Intelligent planning method for energy equipment capacity of zero-carbon park based on energy-carbon collaborative optimization

Abstract

The application provides a zero-carbon park energy equipment capacity intelligent planning method based on energy-carbon collaborative optimization, and relates to the technical field of energy optimization, wherein the method comprises the following steps: constructing a multi-energy flow-carbon flow collaborative comprehensive energy physical model; constructing a energy equipment's flatness resource model for each energy equipment of the zero-carbon park; constructing a multi-energy flow-carbon flow collaborative capacity intelligent planning model based on the comprehensive energy physical model and the flatness resource model of each energy equipment; constructing a low-carbon operation strategy and sensitivity evaluation model; taking the minimum total resource quantity as the target, solving the capacity intelligent planning model based on the comprehensive energy physical model, the flatness resource model and the low-carbon operation strategy and sensitivity evaluation model, and obtaining the optimal capacity planning of each energy equipment, so that the capacity intelligent planning of different energy equipment in the zero-carbon park is realized with the minimum total resource quantity as the target.

Description

Technical Field

[0001] This invention relates to the field of energy optimization technology, and in particular to a method for intelligent planning of energy equipment capacity in zero-carbon industrial parks based on energy-carbon synergistic optimization. Background Technology

[0002] Zero-carbon industrial parks have become important demonstration platforms for building new power systems and integrated energy systems. By introducing various technologies such as wind power, photovoltaics, energy storage, demand response, and carbon capture within a region, zero-carbon parks achieve high-proportion clean energy consumption, cascaded energy utilization, and near-zero carbon emissions, representing a crucial path for the energy system's transformation towards a green, low-carbon, safe, and efficient model. However, the multi-energy flow, multi-temporal scale, and multi-constraint coupling characteristics within these parks make traditional single-energy planning or hierarchical scheduling methods insufficient to meet their synergistic optimization requirements for "dual energy and carbon control." Furthermore, with the gradual integration of emerging resources such as new energy storage, carbon capture, and energy-efficient power plants into the park's energy system, the dispatchability and low-carbon nature of the park's energy system have significantly improved, but this has also increased model complexity. Therefore, how to intelligently plan the capacity of different energy devices within a zero-carbon park while ensuring energy supply security and minimizing total resource consumption is a pressing technical problem that needs to be solved. Summary of the Invention

[0003] To address the problems existing in the prior art, this invention provides a method for intelligent planning of energy equipment capacity in zero-carbon industrial parks based on energy-carbon synergistic optimization.

[0004] This invention provides a method for intelligent planning of energy equipment capacity in zero-carbon industrial parks based on energy-carbon synergistic optimization, comprising the following steps.

[0005] A comprehensive energy physics model is constructed to coordinate multiple energy flows and carbon flows. This comprehensive energy physics model is used to characterize the interaction between electrical energy, thermal energy, cold energy, gas energy, carbon capture, and carbon flows. For each energy device in the zero-carbon park, a levelized resource model is constructed for the energy device. The levelized resource model is used to characterize the impact of the energy device's resource quantity, operation, fuel and emission factors on the total resource quantity. Based on the comprehensive energy physics model and the levelized resource model of each energy device, a capacity intelligent planning model for multi-energy flow-carbon flow coordination is constructed. A low-carbon operation strategy and sensitivity assessment model is constructed, which includes multiple constraints, including demand response constraints, wind and solar curtailment constraints, resource and equipment capacity constraints, and total carbon emission constraints. With the goal of minimizing total resources, the capacity intelligent planning model is solved based on the integrated energy physics model, the levelized resource model, and the low-carbon operation strategy and sensitivity assessment model to obtain the optimal capacity planning for each of the energy devices.

[0006] According to the present invention, a method for intelligent planning of energy equipment capacity in zero-carbon industrial parks based on energy-carbon synergistic optimization is provided, wherein the integrated energy physics model includes: in, express The actual power output of the wind turbine at any given time. express The wind curtailment rate of wind power equipment at all times. express Per-unit output coefficient of wind power equipment at all times This indicates the installable capacity of wind power equipment. express The actual power of the photovoltaic equipment at any given time. express The wind curtailment power of photovoltaic equipment at all times express The per-unit output coefficient of photovoltaic equipment at any given time. This indicates the installable capacity of photovoltaic equipment. express The actual power of the gas turbine at any given time. This indicates the electrical conversion efficiency of the gas turbine. express The gas consumption of the gas turbine at any given time. Indicates the calorific value of natural gas. express The heat of the gas turbine at all times, This indicates the heat conversion efficiency of the gas turbine. This indicates the electrical power of the gas turbine. express The heat output of the gas boiler at all times This indicates the heat conversion efficiency of a gas-fired boiler. express The gas consumption of the gas boiler at all times. This indicates the heat output of the gas-fired boiler. express The cooling capacity of a constant absorption refrigeration unit. The coefficient of performance (COP) of an absorption refrigeration system. express The heat absorption of the constant absorption cooling equipment This indicates the heat absorption capacity of the absorption refrigeration equipment. express The cooling capacity of the electric refrigeration equipment at all times. The coefficient of performance (COP) of an electric refrigeration device. express The power consumption of the refrigeration equipment at all times. Indicates the input power of the electric refrigeration equipment. express The charging power of the electric cooling equipment at all times. Indicates the power of the energy storage device. This indicates the discharge power of the energy storage device. This represents the Big M rule. express The energy storage binary variable of the instantaneous energy storage device. express The binary variable representing the discharge of the energy storage device at any given moment. express The state of charge of the battery in the energy storage device at all times. Indicates the capacity of the energy storage device. Indicates the scheduling period. express The heat absorption capacity of the thermal energy storage device at all times. express The heat released by the thermal energy storage device at all times Indicates the power of the thermal energy storage device. express The battery state of charge of the thermal energy storage device at all times. Indicates the capacity of the thermal energy storage equipment. express The power consumption of thermal energy storage equipment at all times. Indicates the carbon capture and conversion factor. express The quality of carbon capture at all times express Electricity purchased from the grid at any time express Constant electrical load, express Power can be increased in response to demand. express Power reduction should be adjusted in response to demand. express Constant heat load, express Always cold load, express Purchase gas at any time. express Constant air load.

[0007] According to the present invention, a method for intelligent planning of energy equipment capacity in zero-carbon industrial parks based on energy-carbon synergistic optimization is provided, wherein the levelized resource model of the energy equipment includes: in, Indicates the resource recovery coefficient. Indicates energy equipment The discount rate, Indicates energy equipment lifespan, Indicates energy equipment Considering the residual value, the equivalent annual value, Indicates energy equipment The amount of resources invested per unit capacity at one time. Indicates energy equipment residual value rate Indicates energy equipment The average annualized daily resource input Indicates energy equipment The annual fixed operation and maintenance cost per unit capacity.

