A Comprehensive Energy System Optimization and Scheduling Method Based on Carbon Emission Constraints and Efficiency Orientation

Abstract

The application discloses a park comprehensive energy system optimization scheduling method based on carbon emission constraint and efficiency orientation; the method comprises the following steps: constructing an electricity-heat-cold-gas multi-energy flow topology and an energy conversion model set of coupling equipment; obtaining electricity / heat / cold load, wind and light output prediction and carbon factor; establishing a comprehensive optimization model containing economic cost, wind and light abandonment penalty, carbon cost and cascade utilization efficiency item, and setting multi-energy flow balance, energy storage SOC, equipment output / climbing, power grid interaction and carbon emission total amount / intensity constraint; solving to obtain equipment output and energy storage charging and discharging sequence generation scheduling instruction in each period, triggering rolling re-optimization according to the measured carbon emission overrun in operation, enhancing the adaptability to disturbance, forming a low-carbon and high-efficiency closed-loop scheduling which is monitorable, constraintable and deviation-correctable, and realizing safe, economic and low-carbon collaborative operation of the park comprehensive energy system.

Description

Technical Field

[0001] This invention relates to the field of energy dispatching technology, and in particular to a comprehensive energy system optimization dispatching method based on carbon emission constraints and efficiency orientation. Background Technology

[0002] Integrated energy systems typically target industrial parks, industrial enterprises, or urban areas, coordinating the supply of various energy forms such as electricity, heat, cooling, and natural gas within the same energy supply framework to improve energy reliability and overall energy efficiency. With the continuous increase in the scale of new energy sources such as photovoltaics and wind power being integrated into industrial parks, the operation of park energy systems exhibits characteristics of "strong fluctuations in new energy output, multi-dimensional load coupling, and increasingly stringent carbon emission constraints." Traditional scheduling methods focused solely on a single energy flow or a single objective are insufficient to meet the operational demands for low-carbon and highly efficient coordination.

[0003] Therefore, there is an urgent need for a comprehensive energy system optimization and scheduling method that can address the scenarios of new energy fluctuations and multi-energy flow coupling in industrial parks, while meeting the balance of electricity-heat-cooling supply and demand and equipment operation constraints, introducing carbon emission constraints and efficiency-oriented goals, and possessing the ability to perform rolling re-optimization. Summary of the Invention

[0004] To address the aforementioned issues, this invention provides a comprehensive energy system optimization scheduling method based on carbon emission constraints and efficiency orientation.

[0005] To achieve the above-mentioned technical objectives, the technical solution adopted by this invention is as follows: In a first aspect, the present invention proposes an optimized scheduling method for an integrated energy system based on carbon emission constraints and efficiency orientation, applied to an integrated energy system in a park. The integrated energy system includes at least a new energy power generation unit, an external power grid interaction unit, an electric / heat / cold energy storage unit, and an electric-heat-cold coupled energy conversion device. The method is characterized by the following steps: S1. Construct a multi-energy flow topology model of the park's integrated energy system, and determine the energy conversion relationship of various types of equipment based on the multi-energy flow topology model. Establish a set of equipment energy conversion models that include the mapping relationship between electrical power, thermal power, cooling capacity and fuel consumption. S2. Obtain the prediction information within the target scheduling period. The prediction information includes at least: the park's electricity load prediction curve, heat load prediction curve, cooling load prediction curve, and new energy output prediction curve. S3. Establish a comprehensive scheduling optimization model oriented towards the target scheduling cycle. The objective function of the comprehensive scheduling optimization model shall include at least: an economic cost term representing purchased electricity and fuel consumption; a penalty cost term representing wind and solar curtailment; a carbon cost term representing indirect and direct carbon emissions; and an efficiency-oriented term representing energy cascade utilization and equipment efficiency improvement, which is used to improve the efficiency of electricity-heat-cooling coupling utilization and reduce primary energy consumption. S4. Set constraints for the comprehensive scheduling optimization model. The constraints shall include at least: power balance constraints, thermal balance constraints, cold balance constraints, energy storage state of charge constraints, equipment output upper and lower limits constraints, ramping constraints, and carbon emission constraints. S5. Solve the comprehensive scheduling optimization model based on the predicted information to obtain the optimal output sequence and energy storage charging and discharging sequence of each device in each discrete time period within the target scheduling cycle, and generate scheduling instructions for the comprehensive energy system accordingly.

[0006] Furthermore, the new energy power generation unit is used to provide renewable energy power input within the target dispatch cycle; The external power grid interaction unit is used to realize bidirectional power exchange between the park and the external power grid. The energy storage unit is used to perform peak shaving and valley filling and smooth out the fluctuations of new energy sources. When new energy sources are abundant, it absorbs electrical energy for charging and releases electrical energy for discharging when the load is at its peak or when new energy sources are insufficient. The thermal energy storage unit is used to store and release heat output from the heat source equipment in a time-shifted manner. It stores heat during periods of surplus heat source or low electricity price and low carbon factor, and releases heat during periods of increased heating demand or tightening carbon emission constraints. The cold energy storage unit is used to store the cold energy output by the refrigeration equipment in a time-shifted manner and release it on demand. It stores cold energy during periods of surplus refrigeration or surplus renewable energy, and releases cold energy during periods of peak cold load or when the carbon emission factor of electricity purchased from the grid is high. The aforementioned electric-thermal-cold coupled energy conversion equipment is used to realize the cross-energy flow conversion and cascade utilization between electrical energy, thermal energy and cold energy, so as to realize the combined output of fuel to electrical power and thermal power through cogeneration, realize the enhancement of electrical energy to high-grade thermal energy through heat pump, realize the conversion of electrical energy to cold energy through electric chiller, and realize the conversion of thermal energy to cold energy through absorption chiller, thereby supporting the coordinated scheduling of multiple energy flows in the park, improving comprehensive energy efficiency and reducing carbon emissions.

[0007] In some embodiments, step S1 specifically includes the following steps: S11. Abstract the park's integrated energy system into a multi-energy flow network diagram. The node set V includes at least the power grid nodes. heating network nodes Cold network nodes and gas nodes The set of edges E includes at least the power transmission edges. Heat transfer side Cold air transfer side Gas transmission side And cross-energy flow coupling edges; based on the association between nodes and branches, establish a multi-energy flow energy balance expression: ,in , This represents the branch flow vector of energy flow x in time period t. This represents the device output vector of the injection node. This represents the node load demand vector, so that the multi-energy flow network diagram can characterize the injection, transmission, and absorption relationships of various energy flows at each node. S12. For the aforementioned cross-energy flow coupling edge, construct a unified energy conversion model set for the electric-thermal-cold coupling device, and define the input energy flow for each coupling device k. Output energy flow With conversion efficiency parameters This satisfies the mapping relationship between the input energy flow and the output energy flow obtained through transformation: ; The coupling device includes at least one of the following: cogeneration equipment, gas boiler, electric boiler, heat pump, electric chiller, and absorption chiller; and the energy conversion model set can describe the conversion process of fuel input into combined output of electric power and thermal power, electric power into thermal power, electric power into cooling capacity, thermal power into cooling capacity, and electric power being boosted into high-grade thermal power by the heat pump. S13. Establish a time-sharing output boundary model for new energy power generation units so that wind power output and photovoltaic output meet non-negative constraints in each time period and are subject to the upper limit of the predicted available output for the corresponding time period. S14. Establish a state transition model and a charge / discharge power constraint model for the state of charge (SOC) of the electric energy storage unit and the thermal energy storage unit, so that any energy storage unit s satisfies: the state of charge of the energy storage unit in each time period is determined by the state of charge of the previous time period and the charge / discharge power of the current time period. At the same time, limit the upper and lower limits of the state of charge and the upper and lower limits of the charge / discharge power to characterize the dynamics and adjustability of energy storage. S15. Based on the multi-energy flow network diagram and the energy conversion model set, generate a set of system state variables and a set of decision variables for subsequent optimization solutions. The set of system state variables includes at least the energy flow state of the multi-energy flow branches and the energy storage charge state. The set of decision variables includes at least the power purchased and sold by the external power grid, the time-sharing output of each coupled device, the time-sharing charging and discharging power of each energy storage unit, and the time-sharing actual output of wind power and photovoltaic power.

