A rural new-type industrial park comprehensive energy system planning method and device

Abstract

This invention proposes a planning method and apparatus for an integrated energy system in a new type of rural industrial park. Through in-depth analysis and optimization calculations of input data, it provides a scientific basis for capacity planning of distributed multi-energy coupled systems. The core objective of this method is to minimize system costs, maximize energy utilization, and ensure system reliability. By integrating mathematical modeling and optimization algorithms, this method comprehensively considers the characteristics of various energy forms, such as wind power, photovoltaic power, biomass power, energy storage systems, and flexible loads, as well as their interactions. By optimizing the capacity and operating strategies of each component and rationally allocating the energy storage system, it maximizes the utilization of renewable energy and improves overall energy efficiency. By accurately calculating the optimal capacity configuration for each device, this method not only significantly reduces the overall construction and operating costs of the system but also maximizes the utilization rate of renewable energy and reduces dependence on traditional fossil fuels.

Description

Technical Field

[0001] This invention relates to the field of distributed smart energy systems, and in particular to a planning method and apparatus for an integrated energy system in a new type of rural industrial park. Background Technology

[0002] With global warming failing to be effectively curbed and traditional fossil fuels gradually depleting, renewable energy technologies have become crucial for addressing the energy crisis and climate change. Distributed renewable energy, in particular, is characterized by its adaptability to local conditions, local consumption, and grid support, offering broad application scenarios and significant potential. Simultaneously, the development of facility agriculture and new rural industries is increasing the demand in rural areas for reliable power distribution networks, high-quality power, and affordable electricity. Distributed renewable energy is an effective solution for meeting future energy needs in rural areas.

[0003] The establishment of new rural industrial parks can attract diverse new rural industries and leverage their complementarity in energy use, production supply chains, and logistics, thereby improving production efficiency and energy efficiency. However, how to comprehensively coordinate local renewable resource endowments with the flexibility of industrial load and scientifically configure the multi-energy coupling system of new industrial parks remains a complex systems engineering problem. The reliability and economy of multi-energy systems are usually mutually exclusive indicators, requiring scientific methods to find the optimal balance. Traditional planning and design methods often rely on empirical formulas and simplifying assumptions, making it difficult to fully consider the complexity and dynamic changes of the system. This may lead to unreasonable system design, failing to achieve the expected economic and environmental benefits.

[0004] Existing multi-energy system planning methods directly employ empirical formulas and simplified assumptions for capacity configuration, failing to fully consider the spatiotemporal variations of energy resources, the dynamic response of various load types, and the real-time balance constraints of electricity, heat, and cooling. This can lead to a disconnect between system design and actual operational needs, or the coexistence of redundant and insufficient energy storage configurations, thus affecting the system's economic efficiency and energy supply stability. Furthermore, traditional methods often struggle to quantify the trade-off between reliability and cost, limiting the scientific deployment and efficient operation of multi-energy systems in complex rural industrial parks. Summary of the Invention

[0005] The present invention aims to at least partially solve one of the technical problems in the related art.

[0006] Therefore, the first objective of this invention is to propose a planning method for a comprehensive energy system in a new type of rural industrial park.

[0007] The second objective of this invention is to propose a planning device for an integrated energy system in a new type of rural industrial park.

[0008] To achieve the above objectives, a first aspect of the present invention proposes a method for planning a comprehensive energy system for a new type of rural industrial park, comprising:

[0009] S1. Construct a capacity planning model for a distributed multi-energy coupled system. The model takes minimizing the total system cost and maximizing energy utilization as the objective functions, and sets constraints on the spatiotemporal variation characteristics of multiple energy sources, the charging and discharging efficiency and SOC of the energy storage system, the real-time balance of electricity, heat and cold energy, and flexible load adjustment. S2, based on historical meteorological data and prediction models, acquire meteorological data, load data, equipment parameters, and system parameters as input parameters, and input them into the capacity planning model; S3. The dynamic programming model is solved using a convex optimization library and a professional solver to obtain the optimal capacity configuration and operation strategy for each energy device. S4 determines whether the optimal solution condition is met. If not, the solution is solved again until the optimal solution condition is met, and the optimization result is output.

