system operational data at the current moment.

[0212] The second preset time period refers to a real-time adjustment period of 5 minutes or 15 minutes.

[0213] Rapid adjustment refers to the rapid response operation performed by the real-time layer based on the actual deviation.

[0214] Increasing the heat release power of thermal energy storage refers to increasing the power of thermal energy storage devices to release heat energy to the heat load.

[0215] Starting a heat pump or electric boiler to supplement the heat load refers to starting a heat pump or electric boiler to consume electrical energy to generate heat energy to supplement the heat load demand.

[0216] Coordinating battery energy storage with purchased electricity to ensure synchronous balance of electrical and thermal loads means adjusting the charging and discharging power of battery energy storage and the amount of electricity purchased from the external power grid to simultaneously meet the electrical and thermal loads.

[0217] The result of multi-timescale coordinated regulation refers to the final regulation scheme formed after three-level linkage of daily planning, intraday rolling correction and real-time execution.

[0218] The embodiments of the present invention generate a day-ahead baseline scheduling plan based on an initial target scheduling scheme, perform intraday rolling corrections according to a first preset time period, and execute rapid adjustment actions according to a second preset time period. This enables multi-timescale closed-loop control from day-ahead planning to real-time execution, effectively addressing prediction deviations and real-time fluctuations, thereby improving the operational stability and adaptability of the integrated energy system.

[0219] Optionally, the steps of establishing a random scenario set for renewable energy output fluctuations and load forecasting errors, using the random scenario set to evaluate the system operating status under different scenarios, and correcting the multi-timescale coordinated control results based on the scenario evaluation results to obtain the corrected target scheduling scheme include: To address the fluctuations in renewable energy output and load forecasting errors, a random scenario set containing multiple target operating scenarios is constructed. The random scenario set includes at least the following scenarios: normal high photovoltaic load, normal low photovoltaic load, normal photovoltaic heat load increase, low wind and solar power load with sudden increase, and simultaneous increase of multiple energy loads. For the photovoltaic high-load normal scenario, the photovoltaic low-load normal scenario, the photovoltaic normal heat load rising scenario, the wind and solar low power load sudden increase scenario, and the multi-energy load simultaneous increase scenario, the energy conversion equipment output plan and energy storage charging and discharging plan under each scenario are recalculated using the multi-time scale collaborative control results, and a set of operation schemes for each scenario is generated. Based on the set of operation schemes for each scenario, and the probability or risk weight of occurrence for different scenarios, the operation schemes under different scenarios are weighted and fused for evaluation to generate robust scheduling results. The robust scheduling results are used to correct the deviations in the multi-timescale collaborative control results, thereby obtaining the corrected target scheduling scheme.

[0220] The embodiments of the present invention can construct a set of random scenarios containing a variety of typical operating scenarios to address the fluctuations in renewable energy output and load forecasting errors.

[0221] Renewable energy output fluctuations refer to the phenomenon where the actual output of photovoltaic and wind power deviates from the predicted value.

[0222] Load forecasting error refers to the deviation between actual load demand and the forecast value.

[0223] A random scenario set refers to a set of scenarios that includes multiple typical operating conditions, constructed to handle uncertainty.

[0224] A typical scenario where photovoltaic power generation is high and the load is normal refers to a scenario where photovoltaic output is significantly higher than the predicted value while the power and heat load are at a normal level.

[0225] A low-power, normal-load photovoltaic scenario refers to a typical scenario where photovoltaic output is significantly lower than the predicted value, while the electric heating load is at a normal level.

[0226] The photovoltaic normal heat load increase scenario refers to a typical scenario where the photovoltaic output is close to the predicted value but the heat load is significantly higher than the predicted value.

[0227] The scenario of low wind and solar power output and sudden increase in electricity load refers to a typical scenario in which both wind and solar power output are lower than the predicted values ​​and the electricity load suddenly increases.

[0228] The scenario of simultaneous increase in multiple energy loads refers to a typical scenario in which the electrical load and thermal load are both significantly higher than the predicted values.