[0008] According to the present invention, a method for intelligent planning of energy equipment capacity in zero-carbon industrial parks based on energy-carbon synergistic optimization is provided. The intelligent capacity planning model includes: in, This represents the total amount of resources. Indicates the amount of purchased electricity resources. Indicates the amount of gas resources purchased. Indicates the amount of resources required to respond to demand. Indicates the amount of resources invested in equipment. This indicates the amount of wind and solar resources that were abandoned. express Real-time power grid resource availability Indicates the amount of natural gas resources. Indicates the amount of resources required to respond to demand. This represents the average annualized daily resource input for wind power equipment. This indicates the average annualized daily resource input for photovoltaic equipment. This represents the average annualized daily resource input for gas turbines. This indicates the average annualized daily resource input for gas-fired boilers. This indicates the average annualized daily resource input for electric refrigeration equipment. This represents the average annualized daily resource input for absorption refrigeration equipment. This indicates the average annualized daily resource input for carbon capture equipment. This indicates the available installed capacity of carbon capture equipment. This indicates the average annualized daily resource input for energy storage equipment. This indicates the average annualized daily resource input for thermal energy storage equipment. This indicates the amount of wind and solar resources abandoned per unit.

[0009] According to the present invention, a method for intelligent planning of energy equipment capacity in zero-carbon industrial parks based on energy-carbon synergistic optimization is provided, wherein the low-carbon operation strategy and sensitivity assessment model include: in, This represents the maximum coefficient of demand response. Indicates the maximum electrical load. Indicates time difference, This indicates the amount of wind and solar power that has been forfeited. Indicates the maximum amount of electricity that can be purchased. Indicates the maximum cooling load. Indicates the maximum heat load. Indicates carbon emissions. Indicates the carbon emission coefficient of electricity purchase. Indicates the carbon emission coefficient of a gas turbine. Indicates the carbon emission coefficient of a gas-fired boiler. This indicates the carbon emission limit for zero-carbon industrial parks.

[0010] According to the present invention, a method for intelligent capacity planning of energy equipment in a zero-carbon industrial park based on energy-carbon synergistic optimization is provided. The method aims to minimize total resource consumption by solving the intelligent capacity planning model based on the integrated energy physics model, the levelized resource model, and the low-carbon operation strategy and sensitivity assessment model to obtain the optimal capacity planning for each energy device. The method includes: With the goal of minimizing total resources, a mixed-integer linear programming algorithm is used to solve the capacity intelligent planning model based on the comprehensive energy physics model, the levelized resource model, and the low-carbon operation strategy and sensitivity assessment model, thereby obtaining the optimal capacity planning for each of the energy devices.

[0011] According to the present invention, a method for intelligent planning of energy equipment capacity in zero-carbon industrial parks based on energy-carbon synergistic optimization is provided, the method further comprising: With the goal of minimizing total resources, a mixed-integer linear programming algorithm is used to solve the capacity intelligent planning model based on the comprehensive energy physics model, the levelized resource model, and the low-carbon operation strategy and sensitivity assessment model, thereby obtaining the clean energy ratio and carbon emission intensity of the zero-carbon park.

[0012] This invention also provides a smart planning device for energy equipment capacity in zero-carbon industrial parks based on energy-carbon synergistic optimization, comprising: The first building unit is used to construct a comprehensive energy physics model that integrates multiple energy flows and carbon flows. The comprehensive energy physics model is used to characterize the interaction between electrical energy, thermal energy, cold energy, gas energy, carbon capture, and carbon flows. The second construction unit is used to construct a levelized resource model for each energy device in the zero-carbon park. The levelized resource model is used to characterize the impact of the energy device's resource quantity, operation, fuel and emission factors on the total resource quantity. The third building unit is used to build a multi-energy flow-carbon flow coordinated capacity intelligent planning model based on the integrated energy physics model and the levelized resource model of each energy device. The fourth building unit is used to build a low-carbon operation strategy and sensitivity assessment model. The low-carbon operation strategy and sensitivity assessment model includes multiple constraints, including demand response constraints, wind and solar curtailment constraints, resource and equipment capacity constraints, and total carbon emission constraints. The planning unit is used to solve the capacity intelligent planning model based on the integrated energy physics model, the levelized resource model, and the low-carbon operation strategy and sensitivity assessment model, with the goal of minimizing the total resource volume, to obtain the optimal capacity planning for each of the energy devices.

[0013] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the intelligent planning method for energy equipment capacity in zero-carbon parks based on energy-carbon synergistic optimization as described above.

[0014] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the intelligent planning method for energy equipment capacity in zero-carbon parks based on energy-carbon synergistic optimization as described above.

[0015] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the intelligent planning method for energy equipment capacity in zero-carbon industrial parks based on energy-carbon synergistic optimization as described above.

[0016] This invention provides an intelligent capacity planning method for energy equipment in zero-carbon parks based on energy-carbon synergistic optimization. It constructs a comprehensive energy physics model to characterize the interaction between electricity, heat, cooling, gas, carbon capture, and carbon flow. For each energy device in a zero-carbon park, it constructs a leveled resource model to characterize the impact of resource quantity, operation, fuel, and emission factors on the total resource quantity. Based on the comprehensive energy physics model and the leveled resource models of each energy device, it constructs an intelligent capacity planning model for multi-energy-carbon flow synergy. It also constructs a low-carbon operation strategy and sensitivity assessment model including multiple constraints. Finally, with the goal of minimizing the total resource quantity, it solves the intelligent capacity planning model based on the comprehensive energy physics model, the leveled resource model, and the low-carbon operation strategy and sensitivity assessment model to obtain the optimal capacity planning for each energy device. This achieves intelligent capacity planning for different energy devices in a zero-carbon park with the goal of minimizing the total resource quantity, significantly improving the comprehensive utilization efficiency and carbon emission reduction capacity of multi-energy systems in zero-carbon parks, and providing a generalized modeling and decision-making basis for new power systems and zero-carbon park planning. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating the intelligent planning method for energy equipment capacity in zero-carbon industrial parks based on energy-carbon synergistic optimization, provided in an embodiment of the present invention.

[0019] Figure 2 This is a schematic diagram of the physical model of a zero-carbon park provided in an embodiment of the present invention.

[0020] Figure 3 This is a schematic diagram of the structure of the intelligent planning device for zero-carbon park energy equipment capacity based on energy-carbon synergistic optimization provided in an embodiment of the present invention.

[0021] Figure 4 This is a schematic diagram of the physical structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0023] The following is combined with Figure 1 and Figure 2 This invention describes a method for intelligent planning of energy equipment capacity in zero-carbon parks based on energy-carbon synergistic optimization. The executing entity of this method can be an electronic device such as a computer, tablet computer, terminal, or server, or it can be an intelligent planning device for zero-carbon park energy equipment capacity based on energy-carbon synergistic optimization installed in such an electronic device. This intelligent planning device can be implemented through software, hardware, or a combination of both.

