Agricultural micro-grid and pumped storage power station combined dispatching method and device and medium

Abstract

The invention relates to an agricultural micro-grid and pumped storage power station combined scheduling method and device and a medium, and the method comprises the following steps: building an agricultural micro-grid model, a pumped storage power station model and an irrigation system model according to the indexes of the agricultural micro-grid and the pumped storage power station; establishing a joint scheduling model by taking the minimum system operation cost as an optimization target and taking the power of various resources in the system as decision variables, performing uncertainty description on renewable energy sources based on envelope constraints to obtain an interval uncertainty model of renewable energy source output, establishing a robust optimization model based on an information gap decision theory, and performing optimization on the output of the renewable energy sources. And solving to obtain a joint scheduling strategy by combining the agricultural micro-grid model, the pumped storage power station model, the irrigation system model, the joint scheduling model and the robust optimization model. Compared with the prior art, the method has the advantages of high accuracy, high risk resistance and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid dispatching, and in particular to a method, device and medium for jointly dispatching an agricultural microgrid and a pumped storage power station. Background Art

[0002] Agricultural microgrids are an effective means to consume renewable energy, promote local consumption and improve energy management efficiency in rural areas, so maximizing the operational performance of agricultural microgrids has become the current focus in this field. Compared with traditional power grids, the uncertainty of renewable energy makes the optimization and scheduling process of agricultural microgrids more complicated.

[0003] The fluctuation of renewable energy is the main reason for the increased fluctuation of microgrid net load. When there is an imbalance in the supply and demand of the microgrid, it will pose a great risk to the overall stable operation of the system. Adding energy storage systems to microgrids is an effective way to reduce the fluctuation of the system net load and improve the flexibility of system operation. Considering the abundance of water resources in rural areas and the necessary demand for agricultural irrigation, the joint dispatch of small pumped storage power stations and agricultural microgrids has become an effective solution to cope with the fluctuation of agricultural microgrids.

[0004] Most of the existing microgrid day-ahead dispatching models focus on source-load uncertainty modeling and meeting water and electricity load requirements, and rarely consider the coupling characteristics of agricultural microgrids and irrigation systems. Therefore, studying how to comprehensively consider the irrigation characteristics of small pumped-storage power stations and the uncertainty of renewable energy generation, and formulating a more reasonable and economical agricultural microgrid dispatching strategy, is of great significance to improving the economy and flexibility of system operation, promoting the consumption of new energy, and ensuring the safe, economical, and stable operation of the power system. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide a method, device and medium for jointly dispatching an agricultural microgrid and a pumped storage power station.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] A method for jointly dispatching an agricultural microgrid and a pumped storage power station comprises an agricultural microgrid, a pumped storage power station and an irrigation system, wherein the agricultural microgrid is powered by a photovoltaic generator set, a wind generator set and a gas turbine, and the pumped storage power station is used to store excess electricity of the agricultural microgrid and provide irrigation water for the irrigation system, and further comprises the following steps;

[0008] According to the indicators of agricultural microgrid, pumped storage power station and irrigation system, establish agricultural microgrid model, pumped storage power station model and irrigation system model;

[0009] Taking the minimization of the operation cost of the agricultural microgrid as the optimization goal, a joint dispatch model is established with the dispatch output of photovoltaic generators, wind generators and gas turbines as decision variables.

[0010] Based on the envelope constraint, the uncertainty of the dispatch output of photovoltaic generators and wind turbines is described, and the interval uncertainty model of renewable energy output is obtained. A robust optimization model based on information gap decision theory is further established.

[0011] By combining the agricultural microgrid model, pumped storage power station model, irrigation system model, joint scheduling model and robust optimization model, the joint scheduling strategy is solved.

[0012] Furthermore, in the agricultural microgrid model, when the photovoltaic generator set and the wind generator set generate insufficient electricity, the gas turbine will make up for the power shortage; when the photovoltaic generator set and the wind generator set generate excess electricity, the excess electricity will be stored in the pumped-storage power station; when the storage capacity of the pumped-storage power station reaches its upper limit, the excess electricity will be discarded.