[0229] The embodiments of the present invention can recalculate the output plan of energy conversion equipment and the energy storage charging and discharging plan for each scenario, including the normal scenario of high photovoltaic load, the normal scenario of low photovoltaic load, the scenario of normal photovoltaic heat load increase, the scenario of low wind and solar power load increase, and the scenario of simultaneous increase of multiple energy loads, by using the multi-time scale collaborative control results, so as to generate a set of operation schemes for each scenario.

[0230] A typical scenario where photovoltaic power generation is high and the load is normal refers to a scenario where photovoltaic output is significantly higher than the predicted value while the power and heat load are at a normal level.

[0231] A low-power, normal-load photovoltaic scenario refers to a typical scenario where photovoltaic output is significantly lower than the predicted value, while the electric heating load is at a normal level.

[0232] The photovoltaic normal heat load increase scenario refers to a typical scenario where the photovoltaic output is close to the predicted value but the heat load is significantly higher than the predicted value.

[0233] The scenario of low wind and solar power output and sudden increase in electricity load refers to a typical scenario in which both wind and solar power output are lower than the predicted values ​​and the electricity load suddenly increases.

[0234] The scenario of simultaneous increase in multiple energy loads refers to a typical scenario in which the electrical load and thermal load are both significantly higher than the predicted values.

[0235] The result of multi-timescale coordinated regulation refers to the final regulation scheme formed after three-level linkage of daily planning, intraday rolling correction and real-time execution.

[0236] The output plan of energy conversion equipment refers to the output setting value of cogeneration units, heat pumps, electric boilers, electrolyzers and fuel cells in various scenarios.

[0237] The energy storage charging and discharging plan refers to the charging and discharging power of battery energy storage, the heat storage and release power of thermal energy storage, and the hydrogen production and utilization plan of hydrogen energy storage in various scenarios.

[0238] The set of operation plans for each scenario refers to the sum of operation plans calculated for each random scenario.

[0239] The embodiments of the present invention can perform weighted fusion evaluation of the operation schemes under different scenarios based on the set of operation schemes for each scenario and the occurrence probability or risk weight of different scenarios, so as to generate robust scheduling results.

[0240] The set of operation plans for each scenario refers to the sum of operation plans calculated for each random scenario.

[0241] The probability of occurrence refers to the likelihood of each random scenario occurring in actual operation.

[0242] Risk weight refers to the quantitative weight of the impact of each scenario on system security and economy.

[0243] Weighted fusion evaluation refers to the process of comprehensively weighting the operational plans for different scenarios based on probability and risk weights.

[0244] Robust scheduling results refer to scheduling schemes that are highly adaptable to uncertainty after weighted fusion evaluation.

[0245] In this embodiment of the invention, the robust scheduling results can be used to correct the deviations in the multi-timescale collaborative control results, so as to obtain a corrected target scheduling scheme.

[0246] Robust scheduling results refer to scheduling schemes that are highly adaptable to uncertainty after weighted fusion evaluation.

[0247] The result of multi-timescale coordinated regulation refers to the final regulation scheme formed after three-level linkage of daily planning, intraday rolling correction and real-time execution.

[0248] Deviation correction refers to the process of adjusting the original control scheme using robust scheduling results to reduce the impact of uncertainty.

[0249] The revised target scheduling scheme refers to the more robust final scheduling scheme obtained after random scenario evaluation and bias correction.

[0250] The embodiments of the present invention construct a random scenario set, recalculate the operation plan for each scenario, perform weighted fusion evaluation, and finally correct the deviation of the multi-timescale control results. This can effectively cope with the fluctuation of renewable energy output and load forecasting errors, improve the reliability and robustness of the dispatching plan under uncertain conditions, and thus enhance the overall energy supply security and economy of the system.

[0251] To enable those skilled in the art to better understand the embodiments of the present invention, an example is used below to illustrate the embodiments of the present invention.