[0024] Figure 1 This is a flowchart illustrating the intelligent planning method for zero-carbon park energy equipment capacity based on energy-carbon synergistic optimization provided in this embodiment of the invention. Figure 1 As shown, the intelligent planning method for energy equipment capacity in zero-carbon industrial parks based on energy-carbon synergistic optimization includes the following steps: Step 101: Construct a comprehensive energy physics model that integrates multiple energy flows and carbon flows. The comprehensive energy physics model is used to characterize the interaction between electrical energy, thermal energy, cold energy, gas energy, carbon capture, and carbon flows.

[0025] Among them, multiple energy flows include electrical energy, thermal energy, cold energy, gas energy, and carbon capture.

[0026] For example, a comprehensive energy physics model that integrates multiple energy flows and carbon flows is used to construct a quantitative relationship of close coupling between electrical energy, thermal energy, cold energy, gas energy, carbon capture, and carbon flows, so as to characterize their synergistic interaction and achieve a unified description of energy and carbon flows.

[0027] Step 102: For each energy device in the zero-carbon park, construct a levelized resource model for the energy device. The levelized resource model is used to characterize the impact of the energy device's resource quantity, operation, fuel and emission factors on the total resource quantity.

[0028] The energy equipment in the zero-carbon park includes wind power equipment, photovoltaic equipment, gas turbines, gas boilers, absorption refrigeration equipment, electric refrigeration equipment, electric energy storage equipment, thermal energy storage equipment, and carbon capture equipment.

[0029] For example, levelized resource models are constructed for wind power equipment, photovoltaic equipment, gas turbines, gas boilers, absorption refrigeration equipment, electric refrigeration equipment, electric energy storage equipment, thermal energy storage equipment, and carbon capture equipment, respectively.

[0030] Step 103: Based on the comprehensive energy physics model and the levelized resource model of each energy device, construct a capacity intelligent planning model that coordinates multiple energy flows and carbon flows.

[0031] For example, based on the integrated energy physics model and the levelized resource model of each energy device, a multi-energy flow-carbon flow coordinated capacity intelligent planning model is constructed. The capacity intelligent planning model considers the constraints of electricity, heat, cooling and carbon emissions, and aims to minimize the total resource volume to intelligently plan the capacity of each energy device.

[0032] Step 104: Construct a low-carbon operation strategy and sensitivity assessment model. The low-carbon operation strategy and sensitivity assessment model includes multiple constraints, including demand response constraints, wind and solar curtailment constraints, resource and equipment capacity constraints, and total carbon emission constraints.

[0033] For example, the constructed low-carbon operation strategy and sensitivity assessment model includes demand response constraints, wind and solar curtailment constraints, resource and equipment capacity constraints, and total carbon emission constraints, etc., to evaluate the rationality, carbon intensity, and system flexibility of the intelligent planning scheme for energy equipment capacity under multiple constraints.

[0034] Step 105: With the goal of minimizing total resources, based on the integrated energy physics model, the levelized resource model, and the low-carbon operation strategy and sensitivity assessment model, solve the capacity intelligent planning model to obtain the optimal capacity planning for each of the energy devices.

[0035] For example, after constructing the integrated energy physics model, the levelized resource model for each energy device, and the low-carbon operation strategy and sensitivity assessment model, the entire content of the integrated energy physics model, the levelized resource model for each energy device, and the low-carbon operation strategy and sensitivity assessment model can be combined. With the goal of minimizing the total resource quantity, that is, minimizing the sum of the resource quantities of wind power equipment, photovoltaic equipment, gas turbines, gas boilers, absorption refrigeration equipment, electric refrigeration equipment, electric energy storage equipment, thermal energy storage equipment, and carbon capture equipment, the intelligent optimization algorithm is used to solve the capacity intelligent planning model. Under the premise of satisfying the supply and demand balance of various energy sources, equipment operation constraints, carbon emission limits, and system reliability requirements, the optimal capacity plan for each wind power equipment, photovoltaic equipment, gas turbine, gas boiler, absorption refrigeration equipment, electric refrigeration equipment, electric energy storage equipment, thermal energy storage equipment, and carbon capture equipment is finally output. Among them, the optimal capacity planning for wind power equipment refers to the optimal installed power of wind power equipment; the optimal capacity planning for photovoltaic equipment refers to the optimal installed power of photovoltaic equipment; the optimal capacity planning for gas turbines refers to the optimal rated power of gas turbines; the optimal capacity planning for gas boilers refers to the optimal rated power of gas boilers; the optimal capacity planning for absorption refrigeration equipment refers to the optimal refrigeration power of absorption refrigeration equipment; the optimal capacity planning for electric refrigeration equipment refers to the optimal refrigeration power of electric refrigeration equipment; the optimal capacity planning for electric energy storage equipment refers to the optimal rated power and optimal rated capacity of electric energy storage equipment; the optimal capacity planning for thermal energy storage equipment refers to the optimal energy storage power and optimal thermal storage capacity of thermal energy storage equipment; and the optimal capacity planning for carbon capture equipment refers to the optimal capture capacity of carbon capture equipment, that is, the maximum carbon capture per unit time or per year, providing a quantitative decision-making basis for the zero-carbon construction and operation of zero-carbon parks.

[0036] This invention provides an intelligent capacity planning method for energy equipment in zero-carbon parks based on energy-carbon synergistic optimization. It constructs a comprehensive energy physics model to characterize the interaction between electricity, heat, cooling, gas, carbon capture, and carbon flow. For each energy device in a zero-carbon park, it constructs a leveled resource model to characterize the impact of resource quantity, operation, fuel, and emission factors on the total resource quantity. Based on the comprehensive energy physics model and the leveled resource models of each energy device, it constructs an intelligent capacity planning model for multi-energy-carbon flow synergy. It also constructs a low-carbon operation strategy and sensitivity assessment model including multiple constraints. Finally, with the goal of minimizing the total resource quantity, it solves the intelligent capacity planning model based on the comprehensive energy physics model, the leveled resource model, and the low-carbon operation strategy and sensitivity assessment model to obtain the optimal capacity planning for each energy device. This achieves intelligent capacity planning for different energy devices in a zero-carbon park with the goal of minimizing the total resource quantity, significantly improving the comprehensive utilization efficiency and carbon emission reduction capacity of multi-energy systems in zero-carbon parks, and providing a generalized modeling and decision-making basis for new power systems and zero-carbon park planning.

