A multi-scenario risk optimization operation method and system of a source-network-load-storage carbon integrated park energy system

Abstract

The application discloses a kind of source network load carbon integration park energy system multi-scene risk optimization operation method and system, the method includes: obtaining the diversified characteristic data of park energy system;The diversified characteristic data is input into the system unit equipment operation characteristic representation model, standby plan energy transfer constraint model and carbon transfer calculation model pre-constructed, generates operating characteristic parameter, standby constraint condition, carbon emission flow parameter and carbon transfer balance constraint condition;According to the selected optimization target scene, market attribute data, operating characteristic parameter, carbon emission flow parameter, standby constraint condition and carbon transfer balance constraint condition are solved, and the optimal output plan is obtained;According to optimal output plan control unit equipment operation;Invention is through the construction covering electric heating gas carbon characteristic element and source network load unit equipment operation characteristic representation model and carbon transfer integrated calculation model, to realize energy transfer and carbon transfer real-time coupling and collaborative optimization.

Description

Technical Field

[0001] This invention relates to a method and system for multi-scenario risk optimization operation of an integrated energy system for source-grid-load-storage carbon in a park, belonging to the field of energy-carbon synergistic optimization technology for multi-energy systems with electro-thermal-gas-carbon coupling, multi-scenario optimization operation of integrated energy systems for source-grid-load-storage carbon in a park, and resisting imbalance risks. Background Technology

[0002] With the advancement of "dual carbon" goals and the acceleration of new power system construction, industrial park energy systems are developing towards integrated generation, grid, load, and storage, multi-energy complementarity, and low-carbon efficiency. Traditional optimization of industrial park energy systems often focuses on the independent operation or partial coupling of energy forms such as electricity, heat, and gas, aiming for optimal economy or energy efficiency. System models typically center on equipment operating constraints, power balance, and cost minimization. However, integrated generation, grid, load, and storage technology effectively improves the operational flexibility and renewable energy absorption capacity of the energy system by integrating distributed power sources, diverse loads, energy storage, and grid interaction resources. The development of carbon theory provides a theoretical foundation for real-time tracking and accurate measurement of carbon conversion in power energy, enabling energy carbon analysis to evolve from static accounting to dynamic transfer calculation, and providing a new technical path for energy-carbon synergistic optimization.

[0003] In existing technologies, research on the optimization of energy systems in industrial parks mainly focuses on the optimization of single energy forms or simple multi-energy complementarity. Some studies have begun to focus on the economic optimization problem under carbon emission constraints. However, existing technologies still have obvious shortcomings: (1) Most studies are still at the level of coordinated optimization of the four elements of "source, grid, load and storage", and have not integrated the core elements of "carbon transfer" and "energy transfer" into a model and real-time deep coupling optimization. There is a lack of a source-grid-load-storage carbon integrated coordinated optimization model, which leads to inaccurate tracking of system carbon transfer and energy transfer; (2) Existing technologies often adopt simple penalty mechanisms when dealing with source-load uncertainty risks. The backup plan to resist source-load imbalance risks is disconnected from carbon transfer, making it difficult to achieve overall optimization of risk, carbon conversion and energy conversion at the system level; (3) Existing technologies are mostly limited to a single economically optimal or carbon emission-lowest scenario, lacking a comprehensive optimization strategy for flexible configuration of operation modes, which is difficult to adapt to the diversified operation needs of the park; (4) The carbon transfer calculation model in existing technologies is mostly simplified to the static coefficient method. It has not built a dynamic full-process carbon transfer tracking and situation analysis system covering multiple links of source-grid-load-storage integration, which is difficult to support the refined management and control of system carbon transfer and the coordinated optimization of energy and carbon. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a multi-scenario risk optimization operation method and system for an integrated energy system of source, grid, load, storage and carbon in a park. (1) Construct an integrated calculation model of carbon transfer and an operation characteristic characterization model covering the characteristic elements of electricity, heat, gas and carbon and the source, grid, load and storage unit equipment, in order to achieve real-time coupling and collaborative optimization of energy transfer and carbon transfer; (2) Design backup plan energy transfer constraints to resist the risk of source-load power imbalance and combine them with carbon transfer constraints, in order to improve the robustness and low carbon of the system under the risk of source-load power imbalance; (3) Establish an optimized operation mode and its comprehensive index optimization framework for multiple scenarios such as emission reduction, low consumption and cost reduction, in order to support the park energy system to flexibly configure the operation mode requirements according to actual needs.

[0005] To achieve the above objectives, the present invention is implemented using the following technical solution:

[0006] In a first aspect, the present invention provides a multi-scenario risk optimization operation method for an integrated energy system for source-grid-load-storage carbon storage in a park, comprising:

[0007] Acquire diverse characteristic data of the park's energy system, including inherent physical attribute parameters of equipment, initial state of operation optimization and operation optimization control threshold parameters, reference standard configuration parameters, predicted source load time series data, and energy market attribute data.

[0008] The inherent physical attribute parameters of the equipment, the initial state of operation optimization, the operation optimization control threshold parameters, and the reference standard configuration parameters are input into the pre-constructed system unit equipment operation characteristic characterization model to obtain the operation characteristic parameters of each unit equipment in the system.

[0009] The operating characteristic parameters and the predicted source load time series data are input into the pre-built backup plan energy transfer constraint model to generate backup constraint conditions to resist the risk of source load imbalance.

[0010] The operating characteristic parameters and the predicted source load time series data are input into the pre-built carbon transfer calculation model to generate carbon emission flow parameters of each unit device in the system and carbon transfer balance constraints of the system.

[0011] The energy market attribute data is input into a pre-constructed optimized operation target scenario model. The operating characteristic parameters and carbon emission flow parameters are used as equipment self-constraints, the backup constraints and carbon transfer balance constraints are used as system-level constraints, and the optimization objective function is used as the objective to construct and solve the optimization problem, thereby obtaining the optimal output plan of each unit equipment in the system for multiple future time periods. The optimization objective function is determined according to the optimized operation target scenario selected by the user.

[0012] The instructions for each time period in the optimal output plan are output to the actuator, and the control system controls each unit device to operate according to the instructions.

[0013] Furthermore, the system unit equipment includes combined heat and power equipment, waste heat recovery system, system-grid interaction equipment, wind and solar new energy power generation equipment, and energy storage equipment;

[0014] The inherent physical attribute parameters of the equipment include the power generation efficiency, minimum technical output, rated capacity, time conversion coefficient, ramp rate limit, design operating condition baseline value, floating coefficient, and optimized heat-to-power ratio of the combined heat and power equipment; the comprehensive heat conversion efficiency, minimum technical output, and rated capacity of the waste heat recovery system; the self-discharge rate, charging efficiency, discharging efficiency, maximum charging and discharging power, minimum state of charge coefficient, and maximum state of charge coefficient of the energy storage equipment; and the upper limit of the line transmission capacity when the system and the grid interact with each other when purchasing electricity and when returning electricity, as well as the reduction coefficient and increase coefficient.

[0015] The initial state of operational optimization includes the initial energy storage capacity of the energy storage device;

[0016] The operation optimization control threshold parameters include the system optimization operation time interval step size, the system optimization operation time cycle set, the wind and solar new energy power generation and absorption penalty factor, the optimization operation mode index ratio coefficient, and the extreme value calculation relaxation coefficient under the optimization operation mode.

[0017] The predicted source load time series data includes electricity load demand, heat load demand, and predicted power generation of wind and solar new energy power generation equipment.

[0018] The energy market attribute data includes the time-of-use electricity price and the price of natural gas consumed by the system;

[0019] The reference standard configuration parameters include the wind and solar new energy absorption rate and the minimum absorption rate limit of wind and solar new energy, the maximum power shedding load rate, the standard coal conversion factor of the system's purchased electricity, the standard coal conversion factor of the system's natural gas consumption, and the standard coal conversion factor of the wind and solar new energy power generation equipment.

[0020] The system unit equipment operation characteristic characterization model includes a white-box model of the operation process of each unit equipment in the system and a safe operation constraint model, wherein,

[0021] The expression for the white-box model of the combined heat and power equipment operation process is as follows:

[0022] ;

[0023] In the formula: The electrical power output of the combined heat and power equipment in the system during time period t; The power generation efficiency of the combined heat and power equipment in the system; The natural gas input power of the combined heat and power equipment in the system during time period t; The combined heat and power equipment in the system outputs thermal power during time period t. The optimized heat-to-power ratio set for the actual operation and regulation of the combined heat and power equipment in the system; The volumetric flow rate of natural gas during the operation of the combined heat and power (CHP) equipment in the system at time period t; This refers to the natural gas flow-to-power conversion coefficient.

[0024] The expression for the safety operation constraints of the combined heat and power equipment is as follows:

[0025] ;

[0026] In the formula: Let t be the start / stop status variable of the combined heat and power equipment in the system during time period t, where 0 represents shutdown and 1 represents operation; Minimize the technical output of the combined heat and power equipment in the system; The electrical power output of the combined heat and power equipment in the system during time period t; The rated capacity of the combined heat and power equipment in the system; To optimize the system's runtime time interval step size; To optimize the time conversion coefficient between running step size and hill climb; The downhill ramp rate limit for combined heat and power equipment in the system; The electrical power output of the combined heat and power equipment in the system during the t+1 time period; The limit for the upward ramp rate of the combined heat and power equipment in the system; , These are the downward reserve capacities of the combined heat and power equipment in the system for mitigating risks during time period t and time period t+1, respectively. , These are the upward backup capacities of the combined heat and power equipment in the system for mitigating risks during time period t and time period t+1, respectively. The optimized heat-to-power ratio set for the actual operation and regulation of the combined heat and power equipment in the system; This is the design operating condition baseline value for the heat-to-power ratio of the combined heat and power equipment in the system; This is the coefficient for the fluctuation of the heat-to-power ratio of the combined heat and power equipment in the system around the design operating condition benchmark value.

[0027] The expression for the white-box model of the waste heat recovery system operation process is as follows:

[0028] ;

[0029] In the formula: The output thermal power of the waste heat recovery system in the system during time period t; Let be the start / stop status variable of the waste heat recovery system in the system during time period t, where 0 indicates shutdown and 1 indicates operation; The combined heat and power equipment in the system outputs thermal power during time period t. The overall heat conversion efficiency of the waste heat recovery system;

[0030] The expression for the safety operation constraints of the waste heat recovery system is as follows:

[0031] ;

[0032] In the formula: Let be the start / stop status variable of the waste heat recovery system in the system during time period t, where 0 indicates shutdown and 1 indicates operation; Let t be the start / stop status variable of the combined heat and power equipment in the system during time period t, where 0 represents shutdown and 1 represents operation; Minimize the technical output of the waste heat recovery system in the system; The output thermal power of the waste heat recovery system in the system during time period t; This refers to the rated capacity of the waste heat recovery system in the system.

