Agricultural and pastoral zero-carbon park energy scheduling optimization method based on multi-energy cooperation

Abstract

The invention discloses an agricultural and pastoral zero-carbon park energy scheduling optimization method based on multi-energy cooperation. The method comprises the following steps: S1, modeling an integrated energy system of an agricultural and pastoral zero-carbon park to obtain an integrated energy system model; s2, analyzing various energy-using equipment in the agricultural and pastoral zero-carbon park, and constructing a corresponding energy-using equipment model; s3, analyzing a power generation system in the agricultural and pastoral zero-carbon park to construct a corresponding power generation equipment model; s4, constructing a multi-energy scheduling optimization problem with the lowest operation cost of the agricultural and pastoral zero-carbon park as a target based on the comprehensive energy system model, the energy consumption equipment model and the power generation equipment model; s5, solving a multi-energy scheduling optimization problem to obtain an optimal energy scheduling strategy; and realizing energy operation scheduling of the agricultural and pastoral zero-carbon park through the optimal energy scheduling strategy. According to the method, a multi-energy scheduling optimization problem taking the lowest operation cost of the agricultural and pastoral zero-carbon park as a target is constructed, so that the operation cost of the park is effectively reduced, and the economic benefits of the agricultural and pastoral zero-carbon park are improved.

Description

Technical Field

[0001] The present invention relates to the field of energy scheduling in zero-carbon agricultural and animal husbandry parks, and in particular to an energy scheduling optimization method for zero-carbon agricultural and animal husbandry parks based on multi-energy synergy. Background Art

[0002] The agricultural and animal husbandry zero-carbon park aims to achieve energy self-sufficiency and zero carbon emissions growth within the park by integrating advanced technologies such as renewable energy, smart microgrids, and carbon sink management. However, the construction and operation of the agricultural and animal husbandry zero-carbon park is not easy, especially in terms of energy scheduling and optimization, which faces many technical challenges.

[0003] The energy structure of the agricultural and animal husbandry zero-carbon park is complex and diverse. The park usually integrates a variety of renewable energy sources such as solar energy, wind energy, and biomass energy. The supply of these energy sources is intermittent and uncertain, which brings great challenges to the energy scheduling of the park. At the same time, there may also be traditional fossil energy in the park as a backup or supplement. How to balance the use of renewable energy and fossil energy and achieve energy complementarity and optimal configuration is one of the key issues in the energy scheduling of the agricultural and animal husbandry zero-carbon park. At the same time, the energy scheduling of the agricultural and animal husbandry zero-carbon park also needs to consider the dual goals of environmental protection and economic benefits. On the one hand, as a demonstration area for zero-carbon emissions, the park needs to strictly control carbon emissions and promote green, low-carbon and sustainable development. On the other hand, as an economic entity, the park needs to pay attention to operating costs, improve economic benefits, and achieve sustainable development. How to find a balance between environmental protection and economic benefits is an important consideration for energy scheduling in agricultural and animal husbandry zero-carbon parks.

[0004] In addition, with the rapid development of technologies such as smart microgrids, big data, and cloud computing, energy dispatch in zero-carbon agricultural and animal husbandry parks is also facing new opportunities and challenges. Smart microgrid technology can realize the intelligent dispatch and optimal configuration of energy within the park, and improve energy utilization efficiency; big data and cloud computing technology can realize accurate prediction and real-time monitoring of energy demand in the park, and provide strong data support for energy dispatch. However, the application of these technologies has also brought new technical challenges, such as how to ensure the accuracy and security of data, and how to achieve coordination and integration between different technologies.

[0005] Therefore, how to design an energy scheduling optimization method for agricultural and animal husbandry zero-carbon parks that comprehensively considers multiple aspects and multiple levels is a technical problem that needs to be solved urgently. Summary of the invention

[0006] In view of the deficiencies of the above-mentioned prior art, the technical problem to be solved by the present invention is: how to provide an energy scheduling optimization method for an agricultural and animal husbandry zero-carbon park based on multi-energy synergy, and construct a multi-energy scheduling optimization problem with the goal of minimizing the operating cost of the agricultural and animal husbandry zero-carbon park based on a comprehensive energy system model, an energy-consuming equipment model and a power generation equipment model, and then solve the optimal energy scheduling strategy to effectively reduce the operating cost of the park, thereby improving the economic benefits of the agricultural and animal husbandry zero-carbon park.