[0008] In some embodiments, step S2 specifically includes the following steps: S21. Obtain the predicted curve of the park's electricity load within the target scheduling period, and construct the electricity load into a sequence of electricity loads arranged in discrete time periods; further decompose the electricity load into non-adjustable electricity load and adjustable electricity load, wherein the non-adjustable electricity load is used to characterize basic production electricity consumption or rigid electricity demand, and the adjustable electricity load is used to characterize transferable load or interruptible load; at the same time, set energy conservation constraints on the adjustable electricity load to ensure that the total electricity consumption remains unchanged within the allowable adjustment time window, thereby providing predictive input for subsequent demand response and peak shaving and valley filling; S22. Obtain the predicted heat load curve of the park within the target scheduling cycle, and construct the heat load into a heat load sequence arranged in discrete time periods; for the heat load with comfort requirements or process temperature requirements, further determine the allowable heat supply deviation range for the corresponding time period, so that the heat load can be flexibly adjusted within the deviation range, thereby giving the heat load an adjustable space to participate in cascade utilization and rolling optimization. S23. Obtain the predicted curve of the park's cooling load within the target scheduling period, and construct the cooling load into a cooling load sequence arranged by discrete time periods; for the cooling loads related to air conditioning, process cooling, or cold energy storage, further determine the upper limit, lower limit, or shiftable time window of the cooling demand for each time period, so that the cooling load can be used as a scheduling variable to participate in the coordinated regulation of multi-energy flow. S24. Obtain the predicted output curves of new energy sources within the target scheduling period, and determine the upper bound of available output for wind power and photovoltaic power. and And construct an uncertainty range for new energy output to meet its requirements. as well as ,in and These represent the prediction error boundaries for wind power and solar power in time period t, respectively. S25. Obtain the set of parameters related to carbon emission accounting, and express the time-of-use grid carbon emission factor for purchased electricity as follows: The fuel emission factor is expressed as This allows for the calculation of indirect carbon emissions from purchased electricity at any given time period t. and direct carbon emissions from fuel consumption ,in Let t be the power purchased during time period t. This represents the equivalent fuel consumption for time period t. S26. Perform unified time-granularity resampling and time-series alignment processing on the electric load prediction curve, heat load prediction curve, cold load prediction curve, new energy output prediction curve and carbon emission accounting parameter set, so that each prediction sequence satisfies a one-to-one correspondence on the same discrete time period set, thereby forming a unified prediction dataset for subsequent construction and solution of the integrated scheduling optimization model.

[0009] In some embodiments, step S3 specifically includes the following steps: S31. Divide the target scheduling period into several discrete scheduling time periods. And the power purchased from the external power grid for each time period External power grid electricity sales power Actual power of new energy consumption The time-sharing output of each energy conversion device and the time-of-use charging power of each energy storage unit With discharge power These are used as optimization decision variables, thus forming a time series set of decision variables for subsequent solution. Among them, the actual power of new energy consumption Including the actual absorption capacity of photovoltaic and wind power ; S32. Constructing the economic cost item This is used to characterize the electricity purchase cost, fuel consumption cost, and energy storage charging and discharging operation cost within and outside the target scheduling cycle; among them, the economic cost item... The expression is as follows: ; in, For time-of-use electricity pricing, Electricity pricing is based on time-of-use pricing. Let f be the amount of fuel consumed or the equivalent input power during time period t. For fuel prices, This is the energy storage operating cost coefficient. The duration of the time period; S33. Construct a penalty cost item for wind and solar power curtailment. This is used to measure the difference between the predicted available power output of new energy sources and the actual power output of new energy sources, and the difference is defined as the amount of power abandoned by new energy sources. By applying penalty weights to the amount of renewable energy wasted, the optimization model prioritizes the absorption of renewable energy output while satisfying system constraints. The expression is as follows: , ; = ; in, This represents the upper bound of the available power output of new energy sources during time period t. The penalty for abandoning wind and solar power is weighted; S34. Constructing carbon cost items The carbon cost item includes the indirect carbon emission cost caused by purchased electricity and the direct carbon emission cost caused by fuel consumption, wherein the indirect carbon emission cost... The direct carbon emission cost is determined by the time-of-use grid carbon emission factor and the amount of electricity purchased, and is determined by the fuel emission factor and fuel consumption, as expressed in the following formula: Furthermore, carbon emissions are linked to carbon cost weights to achieve price constraints and cost endogenization of carbon emissions; among which, the carbon cost item... The expression is as follows:

[0010] in, As a fuel emission factor, This is the carbon cost weighting coefficient; S35. Constructing Efficiency-Oriented Items This is used to characterize the cascade utilization and comprehensive energy efficiency improvement of multiple energy flows including electricity, heat, and cooling, where the comprehensive primary energy input is defined as: The comprehensive effective energy supply is defined as: Thus, an efficiency-oriented term is constructed in the form of a primary energy input penalty or a comprehensive energy efficiency maximization form, satisfying... ; in, This is the equivalent primary energy conversion factor for fuel. , , These are the electricity / heating / cooling load requirements, , , For the value coefficients of different energy flows, The primary energy penalty weight is used to convert primary energy consumption into a penalty for the objective function; The effective energy supply reward weight is used to convert the effective energy supply into a reward for the objective function; S36. The economic cost item The cost of curtailing wind and solar power Carbon cost item With efficiency-oriented items By integrating the data, a comprehensive scheduling optimization model can be formed with a specific objective function. The expression is as follows:

[0011] This enables the objective function to simultaneously drive the comprehensive scheduling objectives of reducing dependence on purchased electricity and indirect carbon emissions, suppressing wind and solar curtailment, and improving the efficiency of electricity-heat-cooling cascade utilization.