[0033] The expression for the white-box model of the system's interaction with the power grid is as follows:

[0034] ;

[0035] In the formula: The system purchases electrical power in time period t through the external line connection. The system transmits power back during time period t via external line connections;

[0036] The expression for the safety operation constraints of the system interacting with the power grid is as follows:

[0037] ;

[0038] In the formula: The system purchases electrical power in time period t through the external line connection. The system transmits power back during time period t via external line connections; This is the upper limit of the line transmission capacity when the system purchases electricity from the grid. This is the upper limit of the line transmission capacity when the system sends power back to the grid;

[0039] The expression for the white-box model of the operation process of the wind and solar new energy power generation equipment is as follows:

[0040] ;

[0041] In the formula: For the absorption rate of wind and solar new energy; To predict the power generation of wind and solar renewable energy generation equipment in time period t; The actual power generation of the wind and solar renewable energy power generation equipment in the system during time period t; Optimize the set of runtime cycles for the system;

[0042] The expression for the safety operation constraints of the wind and solar new energy power generation equipment is as follows:

[0043] ;

[0044] In the formula: To predict the power generation of wind and solar renewable energy generation equipment in time period t; The actual power generation of the wind and solar renewable energy power generation equipment in the system during time period t; For the absorption rate of wind and solar new energy; This is the minimum absorption rate limit for wind and solar new energy sources;

[0045] The expression for the white-box model of the energy storage device's operation process is as follows:

[0046] ;

[0047] In the formula: Let be the amount of energy stored by the energy storage device in the system during time period t; Let be the amount of electricity stored by the energy storage device in the system during the (t-1)th time period; The self-discharge rate of the energy storage; This represents the charging power of the energy storage device in the system during the (t-1)th time period; The charging efficiency of the energy storage devices in the system; Let be the discharge power of the energy storage device in the system during the (t-1)th time period; The discharge efficiency of the energy storage device in the system; To optimize the system's runtime time interval step size; This represents the amount of electricity stored by the energy storage devices in the system during the first time period. Initialize the energy storage capacity of the energy storage devices in the system; The energy storage device in the system is in the first The amount of electricity stored during the period, of which This indicates the last time period in the optimized runtime cycle;

[0048] The expression for the safety operation constraints of the energy storage device is as follows:

[0049]

[0050] In the formula: Let be the charging power of the energy storage device in the system during time period t; Let be the discharge power of the energy storage device in the system during time period t; This refers to the maximum charging and discharging power of the energy storage devices in the system. To optimize the system's runtime time interval step size; Let be the amount of energy stored by the energy storage device in the system during time period t; This is the minimum state of charge coefficient of the energy storage device in the system; This represents the maximum state of charge coefficient of the energy storage devices in the system. This refers to the rated capacity of the energy storage devices in the system.

[0051] Furthermore, the expression for setting the energy transfer constraint condition of the backup plan to resist the risk of imbalance is as follows:

[0052] ;

[0053] In the formula: The actual power generation of the wind and solar renewable energy power generation equipment in the system during time period t; The system purchases electrical power in time period t through the external line connection. The electrical power output of the combined heat and power equipment in the system during time period t; Let be the charging power of the energy storage device in the system during time period t; Let be the discharge power of the energy storage device in the system during time period t; Let be the electrical load demand of the system in time period t; The system transmits power back during time period t via external line connections; The output thermal power of the waste heat recovery system in the system during time period t; Let t be the system's heat load demand in time period t; This is the maximum load shedding rate during system risk protection. The power reduction coefficient for wind and solar new energy power generation equipment in the system during the formulation of the backup plan to resist imbalance risks; This refers to the power generation coefficient in the system's backup plan for wind and solar new energy power generation equipment to mitigate imbalance risks. This refers to the downlink backup capacity of the combined heat and power equipment in the system to mitigate risks during time period t. This refers to the upward backup capacity of the combined heat and power equipment in the system to mitigate risks during time period t.

[0054] Furthermore, the expression for the carbon transfer calculation model of the combined heat and power equipment is as follows:

[0055] ;

[0056] In the formula: The carbon emission rate of natural gas input to the combined heat and power (CHP) equipment in the system during time period t; The carbon potential of the natural gas input is given to the combined heat and power equipment in the system during time period t. The natural gas input power of the combined heat and power equipment in the system during time period t; The carbon emission flow rate of the combined heat and power equipment in the system during time period t; The carbon potential is the output power of the combined heat and power equipment in the system during time period t. The electrical power output of the combined heat and power equipment in the system during time period t; The carbon emission flow rate of the combined heat and power equipment in the system during time period t; The carbon potential of the combined heat and power equipment in the system during time period t is the output heat power of the equipment. The combined heat and power equipment in the system outputs thermal power during time period t. The carbon potential structural coefficient is the output carbon potential of the combined heat and power equipment in the system. The power generation efficiency of the combined heat and power equipment in the system; The heating efficiency of the combined heat and power equipment in the system; The optimized heat-to-power ratio set for the actual operation and regulation of the combined heat and power equipment in the system;

[0057] The expression for the carbon transfer calculation model of the waste heat recovery system is as follows:

[0058] ;

[0059] In the formula: The thermal power input carbon emission flow rate of the waste heat recovery system in the system during time period t; The carbon potential is input to the thermal power of the waste heat recovery system in the system during time period t. The combined heat and power equipment in the system outputs thermal power during time period t. Let the carbon emission flow rate of the waste heat recovery system in the system be the thermal power output during time period t. The carbon potential of the waste heat recovery system in the system is the thermal power output of the system during time period t. The output thermal power of the waste heat recovery system in the system during time period t; The overall heat conversion efficiency of the waste heat recovery system; The carbon potential of the combined heat and power equipment in the system during time period t is the output heat power of the equipment.

[0060] The expression for the carbon transfer calculation model of the system interaction with the power grid is as follows:

[0061] ;

[0062] In the formula: The system purchases the carbon emission flow rate of electricity in time period t via external line connections; The system purchases the equivalent carbon potential of electricity in time period t through the external line connection. The system purchases electrical power in time period t through the external line connection. The system transmits the carbon emission flow rate back to the system via external line connections during time period t. The system transmits the equivalent carbon potential back through the external line connection at time t. The system transmits power back during time period t via external line connections; Let the system node carbon potential be at time t.

[0063] The expression for the carbon transfer calculation model of the wind and solar new energy power generation equipment is as follows:

[0064] ;

[0065] In the formula: Let be the carbon emission flow rate of the wind and solar new energy power generation equipment in the system during time period t; The carbon potential of the wind and solar power generation equipment in the system during time period t; The actual power generation of the wind and solar renewable energy power generation equipment in the system during time period t;

[0066] The expression for the carbon transfer calculation model of the energy storage device is as follows:

[0067] ;

[0068] In the formula: The carbon emissions generated by the energy storage device in the system during time period t; Let be the charging power of the energy storage device in the system during time period t; The carbon potential is input to charge the energy storage device in the system during time period t. To optimize the system's runtime time interval step size; This represents the carbon emissions generated by the energy storage device in the system during time period t. Let be the discharge power of the energy storage device in the system during time period t; The carbon potential output by the energy storage device in the system during time period t; Let be the internal carbon potential of the energy storage device in the system during time period t; Let be the amount of energy stored by the energy storage device in the system during time period t; The internal carbon potential of the energy storage device in the system during the (t-1)th time period; Let be the amount of electricity stored by the energy storage device in the system during the (t-1)th time period; The discharge efficiency of the energy storage device in the system; The internal carbon potential of the energy storage device in the system during the first time period; The system purchases the equivalent carbon potential of electricity in the first time period through the external line connection; Let be the carbon potential at the system node during time period t.

[0069] Furthermore, the system carbon transfer balance constraint is determined based on the carbon emission flow parameters of each unit device in the system, and the specific expression for setting the system carbon transfer balance constraint is as follows:

[0070] ;

[0071] In the formula: Let be the carbon emission flow rate of the wind and solar new energy power generation equipment in the system during time period t; The system purchases the carbon emission flow rate of electricity in time period t via external line connections; The carbon emission flow rate of the combined heat and power equipment in the system during time period t; Let be the discharge power of the energy storage device in the system during time period t; The carbon potential output by the energy storage device in the system during time period t; Let the system node carbon potential be at time t. Let be the charging power of the energy storage device in the system during time period t; Let be the electrical load demand of the system in time period t; The system transmits power back through the external line connection during time period t.

[0072] Furthermore, the optimized operation target scenarios include optimized operation modes for carbon reduction scenarios, optimized operation modes for low-consumption scenarios, optimized operation modes for cost reduction scenarios, and optimized operation modes for multi-scenario comprehensive indicators.

[0073] The mathematical expression for the optimized operation mode of the emission reduction scenario is as follows:

[0074]

[0075] In the formula: The objective function for the system's total carbon emissions; This represents the total carbon emissions from non-new energy power generation equipment in the system. This represents the equivalent carbon emission reduction of wind and solar power generation equipment in the system. Let be the carbon emissions of non-new energy power generation equipment in the system during period t. This represents the actual power generation of the wind and solar renewable energy generation equipment in the system during time period t. The carbon potential of the wind and solar power generation equipment in the system during time period t; Optimize the set of runtime cycles for the system; To optimize the system's runtime time interval step size;

[0076] Under the optimized operation mode of the emission reduction scenario, the mathematical model expression for the carbon emissions of non-new energy power generation equipment in each time period is as follows:

[0077] ;

[0078] In the formula: Let be the carbon emissions of non-new energy power generation equipment in the system during period t. The equivalent carbon emissions generated by the system in time period t when purchasing electricity through external connection lines; This represents the carbon emissions generated by the combined heat and power (CHP) equipment in the system during time period t. The system purchases the equivalent carbon potential of electricity in time period t through the external line connection. The system purchases electrical power in time period t through the external line connection. To optimize the system's runtime time interval step size; The carbon potential of the natural gas input is given to the combined heat and power equipment in the system during time period t. The natural gas input power of the combined heat and power equipment in the system during time period t;

[0079] The mathematical expression for the optimized operation mode in the low-power scenario is as follows:

[0080] ;

[0081] In the formula: Let be the overall energy consumption objective function of the system; Let be the total energy consumption of the system in time period t; Optimize the set of runtime cycles for the system; The system purchases electrical power in time period t through the external line connection. The standard coal equivalent coefficient for electricity purchased by the system; The volumetric flow rate of natural gas during the operation of the combined heat and power (CHP) equipment in the system at time period t; The standard coal equivalent coefficient for natural gas consumption in the system; This represents the actual power generation of the wind and solar renewable energy generation equipment in the system during time period t. The standard coal equivalent coefficient for power generation from wind and solar new energy power generation equipment; To optimize the system's runtime time interval step size;

[0082] The mathematical expression for the optimized operation mode in the cost reduction scenario is as follows:

[0083] ;

[0084] In the formula: The objective function is the total operating cost of the system. Let be the system's operating cost in time period t; The operating cost of the combined heat and power equipment in the system during time period t; The operating cost of the system during time period t is due to the purchase of electricity and the penalty for curtailment of wind and solar power generation equipment. The system purchases electrical power in time period t through the external line connection. The electricity price is calculated based on the time period purchased during time period t. To predict the power generation of wind and solar renewable energy generation equipment in time period t; The actual power generation of the wind and solar renewable energy power generation equipment in the system during time period t; Penalty factor for the absorption of wind and solar new energy power generation; The volumetric flow rate of natural gas during the operation of the combined heat and power (CHP) equipment in the system at time period t; The price of natural gas consumed by the system; Optimize the set of runtime cycles for the system; To optimize the system's runtime time interval step size;

[0085] The mathematical expression for the multi-scenario comprehensive index optimization operation mode is as follows:

[0086]

[0087] In the formula: Let be the overall energy consumption objective function of the system; The objective function for the system's total carbon emissions; The objective function is the total operating cost of the system. The objective function is the comprehensive index of the system across multiple scenarios; , , These are the percentage coefficients of indicators for optimized operation modes in emission reduction scenarios, optimized operation modes in low-consumption scenarios, and optimized operation modes in cost reduction scenarios, respectively. , , These are the normalized and standardized values ​​of indicators for optimized operation modes in emission reduction scenarios, optimized operation modes in low-consumption scenarios, and optimized operation modes in cost reduction scenarios, respectively. , , These are the maximum values ​​of the objective functions under the optimized operation modes for emission reduction, low energy consumption, and cost reduction scenarios, respectively. , , These are the minimum objective functions for the optimized operation modes in emission reduction, low energy consumption, and cost reduction scenarios, respectively. , , These are the relaxation coefficients for extreme value calculations under the optimized operation modes for emission reduction, low energy consumption, and cost reduction scenarios, respectively. , These represent the total carbon emission objective function values ​​of the system under the low-consumption scenario optimized operation mode and the cost-reduction scenario optimized operation mode, respectively. , These are the total comprehensive energy consumption objective function values ​​of the system under the optimized operation mode for emission reduction scenarios and the optimized operation mode for cost reduction scenarios, respectively. , These represent the objective function values ​​for the total operating cost of the system in the emission reduction scenario optimization operation mode and the low-consumption scenario optimization operation mode, respectively.