[0007] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0008] An energy scheduling optimization method for zero-carbon agricultural and animal husbandry parks based on multi-energy synergy, comprising:

[0009] S1: Model the comprehensive energy system of the agricultural and animal husbandry zero-carbon park to obtain a comprehensive energy system model; calculate the required electricity of the agricultural and animal husbandry zero-carbon park through the comprehensive energy system model;

[0010] S2: Analyze various energy-consuming equipment in the agricultural and animal husbandry zero-carbon park and build corresponding energy-consuming equipment models; calculate the energy consumption and power of various energy-consuming equipment in the agricultural and animal husbandry zero-carbon park through the energy-consuming equipment model;

[0011] S3: Analyze the power generation system in the agricultural and animal husbandry zero-carbon park and build a corresponding power generation equipment model; calculate the power generation of the agricultural and animal husbandry park through the power generation equipment model;

[0012] S4: Based on the comprehensive energy system model, energy-consuming equipment model and power generation equipment model, a multi-energy scheduling optimization problem with the goal of minimizing the operating cost of the agricultural and animal husbandry zero-carbon park is constructed;

[0013] S5: Solve the multi-energy scheduling optimization problem and obtain the optimal energy scheduling strategy; realize the energy operation scheduling of the agricultural and animal husbandry zero-carbon park through the optimal energy scheduling strategy.

[0014] Preferably, in step S1, the formula of the comprehensive energy system model is expressed as:

[0015]

[0016] Where: P t P represents the required electricity in the agricultural and animal husbandry zero-carbon park during period t; t res Indicates the power generation in the maximum power tracking mode; and They represent the natural gas consumption of cogeneration units and gas boiler equipment respectively; and P represents the gas-to-electricity efficiency and gas-to-heat efficiency of the cogeneration unit; t EBIndicates the electricity consumed by the power boiler equipment; Indicates the efficiency of electric boiler; and Respectively represent the discharging and charging power of the energy storage system; and Respectively represent the discharge and charge capacity of the thermal energy storage system; P t curt and represent the power and heat of the thermal energy storage system respectively; and Indicates the time-shiftable power load and heat load; L e,t and L th,t Represent constant electrical load and constant thermal load respectively.

[0017] Preferably, in step S2, the energy-consuming equipment model includes a cogeneration unit model;

[0018] The formula of the combined heat and power unit model is expressed as:

[0019]

[0020] Where: P t CHP represents the output power of the cogeneration unit during period t; represents the natural gas consumption of the cogeneration unit during period t; H ng Indicates the calorific value of natural gas; η gt Indicates the natural gas combustion efficiency.

[0021] Preferably, in step S2, the energy-consuming equipment model includes a gas boiler equipment model;

[0022] The formula of the gas boiler equipment model is expressed as:

[0023]

[0024] Where: P t GF Indicates the output power of the gas boiler equipment during period t; Indicates the natural gas consumption of gas boiler equipment during period t; L ng Indicates the lower calorific value of natural gas; η gb Indicates the thermal efficiency of gas boiler equipment.

[0025] Preferably, in step S3, the power generation equipment model includes a distributed photovoltaic power generation model;

[0026] The formula of distributed photovoltaic power generation model is expressed as:

[0027]

[0028] Where: E PV represents photovoltaic power generation; f PV Indicates the power derating factor of photovoltaics, which is used to characterize the decrease in output power caused by factors such as dust and aging on the photovoltaic surface; P PV,R Represents the peak photovoltaic capacity; G T Indicates actual illuminance; G T,STC Indicates the illuminance under standard test conditions; α P Indicates the power temperature coefficient; T cell Indicates the current temperature of the photovoltaic surface; T cell,STC Indicates the photovoltaic temperature under standard test conditions.

[0029] Preferably, in step S3, the power generation equipment model includes a biomass power generation model;

[0030] The formula of biomass power generation model is expressed as:

[0031]

[0032] Where: E BPG represents biomass electricity generation; P BPG represents the biomass heat; Δt represents the time change; η represents the efficiency; q represents the gas consumption of the gas turbine; Indicates the lower heating value of the gas.