[0013] Furthermore, the pumped storage power station model includes a turbine and a water pump, and the water-to-electricity conversion equation of the turbine and the water pump is:

[0014]

[0015] Where P t PS,HT , P t PS,WP are the power output of the turbine and the power consumed by the pump respectively; a PS , b PS , c PS are the water-to-electricity conversion coefficients of turbines and pumps; is the flow rate flowing through the turbine at time t; is the flow rate through the pump at time t.

[0016] Furthermore, the calculation expression of the power consumption load of the irrigation system is:

[0017]

[0018] Where P t IS The equivalent electrical energy generated by the irrigation system; IS , b IS , c IS is the equivalent power generation coefficient of the irrigation system; The flow rate of the irrigation system.

[0019] Furthermore, the objective function of the joint scheduling model is:

[0020] min C=C RE +C MT +θ PS C PS,HT +(1-θ PS )C PS,WP +C CUR

[0021]

[0022]

[0023] Where, C is the operating cost of the agricultural microgrid system; C RE is the operating cost of renewable energy units; C MT is the operating cost of the micro gas turbine; C PS,HT is the turbine startup cost; C PS,WP is the pump startup cost; C CUR is the cost of wind and solar power abandonment; θ PS is a Boolean variable representing the working state of a small pumped storage power station. When the value is 1, the turbine is working, and when the value is 0, the pump is working; α PV , α WT are the cost coefficients of photovoltaic and wind turbines respectively; P t PV , P t WT are the dispatching outputs of photovoltaic and wind turbines at time t respectively; a MT , b MT 、c MT are the cost coefficients of micro gas turbines; P t MT is the dispatching output of the micro gas turbine at time t; α PV,CUR , α WT,CUR are the penalty cost coefficients for abandoned solar power and abandoned wind power respectively; P t PV,CUR , P t WT,CUR They are the amount of abandoned solar power and the amount of abandoned wind power respectively.

[0024] Furthermore, the joint dispatch model includes power balance constraints, renewable energy unit operation constraints, upper and lower water pool flow rate constraints, upper and lower water pool capacity constraints, upper and lower water pool initial and final capacity constraints, irrigation flow rate constraints, and irrigation system water volume constraints;

[0025] The calculation expression of the electric power balance constraint is:

[0026] P t load =P t PV +P tWT +P t MT +P t PS,HT -P t PS,WP +P IS

[0027] Where P t load is the system power load demand at time t;

[0028] The calculation expression of the operation constraint of renewable energy units is:

[0029]

[0030] Where P t PV,pre , P t WT,pre are the predicted output power of photovoltaic and wind turbines at time t respectively;

[0031] The calculation expression of the flow velocity constraint of the upper and lower pools is:

[0032]

[0033] Where V UP,min 、V UP,max V are the minimum and maximum flow rates for draining or pumping water from the upper pool, respectively; LOW,min 、V LOW,max V are the minimum and maximum flow rates for draining or pumping water from the lower pool, respectively; t UP 、V t LOW are the flow velocities of the upper and lower pools at time t respectively;

[0034] The calculation expression of the upper and lower pool capacity constraints is:

[0035]

[0036] In the formula, Q UP,max , Q UP,min is the upper and lower limits of the water tank capacity; Q LOW,max , Q LOW,min The upper and lower limits of the water tank capacity;

[0037] The calculation expression of the real-time capacity constraint of the upper and lower pools is:

[0038]

[0039] In the formula, is the amount of water taken from the river by the upper pool at time t;

[0040] The calculation expression of the initial and final capacity constraints of the upper and lower pools is:

[0041]

[0042] In the formula, are the initial and final water volumes of the upper pool respectively; are the initial and final water volumes of the lower pool; Q UP,NO , Q LOW,NO The water volume is specified for the upper and lower pools respectively;

[0043] The calculation expression of irrigation flow rate constraint is:

[0044] V IS,min ≤V t IS ≤V IS,max

[0045] Where V t IS is the flow rate of the irrigation system at time t; V IS,max 、V IS,min are the upper and lower limits of the flow rate of the irrigation system; the calculation expression of the water volume constraint of the irrigation system is:

[0046]

[0047] In the formula, Q IS,total is the total water consumption of the irrigation system.