[0088] Furthermore, solving the optimization problem also yields the carbon emission flow distribution of each unit device in the system, including:

[0089] The carbon emission flow distribution of each unit device in the system is input into the pre-constructed system carbon transfer status analysis model to obtain the carbon transfer status assessment result. Based on the assessment result, control instructions for adjusting system operating parameters are generated. The control instructions include capacity configuration optimization parameters or energy storage device maintenance and scheduling information.

[0090] The expression for the system's carbon transfer situation analysis model is as follows:

[0091]

[0092] In the formula: This represents the total carbon input to the system. Optimize the set of runtime cycles for the system; The system purchases the carbon emission flow rate of electricity in time period t via external line connections; The carbon emission rate of natural gas input to the combined heat and power (CHP) equipment in the system during time period t; To optimize the system's runtime time interval step size; This represents the total carbon output of the system. Let the carbon emission flow rate of the waste heat recovery system in the system be the thermal power output during time period t. Let the system node carbon potential be at time t. Let be the electrical load demand of the system in time period t; The system transmits the equivalent carbon potential back through the external line connection at time t. The system transmits power back during time period t via external line connections; This represents the total carbon loss from energy storage devices. The internal carbon potential of the energy storage device in the system during the first time period; For the energy storage device in the system in the first The internal carbon potential of the time period, among which This indicates the last time period in the optimized runtime cycle; Initialize the energy storage capacity of the energy storage devices in the system; This represents the total carbon balance deviation of the system;

[0093] Based on the evaluation results, control instructions are generated for adjusting system operating parameters, including:

[0094] Based on the carbon transfer situation assessment results, the total carbon input of the system, the total carbon loss of energy storage devices, and the proportion of the total carbon output of the system are statistically obtained; based on the statistical results, optimization parameters for unit device capacity configuration are generated, and maintenance and scheduling information of energy storage devices is output.

[0095] Based on the overall carbon balance deviation of the system in the carbon transfer situation assessment results, the degree of carbon-coordinated operation of the system is determined. If the carbon balance deviation is 0, the current operating parameter configuration is maintained. If the carbon balance deviation is not 0, the parameter verification process is initiated and a parameter verification request is generated.

[0096] Secondly, this invention provides a multi-scenario risk optimization operation system for an integrated energy system for industrial parks, comprising:

[0097] Acquisition Module: Used to acquire diversified characteristic data of the park's energy system. The diversified characteristic data includes inherent physical attribute parameters of equipment, initial state of operation optimization and operation optimization control threshold parameters, reference standard configuration parameters, predicted source load time series data, and energy market attribute data.

[0098] The first generation module is used to input the inherent physical attribute parameters of the device, the initial state of operation optimization, the operation optimization control threshold parameters, and the reference standard configuration parameters into the pre-built system unit device operation characteristic characterization model to obtain the operation characteristic parameters of each unit device in the system.

[0099] The second generation module is used to input the operating characteristic parameters and the predicted source load time series data into the pre-built backup plan energy transfer constraint model to generate backup constraint conditions to resist the risk of source load imbalance.

[0100] The third generation module is used to input the operating characteristic parameters and the predicted source load time series data into the pre-built carbon transfer calculation model to generate carbon emission flow parameters of each unit device in the system and carbon transfer balance constraints of the system.

[0101] The optimization module is used to input the energy market attribute data into a pre-built optimized operation target scenario model. It uses the operating characteristic parameters and carbon emission flow parameters as equipment self-constraints, the backup constraints and carbon transfer balance constraints as system-level constraints, and the optimization objective function as the objective to construct and solve the optimization problem, thereby obtaining the optimal output plan and carbon emission flow distribution of each unit equipment in the system in multiple future time periods. The optimization objective function is determined according to the optimized operation target scenario selected by the user.

[0102] Execution module: Used to output instructions for each time period in the optimal output plan to the execution mechanism, and control each unit device of the system to operate according to the instructions;

[0103] Feedback module: Used to input the carbon emission flow distribution of each unit device in the system into the pre-built system carbon transfer status analysis model to obtain the carbon transfer status assessment results, and to optimize the system operating capacity configuration, or to overhaul energy storage equipment or adjust relevant parameters based on the assessment results.

[0104] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described above.

[0105] Fourthly, the present invention provides a computer device, comprising:

[0106] Memory, used to store computer programs / instructions;

[0107] A processor for executing the computer program / instructions to implement the steps of any of the methods described above.

[0108] Fifthly, the present invention provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of any of the methods described above.

[0109] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0110] This invention provides a multi-scenario risk optimization operation method and system for an integrated energy system of source, grid, load, storage and carbon in a park. By constructing an integrated operation characteristic characterization model of source, grid, load and storage and a carbon transfer coupling model, it realizes the deep collaborative optimization operation of integrated source, grid, load and storage carbon, and achieves synchronous collaborative optimization of energy transfer and carbon transfer and tracking, thereby improving the overall energy and carbon operation efficiency of the system.

[0111] This invention provides a multi-scenario risk optimization operation method and system for an integrated energy system for source, grid, load, storage, and carbon in a park. By setting backup plan conditions that are coordinated with carbon transfer to resist imbalance risks, the system's ability to resist source-load power imbalance risks is improved. While ensuring the safe and stable operation of the system, the additional carbon emissions brought about by the backup capacity are reduced.

[0112] This invention provides a multi-scenario risk optimization operation method and system for an integrated energy system for source, grid, load, storage, and carbon in a park. In response to the needs of multi-scenario optimization operation modes in actual engineering, it proposes a flexible configuration strategy for multi-scenario comprehensive index optimization operation modes, supports flexible optimization needs and comprehensive decision-making in multiple scenarios, and improves the overall economic, environmental protection, and energy-saving performance benefits of the system.

[0113] This invention provides a multi-scenario risk optimization operation method and system for an integrated energy system of source, grid, load, storage, and carbon in a park. By constructing a carbon transfer balance constraint and situation assessment model, it realizes refined tracking and situation analysis of the entire carbon transfer process, supports real-time monitoring, assessment, and coordinated operation and control of carbon transfer in the system, and provides technical support and theoretical reference for energy carbon management of energy systems. Attached Figure Description

[0114] Figure 1 This is a flowchart illustrating the implementation of the present invention.

[0115] Figure 2 This is a schematic diagram of the system topology in an embodiment of the present invention.

[0116] Figure 3 This is a graph showing the natural gas price and time-of-use electricity price data consumed by the system in this embodiment of the invention.

[0117] Figure 4 This is a graph showing the predicted power generation data of the wind and solar new energy power generation equipment in an embodiment of the present invention.

[0118] Figure 5 This is a diagram showing the electrical load demand data in an embodiment of the present invention.

[0119] Figure 6 This is a graph showing the heat load demand data in an embodiment of the present invention.

[0120] Figure 7 This is a diagram showing the energy balance data of the system's electrical energy bus in an embodiment of the present invention.

[0121] Figure 8 This is a graph showing the carbon potential and energy storage capacity of the energy storage device in an embodiment of the present invention.

[0122] Figure 9 This is a graph showing the carbon potential data of system nodes in an embodiment of the present invention; Detailed Implementation

[0123] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments and specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations thereof. In the absence of conflict, the embodiments and technical features in the embodiments can be combined with each other.

[0124] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0125] Example 1:

[0126] Figure 1 This is a flowchart of a multi-scenario risk optimization operation method for an integrated energy system in a power generation, grid, load, storage, and carbon storage industrial park, according to Embodiment 1 of the present invention. The multi-scenario risk optimization operation method for an integrated energy system in a power generation, grid, load, storage, and carbon storage industrial park provided in this embodiment can be applied to a terminal and can be executed by a multi-scenario risk optimization operation system for an integrated energy system in a power generation, grid, load, storage, and carbon storage industrial park. This system can be implemented by software and / or hardware and can be integrated into the terminal, such as any smartphone, tablet, or computer device with communication capabilities. See also... Figure 1 The method implemented in this way specifically includes the following steps:

[0127] Acquire diverse characteristic data of the park's energy system, including inherent physical attribute parameters of equipment, initial state of operation optimization and operation optimization control threshold parameters, reference standard configuration parameters, predicted source load time series data, and energy market attribute data;

[0128] The inherent physical attribute parameters of the equipment, the initial state of operation optimization, the operation optimization control threshold parameters, and the reference standard configuration parameters are input into the pre-constructed system unit equipment operation characteristic characterization model to obtain the operation characteristic parameters of each unit equipment in the system.

[0129] The operating characteristic parameters and the predicted source load time series data are input into the pre-built backup plan energy transfer constraint model to generate backup constraint conditions to resist the risk of source load imbalance.

[0130] The operating characteristic parameters and the predicted source load time series data are input into the pre-built carbon transfer calculation model to generate carbon emission flow parameters of each unit device in the system and carbon transfer balance constraints of the system.

[0131] The energy market attribute data is input into a pre-constructed optimized operation target scenario model. The operating characteristic parameters and carbon emission flow parameters are used as equipment self-constraints, the backup constraints and carbon transfer balance constraints are used as system-level constraints, and the optimization objective function is used as the objective to construct and solve the optimization problem, thereby obtaining the optimal output plan and carbon emission flow distribution of each unit equipment in the system in multiple future time periods. The optimization objective function is determined according to the optimized operation target scenario selected by the user.

[0132] The instructions for each time period in the optimal output plan are output to the actuator, and the control system unit equipment operates according to the instructions. When the system operator or system management needs to readjust the scenario operation mode or reset the operation data, the above steps are repeated to update the output instructions and reissue the updated instructions, and the control system unit equipment operates according to the updated instructions.

[0133] The carbon emission flow distribution of each unit device in the system is obtained and input into a pre-built system carbon transfer situation analysis model to obtain the carbon transfer situation assessment results. The system operator or system management party uses the carbon transfer situation assessment results to statistically analyze the total carbon input, total carbon loss of energy storage equipment, and the proportion of total carbon output of the system, and uses the feedback to optimize the capacity configuration of unit devices and energy storage maintenance. The system operator or system management party uses the total carbon balance deviation of the system in the carbon transfer situation assessment results to judge the degree of energy-carbon coordinated operation of the system. A carbon balance deviation of 0 indicates that the system is operating in a deep energy-carbon coordinated optimization manner and has reached the optimal level. A carbon balance deviation of not 0 indicates that there are new unknown carbon emission sources or unknown carbon absorption sources in the system, or that the inherent physical attribute parameters of a certain unit device of the system deviate from the design value, etc., which require the system operator or system management party to investigate in a timely manner.