[0033] Preferably, in step S4, the objective function of the multi-energy scheduling optimization problem is expressed as:

[0034]

[0035] Where: F t represents the operating cost of the agricultural and animal husbandry zero-carbon park during period t; t c ,t e Indicates the start and end time periods; P t E represents the required electricity in the agricultural and animal husbandry zero-carbon park during period t; PV Represents photovoltaic power generation; E BPG represents the amount of electricity generated by biomass; represents the natural gas consumption of the cogeneration unit during period t; Indicates the natural gas consumption of gas boiler equipment during period t; μ e,t and μ g,t They represent the electricity price and natural gas price in period t respectively.

[0036] Preferably, in step S4, the constraints of the multi-energy scheduling optimization problem include:

[0037] Power capacity range:

[0038]

[0039] Where: P t CHP , P t GF , P t EB Respectively represent the output power of cogeneration units, gas boiler equipment and electric boiler equipment; Respectively represent the minimum power range of cogeneration units, gas boiler equipment and power boiler equipment; They represent the maximum power ranges of cogeneration units, gas boiler equipment and electric boiler equipment respectively.

[0040] Preferably, in step S4, the constraints of the multi-energy scheduling optimization problem also include:

[0041] Maximum charging and discharging power constraints of energy storage system:

[0042]

[0043] Where: P t EES , Respectively represent the charging power and charging energy of the energy storage system; and Respectively represent the minimum charging power range and the maximum discharging power range of the energy storage system; and They represent the minimum charging energy and maximum discharging energy of the energy storage system respectively;

[0044] The maximum energy boundary of thermal energy storage system:

[0045]

[0046] Where: and They represent the minimum energy range and the maximum energy range of the thermal energy storage system respectively; Represents the current thermal energy storage energy; α TES Represents the self-discharge rate of the thermal energy storage system; represents the net energy change of thermal energy storage during period t;

[0047] Time-shifted load constraints:

[0048]

[0049] Where: L sl e,t and L sl th,t They represent the time-shiftable electric load and heat load within the day respectively; and Respectively represent the upper limits of the time-shiftable power load and heat load; Ω e and Ω th They represent the scheduling time intervals of the time-shiftable power load and heat load respectively.

[0050] Preferably, in step S4, the constraints of the multi-energy scheduling optimization problem also include:

[0051] Output climbing constraint:

[0052]

[0053] Where: ΔP CHP and ΔP EB Represent the unit ramp rates of cogeneration units and power boiler equipment respectively.

[0054] Compared with the prior art, the energy scheduling optimization method for zero-carbon agricultural and animal husbandry parks based on multi-energy synergy in the present invention has the following beneficial effects:

[0055] First, the present invention models the comprehensive energy system of the agricultural and animal husbandry zero-carbon park to comprehensively examine the energy demand and supply status in the park, provide an accurate data basis for subsequent energy scheduling, and ensure the coordination and cooperation between various energy sources (such as solar energy, biomass energy, etc.); energy scheduling under multi-energy coordination enables the park to meet energy demand while minimizing energy consumption, thereby improving the energy utilization efficiency of the agricultural and animal husbandry zero-carbon park. Then, the present invention conducts an in-depth analysis of various energy-consuming equipment in the agricultural and animal husbandry zero-carbon park and constructs a corresponding energy-consuming equipment model. The model accurately predicts the energy consumption and power demand of the equipment, providing a reliable basis for energy scheduling; by accurately predicting the energy consumption of the equipment, it is possible to achieve dynamic scheduling of energy, ensure that the operation of key equipment is prioritized when the energy supply is tight, and reasonably allocate energy when the energy is sufficient, thereby improving the flexibility and response speed of energy scheduling. Secondly, the present invention has carried out a detailed analysis of the power generation system of the park and constructed a power generation equipment model, which can accurately calculate the power generation of photovoltaic power generation and biomass power generation, and provide a reliable clean energy supply for the park; by optimizing the configuration of the power generation system, the utilization rate of renewable energy is improved, and the dependence on traditional energy is reduced; at the same time, the improved self-sufficiency rate also reduces the operating cost of the park and enhances the economic competitiveness of the park. Finally, based on the comprehensive energy system model, energy-consuming equipment model and power generation equipment model, the present invention constructs a multi-energy scheduling optimization problem with the goal of minimizing the operating cost of the agricultural and animal husbandry zero-carbon park, and then obtains the optimal energy scheduling strategy through mathematical modeling and solution; by implementing the optimal energy scheduling strategy, the operating cost of the park can be effectively reduced, thereby improving the economic benefits of the agricultural and animal husbandry zero-carbon park. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to make the purpose, technical solution and advantages of the invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings, in which:

[0057] Figure 1 This is the logical block diagram of the energy scheduling optimization method for zero-carbon agricultural and animal husbandry parks based on multi-energy synergy. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but only represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0059] It should be noted that similar numbers and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. In the description of the present invention, it should be noted that the orientation or position relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inside", "outside", etc. is based on the orientation or position relationship shown in the drawings, or the orientation or position relationship in which the invention product is usually placed when used, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance. In addition, the terms "horizontal", "vertical", etc. do not mean that the components are required to be absolutely horizontal or suspended, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly tilted. In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0060] The following is a further detailed description through specific implementation methods:

[0061] Example:

[0062] This embodiment discloses an energy scheduling optimization method for an agricultural and animal husbandry zero-carbon park based on multi-energy synergy.

[0063] like Figure 1 As shown in the figure, the energy scheduling optimization method for zero-carbon agricultural and animal husbandry parks based on multi-energy synergy includes:

[0064] S1: Model the comprehensive energy system of the agricultural and animal husbandry zero-carbon park to obtain a comprehensive energy system model; calculate the required electricity of the agricultural and animal husbandry zero-carbon park through the comprehensive energy system model;

[0065] S2: Analyze various energy-consuming equipment in the agricultural and animal husbandry zero-carbon park and build corresponding energy-consuming equipment models; calculate the energy consumption and power of various energy-consuming equipment in the agricultural and animal husbandry zero-carbon park through the energy-consuming equipment model;

[0066] S3: Analyze the power generation system in the agricultural and animal husbandry zero-carbon park and build a corresponding power generation equipment model; calculate the power generation of the agricultural and animal husbandry park through the power generation equipment model;

[0067] S4: Based on the comprehensive energy system model, energy-consuming equipment model and power generation equipment model, a multi-energy scheduling optimization problem with the goal of minimizing the operating cost of the agricultural and animal husbandry zero-carbon park is constructed;

[0068] S5: Solve the multi-energy scheduling optimization problem and obtain the optimal energy scheduling strategy; realize the energy scheduling of the agricultural and animal husbandry zero-carbon park through the optimal energy scheduling strategy.

[0069] In this embodiment, the multi-energy scheduling optimization problem can be solved by a genetic algorithm (such as a particle swarm algorithm).

[0070] First, the present invention models the comprehensive energy system of the agricultural and animal husbandry zero-carbon park to comprehensively examine the energy demand and supply status in the park, provide an accurate data basis for subsequent energy scheduling, and ensure the coordination and cooperation between various energy sources (such as solar energy, biomass energy, etc.); energy scheduling under multi-energy coordination enables the park to meet energy demand while minimizing energy consumption, thereby improving the energy utilization efficiency of the agricultural and animal husbandry zero-carbon park. Then, the present invention conducts an in-depth analysis of various energy-consuming equipment in the agricultural and animal husbandry zero-carbon park and constructs a corresponding energy-consuming equipment model. The model accurately predicts the energy consumption and power demand of the equipment, providing a reliable basis for energy scheduling; by accurately predicting the energy consumption of the equipment, it is possible to achieve dynamic scheduling of energy, ensure that the operation of key equipment is prioritized when the energy supply is tight, and reasonably allocate energy when the energy is sufficient, thereby improving the flexibility and response speed of energy scheduling. Secondly, the present invention has carried out a detailed analysis of the power generation system of the park and constructed a power generation equipment model, which can accurately calculate the power generation of photovoltaic power generation and biomass power generation, and provide a reliable clean energy supply for the park; by optimizing the configuration of the power generation system, the utilization rate of renewable energy is improved, and the dependence on traditional energy is reduced; at the same time, the improved self-sufficiency rate also reduces the operating cost of the park and enhances the economic competitiveness of the park. Finally, based on the comprehensive energy system model, energy-consuming equipment model and power generation equipment model, the present invention constructs a multi-energy scheduling optimization problem with the goal of minimizing the operating cost of the agricultural and animal husbandry zero-carbon park, and then obtains the optimal energy scheduling strategy through mathematical modeling and solution; by implementing the optimal energy scheduling strategy, the operating cost of the park can be effectively reduced, thereby improving the economic benefits of the agricultural and animal husbandry zero-carbon park.