[0048] Furthermore, the calculation expression of the robust optimization model is:

[0049]

[0050] In the formula, is the predicted value of the uncertain parameter; η is the deviation coefficient, that is, the uncertainty fluctuation coefficient; U(η,X) is the fluctuation range of the uncertainty parameter. The weighted sum is used to normalize multiple uncertain parameters into a comprehensive uncertain parameter for solution. The specific model of the uncertain parameter is:

[0051]

[0052] Where η PV , η WT are the predicted power fluctuation amplitudes of the uncertain parameters of photovoltaic and wind turbines, λ PV , WT are the weights of the power fluctuation amplitude predicted by the uncertain parameters of photovoltaic and wind turbines, respectively.

[0053] Furthermore, the robust optimization model includes a risk aversion model, and the calculation expression of the risk aversion model is:

[0054]

[0055] Where: P t PV,true , P t WT,true are the actual output powers of the uncertain parameters of photovoltaic and wind turbines respectively; C0 is the optimal dispatching cost of the deterministic model; ξ is the avoidance coefficient.

[0056] The second aspect of the present invention is a device for jointly dispatching an agricultural microgrid and a pumped-storage power station, comprising a memory, a processor, and a program stored in the memory. When the processor executes the program, it implements any of the above methods for jointly dispatching an agricultural microgrid and a pumped-storage power station.

[0057] A third aspect of the present invention is a storage medium having a program stored thereon, which, when executed, implements any of the above methods for jointly dispatching an agricultural microgrid and a pumped-storage power station.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] 1) The joint dispatching strategy of agricultural microgrid and small pumped-storage power station designed in this invention fully considers the coupling characteristics of agricultural microgrid and irrigation system, constructs a joint dispatching model, comprehensively considers the irrigation characteristics of small pumped-storage power station and the uncertainty of renewable energy, uses information gap decision theory to analyze the uncertainty of wind and solar power, considers the uncertainty of renewable energy, constructs a robust optimization model, and improves the accuracy of the overall model.

[0060] 2) The present invention formulates a more reasonable scheduling strategy through the energy transfer process of electric energy flow and water energy flow, which can reduce the system operation cost, promote the consumption of renewable energy, and improve the economy and risk resistance of distribution network operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a model diagram of the agricultural microgrid of the present invention.

[0062] Figure 2 Schematic diagram of the changes in each generator set and electrical load of the agricultural microgrid on a typical day. DETAILED DESCRIPTION

[0063] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0064] Example 1

[0065] The present invention is a method, device and medium for jointly dispatching an agricultural microgrid and a pumped storage power station. The present invention is further described below from several aspects, including model establishment, design principle, design method, and effectiveness verification.

[0066] (1) Agricultural microgrid model

[0067] like Figure 1 As shown, the agricultural microgrid established by the present invention is composed of a photovoltaic generator set, a wind generator set, a micro gas turbine, a small pumped storage power station, an irrigation system and two pools at different water levels. Since it is impossible to interact with the main power grid, the micro gas turbine can be started to generate electricity to make up for the power shortage when necessary. Among them, the upper pool is connected to the river, and the lower pool is connected to the irrigation system. The water stored in the upper pool comes from the river and the water extracted from the lower pool, and the water demand of the irrigation system is met by the lower pool. In this agricultural microgrid, when wind power generation and photovoltaic power generation are insufficient, the micro gas turbine will make up for the power shortage. Otherwise, the excess electric energy will be stored in the pumped storage power station. In addition, if the excess electric energy is large and exceeds the power storage capacity of the small pumped storage power station, a part of the wind and light will be discarded to achieve power balance.

[0068] (2) Small Pumped Storage Power Station Model

[0069] A small pumped storage power station has a hydraulic turbine for generating electricity between the upper and lower water tanks and a water pump for pumping water from the river or the lower water tank. The water-to-electricity conversion equation for the hydraulic turbine and the water pump is as follows:

[0070]

[0071] Where P t PS,HT , P t PS,WP are the power output of the turbine and the power consumed by the pump respectively; a PS , b PS , c PS The hydroelectric conversion factor for turbines and pumps in small pumped storage power stations; is the flow rate flowing through the turbine at time t; is the flow rate through the pump at time t.

[0072] (3) Irrigation system model

[0073] The power consumption formula of the irrigation system is:

[0074]

[0075] Where P t IS The equivalent electrical energy generated by the irrigation system;IS , b IS , c IS is the equivalent power generation coefficient of the irrigation system; The flow rate of the irrigation system.