[0134] The diversified feature data specifically includes the following parameters:

[0135] (1) Inherent physical property parameters of the equipment

[0136] The inherent physical property parameters of equipment refer to the characteristic parameters of each unit device in the system, which are determined at the factory and remain essentially unchanged during operation. Specifically, these include:

[0137] For combined heat and power (CHP) equipment, the parameters include power generation efficiency, minimum technical output, rated capacity, time conversion factor, ramp rate limit, design operating condition baseline value, fluctuation factor, and optimized heat-to-power ratio. Among these, power generation efficiency determines the conversion relationship between natural gas input and electrical power output; minimum technical output and rated capacity define the normal operating range of the equipment; ramp rate limit constrains the rate of change in equipment output; design operating condition baseline value and fluctuation factor describe the operating characteristics of the equipment under non-design conditions; and the optimized heat-to-power ratio reflects the coupling relationship between electrical output and thermal output.

[0138] For waste heat recovery systems, the parameters include overall heat conversion efficiency, minimum technical output, and rated capacity. Overall heat conversion efficiency determines the proportion of heat power output from combined heat and power (CHP) equipment that can be effectively recovered, while minimum technical output and rated capacity define the normal operating range of the waste heat recovery system.

[0139] For energy storage devices, the metrics include self-discharge rate, charging efficiency, discharging efficiency, maximum charge / discharge power, minimum state of charge (SOC), and maximum SOC. Self-discharge rate describes the natural decay of energy in an energy storage device when idle. Charging and discharging efficiencies calculate energy losses during charging and discharging. Maximum charge / discharge power constrains the power exchange capacity of the energy storage device. The minimum and maximum SOC define the permissible operating range of the energy storage device, preventing overcharging and over-discharging.

[0140] For equipment that interacts with the grid, there are two parameters that constrain the maximum power of the system when purchasing electricity and when returning electricity. These parameters include the upper limit of line transmission capacity when purchasing electricity and the upper limit of line transmission capacity when returning electricity.

[0141] In addition, the inherent physical property parameters of the equipment also include the reduction factor and the increase factor, which are used to describe the available adjustment capability of the equipment when participating in system standby adjustment.

[0142] (2) Initial state of operation optimization and control threshold parameters of operation optimization

[0143] The initial state of operation optimization and the control threshold parameters of operation optimization refer to the startup data for initializing system operation optimization and the control threshold data set during operation.

[0144] The initial state parameters for runtime optimization specifically include:

[0145] The initial energy storage capacity of the energy storage device is the energy storage capacity of the energy storage device at the initial moment during the system's optimized operating cycle. This parameter serves as the initial condition for the dynamic model of the energy storage device and directly affects the optimization decisions in subsequent periods.

[0146] Among them, the operation optimization control threshold parameters refer to the control threshold parameters used to configure the optimization solution process, specifically including:

[0147] The system optimizes the time interval step size to discretize continuous-time problems into time-segment sequences.

[0148] The system optimizes the set of running time periods, such as 288 time periods in the next 24 hours with 5-minute intervals, 96 time periods in the next 24 hours with 15-minute intervals, and 24 time periods in the next 24 hours with 1-hour intervals, which defines the time range for optimization.

[0149] The wind and solar new energy power generation consumption penalty factor is used to impose penalties on wind and solar curtailment behavior in the objective function, guiding the system to prioritize the consumption of clean energy.

[0150] The optimization operation mode indicator proportion coefficient is used for the weighted summation of each sub-objective in the multi-scenario comprehensive indicator optimization operation mode and for the selection of optimization operation mode configuration.

[0151] The relaxation coefficient for extreme value calculation in the optimized operation mode is used to optimize the relaxation of constraints during the solution process, and avoid the inability to solve or the termination of the solution due to illegal operations, such as division by zero.

[0152] (3) Predicting source load time series data

[0153] Predicted source load time series data refers to data for future periods obtained through prediction algorithms or external interfaces. Specifically, it includes:

[0154] Electricity load demand, i.e., the predicted value of the park's electricity demand in future time periods, can be generated by a load forecasting model based on historical data or obtained from external interfaces for future time periods.

[0155] Heat load demand, which is the predicted value of the park's heat demand in future time periods, can be generated by a load forecasting model based on historical data or obtained from external interfaces for future time periods.

[0156] The predicted power generation of wind and solar new energy power generation equipment, that is, the predicted output value of wind and solar new energy power generation equipment in the park in each future time period, can be generated by the wind and solar new energy power generation prediction model based on historical data or obtained from the data of future time periods through external interfaces.

[0157] Those skilled in the art will understand that the prediction of electrical load, thermal load, and wind and solar power generation is the foundation for the optimized operation of the park's energy system, and the accuracy of the prediction directly affects the effectiveness of the optimization results.

[0158] (4) System energy consumption market attribute data

[0159] System energy consumption market attribute data refers to price signals related to system operating costs obtained from external markets. Specifically, this includes:

[0160] The time-of-use electricity price is published by the electricity trading market or the power grid company, and usually changes in cycles of 5 minutes, 15 minutes, 1 hour or the whole day, and is used to calculate the system's electricity purchase cost.

[0161] The price of natural gas consumed by the system is determined by the gas supplier and can be updated monthly or daily, and is used to calculate the system's fuel costs.

[0162] Those skilled in the art will understand that, depending on system configuration requirements, the system's energy market attribute data may also include carbon trading prices, electricity sales prices, demand response compensation prices, etc.

[0163] (5) Refer to standard configuration parameters

[0164] Reference standard configuration parameters refer to fixed parameters determined by government policies, industry standards, or company regulations. Specifically, these include:

[0165] The wind and solar renewable energy absorption rate and the minimum absorption rate limit for wind and solar renewable energy are used to constrain the minimum proportion of renewable energy that the system must absorb, reflecting the policy support requirements for clean energy.

[0166] Maximum load shedding rate, used to constrain the maximum proportion of load that can be shelved in an emergency, is an important indicator for system reliability design.

[0167] The standard coal equivalent coefficients for purchased electricity, natural gas, and wind and solar power generation are used to uniformly convert the consumption of various energy sources into standard coal consumption, facilitating the calculation and evaluation of the system's overall energy consumption.

[0168] Output energy system data includes: output electrical power, natural gas input power, output thermal power, and natural gas volumetric flow rate of cogeneration equipment; output thermal power of waste heat recovery system; purchased electrical power and returned electrical power in the system-grid interaction operation characteristic model; actual power generation and predicted power generation of wind and solar power generation equipment; charging power, discharging power, and energy storage capacity; natural gas input carbon emission flow rate and natural gas input carbon potential; thermal power output carbon potential and thermal power output carbon emission flow rate of cogeneration equipment; and thermal power input carbon emission flow rate of waste heat recovery system. The data includes: equivalent carbon potential of fed-back power, carbon emission flow rate of fed-back power, carbon emission flow rate of purchased power, and equivalent carbon potential of purchased power; carbon emission flow rate of wind and solar power generation equipment at power generation output and carbon potential of wind and solar power generation equipment at power generation output; carbon potential of charging input, carbon emissions generated during charging, carbon emissions generated during discharging, carbon potential of discharging output, and internal carbon potential; the system's total carbon emission objective function value, the system's total comprehensive energy consumption objective function value, and the system's total operating cost objective function value; the system's total carbon input, the system's total carbon output, the total carbon loss of energy storage equipment, and the system's total carbon balance deviation.

[0169] Furthermore, the system unit equipment includes combined heat and power equipment, waste heat recovery system, system-grid interaction equipment, wind and solar new energy power generation equipment, and energy storage equipment;

[0170] The system unit equipment operation characteristic characterization model includes a white-box model of the operation process of each unit equipment in the system and a safe operation constraint model, wherein,

[0171] The expression for the white-box model of the combined heat and power equipment operation process is as follows:

[0172] ;

[0173] In the formula: The electrical power output of the combined heat and power equipment in the system during time period t; The power generation efficiency of the combined heat and power equipment in the system; The natural gas input power of the combined heat and power equipment in the system during time period t; The combined heat and power equipment in the system outputs thermal power during time period t. The optimized heat-to-power ratio set for the actual operation and regulation of the combined heat and power equipment in the system; The volumetric flow rate of natural gas during the operation of the combined heat and power (CHP) equipment in the system at time period t; This refers to the natural gas flow-to-power conversion coefficient.

[0174] The expression for the safety operation constraints of the combined heat and power equipment is as follows:

[0175] ;

[0176] In the formula: Let t be the start / stop status variable of the combined heat and power equipment in the system during time period t, where 0 represents shutdown and 1 represents operation; Minimize the technical output of the combined heat and power equipment in the system; The electrical power output of the combined heat and power equipment in the system during time period t; The rated capacity of the combined heat and power equipment in the system; To optimize the system's runtime time interval step size; To optimize the time conversion coefficient between running step size and hill climb; The downhill ramp rate limit for combined heat and power equipment in the system; The electrical power output of the combined heat and power equipment in the system during the t+1 time period; The limit for the upward ramp rate of the combined heat and power equipment in the system; , These are the downward reserve capacities of the combined heat and power equipment in the system for mitigating risks during time period t and time period t+1, respectively. , These are the upward backup capacities of the combined heat and power equipment in the system for mitigating risks during time period t and time period t+1, respectively. The optimized heat-to-power ratio set for the actual operation and regulation of the combined heat and power equipment in the system; This is the design operating condition baseline value for the heat-to-power ratio of the combined heat and power equipment in the system; This is the coefficient for the fluctuation of the heat-to-power ratio of the combined heat and power equipment in the system around the design operating condition benchmark value.

[0177] The expression for the white-box model of the waste heat recovery system operation process is as follows:

[0178] ;

[0179] In the formula: The output thermal power of the waste heat recovery system in the system during time period t; Let be the start / stop status variable of the waste heat recovery system in the system during time period t, where 0 indicates shutdown and 1 indicates operation; The combined heat and power equipment in the system outputs thermal power during time period t. The overall heat conversion efficiency of the waste heat recovery system;

[0180] The expression for the safety operation constraints of the waste heat recovery system is as follows:

[0181] ;

[0182] In the formula: Let be the start / stop status variable of the waste heat recovery system in the system during time period t, where 0 indicates shutdown and 1 indicates operation; Let t be the start / stop status variable of the combined heat and power equipment in the system during time period t, where 0 represents shutdown and 1 represents operation; Minimize the technical output of the waste heat recovery system in the system; The output thermal power of the waste heat recovery system in the system during time period t; This refers to the rated capacity of the waste heat recovery system in the system.

[0183] The expression for the white-box model of the system's interaction with the power grid is as follows:

[0184] ;

[0185] In the formula: The system purchases electrical power in time period t through the external line connection. Let be the power returned by the system through the external line tie line in time period t; (2c-2) The expression for the safe operation constraint condition of the system and the power grid interaction is as follows:

[0186] ;

[0187] In the formula: The system purchases electrical power in time period t through the external line connection. The system transmits power back during time period t via external line connections; This is the upper limit of the line transmission capacity when the system purchases electricity from the grid. This is the upper limit of the line transmission capacity when the system sends power back to the grid;

[0188] The expression for the white-box model of the operation process of the wind and solar new energy power generation equipment is as follows:

[0189] ;

[0190] In the formula: For the absorption rate of wind and solar new energy; To predict the power generation of wind and solar renewable energy generation equipment in time period t; The actual power generation of the wind and solar renewable energy power generation equipment in the system during time period t; Optimize the set of runtime cycles for the system;

[0191] The expression for the safety operation constraints of the wind and solar new energy power generation equipment is as follows:

[0192] ;

[0193] In the formula: To predict the power generation of wind and solar renewable energy generation equipment in time period t; The actual power generation of the wind and solar renewable energy power generation equipment in the system during time period t; For the absorption rate of wind and solar new energy; This is the minimum absorption rate limit for wind and solar new energy sources;

[0194] The expression for the white-box model of the energy storage device's operation process is as follows:

[0195] ;

[0196] In the formula: Let be the amount of energy stored by the energy storage device in the system during time period t; Let be the amount of electricity stored by the energy storage device in the system during the (t-1)th time period; The self-discharge rate of the energy storage; This represents the charging power of the energy storage device in the system during the (t-1)th time period; The charging efficiency of the energy storage devices in the system; Let be the discharge power of the energy storage device in the system during the (t-1)th time period; The discharge efficiency of the energy storage device in the system; To optimize the system's runtime time interval step size; This represents the amount of electricity stored by the energy storage devices in the system during the first time period. Initialize the energy storage capacity of the energy storage devices in the system; The energy storage device in the system is in the first The amount of electricity stored during the period, of which This indicates the last time period in the optimized runtime cycle;

[0197] The expression for the safety operation constraints of the energy storage device is as follows:

[0198]

[0199] In the formula: Let be the charging power of the energy storage device in the system during time period t; Let be the discharge power of the energy storage device in the system during time period t; This refers to the maximum charging and discharging power of the energy storage devices in the system. To optimize the system's runtime time interval step size; Let be the amount of energy stored by the energy storage device in the system during time period t; This is the minimum state of charge coefficient of the energy storage device in the system; This represents the maximum state of charge coefficient of the energy storage devices in the system. This refers to the rated capacity of the energy storage devices in the system.