[0012] In some embodiments, step S4 specifically includes the following steps: S41. Set power balance constraints to match the power supply and demand of the power side in any discrete period within the target scheduling cycle. The power supply includes at least the output of new energy generation, the power purchased from the external power grid, the power of energy storage discharge, and the power output of the electric-thermal-cold coupling equipment. The power demand includes at least the park's power load, the power of energy storage charging, and the power input of the electric-thermal-cold coupling equipment. S42. Set heat energy balance constraints to match the heat supply and heat demand in any discrete period within the target scheduling cycle. The heat supply includes at least one of the following: the heat output of cogeneration equipment, the heat output of gas boilers, the heat output of electric boilers, the heat output of heat pumps, and the heat release power of thermal energy storage. The heat demand includes at least one of the following: park heat load, the heating demand of absorption chillers, and the heat charging power of thermal energy storage. S43. Set a cooling balance constraint to match the cooling supply and cooling demand in any discrete period within the target scheduling cycle. The cooling supply includes at least one of the following: the cooling output of electric chillers, the cooling output of absorption chillers, and the cooling power of cold storage. The cooling demand includes at least one of the following: the park's cooling load and the cooling power of cold storage. S44. Set energy storage state of charge constraints and power constraints so that the state of charge of the electric energy storage unit and the thermal energy storage unit in each discrete time period meets the upper and lower limit constraints, and limit their charging power and discharging power to meet the corresponding upper and lower limit constraints. At the same time, limit the state of charge of adjacent time periods to meet the state transition relationship. S45. Set upper and lower limits for the output of each energy conversion device and a ramping constraint so that the output power of any energy conversion device in each discrete time period does not exceed the upper limit of the rated output and is not lower than the lower limit of the minimum stable output, and limit the output change of adjacent time periods to not exceed the preset ramping rate threshold. S46. Set external power grid interaction constraints to ensure that the purchased and sold power in each discrete time period meets the power grid interaction capacity limit, and further limit the purchase and sale of power in the same time period to not occur at the same time. S47. Set constraints on new energy consumption and wind and solar curtailment, so that the actual output of photovoltaic and wind power in each discrete time period does not exceed the upper limit of the predicted available output for the corresponding time period, and define the difference between the predicted available output and the actual consumption output as the amount of wind and solar curtailment. S48. Set carbon emission constraints so that the total carbon emissions within the target scheduling period and the carbon emission intensity at any discrete time period meet a preset upper limit threshold, expressed as follows: Total carbon emissions: ;in, Indicates the upper limit of total carbon emissions; Carbon emission intensity for any discrete time period: ;in, The upper limit of carbon emission intensity, Provide comprehensive and effective energy supply for time period t; This enables the integrated scheduling optimization model to achieve controlled carbon emissions while satisfying the supply and demand balance of multiple energy flows.

[0013] In some embodiments, step S5 specifically includes the following steps: S51. Based on the unified prediction dataset formed in step S2, construct a set of decision variable vectors for the target scheduling period. The decision variable vector includes at least: the power purchased from the external power grid. External power grid electricity sales power Actual absorption capacity of photovoltaic and wind power The time-sharing output of each energy conversion device and the time-of-use charging power of each energy storage unit With discharge power The decision variable vectors for each discrete time period within the entire time domain are combined to form the overall decision variable set. ; S52. Unify the comprehensive objective function constructed in step S3 with the constraints set in step S4 to form a standardized optimization problem, which satisfies: the set of all decision variables that satisfy the constraints. In the middle, find the comprehensive objective function. Minimum optimal solution *, where the comprehensive objective function From economic cost item The cost of curtailing wind and solar power Carbon cost item With efficiency-oriented items The result of fusion; S53. The optimization problem is solved iteratively using a preset optimization algorithm. During the iteration process, the convergence condition of the objective function is used as the stopping criterion. The optimal decision variable solution is output when the change in the objective function between two adjacent iterations is less than a preset threshold or the number of iterations reaches a preset upper limit. *; S54. Based on the solution of the optimal decision variables * Generate a time-segmented scheduling sequence set, which includes at least: the actual power consumption sequence of photovoltaic and wind power. Power purchase and sale sequence of the power grid Output sequence of each energy conversion device on the electric / hot / cold side and the charging and discharging power sequence of each energy storage unit. ; S55. Convert the scheduling sequence into a set of dispatchable instructions, and send the set of dispatch instructions to the park energy management system or equipment controller based on a unified time index, so that it can perform new energy consumption, grid interaction, energy storage charging and discharging, and coordinated output control of electric-thermal-cold coupling equipment according to each discrete time period, thereby achieving optimal scheduling execution and controlled carbon emission operation within the target scheduling cycle.

[0014] Furthermore, the preset optimization algorithm mentioned in step S53 includes at least one of linear programming, mixed integer linear programming, nonlinear programming, particle swarm optimization algorithm or genetic algorithm.

[0015] In some embodiments, the integrated energy system optimization scheduling method based on carbon emission constraints and efficiency orientation further includes: S6, during the scheduling execution process, collecting actual measured data of park operation and calculating real-time carbon emissions and real-time carbon emission intensity; when the real-time carbon emissions or real-time carbon emission intensity violates a preset threshold, triggering rolling re-optimization and updating the scheduling instructions, thereby realizing dynamic scheduling of multi-energy flow loads and real-time carbon emission control.

[0016] In a second aspect, the present invention also provides a computer-readable storage medium storing at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, at least one program, code set, or instruction set is loaded and executed by a processor to implement the integrated energy system optimization scheduling method based on carbon emission constraints and efficiency orientation described in the first aspect.

[0017] Compared with the prior art, the present invention has at least the following beneficial effects: This invention proposes an integrated energy system optimization scheduling method based on carbon emission constraints and efficiency orientation. By uniformly modeling an electricity-heat-cooling multi-energy flow network and coupling energy conversion equipment with electricity / heat / cooling energy storage units, it simultaneously incorporates economic cost, wind and solar curtailment penalties, carbon emission costs, and overall energy efficiency improvement orientation into the objective function. At the constraint level, it achieves coordinated constraints on multi-energy flow supply and demand balance, equipment operating boundaries, grid interaction capacity, and total carbon emission / intensity. This improves the absorption capacity of new energy sources, reduces dependence on purchased electricity and indirect carbon emissions, and promotes the cascade utilization of electricity, heat, and cooling systems under scenarios of fluctuating new energy output and load mismatch in industrial parks. Furthermore, it combines operational measurement data to conduct threshold-triggered rolling re-optimization, enhancing adaptability to disturbances and forming a monitorable, constrained, and correctable low-carbon, high-efficiency closed-loop scheduling system, achieving safe, economical, and low-carbon coordinated operation of the integrated energy system in the industrial park. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a simplified flowchart of the integrated energy system optimization scheduling method based on carbon emission constraints and efficiency orientation provided in the embodiments of the present invention; Figure 2 This is a schematic diagram of a computer-readable storage medium provided in an embodiment of the present invention. Detailed Implementation