[0252] Figure 2 This is a flowchart illustrating a method for scheduling equipment in an integrated energy system, as provided in an embodiment of the present invention. S1: Acquisition of system operation data: In this embodiment, the scheduling control unit first acquires basic data of the integrated energy system within the current scheduling cycle, the basic data including: Renewable energy forecast data, including forecasted photovoltaic and wind power output for the next dispatch period; Load forecast data, including predicted electrical load and predicted heat load for the park; Energy price data, including time-of-use electricity prices and natural gas prices; Equipment status data includes the current output of the cogeneration unit, the operating status of the heat pump, the operating status of the electric boiler, the operating status of the electrolyzer, and the operating status of the fuel cell; Energy storage status data, including battery energy storage state of charge (SOC), thermal energy storage remaining capacity, and hydrogen energy storage tank capacity; Environmental parameter data, including outdoor temperature, solar irradiance, wind speed, etc.

[0253] In one example, the system is currently at 10:00 AM, and the scheduling control unit receives the following information: The photovoltaic power output is projected to be high; the current electricity load in the park is moderate; the current heat load is low, but it is projected to increase in the evening; the battery energy storage SOC has reached 80%; the thermal energy storage has a large remaining capacity; the hydrogen energy storage is at a low level; and the current time-of-use electricity price is in the transition phase from off-peak to flat. This indicates that the system is about to face the problem of "how to allocate short-term surplus electricity".

[0254] S2: Establish models for energy conversion and energy storage devices: In this embodiment, to avoid scheduling deviations caused by the use of fixed efficiency and ideal energy storage models in traditional methods, the scheduling control unit establishes energy conversion equipment models and energy storage equipment models respectively.

[0255] 1. Energy conversion equipment model: For combined heat and power (CHP) units, establish a correlation model between gas input and electricity and heat output, and consider efficiency changes under different load rates.

[0256] For heat pumps, establish the relationship between input electrical power and output thermal power, and make its coefficient of performance (COP) change dynamically with ambient temperature and load rate.

[0257] For electric boilers, establish the correspondence between electrical input and heat output, and consider the rated operating range.

[0258] For the electrolyzer, establish the conversion relationship between input electrical power and hydrogen production, taking into account the efficiency differences between low load and rated load ranges.

[0259] For fuel cells, a conversion model for hydrogen input and electrical and thermal output is established.

[0260] 2. Energy storage device model: For battery energy storage, a dynamic relationship between charging power, discharging power, and SOC is established, and a lifespan reduction factor is introduced. This lifespan reduction factor can be calculated based on the depth of charge / discharge, charge / discharge rate, and number of cycles.

[0261] For thermal energy storage, a model is established to represent the relationship between heat storage, heat release, and heat storage loss, taking into account the natural dissipation of heat over time.

[0262] For hydrogen energy storage, establish capacity boundary and efficiency loss models for the processes of hydrogen production, storage and utilization.

[0263] Through the above modeling, the system can no longer regard each device as an ideal device with "constant efficiency and no loss" during the optimization process, but rather reflect the actual engineering operation characteristics as much as possible.

[0264] S3: Establish a unified and collaborative decision-making framework: In this embodiment, the scheduling control unit does not adopt a fixed priority strategy of "prioritizing battery charging, then heat conversion, and finally energy abandonment". Instead, it integrates multiple energy conversion paths and multiple energy storage units into the same decision-making framework.

[0265] Specifically, for the surplus electrical energy present at the current moment, multiple candidate energy destinations are set, including: Excess electrical energy can be directly charged into battery energy storage devices; electrical energy can be converted into heat energy through heat pumps and stored in thermal energy storage devices; electrical energy can be converted into hydrogen energy through electrolysis cells and stored in hydrogen energy storage devices; it can be directly supplied to the current adjustable electrical load; and when necessary, the power generation output of cogeneration units can be reduced or suppressed.