[0200] Furthermore, the expression for setting the energy transfer constraint condition of the backup plan to resist the risk of imbalance is as follows:

[0201] ;

[0202] In the formula: The actual power generation of the wind and solar renewable energy power generation equipment in the system during time period t; The system purchases electrical power in time period t through the external line connection. The electrical power output of the combined heat and power equipment in the system during time period t; Let be the charging power of the energy storage device in the system during time period t; Let be the discharge power of the energy storage device in the system during time period t; Let be the electrical load demand of the system in time period t; The system transmits power back during time period t via external line connections; The output thermal power of the waste heat recovery system in the system during time period t; Let t be the system's heat load demand in time period t; This is the maximum load shedding rate during system risk protection. The power reduction coefficient for wind and solar new energy power generation equipment in the system during the formulation of the backup plan to resist imbalance risks; This refers to the power generation coefficient in the system's backup plan for wind and solar new energy power generation equipment to mitigate imbalance risks. This refers to the downlink backup capacity of the combined heat and power equipment in the system to mitigate risks during time period t. This refers to the upward backup capacity of the combined heat and power equipment in the system to mitigate risks during time period t.

[0203] Furthermore, the expression for the carbon transfer calculation model of the combined heat and power equipment is as follows:

[0204] ;

[0205] In the formula: The carbon emission rate of natural gas input to the combined heat and power (CHP) equipment in the system during time period t; The carbon potential of the natural gas input is given to the combined heat and power equipment in the system during time period t. The natural gas input power of the combined heat and power equipment in the system during time period t; The carbon emission flow rate of the combined heat and power equipment in the system during time period t; The carbon potential is the output power of the combined heat and power equipment in the system during time period t. The electrical power output of the combined heat and power equipment in the system during time period t; The carbon emission flow rate of the combined heat and power equipment in the system during time period t; The carbon potential of the combined heat and power equipment in the system during time period t is the output heat power of the equipment. The combined heat and power equipment in the system outputs thermal power during time period t. The carbon potential structural coefficient is the output carbon potential of the combined heat and power equipment in the system. The power generation efficiency of the combined heat and power equipment in the system; The heating efficiency of the combined heat and power equipment in the system; The optimized heat-to-power ratio set for the actual operation and regulation of the combined heat and power equipment in the system;

[0206] The expression for the carbon transfer calculation model of the waste heat recovery system is as follows:

[0207] ;

[0208] In the formula: The thermal power input carbon emission flow rate of the waste heat recovery system in the system during time period t; The carbon potential is input to the thermal power of the waste heat recovery system in the system during time period t. The combined heat and power equipment in the system outputs thermal power during time period t. Let the carbon emission flow rate of the waste heat recovery system in the system be the thermal power output during time period t. The carbon potential of the waste heat recovery system in the system is the thermal power output of the system during time period t. The output thermal power of the waste heat recovery system in the system during time period t; The overall heat conversion efficiency of the waste heat recovery system; The carbon potential of the combined heat and power equipment in the system during time period t is the output heat power of the equipment.

[0209] The expression for the carbon transfer calculation model of the system interaction with the power grid is as follows:

[0210] ;

[0211] In the formula: The system purchases the carbon emission flow rate of electricity in time period t via external line connections; The system purchases the equivalent carbon potential of electricity in time period t through the external line connection. The system purchases electrical power in time period t through the external line connection. The system transmits the carbon emission flow rate back to the system via external line connections during time period t. The system transmits the equivalent carbon potential back through the external line connection at time t. The system transmits power back during time period t via external line connections; Let the system node carbon potential be at time t.

[0212] The expression for the carbon transfer calculation model of the wind and solar new energy power generation equipment is as follows:

[0213] ;

[0214] In the formula: Let be the carbon emission flow rate of the wind and solar new energy power generation equipment in the system during time period t; The carbon potential of the wind and solar power generation equipment in the system during time period t; The actual power generation of the wind and solar renewable energy power generation equipment in the system during time period t;

[0215] The expression for the carbon transfer calculation model of the energy storage device is as follows:

[0216] ;

[0217] In the formula: The carbon emissions generated by the energy storage device in the system during time period t; Let be the charging power of the energy storage device in the system during time period t; The carbon potential is input to charge the energy storage device in the system during time period t. To optimize the system's runtime time interval step size; This represents the carbon emissions generated by the energy storage device in the system during time period t. Let be the discharge power of the energy storage device in the system during time period t; The carbon potential output by the energy storage device in the system during time period t; Let be the internal carbon potential of the energy storage device in the system during time period t; Let be the amount of energy stored by the energy storage device in the system during time period t; The internal carbon potential of the energy storage device in the system during the (t-1)th time period; Let be the amount of electricity stored by the energy storage device in the system during the (t-1)th time period; The discharge efficiency of the energy storage device in the system; The internal carbon potential of the energy storage device in the system during the first time period; The system purchases the equivalent carbon potential of electricity in the first time period through the external line connection; Let be the carbon potential at the system node during time period t.

[0218] Furthermore, the system carbon transfer balance constraint is determined based on the carbon emission flow parameters of each unit device in the system, and the specific expression for setting the system carbon transfer balance constraint is as follows:

[0219] ;

[0220] In the formula: Let be the carbon emission flow rate of the wind and solar new energy power generation equipment in the system during time period t; The system purchases the carbon emission flow rate of electricity in time period t via external line connections; The carbon emission flow rate of the combined heat and power equipment in the system during time period t; Let be the discharge power of the energy storage device in the system during time period t; The carbon potential output by the energy storage device in the system during time period t; Let the system node carbon potential be at time t. Let be the charging power of the energy storage device in the system during time period t; Let be the electrical load demand of the system in time period t; The system transmits power back through the external line connection during time period t.

[0221] Furthermore, the optimized operation target scenarios include optimized operation modes for carbon reduction scenarios, optimized operation modes for low-consumption scenarios, optimized operation modes for cost reduction scenarios, and optimized operation modes for multi-scenario comprehensive indicators.

[0222] The mathematical expression for the optimized operation mode of the emission reduction scenario is as follows:

[0223]

[0224] In the formula: The objective function for the system's total carbon emissions; This represents the total carbon emissions from non-new energy power generation equipment in the system. This represents the equivalent carbon emission reduction of wind and solar power generation equipment in the system. Let be the carbon emissions of non-new energy power generation equipment in the system during period t. This represents the actual power generation of the wind and solar renewable energy generation equipment in the system during time period t. The carbon potential of the wind and solar power generation equipment in the system during time period t; Optimize the set of runtime cycles for the system; To optimize the system's runtime time interval step size;

[0225] Under the optimized operation mode of the emission reduction scenario, the mathematical model expression for the carbon emissions of non-new energy power generation equipment in each time period is as follows:

[0226] ;

[0227] In the formula: Let be the carbon emissions of non-new energy power generation equipment in the system during period t. The equivalent carbon emissions generated by the system in time period t when purchasing electricity through external connection lines; This represents the carbon emissions generated by the combined heat and power (CHP) equipment in the system during time period t. The system purchases the equivalent carbon potential of electricity in time period t through the external line connection. The system purchases electrical power in time period t through the external line connection. To optimize the system's runtime time interval step size; The carbon potential of the natural gas input is given to the combined heat and power equipment in the system during time period t. The natural gas input power of the combined heat and power equipment in the system during time period t;

[0228] The mathematical expression for the optimized operation mode in the low-power scenario is as follows:

[0229] ;

[0230] In the formula: Let be the overall energy consumption objective function of the system; Let be the total energy consumption of the system in time period t; Optimize the set of runtime cycles for the system; The system purchases electrical power in time period t through the external line connection. The standard coal equivalent coefficient for electricity purchased by the system; The volumetric flow rate of natural gas during the operation of the combined heat and power (CHP) equipment in the system at time period t; The standard coal equivalent coefficient for natural gas consumption in the system; This represents the actual power generation of the wind and solar renewable energy generation equipment in the system during time period t. The standard coal equivalent coefficient for power generation from wind and solar new energy power generation equipment; To optimize the system's runtime time interval step size;

[0231] The mathematical expression for the optimized operation mode in the cost reduction scenario is as follows:

[0232] ;

[0233] In the formula: The objective function is the total operating cost of the system. Let be the system's operating cost in time period t; The operating cost of the combined heat and power equipment in the system during time period t; The operating cost of the system during time period t is due to the purchase of electricity and the penalty for curtailment of wind and solar power generation equipment. The system purchases electrical power in time period t through the external line connection. The electricity price is calculated based on the time period purchased during time period t. To predict the power generation of wind and solar renewable energy generation equipment in time period t; The actual power generation of the wind and solar renewable energy power generation equipment in the system during time period t; Penalty factor for the absorption of wind and solar new energy power generation; The volumetric flow rate of natural gas during the operation of the combined heat and power (CHP) equipment in the system at time period t; The price of natural gas consumed by the system; Optimize the set of runtime cycles for the system; To optimize the system's runtime time interval step size;

[0234] The mathematical expression for the multi-scenario comprehensive index optimization operation mode is as follows:

[0235]

[0236] In the formula: Let be the overall energy consumption objective function of the system; The objective function for the system's total carbon emissions; The objective function is the total operating cost of the system. The objective function is the comprehensive index of the system across multiple scenarios; , , These are the percentage coefficients of indicators for optimized operation modes in emission reduction scenarios, optimized operation modes in low-consumption scenarios, and optimized operation modes in cost reduction scenarios, respectively. , , These are the normalized and standardized values ​​of indicators for optimized operation modes in emission reduction scenarios, optimized operation modes in low-consumption scenarios, and optimized operation modes in cost reduction scenarios, respectively. , , These are the maximum values ​​of the objective functions under the optimized operation modes for emission reduction, low energy consumption, and cost reduction scenarios, respectively. , , These are the minimum objective functions for the optimized operation modes in emission reduction, low energy consumption, and cost reduction scenarios, respectively. , , These are the relaxation coefficients for extreme value calculations under the optimized operation modes for emission reduction, low energy consumption, and cost reduction scenarios, respectively. , These represent the total carbon emission objective function values ​​of the system under the low-consumption scenario optimized operation mode and the cost-reduction scenario optimized operation mode, respectively. , These are the total comprehensive energy consumption objective function values ​​of the system under the optimized operation mode for emission reduction scenarios and the optimized operation mode for cost reduction scenarios, respectively. , These represent the objective function values ​​for the total operating cost of the system in the emission reduction scenario optimization operation mode and the low-consumption scenario optimization operation mode, respectively.