[0020] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be particularly noted that the following embodiments are for illustrative purposes only and do not limit the scope of the invention. Similarly, the following embodiments are only some, not all, embodiments of the present invention, and all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Example 1 See attached document Figure 1 As shown, this embodiment provides an optimized scheduling method for an integrated energy system based on carbon emission constraints and efficiency orientation, applied to an integrated energy system in a park. The integrated energy system in the park includes at least a new energy power generation unit, an external power grid interaction unit, an electric / heat / cold energy storage unit, and an electric-heat-cold coupled energy conversion device; wherein, the new energy power generation unit is used to provide renewable energy power input within the target scheduling cycle; The external power grid interaction unit is used to realize bidirectional power exchange between the park and the external power grid, so as to make electricity purchase compensation when the output of new energy is insufficient or the demand of multi-energy flow load increases, and to send surplus electricity to the grid when new energy is abundant and the grid interaction capacity limit is met. The energy storage unit is used to perform peak shaving and valley filling and smooth out the fluctuations of new energy sources. It absorbs electrical energy to charge when there is a surplus of new energy sources and releases electrical energy to discharge when there is a peak load or a shortage of new energy sources. Thermal energy storage units are used to store heat output from heat source equipment in a time-shifted manner and release it on demand. They store heat during periods of surplus heat source or low electricity price and low carbon factor, and release heat during periods of increased heating demand or tightening carbon emission constraints. The cold energy storage unit is used to store the cold energy output by the refrigeration equipment in a time-shifted manner and release it on demand. It stores cold energy during periods of surplus refrigeration or surplus renewable energy, and releases cold energy during periods of peak cold load or when the carbon emission factor of grid electricity purchase is high. Electric-heat-cold coupled energy conversion equipment is used to realize the cross-energy flow conversion and cascade utilization between electrical energy, heat energy and cold energy. It enables the combined output of fuel to electrical power and thermal power through cogeneration, the enhancement of electrical energy to high-grade heat energy through heat pumps, the conversion of electrical energy to cold energy through electric chillers, and the conversion of heat energy to cold energy through absorption chillers. This supports the coordinated scheduling of multiple energy flows in the park, improves the overall energy efficiency and reduces carbon emissions.

[0022] The integrated energy system optimization scheduling method based on carbon emission constraints and efficiency orientation includes the following steps: S1. Construct a multi-energy flow topology model of the park's integrated energy system, and determine the energy conversion relationships of various types of equipment based on the multi-energy flow topology model. Establish a set of equipment energy conversion models that includes the mapping relationships between electrical power, thermal power, cooling capacity, and fuel consumption. Specifically, this includes the following steps: S11. Abstract the park's integrated energy system into a multi-energy flow network diagram. The node set V includes at least the power grid nodes. heating network nodes Cold network nodes and gas nodes The set of edges E includes at least the power transmission edges. Heat transfer side Cold air transfer side Gas transmission side And cross-energy flow coupling edges (i.e., edges formed by connecting nodes with different energy forms, such as an edge from an electrical node to a thermal node); Based on the correlation between nodes and branches, establish multi-energy flow energy balance expressions: ,in , This represents the branch flow vector of energy flow x in time period t. This represents the device output vector of the injection node. This represents the node load demand vector, enabling the multi-energy flow network diagram to characterize the injection, transmission, and absorption relationships of various energy flows at each node.

[0023] S12. For the aforementioned cross-energy flow coupling edge, construct a unified energy conversion model set for the electric-thermal-cold coupling device, and define the input energy flow for each coupling device k. Output energy flow With conversion efficiency parameters This satisfies the mapping relationship between the input energy flow and the output energy flow obtained through transformation: .

[0024] The coupling equipment includes at least one of the following: cogeneration equipment, gas boiler, electric boiler, heat pump, electric chiller, and absorption chiller. The energy conversion model set can describe the conversion process of fuel input into combined output of electric power and thermal power, electric power into thermal power, electric power into cooling capacity, thermal power into cooling capacity, and electric power being upgraded to high-grade thermal power by the heat pump.

[0025] S13. Establish a time-segmented output boundary model for the new energy power generation unit, ensuring that wind power output and photovoltaic output satisfy non-negativity constraints in each time period and are limited by the upper bound of the predicted available output for the corresponding time period, in order to characterize the volatility and availability of new energy power output; the expression is as follows: ;in, Indicates wind power output. Indicates photovoltaic power output. and These represent the upper bounds of the predicted available output for the corresponding time periods.

[0026] S14. Establish a state transition model and a charge / discharge power constraint model for the state of charge (SOC) of the electrical energy storage unit and the thermal energy storage unit, such that any energy storage unit s satisfies the following: the state of charge of the energy storage unit in each time period is jointly determined by the state of charge of the previous time period and the charge / discharge power of the current time period. At the same time, limit the upper and lower limits of the state of charge and the upper and lower limits of the charge / discharge power to characterize the dynamics and adjustability of energy storage; the expression is as follows: ;

[0027] ; in, and These are charging power and discharging power, respectively. For capacity; and For charge and discharge efficiency; It is in a charged state.

[0028] S15. Based on the multi-energy flow network diagram and the energy conversion model set, generate a set of system state variables and a set of decision variables for subsequent optimization solutions, wherein the set of system state variables includes at least the energy flow state of the multi-energy flow branches and the energy storage charge state: The set of decision variables includes at least the power purchased and sold from the external power grid, the time-sharing output of each coupled device, the time-sharing charging and discharging power of each energy storage unit, and the time-sharing actual output of wind power and photovoltaic power. This enables the park's integrated energy system to characterize the fluctuations of new energy sources, the park's electricity-heat-cold coupling conversion, and the dynamic response relationship of energy storage with a unified mathematical framework.

[0029] S2. Obtain forecast information within the target scheduling period. The forecast information includes at least: the park's electricity load forecast curve, heat load forecast curve, cooling load forecast curve, and new energy output forecast curve. Specifically, this includes the following steps: S21. Obtain the predicted curve of the park's electricity load within the target scheduling period, and construct the electricity load into a sequence of electricity loads arranged in discrete time periods; further decompose the electricity load into non-adjustable electricity load and adjustable electricity load, wherein the non-adjustable electricity load is used to characterize basic production electricity consumption or rigid electricity demand, and the adjustable electricity load is used to characterize transferable load or interruptible load; at the same time, set energy conservation constraints on the adjustable electricity load to ensure that the total electricity consumption remains unchanged within the allowable adjustment time window, thereby providing predictive input for subsequent demand response and peak shaving.

[0030] S22. Obtain the predicted heat load curve of the park within the target scheduling period, and construct the heat load into a heat load sequence arranged in discrete time periods; for the heat load with comfort requirements or process temperature requirements, further determine the allowable heat supply deviation range for the corresponding time period, so that the heat load can be flexibly adjusted within the deviation range, thereby giving the heat load an adjustable space to participate in cascade utilization and rolling optimization.

[0031] S23. Obtain the predicted curve of the park's cooling load within the target scheduling period, and construct the cooling load into a cooling load sequence arranged by discrete time periods; for the cooling loads related to air conditioning, process cooling, or cold storage, further determine the upper limit, lower limit, or shiftable time window of the cooling demand for each time period, so that the cooling load can be used as a scheduling variable to participate in the coordinated regulation of multi-energy flow.

[0032] S24. Obtain the predicted output curves of new energy sources within the target scheduling period, and determine the upper bound of available output for wind power and photovoltaic power. and And construct an uncertainty range for new energy output to meet its requirements. as well as ,in and These represent the prediction error boundaries for wind power and photovoltaic power in time period t, respectively, to characterize the volatility of new energy sources and provide input for subsequent scheduling.

[0033] S25. Obtain the set of parameters related to carbon emission accounting, and express the time-of-use grid carbon emission factor for purchased electricity as follows: The fuel emission factor is expressed as This allows for the calculation of indirect carbon emissions from purchased electricity at any given time period t. and direct carbon emissions from fuel consumption ,in Let t be the power purchased during time period t. This represents the equivalent fuel consumption during time period t.

[0034] S26, Set of parameters for electricity load forecasting curve, heat load forecasting curve, cooling load forecasting curve, renewable energy output forecasting curve, and carbon emission accounting parameters. Perform unified time-granularity resampling and time-series alignment processing to ensure that each predicted sequence satisfies a one-to-one correspondence on the same discrete time period set, thereby forming a unified prediction dataset for subsequent construction and solution of integrated scheduling optimization models.