[0076] (4) Deterministic model for joint dispatch of agricultural microgrids and small pumped storage power stations

[0077] The optimization scheduling problem of agricultural microgrid refers to the reasonable scheduling of various flexible resources to minimize the system operation cost under the premise of meeting the water and electricity load requirements of the system. Its objective function is:

[0078] minC=C RE +C MT +θ PS C PS,HT +(1-θ PS )C PS,WP +C CUR

[0079]

[0080] Where, C is the operating cost of the agricultural microgrid system; C RE is the operating cost of renewable energy units; C MT is the operating cost of the micro gas turbine; C PS,HT is the turbine startup cost; C PS,WP is the pump startup cost; C CUR is the cost of wind and solar power abandonment; θ PS is a Boolean variable representing the working state of a small pumped storage power station. When the value is 1, the turbine is working, and when the value is 0, the pump is working; α PV , α WT is the cost coefficient of photovoltaic and wind turbines; P t PV , P t WT is the dispatching output of photovoltaic and wind turbines at time t; a MT , b MT 、c MT is the cost coefficient of the micro gas turbine; P t MT is the dispatching output of the micro gas turbine at time t; α PV,CUR , α WT,CUR P is the penalty cost coefficient for abandoned wind and solar power of photovoltaic and wind turbines; t PV,CUR , P t WT,CUR is the amount of wind and solar power abandoned by photovoltaic and wind turbines at time t.

[0081] Electric power balance constraints.

[0082] P t load =P t PV +P t WT +P t MT +P t PS,HT -P t PS,WP +P IS

[0083] Where P t load is the system power load demand at time t.

[0084] Operating constraints of renewable energy units.

[0085]

[0086] Where P t PV,pre , P t WT,pre are the predicted output powers of photovoltaic and wind turbines at time t respectively.

[0087] Flow rate constraints for upper and lower pools.

[0088]

[0089] Where V UP,min 、V UP,max V are the minimum and maximum flow rates for draining or pumping water from the upper pool, respectively; LOW,min 、V LOW,max V are the minimum and maximum flow rates for draining or pumping water from the lower pool, respectively; t UP 、V t LOW are the flow velocities of the upper and lower pools at time t respectively.

[0090] Upper and lower pool capacity constraints.

[0091]

[0092] In the formula, Q UP,max , Q UP,min is the upper and lower limits of the water tank capacity; Q LOW,max , Q LOW,min The upper and lower limits of the water tank capacity.

[0093] Real-time capacity constraints for upper and lower pools.

[0094]

[0095] The lower water tank supplies water to the irrigation system to meet the water load demand of the irrigation system, which results in a water shortage in the small pumped storage power station. In order to ensure the normal operation of the small pumped storage power station, the upper water tank takes water from the river to make up for the shortage. is the amount of water the upper pool obtains from the river at time t.

[0096] The upper and lower pools are always subject to capacity constraints.

[0097]

[0098] In order to ensure that the pumped storage power station can operate normally the next day, sufficient water must be stored in the upper and lower pools at the end of the day's operation. are the initial and final water volumes of the upper pool respectively; are the initial and final water volumes of the lower pool; Q UP,NO , Q LOW,NO The water volume is specified for the upper and lower pools respectively.

[0099] Irrigation flow rate constraints.

[0100] V IS,min ≤V t IS ≤V IS,max

[0101] Where V t IS is the flow rate of the irrigation system at time t; V IS,max 、V IS,min The upper and lower limits of the flow rate of the irrigation system.

[0102] Irrigation system water constraints.

[0103]

[0104] In the formula, Q IS,total is the total water consumption of the irrigation system.

[0105] (5) Robust optimization model considering wind and solar uncertainty

[0106] When using information gap decision theory to deal with parameter uncertainty, envelope constraints are usually used to model the uncertainty parameter X, such as:

[0107]

[0108] Where: is the predicted value of the uncertain parameter; η is the deviation coefficient, that is, the uncertainty fluctuation coefficient; U(η,X) is the fluctuation range of the uncertainty parameter.

[0109] Traditional IGDT methods are mostly suitable for processing single uncertain parameters. However, this paper considers two uncertain parameters, photovoltaic and wind turbine power prediction, at the same time. It is a decision-making problem with multiple uncertain parameters. To solve this problem, this paper uses weighted sum to normalize multiple uncertain parameters into comprehensive uncertain parameters for solution. The specific model is as follows:

[0110]

[0111] Where η PV , η WT are the predicted power fluctuation amplitudes of the uncertain parameters of photovoltaic and wind turbines. PV , WT are the weights of the power fluctuation amplitude predicted by the uncertain parameters of photovoltaic and wind turbines, respectively.