[0037] In one embodiment, the integrated energy physics model includes: in, express The actual power output of the wind turbine at any given time. express The wind curtailment rate of wind power equipment at all times. express Per-unit output coefficient of wind power equipment at all times This indicates the installable capacity of wind power equipment. express The actual power of the photovoltaic equipment at any given time. express The wind curtailment power of photovoltaic equipment at all times express The per-unit output coefficient of photovoltaic equipment at any given time. This indicates the installable capacity of photovoltaic equipment. express The actual power of the gas turbine at any given time. This indicates the electrical conversion efficiency of the gas turbine. express The gas consumption of the gas turbine at any given time. Indicates the calorific value of natural gas. express The heat of the gas turbine at all times, This indicates the heat conversion efficiency of the gas turbine. This indicates the electrical power of the gas turbine. express The heat output of the gas boiler at all times This indicates the heat conversion efficiency of a gas-fired boiler. express The gas consumption of the gas boiler at all times. This indicates the heat output of the gas-fired boiler. express The cooling capacity of a constant absorption refrigeration unit. The coefficient of performance (COP) of an absorption refrigeration system. express The heat absorption of the constant absorption cooling equipment This indicates the heat absorption capacity of the absorption refrigeration equipment. express The cooling capacity of the electric refrigeration equipment at all times. The coefficient of performance (COP) of an electric refrigeration device. express The power consumption of the refrigeration equipment at all times. Indicates the input power of the electric refrigeration equipment. express The charging power of the electric cooling equipment at all times. Indicates the power of the energy storage device. This indicates the discharge power of the energy storage device. This represents the Big M rule. express The energy storage binary variable of the instantaneous energy storage device. express The binary variable representing the discharge of the energy storage device at any given moment. express The state of charge of the battery in the energy storage device at all times. Indicates the capacity of the energy storage device. This indicates the scheduling period, which can be one day, one year, etc. express The heat absorption capacity of the thermal energy storage device at all times. express The heat released by the thermal energy storage device at all times Indicates the power of the thermal energy storage device. express The battery state of charge of the thermal energy storage device at all times. Indicates the capacity of the thermal energy storage equipment. express The power consumption of thermal energy storage equipment at all times. Indicates the carbon capture and conversion factor. express The quality of carbon capture at all times express Electricity purchased from the grid at any time express Constant electrical load, express Power can be increased in response to demand. express Power reduction should be adjusted in response to demand. express Constant heat load, express Always cold load, express Purchase gas at any time. express Constant air load.

[0038] For example, the integrated energy physics model systematically integrates the operating characteristics of energy equipment, energy storage dynamics, multi-energy balance and carbon capture process through the above comprehensive system of equality and inequality constraints, thereby realizing a complete physical characterization of the synergistic interaction between electrical energy, thermal energy, cold energy, gas energy and carbon flow.

[0039] In one embodiment, the levelized resource model of the energy equipment includes: in, Indicates the resource recovery coefficient. Indicates energy equipment The discount rate, Indicates energy equipment lifespan, Indicates energy equipment Considering the residual value, the equivalent annual value, Indicates energy equipment The amount of resources invested per unit capacity at one time. Indicates energy equipment residual value rate Indicates energy equipment The average annualized daily resource input Indicates energy equipment The annual fixed operation and maintenance cost per unit capacity.

[0040] For example, by constructing a levelized resource model for energy equipment, the impact of energy equipment's resource quantity, operation, fuel, and emission factors on the total resource quantity can be analyzed.

[0041] In one embodiment, the capacity intelligent planning model includes: in, This represents the total amount of resources. Indicates the amount of purchased electricity resources. Indicates the amount of gas resources purchased. Indicates the amount of resources required to respond to demand. Indicates the amount of resources invested in equipment. This indicates the amount of wind and solar resources that were abandoned. express Real-time power grid resource availability Indicates the amount of natural gas resources. Indicates the amount of resources required to respond to demand. This represents the average annualized daily resource input for wind power equipment. This indicates the average annualized daily resource input for photovoltaic equipment. This represents the average annualized daily resource input for gas turbines. This indicates the average annualized daily resource input for gas-fired boilers. This indicates the average annualized daily resource input for electric refrigeration equipment. This represents the average annualized daily resource input for absorption refrigeration equipment. This indicates the average annualized daily resource input for carbon capture equipment. This indicates the available installed capacity of carbon capture equipment. This indicates the average annualized daily resource input for energy storage equipment. This indicates the average annualized daily resource input for thermal energy storage equipment. This indicates the amount of wind and solar resources abandoned per unit.

[0042] For example, the capacity intelligent planning model aims to minimize the total resource volume and solves the optimal capacity planning for each energy device while considering constraints such as wind and solar power output fluctuations, energy storage peak shaving, demand response flexibility, and carbon capture.

[0043] In one embodiment, the low-carbon operation strategy and sensitivity assessment model includes: in, This represents the maximum coefficient of demand response. Indicates the maximum electrical load. Indicates time difference, This indicates the amount of wind and solar power that has been forfeited. Indicates the maximum amount of electricity that can be purchased. Indicates the maximum cooling load. Indicates the maximum heat load. Indicates carbon emissions. Indicates the carbon emission coefficient of electricity purchase. Indicates the carbon emission coefficient of a gas turbine. Indicates the carbon emission coefficient of a gas-fired boiler. This indicates the carbon emission limit for zero-carbon industrial parks.

[0044] It should be noted that the maximum demand response coefficient, maximum electrical load, wind and solar power curtailment, maximum electricity purchase, maximum cooling load, maximum heating load, carbon emission coefficient of purchased electricity, carbon emission coefficient of gas turbine, carbon emission coefficient of gas boiler, and carbon emission limit of zero-carbon park in the low-carbon operation strategy and sensitivity assessment model can all be adjusted based on actual needs, and this invention does not limit them.

[0045] In one embodiment, step 105 above aims to minimize the total resource quantity. Based on the integrated energy physics model, the levelized resource model, and the low-carbon operation strategy and sensitivity assessment model, the capacity intelligent planning model is solved to obtain the optimal capacity planning for each of the energy devices. This can be achieved in the following ways: With the goal of minimizing total resources, a mixed-integer linear programming algorithm is used to solve the capacity intelligent planning model based on the comprehensive energy physics model, the levelized resource model, and the low-carbon operation strategy and sensitivity assessment model, thereby obtaining the optimal capacity planning for each of the energy devices.

[0046] For example, mixed-integer linear programming is an optimization method that solves for the extrema (maximization or minimization) of a linear objective function under a set of linear equality or inequality constraints. In zero-carbon park capacity planning, this linear programming algorithm takes the minimization of total resources as the objective function. It transforms the energy balance constraints, equipment operation constraints, and carbon emission constraints in the integrated energy physics model into linear equality or inequality constraints. Based on the energy equipment input resources and operation and maintenance costs in the levelized resource model, it determines the average annualized daily input resources of energy equipment. It transforms the multi-scenario conditions in the low-carbon operation strategy and sensitivity assessment model into a parameter set of constraints. Using standard solvers such as the simplex method or interior point method, it searches for the optimal solution that minimizes the objective function within the feasible region that satisfies all constraints. Finally, it outputs the optimal capacity planning for wind power equipment, photovoltaic equipment, gas turbines, gas boilers, absorption refrigeration equipment, electric refrigeration equipment, electric energy storage equipment, thermal energy storage equipment, and carbon capture equipment, providing a quantitative decision-making basis for the zero-carbon construction and operation of zero-carbon parks.