[0237] Furthermore, the expression for the system carbon transfer situation analysis model is as follows:

[0238]

[0239] In the formula: This represents the total carbon input to the system. Optimize the set of runtime cycles for the system; The system purchases the carbon emission flow rate of electricity in time period t via external line connections; The carbon emission rate of natural gas input to the combined heat and power (CHP) equipment in the system during time period t; To optimize the system's runtime time interval step size; This represents the total carbon output of the system. Let the carbon emission flow rate of the waste heat recovery system in the system be the thermal power output during time period t. Let the system node carbon potential be at time t. Let be the electrical load demand of the system in time period t; The system transmits the equivalent carbon potential back through the external line connection at time t. The system transmits power back during time period t via external line connections; This represents the total carbon loss from energy storage devices. The internal carbon potential of the energy storage device in the system during the first time period; For the energy storage device in the system in the first The internal carbon potential of the time period, among which This indicates the last time period in the optimized runtime cycle; Initialize the energy storage capacity of the energy storage devices in the system; This represents the total carbon balance deviation of the system.

[0240] The multi-scenario risk optimization operation method for the integrated energy system of source-grid-load-storage carbon storage in the park provided in this embodiment involves the following steps in its application process:

[0241] (1) Introduction to the basic topology of the embodiment

[0242] Combination Figure 1 The implementation flowchart illustrates the topology of a typical system in the multi-scenario risk optimization operation method for an integrated source-grid-load-storage carbon storage park energy system described in this embodiment of the invention. Figure 2 As shown. Figure 2 The topology includes: a combined heat and power (CHP) system, a waste heat recovery system, wind and solar power generation equipment, and energy storage equipment. The system interacts with the power grid via external interconnection lines. The wind and solar power generation equipment includes photovoltaic (PV) equipment and wind turbines. Natural gas is input into the CHP system, which outputs electrical power and waste heat power. The electrical power output from the CHP system is input into the system's power bus. The waste heat power output from the CHP system is input into the waste heat recovery system, which outputs heat power. The heat power output from the waste heat recovery system meets the heating load demand. Solar energy is input into the PV equipment. Wind energy is input into the wind turbines. The wind and solar power generation equipment outputs electrical power. The electrical power output from the wind and solar power generation equipment is input into the system's power bus. The power output from the system's power bus interacts bidirectionally with the power grid. The power output from the system's power bus interacts bidirectionally with the energy storage equipment. The power output from the system's power bus meets the power load demand. The system's power bus is coupled with the output of the CHP system, the power grid, the output of the wind and solar power generation equipment, the energy storage equipment, and the electrical load.

[0243] (2) Introduction to the system data foundation of the embodiment

[0244] This invention describes an integrated energy system for a carbon-storage industrial park, using a 24-hour period in a northern Chinese city during winter as the optimal operating time cycle. The optimal operating time interval is 1 hour, the day is windless, sunny, and has sufficient sunlight. The system is configured to operate in an emission reduction scenario-optimized mode. Basic energy system data is input into a typical system within the multi-scenario risk optimization operation method of this integrated energy system, including: the price of natural gas consumed by the system, and the time-of-use electricity price. Figure 3 As shown; the predicted power generation of wind and solar new energy power generation equipment is as follows: Figure 4 As shown; electrical load demand is as follows Figure 5 As shown; heat load requirements are as follows Figure 6 As shown; the combined heat and power (CHP) equipment has a power generation efficiency of 0.38, a minimum technical output of 0, a rated capacity of 3800kW, a ramp rate limit of 1520kW / min, and a design baseline value of 1.6 for the operating heat-to-power ratio; the waste heat recovery system has a comprehensive heat conversion efficiency of 0.85, a minimum technical output of 0, and a rated capacity of 8000kW; the upper limit of the line transmission capacity when purchasing electricity and when returning electricity in the system-grid interaction operation characteristic characterization model is 10MW; the minimum absorption rate limit for wind and solar new energy is 0.8; the charging efficiency of the energy storage equipment is... The discharge efficiency of the energy storage device is 0.95; the initial energy storage capacity of the energy storage device is 2400kWh; the maximum charging power is 4MW; the maximum discharge power is 4MW; the minimum state of charge factor is 0.1; and the maximum state of charge factor is 0.9. The standard coal equivalent coefficient of the purchased electricity is 0.1229kgce / kWh; the standard coal equivalent coefficient of the natural gas consumed by the system is 1.12150kgce / m3. The proportion coefficient of the optimized operation mode indicator in the emission reduction scenario is 1; the proportion coefficient of the optimized operation mode indicator in the low consumption scenario is 0; and the proportion coefficient of the optimized operation mode indicator in the cost reduction scenario is 0.

[0245] (3) Analysis of the optimized operation results of the embodiment

[0246] This invention describes an integrated energy system for a park, combining energy sources, grid, load, storage, and carbon storage. The optimized operation time cycle is 24 hours per day in a city park during winter in northern China. The system optimization time interval is 1 hour. The system is optimized using an emission reduction scenario optimization mode. After the system optimization operation is completed, basic energy system data is output from a typical system in the multi-scenario risk optimization operation method of this invention. The output basic energy system data includes: energy storage device discharge power, combined heat and power (CHP) device output power, purchased power, actual power generation of wind and solar power generation equipment, energy storage device charging power, electricity load demand, and feedback power. Figure 7 As shown, according to Figure 7 The purchased electricity mainly occurs at night, and the purchase primarily utilizes off-peak electricity prices. During peak power generation periods of wind and solar power generation, the energy storage devices are charged. During the daytime, especially from 11:00 AM to 2:00 PM, when solar power generation is high and electricity demand is low, the energy storage devices charge and the system feeds back power, achieving energy balance. The system's power bus is balanced in real time, and the system's energy supply and consumption are balanced. Optimized operation methods achieve multi-energy coupling, complementary utilization, conversion, and storage. Data on charging input carbon potential, discharging output carbon potential, internal carbon potential, and energy storage capacity are as follows: Figure 8 As shown, according to Figure 8 A multi-scenario risk optimization operation method for an integrated energy system of source, grid, load, and storage in a park can accurately characterize and calculate the carbon transfer data of energy storage devices, from... Figure 8 As can be seen, the output carbon potential and the internal carbon potential are different, which can reflect the energy storage degradation and efficiency loss. The carbon transfer calculation model of energy storage equipment matches the actual engineering physics application scenario; the carbon potential of system nodes is as follows: Figure 9 As shown, according to Figure 9 The carbon potential at nodes is low during the day and high at night. During the day, this is mainly due to the high photovoltaic power generation diluting the carbon potential output from the power bus. At night, due to off-peak electricity prices, the system purchases more electricity from the grid, resulting in a larger carbon potential of the grid-related power output, thus concentrating the carbon potential at nodes. The total carbon emission objective function value of the system is 51.05377t. The total carbon input of the system is 51.05377t, the total carbon output of the system is 52.56074t, the total carbon loss of energy storage equipment is 1.506972t, and the total carbon balance deviation of the system is 0. The system carbon transfer situation analysis model effectively monitors and evaluates the system's carbon transfer in real time.

[0247] (4) Analysis of the running test effect of the example

[0248] This invention constructs an integrated energy system for a park, encompassing source, grid, load, storage, and carbon transfer. It utilizes a multi-scenario risk optimization operation method for this system. The rationality, effectiveness, and innovation of the invention are verified through input and output energy system data. The test results demonstrate that this invention, by constructing an integrated source-grid-load-storage operation characteristic characterization model and a carbon transfer coupling model, achieves deep collaborative optimization of source-grid-load-storage carbon transfer, realizing synchronous collaborative optimization of energy transfer, carbon transfer, and tracking, thereby improving the overall energy and carbon operation efficiency of the system. This invention also improves the overall energy and carbon operation efficiency by setting up a countermeasure mechanism coordinated with carbon transfer. The backup plan conditions for mitigating imbalance risks enhance the system's ability to withstand source-load power imbalance risks, ensuring the safe and stable operation of the system while reducing the additional carbon emissions from backup capacity. Addressing the needs of optimizing operation modes in multiple scenarios in practical engineering, this invention proposes a flexible configuration strategy for optimizing operation modes based on comprehensive indicators across multiple scenarios, supporting flexible optimization needs and comprehensive decision-making in various scenarios. Furthermore, by constructing a carbon transfer balance constraint and situation assessment model, this invention achieves refined tracking and situation analysis of the entire carbon transfer process, supporting real-time monitoring, assessment, and coordinated energy-carbon operation and control of the system's carbon transfer, providing technical support and theoretical guidance for energy system carbon management.

[0249] Example 2: This example provides a multi-scenario risk optimization operation system for an integrated energy system for source-grid-load-storage carbon storage in a park, including:

[0250] Acquisition Module: Used to acquire diversified characteristic data of the park's energy system. The diversified characteristic data includes inherent physical attribute parameters of equipment, initial state of operation optimization and operation optimization control threshold parameters, reference standard configuration parameters, predicted source load time series data, and energy market attribute data.

[0251] The first generation module is used to input the inherent physical attribute parameters of the device, the initial state of operation optimization, the operation optimization control threshold parameters, and the reference standard configuration parameters into the pre-built system unit device operation characteristic characterization model to obtain the operation characteristic parameters of each unit device in the system.

[0252] The second generation module is used to input the operating characteristic parameters and the predicted source load time series data into the pre-built backup plan energy transfer constraint model to generate backup constraint conditions to resist the risk of source load imbalance.

[0253] The third generation module is used to input the operating characteristic parameters and the predicted source load time series data into the pre-built carbon transfer calculation model to generate carbon emission flow parameters of each unit device in the system and carbon transfer balance constraints of the system.

[0254] The optimization module is used to input the energy market attribute data into a pre-built optimized operation target scenario model. It uses the operating characteristic parameters and carbon emission flow parameters as equipment self-constraints, the backup constraints and carbon transfer balance constraints as system-level constraints, and the optimization objective function as the objective to construct and solve the optimization problem, thereby obtaining the optimal output plan and carbon emission flow distribution of each unit equipment in the system in multiple future time periods. The optimization objective function is determined according to the optimized operation target scenario selected by the user.

[0255] Execution module: Used to output instructions for each time period in the optimal output plan to the execution mechanism, and control each unit device of the system to operate according to the instructions;

[0256] Feedback module: Used to input the carbon emission flow distribution of each unit device in the system into the pre-built system carbon transfer status analysis model to obtain the carbon transfer status assessment results, and to optimize the system operating capacity configuration, or to overhaul energy storage equipment or adjust relevant parameters based on the assessment results.

[0257] The specific functions of each module described above are explained in the relevant content of the method in Embodiment 1, and will not be repeated here.

[0258] Example 3: This example provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any of Examples 1.

[0259] Example 4: This example provides a computer device, including:

[0260] Memory, used to store computer programs / instructions;

[0261] A processor for executing the computer program / instructions to implement the steps of the method described in any of Embodiment 1.

[0262] Example 5: This example provides a computer program product, including a computer program / instructions, which, when executed by a processor, implement the steps of the method described in any of Examples 1.

[0263] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

[0264] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0265] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0266] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0267] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0268] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this disclosure and not to limit its protection scope. Although this disclosure has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading this disclosure, they can still make various changes, modifications or equivalent substitutions to the specific implementation of the invention, but these changes, modifications or equivalent substitutions are all within the protection scope of the pending claims.