[0266] In the event of a power supply shortage in the system, multiple candidate power replenishment paths are also set up, including: The electrical load is met by battery energy storage and discharge; the thermal load is met by thermal energy storage and heat release; the hydrogen energy is converted into electrical energy by starting the fuel cell; the output of the combined heat and power unit is increased; the thermal load is supplemented by starting the electric boiler or heat pump; electricity is purchased from the external power grid or gas is purchased from the gas grid.

[0267] Construct comprehensive evaluation indicators and select pathways: In order to determine the utilization value of the same energy in different paths, this embodiment constructs a comprehensive evaluation index for each candidate path.

[0268] The comprehensive evaluation indicators include at least: Energy conversion efficiency of the current path; remaining capacity of the corresponding energy storage device; losses during energy storage charging, discharging or conversion; equipment lifespan depreciation costs; urgency of demand for the corresponding energy form in the future; current and future electricity and gas prices; carbon emission costs; system energy supply security margin.

[0269] For example, when photovoltaic output is high at 10:00 AM, battery SOC is already high, and heat load is about to increase in the evening, the dispatch control unit makes the following judgment after comprehensive evaluation: If we continue to prioritize charging the battery, although the path is simple, the marginal value of continuing to charge is low because the battery's SOC is already high, and it will accelerate battery charging losses and lifespan degradation. If surplus electrical energy is converted into heat energy and stored in thermal energy storage through a heat pump, it can store heat for the evening heat load in advance, and the current capacity of thermal energy storage is sufficient, so the overall value is high. While hydrogen production via electrolyzers can achieve long-term energy storage across different time periods, the immediate overall value of the hydrogen production route is lower than that of the thermal storage route because the current evening heat load demand is more urgent.

[0270] Based on the above comparison results, the scheduling and control unit prioritizes the operation of the heat pump, converting the first part of the current surplus electrical energy into thermal energy and storing it in the thermal energy storage device; at the same time, the second part of the remaining part is allocated to the electrolyzer to produce hydrogen and supplement hydrogen energy storage; only a small amount of the remaining part is used to supplement battery energy storage.

[0271] In this embodiment, energy allocation is not a fixed single path, but rather a dynamic distribution based on the system state.

[0272] S4: Construct a multi-objective optimization model: In this embodiment, to balance economy, low carbon emissions, equipment health, and energy supply security, the scheduling and control unit constructs a multi-objective optimization model.

[0273] The optimization objectives include: Minimize the total operating cost of the system, which includes electricity purchase cost, natural gas cost, and equipment operation and maintenance cost; minimize carbon emission cost; minimize energy storage life loss cost and depreciation cost caused by frequent equipment start-up and shutdown; minimize the risk of insufficient energy supply or wind and solar curtailment losses; and ensure that the system meets the electricity and heat load requirements during each scheduling period.

[0274] In one example, the optimization result is as follows: During midday, surplus photovoltaic power is prioritized for heat storage and partial hydrogen production. In the evening, when electricity prices rise and heat load increases, thermal energy storage is released first to meet the heat load. At night, depending on electricity prices and hydrogen reserves, the decision is made whether to start fuel cells or combined heat and power units. Throughout the process, high-frequency and deep cycling of battery energy storage is limited to reduce lifespan loss. The result is not only reduced operating costs but also avoids the over-reliance on batteries in traditional strategies.

[0275] S5: Construct a collaborative control process across multiple timescales: Day-to-Day-Real-Time Scales To improve the system's adaptability to prediction errors and dynamic changes, this embodiment adopts a control method that combines the day-ahead layer, intraday layer, and real-time layer.

[0276] 1. Current Layer: The day-ahead layer generates the baseline dispatch plan for the next day based on the wind and solar forecasts, load forecasts, energy prices, and equipment status information for the next 24 hours.

[0277] The baseline scheduling plan includes: Planned output of cogeneration units for each time period; operation plans for heat pumps, electric boilers, electrolyzers, and fuel cells for each time period; charging and discharging plans for battery energy storage, thermal energy storage, and hydrogen energy storage; and optimal allocation schemes for various energy sources at different times.