[0047] In one embodiment, the intelligent planning method for energy equipment capacity in zero-carbon industrial parks based on energy-carbon synergistic optimization further includes the following steps: With the goal of minimizing total resources, a mixed-integer linear programming algorithm is used to solve the capacity intelligent planning model based on the comprehensive energy physics model, the levelized resource model, and the low-carbon operation strategy and sensitivity assessment model, thereby obtaining the clean energy ratio and carbon emission intensity of the zero-carbon park.

[0048] For example, this mixed-integer linear programming algorithm takes minimizing total resources as its objective function. It transforms energy balance constraints, equipment operation constraints, and carbon emission constraints in the integrated energy physics model into linear equality or inequality constraints. Based on the energy equipment's input resources and operation and maintenance costs in the levelized resource model, it determines the average annualized daily input resources of energy equipment. It transforms the multi-scenario conditions in the low-carbon operation strategy and sensitivity assessment model into a parameter set of constraints. Using standard solvers such as the simplex method or interior point method, it searches for the optimal solution that minimizes the objective function within the feasible region that satisfies all constraints. Ultimately, it not only outputs the optimal capacity planning for wind power equipment, photovoltaic equipment, gas turbines, gas boilers, absorption refrigeration equipment, electric refrigeration equipment, electric energy storage equipment, thermal energy storage equipment, and carbon capture equipment, but also directly calculates the two key performance indicators of the zero-carbon park: the proportion of clean energy generation and the ratio of total carbon emissions to total energy supply from the solution results. This provides a scientific basis for zero-carbon park construction.

[0049] Figure 2 This is a schematic diagram of the physical model within a zero-carbon park provided in an embodiment of the present invention, such as... Figure 2As shown, this diagram illustrates the coupling and synergy of multiple energy flows and carbon flows from wind power equipment, photovoltaic equipment, gas turbines, gas boilers, absorption refrigeration equipment, electric refrigeration equipment, electric energy storage equipment, thermal energy storage equipment, and carbon capture equipment. This can also be termed a park model integrating source, grid, load, storage, and carbon. Distributed clean energy, represented by wind power and photovoltaic equipment, together with electricity from the upper-level grid, constitute the green portion of the power flow, driving electric loads, electric refrigeration, carbon capture, and electric energy storage. Natural gas (the yellow portion of the gas flow) serves as the key fuel and regulating energy source, driving gas turbines and gas boilers. The gas turbines achieve combined heat and power (CHP), producing electricity and heat (the red portion of the heat flow). The gas boilers supplement heat production; the heat can be directly supplied to the heat load or stored in thermal storage equipment or used to drive absorption refrigeration equipment for cooling (the blue portion of the cold flow). The cooling side is met by both electric refrigeration equipment and absorption refrigeration equipment. Carbon emissions (the brown carbon stream) generated by gas-fired boilers and gas turbines are directed to carbon capture equipment for centralized treatment. This significantly reduces the carbon intensity of the system while meeting the demands of various loads, including electricity, heat, cooling, and gas, providing a clear physical framework for achieving energy-efficient utilization and near-zero carbon emission operation.

[0050] Based on the integrated source-grid-load-storage-carbon park model and all the constraints in the above models, this invention uses a mixed-integer linear programming algorithm with the minimum total resource quantity as the objective function to solve the capacity intelligent planning model. It outputs the optimal capacity planning for each of the following: wind power equipment, photovoltaic equipment, gas turbines, gas boilers, absorption refrigeration equipment, electric refrigeration equipment, electric energy storage equipment, thermal energy storage equipment, and carbon capture equipment; the clean energy ratio and carbon emission intensity of the zero-carbon park; etc.

[0051] To align with engineering applications, a scheduling scenario set can be constructed using hourly and annual equivalent operating condition extensions. This allows for joint simulation of wind power, photovoltaics, upstream power grid, gas turbines, gas boilers, electric energy storage, thermal energy storage, electric refrigeration and absorption refrigeration, carbon capture and utilization (CCUS), and demand response (DR). CCUS is also known as Carbon Capture Utilization and Storage, as shown in the comprehensive energy physics model of multi-energy flow-carbon flow coordination described above. Furthermore, a levelized resource model for each of the aforementioned energy devices is used to achieve consistent measurement of resource quantity and carbon emissions. The hourly electricity load, heat load, cooling load, and gas load for the target year can be obtained from historical or predicted load curves. The long-term operating results of the wind and solar power output curves are used as external boundaries (including the per-unit output coefficients of wind power equipment and photovoltaic equipment, etc.). Explicit constraints are imposed on mutual exclusion of energy storage charging and discharging, state of charge (SOC), demand response up-adjustment power, demand response down-adjustment power, external power purchase limits, and carbon emission accounting, as shown in the expression of the integrated energy physics model above. This includes constraints related to each energy device. To reflect the transmission capacity of a high proportion of renewable energy systems, a power generation cut-off mechanism is introduced into the integrated energy physics model. , When renewable energy sources are unable to be transmitted to other regions or are limited in internal consumption during certain periods, the output of some generating units will be cut off and included in the curtailment penalty, thereby truly assessing the impact of curtailment and resource volume on scenarios with high penetration of new energy.

[0052] The present invention provides an intelligent planning method for energy equipment capacity in zero-carbon industrial parks based on energy-carbon synergistic optimization. This method establishes an intelligent planning approach that balances energy flow and carbon flow while minimizing total energy consumption, based on achieving multi-energy complementary operation and coordinated carbon emission control in zero-carbon industrial parks. Taking a multi-energy system with five energy flows—electricity, heat, cooling, gas, and carbon—as the research object, it comprehensively considers factors such as wind power, photovoltaics, energy storage, gas turbines, gas boilers, refrigeration systems, carbon capture, and demand response. Through mixed-integer linear programming, it achieves capacity configuration and operational optimization of the source-grid-load-storage-carbon system, solving the problem of insufficient carbon constraints and flexibility coordination in existing integrated energy system planning models.

[0053] It should be noted that all resource quantities involved in this invention can be understood as costs.

[0054] The following describes the intelligent planning device for energy equipment capacity in zero-carbon parks based on energy-carbon synergistic optimization provided by the present invention. The intelligent planning device for energy equipment capacity in zero-carbon parks based on energy-carbon synergistic optimization described below can be referred to in correspondence with the intelligent planning method for energy equipment capacity in zero-carbon parks based on energy-carbon synergistic optimization described above.