Claims

1. A multi-scenario risk optimization operation method for an integrated energy system of source-grid-load-storage carbon storage in a park, characterized in that, include: Acquire diverse characteristic data of the park's energy system, including inherent physical attribute parameters of equipment, initial state of operation optimization and operation optimization control threshold parameters, reference standard configuration parameters, predicted source load time series data, and energy market attribute data. The inherent physical attribute parameters of the equipment, the initial state of operation optimization, the operation optimization control threshold parameters, and the reference standard configuration parameters are input into the pre-constructed system unit equipment operation characteristic characterization model to obtain the operation characteristic parameters of each unit equipment in the system. The operating characteristic parameters and the predicted source load time series data are input into the pre-built backup plan energy transfer constraint model to generate backup constraint conditions to resist the risk of source load imbalance. The operating characteristic parameters and the predicted source load time series data are input into the pre-built carbon transfer calculation model to generate carbon emission flow parameters of each unit device in the system and carbon transfer balance constraints of the system. The energy market attribute data is input into a pre-constructed optimized operation target scenario model. The operating characteristic parameters and carbon emission flow parameters are used as equipment self-constraints, the backup constraints and carbon transfer balance constraints are used as system-level constraints, and the optimization objective function is used as the objective to construct and solve the optimization problem, thereby obtaining the optimal output plan of each unit equipment in the system for multiple future time periods. The optimization objective function is determined according to the optimized operation target scenario selected by the user. The instructions for each time period in the optimal output plan are output to the actuator, and the control system controls each unit device to operate according to the instructions.

2. The multi-scenario risk optimization operation method for the integrated energy system of source-grid-load-storage carbon storage park as described in claim 1, characterized in that, The system unit equipment includes combined heat and power equipment, waste heat recovery system, system-grid interaction equipment, wind and solar new energy power generation equipment, and energy storage equipment. The inherent physical attribute parameters of the equipment include the power generation efficiency, minimum technical output, rated capacity, time conversion coefficient, ramp rate limit, design operating condition baseline value, floating coefficient, and optimized heat-to-power ratio of the combined heat and power equipment; the comprehensive heat conversion efficiency, minimum technical output, and rated capacity of the waste heat recovery system; the self-discharge rate, charging efficiency, discharging efficiency, maximum charging and discharging power, minimum state of charge coefficient, and maximum state of charge coefficient of the energy storage equipment; and the upper limit of the line transmission capacity when the system and the grid interact with each other when purchasing electricity and when returning electricity, as well as the reduction coefficient and increase coefficient. The initial state of operational optimization includes the initial energy storage capacity of the energy storage device; The operation optimization control threshold parameters include the system optimization operation time interval step size, the system optimization operation time cycle set, the wind and solar new energy power generation and absorption penalty factor, the optimization operation mode index ratio coefficient, and the extreme value calculation relaxation coefficient under the optimization operation mode. The predicted source load time series data includes electricity load demand, heat load demand, and predicted power generation of wind and solar new energy power generation equipment. The energy market attribute data includes the time-of-use electricity price and the price of natural gas consumed by the system; The reference standard configuration parameters include the wind and solar new energy absorption rate and the minimum absorption rate limit of wind and solar new energy, the maximum power shedding load rate, the standard coal conversion factor of the system's purchased electricity, the standard coal conversion factor of the system's natural gas consumption, and the standard coal conversion factor of the wind and solar new energy power generation equipment. The system unit equipment operation characteristic characterization model includes a white-box model of the operation process of each unit equipment in the system and a safe operation constraint model, wherein, The expression for the white-box model of the combined heat and power equipment operation process is as follows: ； In the formula: The electrical power output of the combined heat and power equipment in the system during time period t; The power generation efficiency of the combined heat and power equipment in the system; The natural gas input power of the combined heat and power equipment in the system during time period t; The combined heat and power equipment in the system outputs thermal power during time period t. The optimized heat-to-power ratio set for the actual operation and regulation of the combined heat and power equipment in the system; The volumetric flow rate of natural gas during the operation of the combined heat and power (CHP) equipment in the system at time period t; This refers to the natural gas flow-to-power conversion coefficient. The expression for the safety operation constraints of the combined heat and power equipment is as follows: ； In the formula: Let t be the start / stop status variable of the combined heat and power equipment in the system during time period t, where 0 represents shutdown and 1 represents operation; Minimize the technical output of the combined heat and power equipment in the system; The electrical power output of the combined heat and power equipment in the system during time period t; The rated capacity of the combined heat and power equipment in the system; To optimize the system's runtime time interval step size; To optimize the time conversion coefficient between running step size and hill climb; The downhill ramp rate limit for combined heat and power equipment in the system; The electrical power output of the combined heat and power equipment in the system during the t+1 time period; The limit for the upward ramp rate of the combined heat and power equipment in the system; , These are the downward reserve capacities of the combined heat and power equipment in the system for mitigating risks during time period t and time period t+1, respectively. , These are the upward backup capacities of the combined heat and power equipment in the system for mitigating risks during time period t and time period t+1, respectively. The optimized heat-to-power ratio set for the actual operation and regulation of the combined heat and power equipment in the system; This is the design operating condition baseline value for the heat-to-power ratio of the combined heat and power equipment in the system; This is the coefficient for the fluctuation of the heat-to-power ratio of the combined heat and power equipment in the system around the design operating condition benchmark value. The expression for the white-box model of the waste heat recovery system operation process is as follows: ； In the formula: The output thermal power of the waste heat recovery system in the system during time period t; Let be the start / stop status variable of the waste heat recovery system in the system during time period t, where 0 indicates shutdown and 1 indicates operation; The combined heat and power equipment in the system outputs thermal power during time period t. The overall heat conversion efficiency of the waste heat recovery system; The expression for the safety operation constraints of the waste heat recovery system is as follows: ； In the formula: Let be the start / stop status variable of the waste heat recovery system in the system during time period t, where 0 indicates shutdown and 1 indicates operation; Let t be the start / stop status variable of the combined heat and power equipment in the system during time period t, where 0 represents shutdown and 1 represents operation; Minimize the technical output of the waste heat recovery system in the system; The output thermal power of the waste heat recovery system in the system during time period t; This refers to the rated capacity of the waste heat recovery system in the system. The expression for the white-box model of the system's interaction with the power grid is as follows: ； In the formula: The system purchases electrical power in time period t through the external line connection. The system transmits power back during time period t via external line connections; The expression for the safety operation constraints of the system interacting with the power grid is as follows: ； In the formula: The system purchases electrical power in time period t through the external line connection. The system transmits power back during time period t via external line connections; This is the upper limit of the line transmission capacity when the system purchases electricity from the grid. This is the upper limit of the line transmission capacity when the system sends power back to the grid; The expression for the white-box model of the operation process of the wind and solar new energy power generation equipment is as follows: ； In the formula: For the absorption rate of wind and solar new energy; To predict the power generation of wind and solar renewable energy generation equipment in time period t; The actual power generation of the wind and solar renewable energy power generation equipment in the system during time period t; Optimize the set of runtime cycles for the system; The expression for the safety operation constraints of the wind and solar new energy power generation equipment is as follows: ； In the formula: To predict the power generation of wind and solar renewable energy generation equipment in time period t; The actual power generation of the wind and solar renewable energy power generation equipment in the system during time period t; For the absorption rate of wind and solar new energy; This is the minimum absorption rate limit for wind and solar new energy sources; The expression for the white-box model of the energy storage device's operation process is as follows: ； In the formula: Let be the amount of energy stored by the energy storage device in the system during time period t; Let be the amount of electricity stored by the energy storage device in the system during the (t-1)th time period; The self-discharge rate of the energy storage; This represents the charging power of the energy storage device in the system during the (t-1)th time period; The charging efficiency of the energy storage devices in the system; Let be the discharge power of the energy storage device in the system during the (t-1)th time period; The discharge efficiency of the energy storage device in the system; To optimize the system's runtime time interval step size; This represents the amount of electricity stored by the energy storage devices in the system during the first time period. Initialize the energy storage capacity of the energy storage devices in the system; The energy storage device in the system is in the first The amount of electricity stored during the period, of which This indicates the last time period in the optimized runtime cycle; The expression for the safety operation constraints of the energy storage device is as follows: ； In the formula: Let be the charging power of the energy storage device in the system during time period t; Let be the discharge power of the energy storage device in the system during time period t; This refers to the maximum charging and discharging power of the energy storage devices in the system. To optimize the system's runtime time interval step size; Let be the amount of energy stored by the energy storage device in the system during time period t; This is the minimum state of charge coefficient of the energy storage device in the system; This represents the maximum state of charge coefficient of the energy storage devices in the system. This refers to the rated capacity of the energy storage devices in the system.

3. The multi-scenario risk optimization operation method for the integrated energy system of source-grid-load-storage carbon storage park according to claim 2, characterized in that, The specific expression for setting the energy transfer constraint condition of the backup plan to resist the risk of imbalance is as follows: ； In the formula: The actual power generation of the wind and solar renewable energy power generation equipment in the system during time period t; The system purchases electrical power in time period t through the external line connection. The electrical power output of the combined heat and power equipment in the system during time period t; Let be the charging power of the energy storage device in the system during time period t; Let be the discharge power of the energy storage device in the system during time period t; Let be the electrical load demand of the system in time period t; The system transmits power back during time period t via external line connections; The output thermal power of the waste heat recovery system in the system during time period t; Let t be the system's heat load demand in time period t; This is the maximum load shedding rate during system risk protection. The power reduction coefficient for wind and solar new energy power generation equipment in the system's backup plan to mitigate imbalance risks; This refers to the power generation coefficient in the system's backup plan for wind and solar new energy power generation equipment to mitigate imbalance risks. This refers to the downlink backup capacity of the combined heat and power equipment in the system to mitigate risks during time period t. This refers to the upward backup capacity of the combined heat and power equipment in the system to mitigate risks during time period t.

4. The multi-scenario risk optimization operation method for the integrated energy system of source-grid-load-storage carbon storage park according to claim 1, characterized in that, The expression for the carbon transfer calculation model of the combined heat and power equipment is as follows: ； In the formula: The carbon emission rate of natural gas input to the combined heat and power (CHP) equipment in the system during time period t; The carbon potential of the natural gas input is given to the combined heat and power equipment in the system during time period t. The natural gas input power of the combined heat and power equipment in the system during time period t; The carbon emission flow rate of the combined heat and power equipment in the system during time period t; The carbon potential is the output power of the combined heat and power equipment in the system during time period t. The electrical power output of the combined heat and power equipment in the system during time period t; The carbon emission flow rate of the combined heat and power equipment in the system during time period t; The carbon potential of the combined heat and power equipment in the system during time period t is the output heat power of the equipment. The combined heat and power equipment in the system outputs thermal power during time period t. The carbon potential structural coefficient is the output carbon potential of the combined heat and power equipment in the system. The power generation efficiency of the combined heat and power equipment in the system; The heating efficiency of the combined heat and power equipment in the system; The optimized heat-to-power ratio set for the actual operation and regulation of the combined heat and power equipment in the system; The expression for the carbon transfer calculation model of the waste heat recovery system is as follows: ； In the formula: The thermal power input carbon emission flow rate of the waste heat recovery system in the system during time period t; The carbon potential is input to the thermal power of the waste heat recovery system in the system during time period t. The combined heat and power equipment in the system outputs thermal power during time period t. Let the carbon emission flow rate of the waste heat recovery system in the system be the thermal power output during time period t. The carbon potential of the waste heat recovery system in the system is the thermal power output of the system during time period t. The output thermal power of the waste heat recovery system in the system during time period t; The overall heat conversion efficiency of the waste heat recovery system; The carbon potential of the combined heat and power equipment in the system during time period t is the output heat power of the equipment. The expression for the carbon transfer calculation model of the system interaction with the power grid is as follows: ； In the formula: The system purchases the carbon emission flow rate of electricity in time period t via external line connections; The system purchases the equivalent carbon potential of electricity in time period t through the external line connection. The system purchases electrical power in time period t through the external line connection. The system transmits the carbon emission flow rate back to the system via external line connections during time period t. The system transmits the equivalent carbon potential back through the external line connection at time t. The system transmits power back during time period t via external line connections; Let the system node carbon potential be at time t. The expression for the carbon transfer calculation model of the wind and solar new energy power generation equipment is as follows: ； In the formula: Let be the carbon emission flow rate of the wind and solar new energy power generation equipment in the system during time period t; The carbon potential of the wind and solar power generation equipment in the system during time period t; The actual power generation of the wind and solar renewable energy power generation equipment in the system during time period t; The expression for the carbon transfer calculation model of the energy storage device is as follows: ； In the formula: The carbon emissions generated by the energy storage device in the system during time period t; Let be the charging power of the energy storage device in the system during time period t; The carbon potential is input to charge the energy storage device in the system during time period t. To optimize the system's runtime time interval step size; This represents the carbon emissions generated by the energy storage device in the system during time period t. Let be the discharge power of the energy storage device in the system during time period t; The carbon potential output by the energy storage device in the system during time period t; Let be the internal carbon potential of the energy storage device in the system during time period t; Let be the amount of energy stored by the energy storage device in the system during time period t; The internal carbon potential of the energy storage device in the system during the (t-1)th time period; Let be the amount of electricity stored by the energy storage device in the system during the (t-1)th time period; The discharge efficiency of the energy storage device in the system; The internal carbon potential of the energy storage device in the system during the first time period; The system purchases the equivalent carbon potential of electricity in the first time period through the external line connection; Let be the carbon potential at the system node during time period t.