[0278] 2. Intra-day layer: The intra-day layer acquires updated wind and solar power forecasts and load forecasts every hour or 30 minutes, and makes rolling corrections to the day-ahead plan. For example, if the cloud cover suddenly thickens at 2 pm on a certain day, and the actual photovoltaic output is lower than the forecast, the intra-day layer will recalculate.

[0279] 3. Real-time layer: The real-time layer performs rapid adjustments based on the latest measurements, with a cycle of 5 or 15 minutes.

[0280] For example, if the park's heat load suddenly increases beyond prediction at 6 PM, the real-time layer can immediately: Increase the heat release capacity of thermal energy storage; start heat pumps to supplement the heat load; increase the output of electric boilers or cogeneration units when necessary; and coordinate battery energy storage and purchased electricity to ensure synchronous balance of electric and heat loads.

[0281] Through the above three-layer linkage, continuous control is achieved from advance planning to mid-course correction and then to real-time compensation.

[0282] Introducing a stochastic scenario mechanism to handle uncertainty: In this embodiment, to improve reliability under conditions of renewable energy and load fluctuations, the dispatch control unit further establishes a set of random scenarios.

[0283] For example, for a next-day run, the following scenario can be constructed: Scenario A: High solar power generation, normal load; Scenario B: Low solar power generation, normal load; Scenario C: Photovoltaic system is functioning normally, but heat load is increasing; Scenario D: Low wind and solar power, sudden increase in electrical load; Scenario E: Multi-energy loads increase synchronously.

[0284] During the optimization process, the scheduling control unit calculates the operation plan for each scenario and generates robust scheduling results based on scenario probability or risk weight.

[0285] For example, if multiple scenarios indicate a high risk of heat load in the evening, the system will prioritize reserving a portion of the surplus electricity at noon, even if the battery still has some charging capacity, to convert the surplus electricity into heat energy via the heat pump and store it in the thermal energy storage, in order to improve the heating guarantee capability under subsequent heat load fluctuations.

[0286] Thus, the scheduling decision in this embodiment is no longer based on a single predicted value, but takes into account multiple possible scenarios.

[0287] S7: Generation and execution of control commands: After completing the optimization calculation, the scheduling and control unit generates control commands and sends them to each execution unit.

[0288] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0289] Reference Figure 3 The diagram illustrates a structural block diagram of an equipment scheduling device for an integrated energy system provided in an embodiment of the present invention, which may specifically include the following modules: The operation status information acquisition module 301 is used to acquire the operation status information of the integrated energy system; The model building module 302 is used to establish an energy conversion equipment operation model that characterizes the non-constant conversion efficiency characteristics under different load conditions using the operation status information, and an energy storage equipment operation model that characterizes the energy storage capacity constraints, charging and discharging losses and lifespan decay characteristics. The candidate energy flow direction determination module 303 is used to determine one or more candidate energy flow directions that express all possible destinations of the currently available energy in the integrated energy system, using the energy conversion equipment operation model and the energy storage equipment operation model; the integrated energy system includes energy conversion equipment and energy storage equipment; The global multi-energy flow collaborative scheduling instruction generation module 304 is used to generate global multi-energy flow collaborative scheduling instructions for the energy conversion device and the energy storage device based on the candidate energy flow direction.

[0290] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0291] In addition, embodiments of the present invention also provide an electronic device, such as... Figure 4 As shown, it includes a processor 401, a communication interface 402, a memory 403, and a communication bus 404, wherein the processor 401, the communication interface 402, and the memory 403 communicate with each other through the communication bus 404. Memory 403 is used to store computer programs; When the processor 401 executes the program stored in the memory 403, it implements any of the equipment scheduling methods for the integrated energy system described in the above embodiments: The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0292] The communication interface is used for communication between the aforementioned terminal and other devices.

[0293] The memory may include random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0294] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0295] like Figure 5 As shown, in another embodiment of the present invention, a computer-readable storage medium 501 is also provided, which stores instructions that, when executed on a computer, cause the computer to perform the equipment scheduling method for the integrated energy system described in the above embodiments.