[0010] Further, in one embodiment of the present invention, S1 includes: The objective function is: ; ; ; The constraints of the objective function include: Constraints of wind power generation:

[0011] Constraints of photovoltaic power generation:

[0012] Energy storage constraints: ; Electric boiler constraints:

[0013] Constraints of gas-fired boilers: ; HVAC constraints: ; Constraints of combined cooling, heating and power units:

[0014] Constraints of thermal storage systems: ; Power balance constraints: Thermal energy balance constraints:

[0015] Cold energy balance constraints:

[0016] Flexibility load constraints: .

[0017] Furthermore, in one embodiment of the present invention, the meteorological data, load data, equipment parameters, and system parameters include: meteorological data including wind speed, temperature, and solar radiation; load data including user power consumption, cooling power consumption, heating power consumption, and flexibility indicators; equipment parameters including cost, lifespan, and efficiency of wind turbines, photovoltaic panels, energy storage systems, and biomass power generation equipment; and system parameters including real-time electricity prices and gas prices.

[0018] Furthermore, in one embodiment of the present invention, S3 includes: a convex optimization library using CVXPY for modeling, the model supporting linear, quadratic and mixed integer programming forms to adapt to the combined requirements of different optimization objectives; a specialized solver including at least one of CPLEX, MOSEK and SCIPY, the solver automatically selecting the optimal solution path according to the model complexity to improve computational efficiency.

[0019] Furthermore, in one embodiment of the present invention, S4 includes: outputting optimization results, including detailed data of each device such as wind turbine, photovoltaic, biomass power generation, electric energy storage, and thermal storage system, wherein the detailed data includes capacity configuration, power generation capacity, daily power generation, cost, and energy storage status.

[0020] To achieve the above objectives, another aspect of the present invention provides a planning device for a comprehensive energy system in a new type of rural industrial park, comprising: The multi-energy coupling system modeling module is used to construct a capacity planning model for a distributed multi-energy coupling system. The model takes minimizing the total system cost and maximizing energy utilization as objective functions, and sets constraints on the spatiotemporal variation characteristics of multiple energy sources, the charging and discharging efficiency and SOC of the energy storage system, the real-time balance of electricity, heat and cold energy, and flexible load adjustment. The energy input parameter acquisition module acquires meteorological data, load data, equipment parameters, and system parameters as input parameters based on historical meteorological data and prediction models, and inputs them into the capacity planning model. The optimization solution module uses a convex optimization library and a professional solver to solve the dynamic programming model and obtain the optimal capacity configuration and operation strategy for each energy device. The optimization result output module determines whether the optimal solution conditions are met. If not, it performs the solution again until the optimal solution conditions are met, and then outputs the optimization result.

[0021] The present invention discloses a method and apparatus for planning a comprehensive energy system for a new type of rural industrial park, which can realize the scientific optimization configuration of a multi-energy coupling system in a new type of rural industrial park, significantly reduce the total system cost and improve the utilization rate of renewable energy while ensuring the reliability of energy supply.

[0022] The features and beneficial effects of this invention are as follows: This invention assesses distributed wind and solar energy resources through mathematical modeling and optimization algorithms, using historical data and predictive models to estimate future power generation. Simultaneously, it combines load modeling and forecasting techniques to accurately predict the system's energy demand and flexibility. Based on this, optimization algorithms determine the optimal capacity configuration for distributed renewable energy systems, energy storage systems, etc., to ensure that the system meets load demands while minimizing costs or maximizing energy efficiency.

[0023] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0024] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a comprehensive energy system planning method for a new type of rural industrial park according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating the specific solution process according to an embodiment of the present invention; Figure 3 This is a structural diagram of a comprehensive energy system planning device for a new type of rural industrial park according to an embodiment of the present invention. Detailed Implementation

[0025] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0026] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0027] The following description, with reference to the accompanying drawings, describes a planning method and apparatus for an integrated energy system in a new type of rural industrial park according to an embodiment of the present invention.

[0028] Figure 1 This is a flowchart of a comprehensive energy system planning method for a new type of rural industrial park according to an embodiment of the present invention, such as... Figure 1 As shown, it includes: S1. Construct a capacity planning model for a distributed multi-energy coupled system. The model takes minimizing the total system cost and maximizing energy utilization as the objective functions, and sets constraints on the spatiotemporal variation characteristics of multiple energy sources, the charging and discharging efficiency and SOC of the energy storage system, the real-time balance of electricity, heat and cold energy, and flexible load adjustment. S2, based on historical meteorological data and prediction models, acquire meteorological data, load data, equipment parameters, and system parameters as input parameters, and input them into the capacity planning model; S3. The dynamic programming model is solved using a convex optimization library and a professional solver to obtain the optimal capacity configuration and operation strategy for each energy device. S4 determines whether the optimal solution condition is met. If not, the solution is solved again until the optimal solution condition is met, and the optimization result is output.