[0071] In the specific implementation process, the formula of the comprehensive energy system model is expressed as:

[0072]

[0073] Where: P t represents the power required by the agricultural and animal husbandry zero-carbon park during period t, P t <0 means that the integrated energy system of the zero-carbon agricultural and animal husbandry park sells surplus electricity to the grid; P t res Indicates the power generation in the maximum power tracking mode; and They represent the natural gas consumption of cogeneration units and gas boiler equipment respectively; and P represents the gas-to-electricity efficiency and gas-to-heat efficiency of the cogeneration unit; t EB Indicates the electricity consumed by the power boiler equipment; Indicates the efficiency of electric boiler; and Respectively represent the discharging and charging power of the energy storage system; and Respectively represent the discharge and charge capacity of the thermal energy storage system; P t curt and represent the power and heat of the thermal energy storage system respectively; and Indicates the time-shiftable power load and heat load; L e,t and L th,t Represent constant electrical load and constant thermal load respectively.

[0074] During the specific implementation process, the energy-consuming equipment model includes the cogeneration unit model;

[0075] The formula of the combined heat and power unit model is expressed as:

[0076]

[0077] Where: P t CHP represents the output power of the cogeneration unit during period t; represents the natural gas consumption of the cogeneration unit during period t; H ng Indicates the calorific value of natural gas; η gt Indicates the natural gas combustion efficiency.

[0078] During the specific implementation process, the energy-consuming equipment model includes the gas boiler equipment model;

[0079] The formula of the gas boiler equipment model is expressed as:

[0080]

[0081] Where: P t GF Indicates the output power of the gas boiler equipment during period t; Indicates the natural gas consumption of gas boiler equipment during period t; L ng Indicates the lower calorific value of natural gas; η gb Indicates the thermal efficiency of gas boiler equipment.

[0082] During the specific implementation, the power generation equipment model includes a distributed photovoltaic power generation model;

[0083] The formula of distributed photovoltaic power generation model is expressed as:

[0084]

[0085] Where: E PV represents photovoltaic power generation; f PVIt represents the power derating factor of photovoltaic, which is used to characterize the output power reduction caused by factors such as dust and aging on the photovoltaic surface, and is taken as 0.9; P PV,R Represents the peak photovoltaic capacity in kWp; G T Indicates the actual illuminance in kW / m 2 ; G T,STC Indicates the illuminance under standard test conditions, generally 1kW / m 2 ; α P Indicates the power temperature coefficient, the unit is % / ℃; T cell Indicates the current temperature of the photovoltaic surface, in °C; T cell,STC It indicates the photovoltaic temperature under standard test conditions, usually 25℃.

[0086] During the specific implementation, the power generation equipment model includes a biomass power generation model;

[0087] The formula of biomass power generation model is expressed as:

[0088]

[0089] Where: E BPG represents biomass electricity generation; P BPG represents the biomass heat; Δt represents the time change; η represents the efficiency; q represents the gas consumption of the gas turbine; Indicates the lower heating value of the gas.

[0090] In the specific implementation process, the objective function of the multi-energy scheduling optimization problem is expressed as:

[0091]

[0092] Where: F t represents the operating cost of the agricultural and animal husbandry zero-carbon park during period t; t c ,t e Indicates the start and end time periods; P t E represents the required electricity in the agricultural and animal husbandry zero-carbon park during period t; PV Represents photovoltaic power generation; E BPG represents the amount of electricity generated by biomass; represents the natural gas consumption of the cogeneration unit during period t; Indicates the natural gas consumption of gas boiler equipment during period t; μ e,t and μ g,t They represent the electricity price and natural gas price in period t respectively.

[0093] In the specific implementation process, the constraint functions of the multi-energy scheduling optimization problem include:

[0094] 1) Power capacity limitation:

[0095] -P out ≤P t ≤P in ;

[0096] Where P out , P in Indicates the maximum and minimum exchange power on the tie line;

[0097] 2) Power capacity range:

[0098]

[0099] Where: P t CHP , P t GF , P t EB Respectively represent the output power of cogeneration units, gas boiler equipment and electric boiler equipment; Respectively represent the minimum power range of cogeneration units, gas boiler equipment and power boiler equipment; Respectively represent the maximum power range of cogeneration units, gas boiler equipment and power boiler equipment;