[0296] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-described embodiment of the equipment scheduling method for integrated energy systems, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0297] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0298] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0299] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0300] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for equipment scheduling in an integrated energy system, characterized in that, include: Obtain operational status information of the integrated energy system; Using the aforementioned operating status information, an operating model for energy conversion equipment is established to characterize the non-constant conversion efficiency characteristics under different load conditions, as well as an operating model for energy storage equipment to characterize energy storage capacity constraints, charging and discharging losses, and lifespan degradation characteristics. The energy conversion device operation model and the energy storage device operation model are used to determine one or more candidate energy flows to represent all possible destinations of the currently available energy in the integrated energy system; the integrated energy system includes energy conversion devices and energy storage devices. Global multi-energy flow collaborative scheduling instructions for the energy conversion device and the energy storage device are generated based on the candidate energy flow directions.

2. The method according to claim 1, characterized in that, The step of determining one or more candidate energy flows to represent all possible destinations of the currently available energy in the integrated energy system using the energy conversion equipment operation model and the energy storage equipment operation model includes: Using the energy conversion equipment operation model and the energy storage equipment operation model, the non-constant conversion efficiency characteristics and operating range constraints of the energy conversion equipment under different load conditions, as well as the capacity constraints, charging and discharging losses and lifespan degradation characteristics of the energy storage equipment, are extracted to generate a quantitative dataset of the actual operating characteristics of the equipment. The actual operating characteristics of the equipment are used to quantify the dataset, identify the available energy to be allocated in the integrated energy system during the current period, and generate available energy information for the current period. The actual operating characteristics of the equipment are quantified using the dataset and the available energy information for the current time period. All feasible energy conversion paths for surplus electrical energy are enumerated to generate a set of candidate destination paths for surplus electrical energy. The feasible energy conversion paths include paths that directly charge the battery energy storage device, paths that convert electrical energy into heat energy through a heat pump and store it in a thermal energy storage device, paths that convert electrical energy into hydrogen energy through an electrolyzer and store it in a hydrogen energy storage device, paths that directly supply the current adjustable electrical load, and paths that reduce purchased electricity or suppress the power generation output of the combined heat and power unit. The actual operating characteristics of the equipment are used to quantify the dataset and the available energy information for the current time period. All feasible energy replenishment paths for the energy supply gap are enumerated to generate a set of candidate energy replenishment paths for the energy supply gap. The feasible energy replenishment paths include paths that satisfy the electrical load by discharging battery energy storage, paths that satisfy the thermal load by releasing heat from thermal energy storage, paths that start fuel cells to convert hydrogen energy into electrical energy and thermal energy, paths that increase the output of cogeneration units, paths that start electric boilers or heat pumps to supplement the thermal load, and paths that purchase electricity from the external power grid or gas from the gas grid. The set of candidate destination paths for surplus electrical energy and the set of candidate replenishment paths for energy supply gaps are merged and deduplicated. The conversion efficiency, loss and lifespan impact parameters in the dataset are quantified in combination with the actual operating characteristics of the equipment. A comprehensive marginal value assessment index is calculated for each path to generate a set of candidate energy flow directions with assessment indexes. Using the aforementioned set of candidate energy flow directions with evaluation indicators, one or more candidate energy flow directions are determined to represent all possible destinations of the currently available energy in the integrated energy system.