[0029] The present invention provides a planning method for a comprehensive energy system in a new type of rural industrial park, which can achieve scientific optimization of the configuration of a multi-energy coupling system in the new type of rural industrial park, significantly reduce the total system cost and improve the utilization rate of renewable energy while ensuring the reliability of energy supply.

[0030] The following describes in detail, with reference to the accompanying drawings, a method for planning a comprehensive energy system for a new type of rural industrial park according to an embodiment of the present invention.

[0031] The purpose of this invention is to provide a method that offers a scientific basis for capacity planning in distributed multi-energy coupled systems through in-depth analysis and optimization calculations of input data. The core objective of this method is to minimize system costs, maximize energy utilization, and ensure system reliability. Specifically, by integrating mathematical modeling and optimization algorithms, this method comprehensively considers the characteristics of various energy forms, such as wind power, photovoltaic power, biomass power, energy storage systems, and flexible loads, as well as their interactions. By optimizing the capacity and operating strategies of each component and rationally allocating energy storage systems, it maximizes the utilization of renewable energy and improves overall energy efficiency. By accurately calculating the optimal capacity configuration for each device, this method not only significantly reduces the overall construction and operating costs of the system but also maximizes the utilization rate of renewable energy and reduces dependence on traditional fossil fuels.

[0032] The method includes the following steps: 1. Construct a capacity planning model for a distributed multi-energy coupled system: 1) Objective function: This method aims to minimize the total system cost, including annualized construction cost and gas purchase cost: (1) in The system's designed service life (in years); Rated capacity of the fan (kW). The unit capacity cost of the wind turbine (RMB / kW) is Rated photovoltaic capacity (kW). Cost per unit capacity of photovoltaic power (RMB / kW); This is the minimum power capacity (kW) for electrical energy storage. The unit cost per PCS (RMB / kW) This is the minimum energy capacity (kWh) for electrical energy storage. Cost per unit capacity of electric energy storage (RMB / kWh); Rated capacity (kW) of the electric boiler. Cost per unit capacity of electric boiler (RMB / kW); Rated capacity (kW) of gas-fired boiler. Cost per unit capacity of gas-fired boiler (RMB / kW); Rated capacity of HVAC (kW). Cost per unit capacity of HVAC (RMB / kW); Rated capacity (kW) of combined cooling, heating and power (CCHP) units. Cost per unit capacity of a combined cooling, heating and power (CCHP) unit (RMB / kW). Rated capacity (MJ) of the thermal storage system. Cost per unit capacity of thermal storage system (RMB / MJ); The total heat output of the gas-fired boiler is expressed in MJ. The combustion efficiency of the gas-fired boiler (MJ / m^3). Gas purchase cost (yuan / m^3); This refers to the total power generation of the combined cooling, heating and power (CCHP) unit (MWh). The power generation efficiency (MWh / m^3) of a combined cooling, heating and power (CCHP) unit; The annual electricity purchase cost is (in yuan).

[0033] 2) Constraints: Wind power generation constraints: For each day d and each hour h, the wind turbine power generation is between 0 and the rated power. (2) in, This refers to the daily and hourly wind power generation capacity. The wind power generation characteristic coefficient is given for each day and hour (between 0 and 1).

[0034] Photovoltaic power generation constraints: For each day d and each hour h, the photovoltaic power generation is between 0 and the rated power generation. (3) in, This refers to the daily and hourly wind power generation capacity. The wind power generation characteristic coefficient is given for each day and hour (between 0 and 1).

[0035] Energy storage constraints: (4) in, , These are the minimum and maximum values ​​of the State of Charge (SOC) for electrical energy storage. To improve the charging efficiency of electric energy storage, For the discharge efficiency of electrical energy storage, For daily and hourly electrical energy storage, energy (kWh) is stored. The daily hourly charging power of the electrical storage system (kW). The daily / hourly energy storage discharge power (kW).

[0036] Electric boiler constraints: for each day d and each hour h, (5) in, This refers to the daily and hourly power output of the electric boiler.