[0035] S3. Establish a comprehensive scheduling optimization model oriented towards the target scheduling cycle. The objective function of the comprehensive scheduling optimization model shall include at least: an economic cost term representing purchased electricity and fuel consumption; a penalty cost term representing wind and solar curtailment; a carbon cost term representing indirect and direct carbon emissions; and an efficiency-oriented term representing energy cascade utilization and equipment efficiency improvement, used to improve the efficiency of electricity-heat-cooling coupling utilization and reduce primary energy consumption. Specifically, it includes the following steps: S31. Divide the target scheduling period into several discrete scheduling time periods. And the power purchased from the external power grid for each time period External power grid electricity sales power Actual power of new energy consumption The time-sharing output of each energy conversion device and the time-of-use charging power of each energy storage unit With discharge power These are used as optimization decision variables, thus forming a time series set of decision variables for subsequent solution. Among them, the actual power of new energy consumption Including the actual absorption capacity of photovoltaic and wind power .

[0036] S32. Constructing the economic cost item This is used to characterize the electricity purchase cost, fuel consumption cost, and energy storage charging and discharging operation cost within and outside the target scheduling cycle. The economic cost item is included. The expression is as follows: ; in, For time-of-use electricity pricing, Electricity pricing is based on time-of-use pricing. Let f be the amount of fuel consumed or the equivalent input power during time period t. For fuel prices, This is the energy storage operating cost coefficient. This represents the duration of the time period.

[0037] S33. Construct a penalty cost item for wind and solar power curtailment. This is used to measure the difference between the predicted available output of new energy and the actual output of new energy, and this difference is defined as the amount of new energy curtailment. By applying penalty weights to the amount of renewable energy curtailed, the optimization model prioritizes the absorption of renewable energy output while satisfying system constraints, thereby reducing wind and solar curtailment caused by the mismatch between renewable energy output fluctuations and the park's load curve; the expression is as follows: , ; = ; in, This represents the upper bound of the available power output of new energy sources during time period t. The penalty weight for abandoning wind and solar power.

[0038] S34. Constructing carbon cost items The carbon cost item includes the indirect carbon emission costs caused by purchased electricity and the direct carbon emission costs caused by fuel consumption, of which the indirect carbon emission costs are... The direct carbon emission cost is determined by the time-of-use grid carbon emission factor and the amount of electricity purchased, and is determined by the fuel emission factor and fuel consumption, as expressed in the following formula: Furthermore, carbon emissions are linked to carbon cost weights to achieve price constraints and cost endogenization of carbon emissions; among which, the carbon cost item... The expression is as follows:

[0039] in, Fuel emission factor; This is the carbon cost weighting coefficient, used to convert the indirect carbon emissions caused by purchased electricity and the direct carbon emissions caused by fuel consumption into the carbon emission cost in the objective function.

[0040] S35. Constructing Efficiency-Oriented Items This is used to characterize the cascade utilization and comprehensive energy efficiency improvement of multiple energy flows including electricity, heat, and cooling, where the comprehensive primary energy input is defined as: The comprehensive effective energy supply is defined as: Thus, an efficiency-oriented term is constructed in the form of a primary energy input penalty or a comprehensive energy efficiency maximization form, satisfying... ; in, The equivalent primary energy conversion factor is used to convert the consumption of different types of fuels into a unified primary energy input based on their standard coal equivalent, so as to be used for comprehensive energy efficiency calculation and primary energy minimization optimization. , , These are the electricity / heating / cooling load requirements, , , The value coefficients for different energy flows are used to characterize the contribution weights of electrical energy, thermal energy and cooling energy to the overall effective energy supply, so as to achieve a unified measurement of different energy flows under the conditions of economy, energy supply security level or equivalent energy consumption. This is the primary energy penalty weight, typically expressed in yuan / (kWh), used to convert primary energy consumption into a penalty for the objective function. The effective energy supply reward weight is generally expressed in yuan / (kWh), and is used to convert the effective energy supply into a reward based on the objective function. The equivalent unit price of primary energy is determined by converting the primary energy price in the park with the low calorific value. When there are multiple primary energy sources, the weighted average is calculated based on the consumption ratio of the benchmark cycle. The equivalent unit price for electricity, heat, and cooling services is determined by a weighted average of the effective energy supply ratio over the benchmark period.

[0041] S36. Include the economic cost item. The cost of curtailing wind and solar power Carbon cost item With efficiency-oriented items By integrating the data, a comprehensive scheduling optimization model can be formed with a specific objective function. The expression is as follows:

[0042] This enables the objective function to simultaneously drive the comprehensive scheduling objectives of reducing dependence on purchased electricity and indirect carbon emissions, suppressing wind and solar curtailment, and improving the efficiency of electricity-heat-cooling cascade utilization.

[0043] S4. Set constraints for the comprehensive scheduling optimization model. These constraints include at least: electrical balance constraints, thermal balance constraints, cold balance constraints, energy storage state of charge constraints, equipment output upper and lower limits constraints, ramping constraints, and carbon emission constraints. Specifically, this includes the following steps: S41. Set power balance constraints to match the power supply and demand in any discrete time period within the target scheduling cycle. The power supply includes at least the output of new energy generation, the power purchased from the external power grid, the power of energy storage discharge, and the power output of the electric-thermal-cold coupling equipment. The power demand includes at least the park's power load, the power of energy storage charging, and the power input of the electric-thermal-cold coupling equipment.

[0044] S42. Set thermal energy balance constraints to match the thermal supply and thermal demand for any discrete period within the target scheduling cycle. The thermal supply includes at least one of the following: thermal output of cogeneration equipment, thermal output of gas boiler, thermal output of electric boiler, thermal output of heat pump, and thermal energy storage heat release power. The thermal demand includes at least one of the following: park heat load, heating demand of absorption chiller, and thermal energy storage charging power.

[0045] S43. Set a cooling capacity balance constraint to match the cooling supply and cooling demand for any discrete time period within the target scheduling cycle. The cooling supply includes at least one of the following: the cooling output of electric chillers, the cooling output of absorption chillers, and the cooling power of cold storage. The cooling demand includes at least one of the following: the park's cooling load and the cooling power of cold storage.

[0046] S44. Set energy storage state of charge (SOC) constraints and power constraints to ensure that the SOC of electrical and thermal energy storage units in each discrete time period meets the upper and lower limits, and limit their charging and discharging power to meet the corresponding upper and lower limits. Simultaneously, limit the SOC of adjacent time periods to meet the state transition relationship to avoid unreachable charging and discharging scheduling results; the expression is as follows:

[0047] ; .

[0048] S45. Set upper and lower limits for the output of each energy conversion device and a ramping constraint to ensure that the output power of any energy conversion device in each discrete time period does not exceed the upper limit of the rated output and is not lower than the lower limit of the minimum stable output, and limit the output change of adjacent time periods to not exceed the preset ramping rate threshold, so as to ensure that the scheduling instructions meet the equipment operability requirements.

[0049] S46. Set external power grid interaction constraints to ensure that the purchased and sold power in each discrete time period meets the power grid interaction capacity limit. Furthermore, it can limit the purchase and sale of power in the same time period to avoid generating scheduling results that do not conform to the actual operating logic.