5. The multi-scenario risk optimization operation method for the integrated energy system of source-grid-load-storage carbon storage park according to claim 1, characterized in that, The system carbon transfer balance constraint is determined based on the carbon emission flow parameters of each unit device in the system, and the specific expression for setting the system carbon transfer balance constraint is as follows: ； In the formula: Let be the carbon emission flow rate of the wind and solar new energy power generation equipment in the system during time period t; The system purchases the carbon emission flow rate of electricity in time period t via external line connections; The carbon emission flow rate of the combined heat and power equipment in the system during time period t; Let be the discharge power of the energy storage device in the system during time period t; The carbon potential output by the energy storage device in the system during time period t; Let the system node carbon potential be at time t. Let be the charging power of the energy storage device in the system during time period t; Let be the electrical load demand of the system in time period t; The system transmits power back through the external line connection during time period t.

6. The multi-scenario risk optimization operation method for the integrated energy system of source-grid-load-storage carbon storage park according to claim 1, characterized in that, The optimized operation target scenarios include optimized operation modes for carbon reduction scenarios, optimized operation modes for low-consumption scenarios, optimized operation modes for cost reduction scenarios, and optimized operation modes for multiple scenarios with comprehensive indicators. The mathematical expression for the optimized operation mode of the emission reduction scenario is as follows: ； In the formula: The objective function for the system's total carbon emissions; This represents the total carbon emissions from non-new energy power generation equipment in the system. This represents the equivalent carbon emission reduction of wind and solar power generation equipment in the system. Let be the carbon emissions of non-new energy power generation equipment in the system during period t. This represents the actual power generation of the wind and solar renewable energy generation equipment in the system during time period t. The carbon potential of the wind and solar power generation equipment in the system during time period t; Optimize the set of runtime cycles for the system; To optimize the system's runtime time interval step size; Under the optimized operation mode of the emission reduction scenario, the mathematical model expression for the carbon emissions of non-new energy power generation equipment in each time period is as follows: ； In the formula: Let be the carbon emissions of non-new energy power generation equipment in the system during period t. The equivalent carbon emissions generated by the system in time period t when purchasing electricity through external connection lines; This represents the carbon emissions generated by the combined heat and power (CHP) equipment in the system during time period t. The system purchases the equivalent carbon potential of electricity in time period t through the external line connection. The system purchases electrical power in time period t through the external line connection. To optimize the system's runtime time interval step size; The carbon potential of the natural gas input is given to the combined heat and power equipment in the system during time period t. The natural gas input power of the combined heat and power equipment in the system during time period t; The mathematical expression for the optimized operation mode in the low-power scenario is as follows: ； In the formula: Let be the overall energy consumption objective function of the system; Let be the total energy consumption of the system in time period t; Optimize the set of runtime cycles for the system; The system purchases electrical power in time period t through the external line connection. The standard coal equivalent coefficient for electricity purchased by the system; The volumetric flow rate of natural gas during the operation of the combined heat and power (CHP) equipment in the system at time period t; The standard coal equivalent coefficient for natural gas consumption in the system; This represents the actual power generation of the wind and solar renewable energy generation equipment in the system during time period t. The standard coal equivalent coefficient for power generation from wind and solar new energy power generation equipment; To optimize the system's runtime time interval step size; The mathematical expression for the optimized operation mode in the cost reduction scenario is as follows: ； In the formula: The objective function is the total operating cost of the system. Let be the system's operating cost in time period t; The operating cost of the combined heat and power equipment in the system during time period t; The operating cost of the system during time period t is due to the purchase of electricity and the penalty for curtailment of wind and solar power generation equipment. The system purchases electrical power in time period t through the external line connection. The electricity price is calculated based on the time period purchased during time period t. To predict the power generation of wind and solar renewable energy generation equipment in time period t; The actual power generation of the wind and solar renewable energy power generation equipment in the system during time period t; The penalty factor for the absorption of wind and solar new energy power generation; The volumetric flow rate of natural gas during the operation of the combined heat and power (CHP) equipment in the system at time period t; The price of natural gas consumed by the system; Optimize the set of runtime cycles for the system; To optimize the system's runtime time interval step size; The mathematical expression for the multi-scenario comprehensive index optimization operation mode is as follows: ； In the formula: Let be the overall energy consumption objective function of the system; The objective function for the system's total carbon emissions; The objective function is the total operating cost of the system. The objective function is the comprehensive index of the system across multiple scenarios; , , These are the percentage coefficients of indicators for optimized operation modes in emission reduction scenarios, optimized operation modes in low-consumption scenarios, and optimized operation modes in cost reduction scenarios, respectively. , , These are the normalized and standardized values ​​of indicators for optimized operation modes in emission reduction scenarios, optimized operation modes in low-consumption scenarios, and optimized operation modes in cost reduction scenarios, respectively. , , These are the maximum values ​​of the objective functions under the optimized operation modes for emission reduction, low energy consumption, and cost reduction scenarios, respectively. , , These are the minimum objective functions for the optimized operation modes in emission reduction, low energy consumption, and cost reduction scenarios, respectively. , , These are the relaxation coefficients for extreme value calculations under the optimized operation modes for emission reduction, low energy consumption, and cost reduction scenarios, respectively. , These represent the total carbon emission objective function values ​​of the system under the low-consumption scenario optimized operation mode and the cost-reduction scenario optimized operation mode, respectively. , These are the total comprehensive energy consumption objective function values ​​of the system under the optimized operation mode for emission reduction scenarios and the optimized operation mode for cost reduction scenarios, respectively. , These represent the objective function values ​​for the total operating cost of the system in the emission reduction scenario optimization operation mode and the low-consumption scenario optimization operation mode, respectively.

7. The multi-scenario risk optimization operation method for the integrated energy system of source-grid-load-storage carbon storage park according to claim 1, characterized in that, Solving the optimization problem also yields the carbon emission flow distribution of each unit device in the system, and includes: The carbon emission flow distribution of each unit device in the system is input into the pre-constructed system carbon transfer status analysis model to obtain the carbon transfer status assessment result. Based on the assessment result, control instructions for adjusting system operating parameters are generated. The control instructions include capacity configuration optimization parameters or energy storage device maintenance and scheduling information. The expression for the system's carbon transfer situation analysis model is as follows: ； In the formula: This represents the total carbon input to the system. Optimize the set of runtime cycles for the system; The system purchases the carbon emission flow rate of electricity in time period t via external line connections; The carbon emission rate of natural gas input to the combined heat and power (CHP) equipment in the system during time period t; To optimize the system's runtime time interval step size; This represents the total carbon output of the system. Let the carbon emission flow rate of the waste heat recovery system in the system be the thermal power output during time period t. Let the system node carbon potential be at time t. Let be the electrical load demand of the system in time period t; The system transmits the equivalent carbon potential back through the external line connection at time t. The system transmits power back during time period t via external line connections; This represents the total carbon loss from energy storage devices. The internal carbon potential of the energy storage device in the system during the first time period; For the energy storage device in the system in the first The internal carbon potential of the time period, among which This indicates the last time period in the optimized runtime cycle; Initialize the energy storage capacity of the energy storage devices in the system; This represents the total carbon balance deviation of the system; Based on the evaluation results, control instructions are generated for adjusting system operating parameters, including: Based on the carbon transfer situation assessment results, the total carbon input of the system, the total carbon loss of energy storage devices, and the proportion of the total carbon output of the system are statistically obtained; based on the statistical results, optimization parameters for unit device capacity configuration are generated, and maintenance and scheduling information of energy storage devices is output. Based on the overall carbon balance deviation of the system in the carbon transfer situation assessment results, the degree of carbon-coordinated operation of the system is determined. If the carbon balance deviation is 0, the current operating parameter configuration is maintained. If the carbon balance deviation is not 0, the parameter verification process is initiated and a parameter verification request is generated.

8. A multi-scenario risk optimization operation system for an integrated energy system for source-grid-load-storage carbon storage in a park, characterized in that, include: Acquisition Module: Used to acquire diversified characteristic data of the park's energy system. The diversified characteristic data includes inherent physical attribute parameters of equipment, initial state of operation optimization and operation optimization control threshold parameters, reference standard configuration parameters, predicted source load time series data, and energy market attribute data. The first generation module is used to input the inherent physical attribute parameters of the device, the initial state of operation optimization, the operation optimization control threshold parameters, and the reference standard configuration parameters into the pre-built system unit device operation characteristic characterization model to obtain the operation characteristic parameters of each unit device in the system. The second generation module is used to input the operating characteristic parameters and the predicted source load time series data into the pre-built backup plan energy transfer constraint model to generate backup constraint conditions to resist the risk of source load imbalance. The third generation module is used to input the operating characteristic parameters and the predicted source load time series data into the pre-built carbon transfer calculation model to generate carbon emission flow parameters of each unit device in the system and carbon transfer balance constraints of the system. The optimization module is used to input the energy market attribute data into a pre-built optimized operation target scenario model. It uses the operating characteristic parameters and carbon emission flow parameters as equipment self-constraints, the backup constraints and carbon transfer balance constraints as system-level constraints, and the optimization objective function as the objective to construct and solve the optimization problem, thereby obtaining the optimal output plan and carbon emission flow distribution of each unit equipment in the system in multiple future time periods. The optimization objective function is determined according to the optimized operation target scenario selected by the user. Execution module: Used to output instructions for each time period in the optimal output plan to the execution mechanism, and control each unit device of the system to operate according to the instructions; Feedback module: Used to input the carbon emission flow distribution of each unit device in the system into the pre-built system carbon transfer status analysis model to obtain the carbon transfer status assessment results, and to optimize the system operating capacity configuration, or to overhaul energy storage equipment or adjust relevant parameters based on the assessment results.

9. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the steps of the method described in any one of claims 1-7.

10. A computer device, characterized in that, include: Memory, used to store computer programs / instructions; A processor for executing the computer program / instructions to implement the steps of the method according to any one of claims 1-7.

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