[0037] Gas boiler constraints: for each day d and each hour h, (6) in, This refers to the daily and hourly power output of the gas-fired boiler.

[0038] HVAC constraints: for each day d and each hour h, (7) in, This refers to the daily and hourly electrical power consumption of the HVAC system. This refers to the heating capacity of the HVAC system per day and hour. This refers to the cooling capacity of the HVAC system per day and hour. For the electro-thermal conversion efficiency of HVAC systems, The efficiency of the electro-cooling conversion in HVAC systems.

[0039] Combined cooling, heating and power (CCHP) unit constraints: for each day d and each hour h, (8) in, This refers to the daily and hourly electrical power output of the combined cooling, heating and power (CCHP) unit. This refers to the daily and hourly heat production capacity of the combined cooling, heating and power (CCHP) unit. This refers to the daily and hourly cooling capacity of the combined cooling, heating and power (CCHP) unit. For the gas-heat conversion efficiency of combined cooling, heating and power units, The gas-cooling conversion efficiency of a combined cooling, heating and power (CCHP) unit.

[0040] Thermal storage system constraints: for each day d and each hour h, (9) in, , These represent the minimum and maximum temperatures (K) of the medium in the thermal storage system. For thermal energy storage, thermal storage efficiency, For the efficiency of electric energy storage and heat release, Daily and hourly thermal energy storage capacity (kW) t ), The daily hourly thermal energy storage heat release power (kW) t ), The heat capacity of the thermal storage medium (kWh / K).

[0041] Power balance constraint: The sum of the average daily and hourly wind turbine power generation, photovoltaic power generation, electric energy storage discharge power, and purchased power must be equal to the power of the electric boiler, the charging power of the electric energy storage, the power of the HVAC system, and the load power.

[0042] (10) Thermal balance constraint: for each day d and each hour h, (11) Cold energy balance constraint: for each day d and each hour h, (12) Flexibility load constraints: for each day d and each hour h, (13) 2. Optimized solution: The data input module includes four categories: meteorological data, load data, equipment parameters, and system parameters. Meteorological data includes wind speed, temperature, and solar radiation; load data includes users' power consumption, cooling power consumption, heating power consumption, and flexibility indicators; equipment parameters include the cost, lifespan, and efficiency of equipment such as wind turbines, photovoltaic panels, energy storage systems, and biomass power generation; system parameters include real-time electricity prices and gas prices. After inputting the parameters, the mathematical model is solved using a convex optimization library (CVXPY) and solvers (CPLEX, MOSEK, SCIPY, etc.) under the requirements of the objective function, which can efficiently obtain the optimal system configuration scheme.

[0043] 3. Result Output: The output optimization results include detailed data such as capacity configuration, power generation capacity, daily power generation, cost, and energy storage status of various equipment such as wind turbines, photovoltaics, biomass power generation, electric energy storage, and thermal storage systems to ensure that the system can meet load demand while minimizing costs or maximizing energy efficiency.

[0044] The specific solution process is as follows: Figure 2 As shown, the flowchart is explained below: start.

[0045] Parameter acquisition: Input meteorological data includes wind speed, temperature, solar radiation, etc.; load data includes users' energy consumption power; equipment parameters include the cost, lifespan, efficiency, etc. of equipment such as wind turbines, photovoltaic panels, energy storage systems, and biomass power generation; system parameters include real-time electricity prices, gas prices, etc.

[0046] Optimization objective solution: Solve the mathematical model using a convex optimization library (CVXPY) and solvers (CPLEX, MOSEK, SCIPY, etc.).

[0047] Output optimal equipment capacity configuration and power output: Obtain the capacity configuration, power generation efficiency, and daily power output of various equipment such as wind turbines, photovoltaics, biomass power generation, electric energy storage, and thermal storage systems. The system can fully meet the load demand while achieving the goal of minimizing costs or maximizing energy efficiency.

[0048] Finish.

[0049] According to an embodiment of the present invention, a planning method for an integrated energy system in a new rural industrial park can achieve scientific optimization of the configuration of a multi-energy coupling system in the new rural industrial park, significantly reducing the total system cost and improving the utilization rate of renewable energy while ensuring the reliability of energy supply.

[0050] To achieve the above embodiments, such as Figure 3As shown, this embodiment also provides a comprehensive energy system planning device 10 for a new type of rural industrial park. The device 10 includes a multi-energy coupling system modeling module 100, an energy input parameter acquisition module 200, an optimization solution module 300, and an optimization result output module 400.