[0055] Figure 3 This is a schematic diagram of the intelligent planning device for zero-carbon park energy equipment capacity based on energy-carbon synergistic optimization provided in an embodiment of the present invention, as shown below. Figure 3 As shown, the intelligent planning device 300 for zero-carbon park energy equipment capacity based on energy-carbon synergistic optimization includes a first building unit 301, a second building unit 302, a third building unit 303, a fourth building unit 304, and a planning unit 305; wherein: The first building unit 301 is used to build a comprehensive energy physics model of multi-energy flow-carbon flow synergy, wherein the comprehensive energy physics model is used to characterize the interaction relationship between electrical energy, thermal energy, cold energy, gas energy, carbon capture and carbon flow; The second construction unit 302 is used to construct a levelized resource model for each energy device in the zero-carbon park. The levelized resource model is used to characterize the impact of the energy device's resource quantity, operation, fuel and emission factors on the total resource quantity. The third construction unit 303 is used to construct a multi-energy flow-carbon flow coordinated capacity intelligent planning model based on the integrated energy physics model and the levelized resource model of each energy device. The fourth building unit 304 is used to build a low-carbon operation strategy and sensitivity assessment model. The low-carbon operation strategy and sensitivity assessment model includes multiple constraints, including demand response constraints, wind and solar curtailment constraints, resource and equipment capacity constraints, and total carbon emission constraints. Planning unit 305 is used to solve the capacity intelligent planning model based on the integrated energy physics model, the levelized resource model, and the low-carbon operation strategy and sensitivity assessment model with the goal of minimizing total resources, so as to obtain the optimal capacity planning for each of the energy devices.

[0056] This invention provides an intelligent capacity planning device for energy equipment in zero-carbon parks based on energy-carbon synergistic optimization. It constructs a comprehensive energy physics model to characterize the interaction between electricity, heat, cold, gas, carbon capture, and carbon flow. For each energy device in a zero-carbon park, it constructs a leveled resource model to characterize the impact of resource quantity, operation, fuel, and emission factors of the energy device on the total resource quantity. Based on the comprehensive energy physics model and the leveled resource models of each energy device, it constructs an intelligent capacity planning model for multi-energy flow-carbon flow synergy. It also constructs a low-carbon operation strategy and sensitivity assessment model including multiple constraints. Finally, with the goal of minimizing the total resource quantity, it solves the intelligent capacity planning model based on the comprehensive energy physics model, the leveled resource model, and the low-carbon operation strategy and sensitivity assessment model to obtain the optimal capacity planning for each energy device. This achieves intelligent capacity planning for different energy devices in a zero-carbon park with the goal of minimizing the total resource quantity, significantly improving the comprehensive utilization efficiency and carbon emission reduction capacity of multi-energy systems in zero-carbon parks, and providing a generalized modeling and decision-making basis for new power systems and zero-carbon park planning.

[0057] Based on any of the above embodiments, the integrated energy physics model includes: in, express The actual power output of the wind turbine at any given time. express The wind curtailment rate of wind power equipment at all times. express Per-unit output coefficient of wind power equipment at all times This indicates the installable capacity of wind power equipment. express The actual power of the photovoltaic equipment at any given time. express The wind curtailment power of photovoltaic equipment at all times express The per-unit output coefficient of photovoltaic equipment at any given time. This indicates the installable capacity of photovoltaic equipment. express The actual power of the gas turbine at any given time. This indicates the electrical conversion efficiency of the gas turbine. express The gas consumption of the gas turbine at any given time. Indicates the calorific value of natural gas. express The heat of the gas turbine at all times, This indicates the heat conversion efficiency of the gas turbine. This indicates the electrical power of the gas turbine. express The heat output of the gas boiler at all times This indicates the heat conversion efficiency of a gas-fired boiler. express The gas consumption of the gas boiler at all times. This indicates the heat output of the gas-fired boiler. express The cooling capacity of a constant absorption refrigeration unit. The coefficient of performance (COP) of an absorption refrigeration system. express The heat absorption of the constant absorption cooling equipment This indicates the heat absorption capacity of the absorption refrigeration equipment. express The cooling capacity of the electric refrigeration equipment at all times. The coefficient of performance (COP) of an electric refrigeration device. express The power consumption of the refrigeration equipment at all times. Indicates the input power of the electric refrigeration equipment. express The charging power of the electric cooling equipment at all times. Indicates the power of the energy storage device. This indicates the discharge power of the energy storage device. This represents the Big M rule. express The energy storage binary variable of the instantaneous energy storage device. express The binary variable representing the discharge of the energy storage device at any given moment. express The state of charge of the battery in the energy storage device at all times. Indicates the capacity of the energy storage device. Indicates the scheduling period. express The heat absorption capacity of the thermal energy storage device at all times. express The heat released by the thermal energy storage device at all times Indicates the power of the thermal energy storage device. express The battery state of charge of the thermal energy storage device at all times. Indicates the capacity of the thermal energy storage equipment. express The power consumption of thermal energy storage equipment at all times. Indicates the carbon capture and conversion factor. express The quality of carbon capture at all times express Electricity purchased from the grid at any time express Constant electrical load, express Power can be increased in response to demand. express Power reduction should be adjusted in response to demand. express Constant heat load, express Always cold load, express Purchase gas at any time. express Constant air load.

[0058] Based on any of the above embodiments, the levelized resource model of the energy equipment includes: in, Indicates the resource recovery coefficient. Indicates energy equipment The discount rate, Indicates energy equipment lifespan, Indicates energy equipment Considering the residual value, the equivalent annual value, Indicates energy equipment The amount of resources invested per unit capacity at one time. Indicates energy equipment residual value rate Indicates energy equipment The average annualized daily resource input Indicates energy equipment The annual fixed operation and maintenance cost per unit capacity.

[0059] Based on any of the above embodiments, the capacity intelligent planning model includes: in, This represents the total amount of resources. Indicates the amount of purchased electricity resources. Indicates the amount of gas resources purchased. Indicates the amount of resources required to respond to demand. Indicates the amount of resources invested in equipment. This indicates the amount of wind and solar resources that were abandoned. express Real-time power grid resource availability Indicates the amount of natural gas resources. Indicates the amount of resources required to respond to demand. This represents the average annualized daily resource input for wind power equipment. This indicates the average annualized daily resource input for photovoltaic equipment. This represents the average annualized daily resource input for gas turbines. This indicates the average annualized daily resource input for gas-fired boilers. This indicates the average annualized daily resource input for electric refrigeration equipment. This represents the average annualized daily resource input for absorption refrigeration equipment. This indicates the average annualized daily resource input for carbon capture equipment. This indicates the available installed capacity of carbon capture equipment. This indicates the average annualized daily resource input for energy storage equipment. This indicates the average annualized daily resource input for thermal energy storage equipment. This indicates the amount of wind and solar resources abandoned per unit.

[0060] Based on any of the above embodiments, the low-carbon operation strategy and sensitivity assessment model includes: in, This represents the maximum coefficient of demand response. Indicates the maximum electrical load. Indicates time difference, This indicates the amount of wind and solar power that has been forfeited. Indicates the maximum amount of electricity that can be purchased. Indicates the maximum cooling load. Indicates the maximum heat load. Indicates carbon emissions. Indicates the carbon emission coefficient of electricity purchase. Indicates the carbon emission coefficient of a gas turbine. Indicates the carbon emission coefficient of a gas-fired boiler. This indicates the carbon emission limit for zero-carbon industrial parks.