3. The method according to claim 2, characterized in that, The step of generating global multi-energy flow collaborative scheduling instructions for the energy conversion device and the energy storage device based on the candidate energy flow directions includes: Determine the specific path combination, expected conversion efficiency, loss cost, lifetime reduction, and future demand matching degree corresponding to the candidate energy flow direction; Based on the candidate energy flow direction, a multi-objective optimization model is constructed that takes into account operating economy, carbon emission level, equipment health status and energy supply security requirements. Using the specific path combination, the expected conversion efficiency, the loss cost, the lifespan reduction, and the future demand matching degree as constraints, the energy conversion equipment and the energy storage equipment are used in a collaborative optimization solution under different candidate energy flow directions to obtain an initial target scheduling scheme; When the initial target scheduling scheme is determined to meet the preset conditions, a global multi-energy flow collaborative scheduling instruction corresponding to each energy conversion device and each energy storage device is generated according to the initial target scheduling scheme. The global multi-energy flow collaborative scheduling instruction is used to control each energy conversion device in the integrated energy system to perform corresponding energy conversion operations, and is also used to control each energy storage device in the integrated energy system to perform corresponding charging and discharging operations, and is also used to control the load units in the integrated energy system to perform corresponding load supply operations.

4. The method according to claim 3, characterized in that, Also includes: Based on the initial target scheduling scheme, a multi-timescale collaborative control mechanism combining daily planning, intraday rolling correction, and real-time execution is constructed to dynamically update and control the initial target scheduling scheme and generate multi-timescale collaborative control results. A set of random scenarios is established to address the fluctuations in renewable energy output and load forecasting errors. The system operating status under different scenarios is evaluated using the set of random scenarios. Based on the scenario evaluation results, the multi-time-scale collaborative control results are corrected to obtain the corrected target scheduling scheme. Based on the revised target scheduling scheme, a global multi-energy flow collaborative scheduling instruction corresponding to each energy conversion device and each energy storage device is generated.

5. The method according to claim 3, characterized in that, The steps for constructing a multi-objective optimization model based on the candidate energy flow direction that takes into account operational economy, carbon emission levels, equipment health status, and energy supply security requirements include: Using the candidate energy flow direction, combined with the non-constant conversion efficiency characteristics, operating range constraints, capacity constraints, charging and discharging losses, and lifespan decay characteristics in the energy conversion equipment operation model and the energy storage equipment operation model, as well as the renewable energy predicted output information, load prediction information, external energy price information, and environmental parameter information for the current period, the operating cost, conversion efficiency, energy storage status, equipment lifespan impact parameters, carbon emission factors, and future demand value corresponding to each candidate path are extracted to generate a path comprehensive evaluation parameter set; Using the path comprehensive evaluation parameter set and the external energy price information, an economic objective function is constructed with minimizing the total system operating cost as the first optimization objective, thus generating an operational economic objective function; wherein the total operating cost includes electricity purchase cost, natural gas cost, and equipment operation and maintenance cost; By utilizing the comprehensive evaluation parameter set of the aforementioned path and the carbon emission factors of each energy conversion path, a carbon emission objective function is constructed with minimizing carbon emission costs as the second optimization objective, thereby generating a carbon emission level objective function; By utilizing the path comprehensive evaluation parameter set and the lifetime degradation characteristics and equipment start-stop frequency parameters in the energy storage device operation model, an equipment health objective function is constructed with minimizing the energy storage lifetime loss cost and the equipment frequent start-stop depreciation cost as the third optimization objective, and an equipment health status objective function is generated. Using the path comprehensive evaluation parameter set, the load forecast information, and the energy supply security margin requirements, an energy supply security objective function is constructed with minimizing the risk of insufficient energy supply or wind and solar curtailment losses as the fourth optimization objective, and an energy supply security requirement objective function is generated. The objective functions of operational economy, carbon emission level, equipment health status, and energy supply safety requirements are combined into a set of optimization objectives for a multi-objective optimization model, thereby generating a multi-objective optimization objective set. Using the energy conversion equipment operation model, the energy storage equipment operation model, the candidate energy flow direction, and the system power balance requirements of the integrated energy system, a set of constraints for a multi-objective optimization model is constructed. The set of constraints for the multi-objective optimization model includes the operating range constraints of each energy conversion equipment, the capacity boundary constraints and state of charge constraints of each energy storage equipment, and the supply and demand balance constraints of various energy forms. Using the set of multi-objective optimization objectives and the set of constraints of the multi-objective optimization model, a multi-objective optimization model that takes into account the requirements of operational economy, carbon emission level, equipment health status and energy supply security is constructed, and a multi-objective optimization model is generated.