[0051] The multi-energy coupling system modeling module 100 is used to construct a capacity planning model for a distributed multi-energy coupling system. The model takes minimizing the total system cost and maximizing energy utilization as objective functions, and sets constraints on the spatiotemporal variation characteristics of multiple energy sources, the charging and discharging efficiency and SOC of the energy storage system, the real-time balance constraints of electricity, heat and cold energy, and flexible load adjustment constraints. The energy input parameter acquisition module 200 acquires meteorological data, load data, equipment parameters, and system parameters as input parameters based on historical meteorological data and prediction models, and inputs them into the capacity planning model. The optimization solution module 300 uses a convex optimization library and a professional solver to solve the dynamic programming model and obtain the optimal capacity configuration and operation strategy of each energy device. The optimization result output module 400 determines whether the optimal solution conditions are met. If not, it performs the solution again until the optimal solution conditions are met, and then outputs the optimization result.

[0052] Furthermore, the multi-energy coupled system modeling module 100 is also used for: The objective function is: ; ; ; The constraints of the objective function include: Constraints of wind power generation:

[0053] Constraints of photovoltaic power generation:

[0054] Energy storage constraints: ; Electric boiler constraints:

[0055] Constraints of gas-fired boilers: ; HVAC constraints: ; Constraints of combined cooling, heating and power units:

[0056] Constraints of thermal storage systems: ; Power balance constraints: Thermal energy balance constraints:

[0057] Cold energy balance constraints:

[0058] Flexibility load constraints: .

[0059] The objective function is: ; ; ; The constraints of the objective function include: Constraints of wind power generation:

[0060] Constraints of photovoltaic power generation:

[0061] Energy storage constraints: ; Electric boiler constraints:

[0062] Constraints of gas-fired boilers: ; HVAC constraints: ; Constraints of combined cooling, heating and power units:

[0063] Constraints of thermal storage systems: ; Power balance constraints: Thermal energy balance constraints:

[0064] Cold energy balance constraints:

[0065] Flexibility load constraints: .

[0066] Furthermore, the meteorological data, load data, equipment parameters, and system parameters also include: Meteorological data includes wind speed, temperature, and solar radiation; Load data includes users' power consumption, cooling power consumption, heating power consumption, and flexibility indicators; Equipment parameters include the cost, lifespan, and efficiency of wind turbines, photovoltaic panels, energy storage systems, and biomass power generation equipment; System parameters include real-time electricity price and gas price.

[0067] Furthermore, the optimization solution module 300 also includes: The convex optimization library uses CVXPY for modeling, and the model supports linear, quadratic and mixed integer programming forms to adapt to the combination requirements of different optimization objectives; Specialized solvers include at least one of CPLEX, MOSEK, and SCIPY, which automatically select the optimal solution path based on model complexity to improve computational efficiency.

[0068] Furthermore, the optimization result output module 400 also includes: The output optimization results include detailed data for each device in the wind turbine, photovoltaic, biomass power generation, electric energy storage, and thermal energy storage system. The detailed data includes capacity configuration, power generation capacity, daily power generation, cost, and energy storage status.

[0069] The present invention discloses a planning device for a comprehensive energy system in a new rural industrial park, which can realize the scientific optimization of the multi-energy coupling system in the new rural industrial park, significantly reduce the total system cost and improve the utilization rate of renewable energy while ensuring the reliability of energy supply.