[0100] 3) Output climbing constraint:

[0101]

[0102] Where: ΔP CHP and ΔP EB They represent the unit ramp rates of the cogeneration unit and the power boiler equipment respectively;

[0103] 4) Maximum charging and discharging power constraints of energy storage systems:

[0104]

[0105] Where: P t EES , Respectively represent the charging power and charging energy of the energy storage system; and Respectively represent the minimum charging power range and the maximum discharging power range of the energy storage system; and They represent the minimum charging energy and maximum discharging energy of the energy storage system respectively;

[0106] 5) Maximum energy boundary of thermal energy storage system:

[0107]

[0108] Where: and They represent the minimum energy range and the maximum energy range of the thermal energy storage system respectively; Represents the current thermal energy storage energy; α TES Represents the self-discharge rate of the thermal energy storage system; represents the net energy change of thermal energy storage during period t;

[0109] 6) Time-shift load constraints:

[0110]

[0111] Where: L sl e,t and L sl th,t They represent the time-shiftable electric load and heat load within the day respectively; and Respectively represent the upper limits of the time-shiftable power load and heat load; Ω e and Ω th They represent the scheduling time intervals of the time-shiftable power load and heat load respectively.

[0112] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit the technical solution. Those skilled in the art should understand that those modifications or equivalent substitutions of the technical solution of the present invention that do not depart from the purpose and scope of the technical solution should be included in the scope of the claims of the present invention.

Claims

1. A method for optimizing energy scheduling in an agricultural and animal husbandry zero-carbon park based on multi-energy synergy, characterized in that: include: S1: Model the comprehensive energy system of the agricultural and animal husbandry zero-carbon park to obtain a comprehensive energy system model; calculate the required electricity of the agricultural and animal husbandry zero-carbon park through the comprehensive energy system model; S2: Analyze various energy-consuming equipment in the agricultural and animal husbandry zero-carbon park and build corresponding energy-consuming equipment models; calculate the energy consumption and power of various energy-consuming equipment in the agricultural and animal husbandry zero-carbon park through the energy-consuming equipment model; S3: Analyze the power generation system in the agricultural and animal husbandry zero-carbon park and build a corresponding power generation equipment model; calculate the power generation of the agricultural and animal husbandry park through the power generation equipment model; S4: Based on the comprehensive energy system model, energy-consuming equipment model and power generation equipment model, a multi-energy scheduling optimization problem with the goal of minimizing the operating cost of the agricultural and animal husbandry zero-carbon park is constructed; S5: Solve the multi-energy scheduling optimization problem and obtain the optimal energy scheduling strategy; realize the energy operation scheduling of the agricultural and animal husbandry zero-carbon park through the optimal energy scheduling strategy.

2. The method for optimizing energy dispatching in zero-carbon agricultural and animal husbandry parks based on multi-energy synergy as claimed in claim 1, characterized in that: In step S1, the formula of the comprehensive energy system model is expressed as: Where: P t P represents the required electricity in the agricultural and animal husbandry zero-carbon park during period t; t res Indicates the power generated in the maximum power tracking mode; G t CHP and They represent the natural gas consumption of cogeneration units and gas boiler equipment respectively; and P represents the gas-to-electricity efficiency and gas-to-heat efficiency of the cogeneration unit; t EB Indicates the electricity consumed by the power boiler equipment; Indicates the efficiency of electric boiler; and Respectively represent the discharging and charging power of the energy storage system; and Respectively represent the discharge and charge capacity of the thermal energy storage system; P t curt and represent the power and heat of the thermal energy storage system respectively; and Indicates the time-shiftable power load and heat load; L e,t and L th,t Represent constant electrical load and constant thermal load respectively.

3. The method for optimizing energy dispatching in zero-carbon agricultural and animal husbandry parks based on multi-energy synergy according to claim 1, characterized in that: In step S2, the energy-consuming equipment model includes a cogeneration unit model; The formula of the combined heat and power unit model is expressed as: Where: P t CHP represents the output power of the cogeneration unit during period t; represents the natural gas consumption of the cogeneration unit during period t; H ng Indicates the calorific value of natural gas; η gt Indicates the natural gas combustion efficiency.