[0050] S47. Set constraints on renewable energy consumption and wind and solar curtailment, ensuring that the actual output of photovoltaic and wind power in each discrete time period does not exceed the upper limit of the predicted available output for the corresponding time period, and define the difference between the predicted available output and the actual consumption output as the amount of wind and solar curtailment. This is to penalize wind and solar curtailment in the objective function and guide the system to prioritize the consumption of new energy sources.

[0051] S48. Set carbon emission constraints so that the total carbon emissions within the target scheduling period and the carbon emission intensity at any discrete time period meet a preset upper limit threshold, expressed as follows: Total carbon emissions: ;in, Indicates the upper limit of total carbon emissions; Carbon emission intensity for any discrete time period: ;in, The upper limit of carbon emission intensity, Provide comprehensive and effective energy supply for time period t; This enables the integrated scheduling optimization model to achieve controlled carbon emissions while satisfying the supply and demand balance of multiple energy flows.

[0052] S5. Solve the integrated scheduling optimization model based on the predicted information to obtain the optimal output sequence and energy storage charging / discharging sequence of each device in each discrete time period within the target scheduling cycle, and generate scheduling instructions for the integrated energy system accordingly; specifically including the following steps: S51. Based on the unified prediction dataset formed in step S2, construct a set of decision variable vectors for the target scheduling period. The decision variable vector includes at least: the power purchased from the external power grid. External power grid electricity sales power Actual absorption capacity of photovoltaic and wind power The time-sharing output of each energy conversion device and the time-of-use charging power of each energy storage unit With discharge power The decision variable vectors for each discrete time period within the entire time domain are combined to form the overall decision variable set. .

[0053] S52. Unify the comprehensive objective function constructed in step S3 with the constraints set in step S4 to form a standardized optimization problem, which satisfies: the set of all decision variables that satisfy the constraints. In the middle, find the comprehensive objective function. Minimum optimal solution *, where the comprehensive objective function From economic cost item The cost of curtailing wind and solar power Carbon cost item With efficiency-oriented items It was obtained through fusion.

[0054] S53. The optimization problem is solved iteratively using a preset optimization algorithm. During the iteration process, the convergence condition of the objective function is used as the stopping criterion. The optimal decision variable solution is output when the change in the objective function between two adjacent iterations is less than a preset threshold or the number of iterations reaches a preset upper limit. * The preset optimization algorithm includes at least one of linear programming, mixed-integer linear programming, nonlinear programming, particle swarm optimization, or genetic algorithm; particle swarm optimization is preferred.

[0055] S54. Based on the solution of the optimal decision variables * Generate a time-segmented scheduling sequence set, which includes at least: the actual power consumption sequence of photovoltaic and wind power. Power purchase and sale sequence of the power grid Output sequence of each energy conversion device on the electric / hot / cold side and the charging and discharging power sequence of each energy storage unit. This results in a fully executable, lifecycle scheduling plan.

[0056] S55. Convert the scheduling sequence into a set of dispatchable instructions, and send the set of dispatch instructions to the park energy management system or equipment controller based on a unified time index, so that it can perform new energy consumption, grid interaction, energy storage charging and discharging, and coordinated output control of electric-thermal-cold coupling equipment according to each discrete time period, thereby achieving optimal scheduling execution and controlled carbon emission operation within the target scheduling cycle.

[0057] S6. During the scheduling process, real-time carbon emissions and carbon emission intensity are collected from the park's actual operation data. When the real-time carbon emissions or carbon emission intensity violates the preset threshold, rolling re-optimization is triggered and the scheduling instructions are updated, thereby realizing dynamic scheduling of multi-energy flow loads and real-time carbon emission control.

[0058] Example 2 See attached document Figure 2 As shown, based on the same inventive concept, this embodiment of the invention provides a computer-readable storage medium storing computer instructions. When the computer instructions are executed on a computer, the computer causes the computer to perform the integrated energy system optimization scheduling method based on carbon emission constraints and efficiency orientation as described in Embodiment 1.

[0059] In specific implementation, computer-readable storage media include: Universal Serial Bus flash drive (USB), portable hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, and other storage media that can store program code.

[0060] The device embodiments described above are merely illustrative. The units / modules described as separate components may or may not be physically separate. The components shown as units / modules may or may not be physical units / modules; that is, they may be located in one place or distributed across multiple network units / modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0061] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, 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 can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0062] The above description is only a part of the embodiments of the present invention and does not limit the scope of protection of the present invention. Any equivalent device or equivalent process transformation made based on the content of the present invention specification and drawings, or direct or indirect application in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for optimizing and scheduling an integrated energy system based on carbon emission constraints and efficiency orientation, applied to an integrated energy system in a park, wherein the integrated energy system includes at least a new energy power generation unit, an external power grid interaction unit, an electric / heat / cold energy storage unit, and an electric-heat-cold coupled energy conversion device, characterized in that, The method includes the following steps: S1. Construct a multi-energy flow topology model of the park's integrated energy system, and determine the energy conversion relationship of various types of equipment based on the multi-energy flow topology model. Establish a set of equipment energy conversion models that include the mapping relationship between electrical power, thermal power, cooling capacity and fuel consumption. S2. Obtain the prediction information within the target scheduling period. The prediction information includes at least: the park's electricity load prediction curve, heat load prediction curve, cooling load prediction curve, and new energy output prediction curve. S3. Establish a comprehensive scheduling optimization model oriented towards the target scheduling cycle. The objective function of the comprehensive scheduling optimization model shall include at least: an economic cost term representing purchased electricity and fuel consumption; a penalty cost term representing wind and solar curtailment; a carbon cost term representing indirect and direct carbon emissions; and an efficiency-oriented term representing energy cascade utilization and equipment efficiency improvement, which is used to improve the efficiency of electricity-heat-cooling coupling utilization and reduce primary energy consumption. S4. Set constraints for the comprehensive scheduling optimization model. The constraints shall include at least: power balance constraints, thermal balance constraints, cold balance constraints, energy storage state of charge constraints, equipment output upper and lower limits constraints, ramping constraints, and carbon emission constraints. S5. Solve the comprehensive scheduling optimization model based on the predicted information to obtain the optimal output sequence and energy storage charging and discharging sequence of each device in each discrete time period within the target scheduling cycle, and generate scheduling instructions for the comprehensive energy system accordingly.

2. The integrated energy system optimization scheduling method based on carbon emission constraints and efficiency orientation as described in claim 1, characterized in that, The new energy power generation unit is used to provide renewable energy power input within the target dispatch cycle; The external power grid interaction unit is used to realize bidirectional power exchange between the park and the external power grid. The energy storage unit is used to perform peak shaving and valley filling and smooth out the fluctuations of new energy sources. When new energy sources are abundant, it absorbs electrical energy for charging and releases electrical energy for discharging when the load is at its peak or when new energy sources are insufficient. The thermal energy storage unit is used to store and release heat output from the heat source equipment in a time-shifted manner. It stores heat during periods of surplus heat source or low electricity price and low carbon factor, and releases heat during periods of increased heating demand or tightening carbon emission constraints. The cold energy storage unit is used to store the cold energy output by the refrigeration equipment in a time-shifted manner and release it on demand. It stores cold energy during periods of surplus refrigeration or surplus renewable energy, and releases cold energy during periods of peak cold load or when the carbon emission factor of electricity purchased from the grid is high. The aforementioned electric-thermal-cold coupled energy conversion equipment is used to realize the cross-energy flow conversion and cascade utilization between electrical energy, thermal energy and cold energy, so as to realize the combined output of fuel to electrical power and thermal power through cogeneration, realize the enhancement of electrical energy to high-grade thermal energy through heat pump, realize the conversion of electrical energy to cold energy through electric chiller, and realize the conversion of thermal energy to cold energy through absorption chiller, thereby supporting the coordinated scheduling of multiple energy flows in the park, improving comprehensive energy efficiency and reducing carbon emissions.