[0070] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0071] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. A rural new-type industrial park comprehensive energy system planning method, characterized in that, include: S1. Construct a capacity planning model for a distributed multi-energy coupled system. The model takes minimizing the total system cost and maximizing energy utilization as the objective functions, and sets constraints on the spatiotemporal variation characteristics of multiple energy sources, the charging and discharging efficiency and SOC of the energy storage system, the real-time balance of electricity, heat and cold energy, and flexible load adjustment. S2, based on historical meteorological data and prediction models, acquire meteorological data, load data, equipment parameters, and system parameters as input parameters, and input them into the capacity planning model; S3. The dynamic programming model is solved using a convex optimization library and a professional solver to obtain the optimal capacity configuration and operation strategy for each energy device. S4 determines whether the optimal solution condition is met. If not, the solution is solved again until the optimal solution condition is met, and the optimization result is output. 2.The rural new-type industrial park integrated energy system planning method of claim 1, wherein, S1 includes: The objective function is: ； ； ； The constraints of the objective function include: Constraints of wind power generation: Constraints of photovoltaic power generation: Energy storage constraints: ； Electric boiler constraints: Constraints of gas-fired boilers: ； HVAC constraints: ； Constraints of combined cooling, heating and power units: Constraints of thermal storage systems: ； Power balance constraints: Thermal energy balance constraints: Cold energy balance constraint: Flexibility load constraints: 。 3. The integrated energy system planning method for new rural industrial parks as described in claim 1, characterized in that, The meteorological data, load data, equipment parameters, and system parameters include: Meteorological data includes wind speed, temperature, and solar radiation; Load data includes users' power consumption, cooling power consumption, heating power consumption, and flexibility indicators; Equipment parameters include the cost, lifespan, and efficiency of wind turbines, photovoltaic panels, energy storage systems, and biomass power generation equipment; System parameters include real-time electricity price and gas price. 4.The rural new-type industrial park integrated energy system planning method of claim 1, wherein, The S3 includes: The convex optimization library uses CVXPY for modeling, and the model supports linear, quadratic and mixed integer programming forms to adapt to the combination requirements of different optimization objectives. Specialized solvers include at least one of CPLEX, MOSEK, and SCIPY, which automatically select the optimal solution path based on model complexity to improve computational efficiency.

5. The rural new-type industrial park integrated energy system planning method of claim 1, wherein, The S4 includes: The output optimization results include detailed data for each device in the wind turbine, photovoltaic, biomass power generation, electric energy storage, and thermal energy storage system. The detailed data includes capacity configuration, power generation capacity, daily power generation, cost, and energy storage status.

6. A rural new-type industrial park comprehensive energy system planning device, characterized in that, include: The multi-energy coupling system modeling module is used to construct a capacity planning model for a distributed multi-energy coupling system. The model takes minimizing the total system cost and maximizing energy utilization as objective functions, and sets constraints on the spatiotemporal variation characteristics of multiple energy sources, the charging and discharging efficiency and SOC of the energy storage system, the real-time balance of electricity, heat and cold energy, and flexible load adjustment. The energy input parameter acquisition module acquires meteorological data, load data, equipment parameters, and system parameters as input parameters based on historical meteorological data and prediction models, and inputs them into the capacity planning model. The optimization solution module uses a convex optimization library and a professional solver to solve the dynamic programming model and obtain the optimal capacity configuration and operation strategy for each energy device. The optimization result output module determines whether the optimal solution conditions are met. If not, it performs the solution again until the optimal solution conditions are met, and then outputs the optimization result. 7.The rural new-type industrial park comprehensive energy system planning device of claim 6, wherein, The multi-energy coupled system modeling module includes: The objective function is: ； ； ； The constraints of the objective function include: Constraints of wind power generation: Constraints of photovoltaic power generation: Energy storage constraints: ； Electric boiler constraints: Constraints of gas-fired boilers: ； HVAC constraints: ； Constraints of combined cooling, heating and power units: Constraints of thermal storage systems: ； Power balance constraints: Thermal energy balance constraints: Cold energy balance constraint: Flexibility load constraints: 。 8.The rural new-type industrial park comprehensive energy system planning device of claim 6, wherein, The meteorological data, load data, equipment parameters, and system parameters also include: Meteorological data includes wind speed, temperature, and solar radiation; Load data includes users' power consumption, cooling power consumption, heating power consumption, and flexibility indicators; Equipment parameters include the cost, lifespan, and efficiency of wind turbines, photovoltaic panels, energy storage systems, and biomass power generation equipment; System parameters include real-time electricity price and gas price. 9.The rural new-type industrial park comprehensive energy system planning device of claim 6, wherein, The optimization solution module also includes: The convex optimization library uses CVXPY for modeling, and the model supports linear, quadratic and mixed integer programming forms to adapt to the combination requirements of different optimization objectives. Specialized solvers include at least one of CPLEX, MOSEK, and SCIPY, which automatically select the optimal solution path based on model complexity to improve computational efficiency.

10. The integrated energy system planning device for new rural industrial parks as described in claim 6, characterized in that, The optimization result output module also includes: The output optimization results include detailed data for each device in the wind turbine, photovoltaic, biomass power generation, electric energy storage, and thermal energy storage system. The detailed data includes capacity configuration, power generation capacity, daily power generation, cost, and energy storage status.

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