4. The method for optimizing energy dispatching in zero-carbon agricultural and animal husbandry parks based on multi-energy synergy as claimed in claim 2, characterized in that: In step S2, the energy-consuming equipment model includes a gas boiler equipment model; The formula of the gas boiler equipment model is expressed as: Where: P t GF Indicates the output power of the gas boiler equipment during period t; G t GF Indicates the natural gas consumption of gas boiler equipment during period t; L ng Indicates the lower calorific value of natural gas; η gb Indicates the thermal efficiency of gas boiler equipment.

5. The method for optimizing energy dispatching in zero-carbon agricultural and animal husbandry parks based on multi-energy synergy according to claim 1, characterized in that: In step S3, the power generation equipment model includes a distributed photovoltaic power generation model; The formula of distributed photovoltaic power generation model is expressed as: Where: E PV represents photovoltaic power generation; f PV Indicates the power derating factor of photovoltaics, which is used to characterize the decrease in output power caused by factors such as dust and aging on the photovoltaic surface; P PV,R Represents the peak photovoltaic capacity; G T Indicates actual illuminance; G T,STC Indicates the illuminance under standard test conditions; α P Indicates the power temperature coefficient; T cell Indicates the current temperature of the photovoltaic surface; T cell,STC Indicates the photovoltaic temperature under standard test conditions.

6. The method for optimizing energy dispatching in zero-carbon agricultural and animal husbandry parks based on multi-energy synergy according to claim 5, characterized in that: In step S3, the power generation equipment model includes a biomass power generation model; The formula of biomass power generation model is expressed as: Where: E BPG represents biomass electricity generation; P BPG represents biomass heat; Δt represents the time change; η represents efficiency; q represents gas consumption of gas turbine; Indicates the lower heating value of the gas.

7. The method for optimizing energy dispatching in zero-carbon agricultural and animal husbandry parks based on multi-energy synergy according to claim 1, characterized in that: In step S4, the objective function of the multi-energy scheduling optimization problem is expressed as: Where: F t represents the operating cost of the agricultural and animal husbandry zero-carbon park during period t; t c ,t e Indicates the start and end time periods; P t E represents the required electricity in the agricultural and animal husbandry zero-carbon park during period t; PV Represents photovoltaic power generation; E BPG represents the amount of electricity generated by biomass; represents the natural gas consumption of the cogeneration unit during period t; G t GF Indicates the natural gas consumption of gas boiler equipment during period t; μ e,t and μ g,t They represent the electricity price and natural gas price in period t respectively.

8. The method for optimizing energy dispatching in zero-carbon agricultural and animal husbandry parks based on multi-energy synergy according to claim 7, characterized in that: In step S4, the constraints of the multi-energy scheduling optimization problem include: Power capacity range: Where: P t CHP , P t GF , P t EB Respectively represent the output power of cogeneration units, gas boiler equipment and electric boiler equipment; Respectively represent the minimum power range of cogeneration units, gas boiler equipment and power boiler equipment; They represent the maximum power ranges of cogeneration units, gas boiler equipment and electric boiler equipment respectively.

9. The method for optimizing energy dispatching in zero-carbon agricultural and animal husbandry parks based on multi-energy synergy as claimed in claim 8, characterized in that: In step S4, the constraints of the multi-energy scheduling optimization problem also include: Maximum charging and discharging power constraints of energy storage system: Where: P t EES , Respectively represent the charging power and charging energy of the energy storage system; and Respectively represent the minimum charging power range and the maximum discharging power range of the energy storage system; and They represent the minimum charging energy and maximum discharging energy of the energy storage system respectively; The maximum energy boundary of thermal energy storage system: Where: and They represent the minimum energy range and the maximum energy range of the thermal energy storage system respectively; Represents the current thermal energy storage energy; α TES Represents the self-discharge rate of the thermal energy storage system; represents the net energy change of thermal energy storage during period t; Time-shifted load constraints: Where: L sl e,t and L sl th,t They represent the time-shiftable electric load and heat load within the day respectively; and Respectively represent the upper limits of the time-shiftable power load and heat load; Ω e and Ω th They represent the scheduling time intervals of the time-shiftable power load and heat load respectively.

10. The method for optimizing energy dispatching in zero-carbon agricultural and animal husbandry parks based on multi-energy synergy according to claim 9, characterized in that: In step S4, the constraints of the multi-energy scheduling optimization problem also include: Output climbing constraint: Where: ΔP CHP and ΔP EB Represent the unit ramp rates of cogeneration units and power boiler equipment respectively.

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