3. The integrated energy system optimization scheduling method based on carbon emission constraints and efficiency orientation as described in claim 1, characterized in that, Step S1 specifically includes the following steps: S11. Abstract the park's integrated energy system into a multi-energy flow network diagram. The node set V includes at least the power grid nodes. heating network nodes Cold network nodes and gas nodes The set of edges E includes at least the power transmission edges. Heat transfer side Cold air transfer side Gas transmission side And the cross-energy flow coupling edge; A multi-energy flow energy balance expression is established based on the relationship between nodes and branches: ,in , This represents the branch flow vector of energy flow x in time period t. This represents the device output vector of the injection node. This represents the node load demand vector, so that the multi-energy flow network diagram can characterize the injection, transmission, and absorption relationships of various energy flows at each node. S12. For the aforementioned cross-energy flow coupling edge, construct a unified energy conversion model set for the electric-thermal-cold coupling device, and define the input energy flow for each coupling device k. Output energy flow With conversion efficiency parameters This ensures that the input energy flow is transformed into the output energy flow, satisfying the mapping relationship: ; The coupling device includes at least one of the following: cogeneration equipment, gas boiler, electric boiler, heat pump, electric chiller, and absorption chiller; and the energy conversion model set can describe the conversion process of fuel input into combined output of electric power and thermal power, electric power into thermal power, electric power into cooling capacity, thermal power into cooling capacity, and electric power being boosted into high-grade thermal power by the heat pump. S13. Establish a time-sharing output boundary model for new energy power generation units so that wind power output and photovoltaic output meet non-negative constraints in each time period and are subject to the upper limit of the predicted available output for the corresponding time period. S14. Establish a state transition model and a charge / discharge power constraint model for the state of charge (SOC) of the electric energy storage unit and the thermal energy storage unit, so that any energy storage unit s satisfies: the state of charge of the energy storage unit in each time period is determined by the state of charge of the previous time period and the charge / discharge power of the current time period. At the same time, limit the upper and lower limits of the state of charge and the upper and lower limits of the charge / discharge power to characterize the dynamics and adjustability of energy storage. S15. Based on the multi-energy flow network diagram and the energy conversion model set, generate a set of system state variables and a set of decision variables for subsequent optimization solutions. The set of system state variables includes at least the energy flow state of the multi-energy flow branches and the energy storage charge state. The set of decision variables includes at least the power purchased and sold by the external power grid, the time-sharing output of each coupled device, the time-sharing charging and discharging power of each energy storage unit, and the time-sharing actual output of wind power and photovoltaic power.

4. The integrated energy system optimization scheduling method based on carbon emission constraints and efficiency orientation as described in claim 1, characterized in that, Step S2 specifically includes the following steps: S21. Obtain the predicted curve of the park's electricity load within the target scheduling period, and construct the electricity load into a sequence of electricity loads arranged in discrete time periods; further decompose the electricity load into non-adjustable electricity load and adjustable electricity load, wherein the non-adjustable electricity load is used to characterize basic production electricity consumption or rigid electricity demand, and the adjustable electricity load is used to characterize transferable load or interruptible load; at the same time, set energy conservation constraints on the adjustable electricity load to ensure that the total electricity consumption remains unchanged within the allowable adjustment time window, thereby providing predictive input for subsequent demand response and peak shaving and valley filling; S22. Obtain the predicted heat load curve of the park within the target scheduling cycle, and construct the heat load into a heat load sequence arranged in discrete time periods; for the heat load with comfort requirements or process temperature requirements, further determine the allowable heat supply deviation range for the corresponding time period, so that the heat load can be flexibly adjusted within the deviation range, thereby giving the heat load an adjustable space to participate in cascade utilization and rolling optimization. S23. Obtain the predicted curve of the park's cooling load within the target scheduling period, and construct the cooling load into a cooling load sequence arranged by discrete time periods; for the cooling loads related to air conditioning, process cooling, or cold energy storage, further determine the upper limit, lower limit, or shiftable time window of the cooling demand for each time period, so that the cooling load can be used as a scheduling variable to participate in the coordinated regulation of multi-energy flow. S24. Obtain the predicted output curves of new energy sources within the target scheduling period, and determine the upper bound of available output for wind power and photovoltaic power. and And construct an uncertainty range for new energy output to meet its requirements. as well as ,in and These represent the prediction error boundaries for wind power and solar power in time period t, respectively. S25. Obtain the set of parameters related to carbon emission accounting, and express the time-of-use grid carbon emission factor for purchased electricity as follows: The fuel emission factor is expressed as This allows for the calculation of indirect carbon emissions from purchased electricity at any given time period t. and direct carbon emissions from fuel consumption ,in Let t be the power purchased during time period t. This represents the equivalent fuel consumption for time period t. S26. Perform unified time-granularity resampling and time-series alignment processing on the electric load prediction curve, heat load prediction curve, cold load prediction curve, new energy output prediction curve and carbon emission accounting parameter set, so that each prediction sequence satisfies a one-to-one correspondence on the same discrete time period set, thereby forming a unified prediction dataset for subsequent construction and solution of the integrated scheduling optimization model.

5. The integrated energy system optimization scheduling method based on carbon emission constraints and efficiency orientation as described in claim 1, characterized in that, Step S3 specifically includes the following steps: S31. Divide the target scheduling period into several discrete scheduling time periods. And the power purchased from the external power grid for each time period External power grid electricity sales power Actual power of new energy consumption The time-sharing output of each energy conversion device and the time-of-use charging power of each energy storage unit With discharge power These are used as optimization decision variables, thus forming a time series set of decision variables for subsequent solution. Among them, the actual power of new energy consumption Including the actual absorption capacity of photovoltaic and wind power ; S32. Constructing the economic cost item This is used to characterize the electricity purchase cost, fuel consumption cost, and energy storage charging and discharging operation cost within and outside the target scheduling cycle; among which, the economic cost item... The expression is as follows: ； in, For time-of-use electricity pricing, Electricity pricing is based on time-of-use pricing. Let f be the amount of fuel consumed or the equivalent input power during time period t. For fuel prices, This is the energy storage operating cost coefficient. The duration of the time period; S33. Construct a penalty cost item for wind and solar power curtailment. This is used to measure the difference between the predicted available power output of new energy sources and the actual power output of new energy sources, and the difference is defined as the amount of power abandoned by new energy sources. By applying penalty weights to the amount of renewable energy wasted, the optimization model prioritizes the absorption of renewable energy output while satisfying system constraints. The expression is as follows: ， ； = ； in, This represents the upper bound of the available power output of new energy sources during time period t. The penalty for abandoning wind and solar power is weighted; S34. Constructing carbon cost items The carbon cost item includes the indirect carbon emission cost caused by purchased electricity and the direct carbon emission cost caused by fuel consumption, wherein the indirect carbon emission cost... The direct carbon emission cost is determined by the time-of-use grid carbon emission factor and the amount of electricity purchased, and is determined by the fuel emission factor and fuel consumption, as expressed in the following formula: Furthermore, carbon emissions are linked to carbon cost weights to achieve price constraints and cost endogenization of carbon emissions; among which, the carbon cost item... The expression is as follows: in, As a fuel emission factor, This is the carbon cost weighting coefficient; S35. Constructing Efficiency-Oriented Items This is used to characterize the cascade utilization and comprehensive energy efficiency improvement of multiple energy flows including electricity, heat, and cooling, where the comprehensive primary energy input is defined as: The comprehensive effective energy supply is defined as: Thus, an efficiency-oriented term is constructed in the form of a primary energy input penalty or a comprehensive energy efficiency maximization form, satisfying... ; in, This is the equivalent primary energy conversion factor for fuel. , , These are the electricity / heating / cooling load requirements, , , For different energy flows, The primary energy penalty weight is used to convert primary energy consumption into a penalty for the objective function; The effective energy supply reward weight is used to convert the effective energy supply into a reward for the objective function; S36. The economic cost item The cost of curtailing wind and solar power Carbon cost item With efficiency-oriented items By integrating the data, a comprehensive scheduling optimization model can be formed with a specific objective function. The expression is as follows: This enables the objective function to simultaneously drive the comprehensive scheduling objectives of reducing dependence on purchased electricity and indirect carbon emissions, suppressing wind and solar curtailment, and improving the efficiency of electricity-heat-cooling cascade utilization.