[0061] Based on any of the above embodiments, the planning unit 305 is specifically used for: With the goal of minimizing total resources, a mixed-integer linear programming algorithm is used to solve the capacity intelligent planning model based on the comprehensive energy physics model, the levelized resource model, and the low-carbon operation strategy and sensitivity assessment model, thereby obtaining the optimal capacity planning for each of the energy devices.

[0062] Based on any of the above embodiments, the planning unit 305 is further configured to: With the goal of minimizing total resources, a mixed-integer linear programming algorithm is used to solve the capacity intelligent planning model based on the comprehensive energy physics model, the levelized resource model, and the low-carbon operation strategy and sensitivity assessment model, thereby obtaining the clean energy ratio and carbon emission intensity of the zero-carbon park.

[0063] Figure 4 This is a schematic diagram of the physical structure of the electronic device provided in the embodiments of the present invention, such as... Figure 4 As shown, the electronic device may include: a processor 410, a communication interface 420, a memory 430, and a communication bus 440, wherein the processor 410, the communication interface 420, and the memory 430 communicate with each other through the communication bus 440. The processor 410 can call logical instructions in the memory 430 to execute a zero-carbon park energy equipment capacity intelligent planning method based on energy-carbon synergistic optimization. This method includes: constructing a comprehensive energy physics model of multi-energy flow-carbon flow synergy, wherein the comprehensive energy physics model is used to characterize the interaction relationship between electrical energy, thermal energy, cold energy, gas energy, carbon capture, and carbon flow; For each energy device in the zero-carbon park, a levelized resource model is constructed for the energy device. The levelized resource model is used to characterize the impact of the energy device's resource quantity, operation, fuel and emission factors on the total resource quantity. Based on the comprehensive energy physics model and the levelized resource model of each energy device, a capacity intelligent planning model for multi-energy flow-carbon flow coordination is constructed. A low-carbon operation strategy and sensitivity assessment model is constructed, which includes multiple constraints, including demand response constraints, wind and solar curtailment constraints, resource and equipment capacity constraints, and total carbon emission constraints. With the goal of minimizing total resources, the capacity intelligent planning model is solved based on the integrated energy physics model, the levelized resource model, and the low-carbon operation strategy and sensitivity assessment model to obtain the optimal capacity planning for each of the energy devices.

[0064] Furthermore, the logical instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0065] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the intelligent planning method for energy equipment capacity of zero-carbon parks based on energy-carbon synergistic optimization provided by the above methods. The method includes: constructing a comprehensive energy physics model of multi-energy flow-carbon flow synergy, wherein the comprehensive energy physics model is used to characterize the interaction relationship between electrical energy, thermal energy, cold energy, gas energy, carbon capture and carbon flow. For each energy device in the zero-carbon park, a levelized resource model is constructed for the energy device. The levelized resource model is used to characterize the impact of the energy device's resource quantity, operation, fuel and emission factors on the total resource quantity. Based on the comprehensive energy physics model and the levelized resource model of each energy device, a capacity intelligent planning model for multi-energy flow-carbon flow coordination is constructed. A low-carbon operation strategy and sensitivity assessment model is constructed, which includes multiple constraints, including demand response constraints, wind and solar curtailment constraints, resource and equipment capacity constraints, and total carbon emission constraints. With the goal of minimizing total resources, the capacity intelligent planning model is solved based on the integrated energy physics model, the levelized resource model, and the low-carbon operation strategy and sensitivity assessment model to obtain the optimal capacity planning for each of the energy devices.

[0066] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the intelligent planning method for energy equipment capacity in zero-carbon parks based on energy-carbon synergistic optimization provided by the above methods. The method includes: constructing a comprehensive energy physics model of multi-energy flow-carbon flow synergy, wherein the comprehensive energy physics model is used to characterize the interaction relationship between electrical energy, thermal energy, cold energy, gas energy, carbon capture and carbon flow. For each energy device in the zero-carbon park, a levelized resource model is constructed for the energy device. The levelized resource model is used to characterize the impact of the energy device's resource quantity, operation, fuel and emission factors on the total resource quantity. Based on the comprehensive energy physics model and the levelized resource model of each energy device, a capacity intelligent planning model for multi-energy flow-carbon flow coordination is constructed. A low-carbon operation strategy and sensitivity assessment model is constructed, which includes multiple constraints, including demand response constraints, wind and solar curtailment constraints, resource and equipment capacity constraints, and total carbon emission constraints. With the goal of minimizing total resources, the capacity intelligent planning model is solved based on the integrated energy physics model, the levelized resource model, and the low-carbon operation strategy and sensitivity assessment model to obtain the optimal capacity planning for each of the energy devices.

[0067] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0068] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0069] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for intelligent planning of energy equipment capacity in zero-carbon industrial parks based on energy-carbon synergistic optimization, characterized in that, include: A comprehensive energy physics model is constructed to coordinate multiple energy flows and carbon flows. This comprehensive energy physics model is used to characterize the interaction between electrical energy, thermal energy, cold energy, gas energy, carbon capture, and carbon flows. For each energy device in the zero-carbon park, a levelized resource model is constructed for the energy device. The levelized resource model is used to characterize the impact of the energy device's resource quantity, operation, fuel and emission factors on the total resource quantity. Based on the comprehensive energy physics model and the levelized resource model of each energy device, a capacity intelligent planning model for multi-energy flow-carbon flow coordination is constructed. A low-carbon operation strategy and sensitivity assessment model is constructed, which includes multiple constraints, including demand response constraints, wind and solar curtailment constraints, resource and equipment capacity constraints, and total carbon emission constraints. With the goal of minimizing total resources, the capacity intelligent planning model is solved based on the integrated energy physics model, the levelized resource model, and the low-carbon operation strategy and sensitivity assessment model to obtain the optimal capacity planning for each of the energy devices.