6. The method according to claim 3, characterized in that, The steps of constructing a multi-timescale collaborative control mechanism that combines daily planning, intraday rolling correction, and real-time execution based on the initial target scheduling scheme, dynamically updating and controlling the initial target scheduling scheme in a closed loop, and generating multi-timescale collaborative control results include: Based on the initial target scheduling scheme, obtain the renewable energy forecast output information, load forecast information, external energy price information, and status information of energy conversion equipment and energy storage equipment for the next day, and generate the day-ahead layer input dataset; The day-ahead baseline scheduling plan is generated using the day-ahead layer input dataset. The day-ahead baseline scheduling plan includes the planned output of cogeneration units for each time period, the operation plans of heat pumps, electric boilers, electrolyzers and fuel cells for each time period, the charging and discharging plans of battery energy storage, thermal energy storage and hydrogen energy storage, and the optimal allocation schemes of various energy sources for different time periods. Based on the day-ahead baseline scheduling plan and the real-time updated renewable energy forecast output information and load forecast information, the latest forecast data is obtained according to the first preset time period, and the day-ahead baseline scheduling plan is rolled over and revised to generate an intraday rolling revised scheduling plan. Based on the intraday rolling adjustment scheduling plan and the latest measured real-time operating status information, rapid adjustment actions are executed at a second preset time period, and multi-time-scale collaborative control results are generated; the rapid adjustment actions include increasing the heat release power of thermal energy storage, starting heat pumps or electric boilers to supplement the heat load, and coordinating battery energy storage and purchased electricity to ensure synchronous balance of electric and heat loads.

7. The method according to claim 6, characterized in that, The steps of establishing a random scenario set to address renewable energy output fluctuations and load forecasting errors, evaluating the system operating status under different scenarios using the random scenario set, and correcting the multi-timescale coordinated control results based on the scenario evaluation results to obtain the corrected target scheduling scheme include: To address the fluctuations in renewable energy output and load forecasting errors, a random scenario set containing multiple target operating scenarios is constructed. The random scenario set includes at least the following scenarios: normal high photovoltaic load, normal low photovoltaic load, normal photovoltaic heat load increase, low wind and solar power load with sudden increase, and simultaneous increase of multiple energy loads. For the photovoltaic high-load normal scenario, the photovoltaic low-load normal scenario, the photovoltaic normal heat load rising scenario, the wind and solar low power load sudden increase scenario, and the multi-energy load simultaneous increase scenario, the energy conversion equipment output plan and energy storage charging and discharging plan under each scenario are recalculated using the multi-time scale collaborative control results, and a set of operation schemes for each scenario is generated. Based on the set of operation schemes for each scenario, and the probability or risk weight of occurrence for different scenarios, the operation schemes under different scenarios are weighted and fused for evaluation to generate robust scheduling results. The robust scheduling results are used to correct the deviations in the multi-timescale collaborative control results, thereby obtaining the corrected target scheduling scheme.

8. An equipment dispatching device for an integrated energy system, characterized in that, include: The operation status information acquisition module is used to acquire the operation status information of the integrated energy system; The model building module is used to establish an energy conversion equipment operation model that characterizes the non-constant conversion efficiency characteristics under different load conditions using the operation status information, and an energy storage equipment operation model that characterizes the energy storage capacity constraints, charging and discharging losses and lifespan degradation characteristics. The candidate energy flow direction determination module is used to determine one or more candidate energy flow directions that express all possible destinations of the currently available energy in the integrated energy system, using the energy conversion equipment operation model and the energy storage equipment operation model; the integrated energy system includes energy conversion equipment and energy storage equipment; A global multi-energy flow collaborative scheduling instruction generation module is used to generate global multi-energy flow collaborative scheduling instructions for the energy conversion device and the energy storage device based on the candidate energy flow direction.

9. An electronic device, characterized in that, It includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the method as described in claims 1-7.

10. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the method as described in claims 1-7.

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