6. The integrated energy system optimization scheduling method based on carbon emission constraints and efficiency orientation as described in claim 1, characterized in that, Step S4 specifically includes the following steps: S41. Set power balance constraints to match the power supply and demand of the power side in any discrete period within the target scheduling cycle. The power supply includes at least the output of new energy generation, the power purchased from the external power grid, the power of energy storage discharge, and the power output of the electric-thermal-cold coupling equipment. The power demand includes at least the park's power load, the power of energy storage charging, and the power input of the electric-thermal-cold coupling equipment. S42. Set heat energy balance constraints to match the heat supply and heat demand in any discrete period within the target scheduling cycle. The heat supply includes at least one of the following: the heat output of cogeneration equipment, the heat output of gas boilers, the heat output of electric boilers, the heat output of heat pumps, and the heat release power of thermal energy storage. The heat demand includes at least one of the following: park heat load, the heating demand of absorption chillers, and the heat charging power of thermal energy storage. S43. Set a cooling balance constraint to match the cooling supply and cooling demand in any discrete period within the target scheduling cycle. The cooling supply includes at least one of the following: the cooling output of electric chillers, the cooling output of absorption chillers, and the cooling power of cold storage. The cooling demand includes at least one of the following: the park's cooling load and the cooling power of cold storage. S44. Set energy storage state of charge constraints and power constraints so that the state of charge of the electric energy storage unit and the thermal energy storage unit in each discrete time period meets the upper and lower limit constraints, and limit their charging power and discharging power to meet the corresponding upper and lower limit constraints. At the same time, limit the state of charge of adjacent time periods to meet the state transition relationship. S45. Set upper and lower limits for the output of each energy conversion device and a ramping constraint so that the output power of any energy conversion device in each discrete time period does not exceed the upper limit of the rated output and is not lower than the lower limit of the minimum stable output, and limit the output change of adjacent time periods to not exceed the preset ramping rate threshold. S46. Set external power grid interaction constraints to ensure that the purchased and sold power in each discrete time period meets the power grid interaction capacity limit, and further limit the purchase and sale of power in the same time period from occurring simultaneously. S47. Set constraints on new energy consumption and wind and solar curtailment, so that the actual output of photovoltaic and wind power in each discrete time period does not exceed the upper limit of the predicted available output for the corresponding time period, and define the difference between the predicted available output and the actual consumption output as the amount of wind and solar curtailment. S48. Set carbon emission constraints so that the total carbon emissions within the target scheduling period and the carbon emission intensity at any discrete time period meet a preset upper limit threshold, expressed as follows: Total carbon emissions: ;in, Indicates the upper limit of total carbon emissions; Carbon emission intensity for any discrete time period: ;in, The upper limit of carbon emission intensity, Provide comprehensive and effective energy supply for time period t; This enables the integrated scheduling optimization model to achieve controlled carbon emissions while satisfying the supply and demand balance of multiple energy flows.

7. The integrated energy system optimization scheduling method based on carbon emission constraints and efficiency orientation as described in claim 1, characterized in that, Step S5 specifically includes the following steps: S51. Based on the unified prediction dataset formed in step S2, construct a set of decision variable vectors for the target scheduling period. The decision variable vector includes at least: the power purchased from the external power grid. External power grid electricity sales power Actual absorption capacity of photovoltaic and wind power The time-sharing output of each energy conversion device and the time-of-use charging power of each energy storage unit With discharge power The decision variable vectors for each discrete time period within the entire time domain are combined to form the overall decision variable set. ; S52. Unify the comprehensive objective function constructed in step S3 with the constraints set in step S4 to form a standardized optimization problem, which satisfies: the set of all decision variables that satisfy the constraints. In the middle, find the comprehensive objective function. Minimum optimal solution *, where the comprehensive objective function From economic cost item The cost of curtailing wind and solar power Carbon cost item With efficiency-oriented items The result of fusion; S53. The optimization problem is solved iteratively using a preset optimization algorithm. During the iteration process, the convergence condition of the objective function is used as the stopping criterion. The optimal decision variable solution is output when the change in the objective function between two adjacent iterations is less than a preset threshold or the number of iterations reaches a preset upper limit. *; S54. Based on the solution of the optimal decision variables * Generate a time-segmented scheduling sequence set, which includes at least: the actual power consumption output sequences of photovoltaic and wind power. Power purchase and sale sequence of the power grid Output sequence of each energy conversion device on the electric / hot / cold side and the charging and discharging power sequence of each energy storage unit. ; S55. Convert the scheduling sequence into a set of dispatchable instructions, and send the set of dispatch instructions to the park energy management system or equipment controller based on a unified time index, so that it can perform new energy consumption, grid interaction, energy storage charging and discharging, and coordinated output control of electric-thermal-cold coupling equipment according to each discrete time period, thereby achieving optimal scheduling execution and controlled carbon emission operation within the target scheduling cycle.

8. The integrated energy system optimization scheduling method based on carbon emission constraints and efficiency orientation as described in claim 7, characterized in that, The preset optimization algorithm mentioned in step S53 includes at least one of linear programming, mixed integer linear programming, nonlinear programming, particle swarm optimization algorithm or genetic algorithm.

9. The integrated energy system optimization scheduling method based on carbon emission constraints and efficiency orientation as described in claim 1, characterized in that, Also includes: S6. During the scheduling process, real-time carbon emissions and carbon emission intensity are collected from the park's actual operation data. When the real-time carbon emissions or carbon emission intensity violates the preset threshold, rolling re-optimization is triggered and the scheduling instructions are updated, thereby realizing dynamic scheduling of multi-energy flow loads and real-time carbon emission control.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the integrated energy system optimization scheduling method based on carbon emission constraints and efficiency orientation as described in any one of claims 1 to 9.

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