2. The intelligent planning method for energy equipment capacity in zero-carbon industrial parks based on energy-carbon synergistic optimization according to claim 1, characterized in that, The integrated energy physics model includes: in, express The actual power output of the wind turbine at any given time. express The wind curtailment rate of wind power equipment at all times. express Per-unit output coefficient of wind power equipment at all times This indicates the installable capacity of wind power equipment. express The actual power of the photovoltaic equipment at any given time. express The wind curtailment power of photovoltaic equipment at all times express The per-unit output coefficient of photovoltaic equipment at any given time. This indicates the installable capacity of photovoltaic equipment. express The actual power of the gas turbine at any given time. This indicates the electrical conversion efficiency of the gas turbine. express The gas consumption of the gas turbine at any given time. Indicates the calorific value of natural gas. express The heat of the gas turbine at all times, This indicates the heat conversion efficiency of the gas turbine. This indicates the electrical power of the gas turbine. express The heat output of the gas boiler at all times This indicates the heat conversion efficiency of a gas-fired boiler. express The gas consumption of the gas boiler at all times. This indicates the heat output of the gas-fired boiler. express The cooling capacity of a constant absorption refrigeration unit. The coefficient of performance (COP) of an absorption refrigeration system. express The heat absorption of the constant absorption cooling equipment This indicates the heat absorption capacity of the absorption refrigeration equipment. express The cooling capacity of the electric refrigeration equipment at all times. The coefficient of performance (COP) of an electric refrigeration device. express The power consumption of the refrigeration equipment at all times. Indicates the input power of the electric refrigeration equipment. express The charging power of the electric cooling equipment at all times. Indicates the power of the energy storage device. This indicates the discharge power of the energy storage device. This represents the Big M rule. express The energy storage binary variable of the instantaneous energy storage device. express The binary variable representing the discharge of the energy storage device at any given moment. express The state of charge of the battery in the energy storage device at all times. Indicates the capacity of the energy storage device. Indicates the scheduling period. express The heat absorption capacity of the thermal energy storage device at all times. express The heat released by the thermal energy storage device at all times Indicates the power of the thermal energy storage device. express The battery state of charge of the thermal energy storage device at all times. Indicates the capacity of the thermal energy storage equipment. express The power consumption of thermal energy storage equipment at all times. Indicates the carbon capture and conversion factor. express The quality of carbon capture at all times express Electricity purchased from the grid at any time express Constant electrical load, express Power can be increased in response to demand. express Power reduction should be adjusted in response to demand. express Constant heat load, express Always cold load, express Purchase gas at any time. express Constant air load.

3. The intelligent planning method for energy equipment capacity in zero-carbon industrial parks based on energy-carbon synergistic optimization according to claim 2, characterized in that, The levelized resource model for the energy equipment includes: in, Indicates the resource recovery coefficient. Indicates energy equipment The discount rate, Indicates energy equipment lifespan, Indicates energy equipment Considering the residual value, the equivalent annual value, Indicates energy equipment The amount of resources invested per unit capacity at one time. Indicates energy equipment residual value rate Indicates energy equipment The average annualized daily resource input Indicates energy equipment The annual fixed operation and maintenance cost per unit capacity.

4. The intelligent planning method for energy equipment capacity in zero-carbon industrial parks based on energy-carbon synergistic optimization according to claim 3, characterized in that, The capacity intelligent planning model includes: in, This represents the total amount of resources. Indicates the amount of purchased electricity resources. Indicates the amount of gas resources purchased. Indicates the amount of resources required to respond to demand. Indicates the amount of resources invested in equipment. This indicates the amount of wind and solar resources that were abandoned. express Real-time power grid resource availability Indicates the amount of natural gas resources. Indicates the amount of resources required to respond to demand. This represents the average annualized daily resource input for wind power equipment. This indicates the average annualized daily resource input for photovoltaic equipment. This represents the average annualized daily resource input for gas turbines. This indicates the average annualized daily resource input for gas-fired boilers. This indicates the average annualized daily resource input for electric refrigeration equipment. This represents the average annualized daily resource input for absorption refrigeration equipment. This indicates the average annualized daily resource input for carbon capture equipment. This indicates the available installed capacity of carbon capture equipment. This indicates the average annualized daily resource input for energy storage equipment. This indicates the average annualized daily resource input for thermal energy storage equipment. This indicates the amount of wind and solar resources abandoned per unit.

5. The intelligent planning method for energy equipment capacity in zero-carbon industrial parks based on energy-carbon synergistic optimization according to claim 4, characterized in that, The low-carbon operation strategy and sensitivity assessment model include: in, This represents the maximum coefficient of demand response. Indicates the maximum electrical load. Indicates time difference, This indicates the amount of wind and solar power that has been forfeited. Indicates the maximum amount of electricity that can be purchased. Indicates the maximum cooling load. Indicates the maximum heat load. Indicates carbon emissions. Indicates the carbon emission coefficient of electricity purchase. Indicates the carbon emission coefficient of a gas turbine. Indicates the carbon emission coefficient of a gas-fired boiler. This indicates the carbon emission limit for zero-carbon industrial parks.

6. The intelligent planning method for energy equipment capacity in zero-carbon industrial parks based on energy-carbon synergistic optimization according to any one of claims 1-5, characterized in that, The method, with the goal of minimizing total resources, is based on the integrated energy physics model, the levelized resource model, and the low-carbon operation strategy and sensitivity assessment model. It then solves the intelligent capacity planning model to obtain the optimal capacity planning for each of the energy devices, including: With the goal of minimizing total resources, a mixed-integer linear programming algorithm is used to solve the capacity intelligent planning model based on the comprehensive energy physics model, the levelized resource model, and the low-carbon operation strategy and sensitivity assessment model, thereby obtaining the optimal capacity planning for each of the energy devices.

7. The intelligent planning method for energy equipment capacity in zero-carbon industrial parks based on energy-carbon synergistic optimization according to claim 6, characterized in that, The method further includes: With the goal of minimizing total resources, a mixed-integer linear programming algorithm is used to solve the capacity intelligent planning model based on the comprehensive energy physics model, the levelized resource model, and the low-carbon operation strategy and sensitivity assessment model, thereby obtaining the clean energy ratio and carbon emission intensity of the zero-carbon park.

8. A smart planning device for energy equipment capacity in a zero-carbon industrial park based on energy-carbon synergistic optimization, characterized in that, include: The first building unit is used to construct a comprehensive energy physics model that integrates multiple energy flows and carbon flows. The comprehensive energy physics model is used to characterize the interaction between electrical energy, thermal energy, cold energy, gas energy, carbon capture, and carbon flows. The second construction unit is used to construct a levelized resource model for each energy device in the zero-carbon park. The levelized resource model is used to characterize the impact of the energy device's resource quantity, operation, fuel and emission factors on the total resource quantity. The third building unit is used to build a multi-energy flow-carbon flow coordinated capacity intelligent planning model based on the integrated energy physics model and the levelized resource model of each energy device. The fourth building unit is used to build a low-carbon operation strategy and sensitivity assessment model. The low-carbon operation strategy and sensitivity assessment model includes multiple constraints, including demand response constraints, wind and solar curtailment constraints, resource and equipment capacity constraints, and total carbon emission constraints. The planning unit is used to solve the capacity intelligent planning model based on the integrated energy physics model, the levelized resource model, and the low-carbon operation strategy and sensitivity assessment model, with the goal of minimizing the total resource volume, to obtain the optimal capacity planning for each of the energy devices.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the intelligent planning method for energy equipment capacity in zero-carbon parks based on energy-carbon synergistic optimization as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the intelligent planning method for energy equipment capacity in zero-carbon parks based on energy-carbon synergistic optimization as described in any one of claims 1 to 7.

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