[0112] The risk aversion model must ensure that the system operating cost does not exceed the expected scheduling target, while trying to avoid the impact of uncertainty on the optimization results. Therefore, the risk aversion model takes the maximum deviation coefficient η as the optimization target. The larger the calculated deviation coefficient, the stronger the risk tolerance of the system, but the corresponding operating cost is also greater. Its mathematical model is as follows:

[0113]

[0114] Where P t PV,true , P t WT,true are the actual output power of the uncertain parameters of photovoltaic and wind turbines respectively; C0 is the benchmark value, i.e. the optimal dispatch cost of the deterministic model; ξ is the avoidance coefficient; χ is the set of parameters in this model; H(P t PV,true ,P t WT,true ,χ)=0 is the set of system equality constraints; G(P t PV,true ,P t WT,true ,χ)≤0 is the set of system inequality constraints.

[0115] The corresponding uncertainty range is as follows:

[0116]

[0117] It can be seen that the mathematical model constructed by the information gap decision theory is a two-layer model. When the uncertainty fluctuation coefficient η is determined, its maximum cost occurs when each uncertainty is (1±η) times. Therefore, the above two-layer model can be converted into a single-layer model as follows:

[0118]

[0119] The model established in the present invention is a mixed integer linear programming problem, and the model can be solved by calling the GUROBI solver using the YALMIP toolbox in MATLAB.

[0120] (6) Example analysis

[0121] In this embodiment, Figure 2 As shown in Figure 1, the output and power load of the agricultural microgrid generator set on a typical day, and the specific operating parameters of the agricultural microgrid are shown in Table 1.

[0122] Table 1 Agricultural microgrid operating parameters

[0123]

[0124] In order to verify the economy and effectiveness of the joint dispatching model of agricultural microgrid and small pumped storage power station taking into account the uncertainty of wind and solar power proposed in this invention, the following three schemes are set up for comparative analysis:

[0125] Solution 1: Without considering the uncertainty of wind and solar power, calculate the system costs after the agricultural microgrid is jointly dispatched with the small pumped-storage power station, and formulate the unit dispatch output strategy.

[0126] Option 2: Without considering the joint dispatch of agricultural microgrids and small pumped-storage power stations, calculate the system costs of agricultural microgrids while considering the uncertainty of wind and solar power, and formulate unit dispatch output strategies.

[0127] Option 3: Comprehensively consider the uncertainty of wind and solar power and the joint dispatch of agricultural microgrids and small pumped-storage power stations, calculate the various costs of the agricultural microgrid system, and formulate a unit dispatch output strategy.

[0128] The comparison results are shown in Table 2.

[0129] Table 2 Comparison of results of various schemes

[0130]

[0131] The joint dispatching strategy of agricultural microgrid and small pumped storage power station designed by the present invention based on information gap decision theory is based on the richness of water resources in rural areas and the particularity of geographical location. Under the condition of uncertainty of renewable energy, it comprehensively considers factors such as photovoltaic power generation, wind power generation, power load demand and water demand of irrigation system, and establishes a robust optimization dispatching model of agricultural microgrid. The optimization goal is to minimize the comprehensive operation cost of agricultural microgrid system, and the YALMIP toolbox in MATLAB is used to call the GUROBI solver to solve the model. The simulation results show that the joint dispatching of agricultural microgrid and small pumped storage power station can effectively reduce the total cost of system operation, reduce the phenomenon of wind and solar power abandonment, and improve the economic efficiency of agricultural microgrid operation. At the same time, considering the uncertainty of renewable energy is helpful to formulate a more reasonable flexible resource dispatching output strategy and improve the risk resistance and stability of system operation.

[0132] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0133] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0134] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A method for jointly dispatching an agricultural microgrid and a pumped storage power station, comprising an agricultural microgrid, a pumped storage power station and an irrigation system, wherein the agricultural microgrid is powered by a photovoltaic generator set, a wind generator set and a gas turbine, and the pumped storage power station is used to store excess electricity of the agricultural microgrid and provide irrigation water for the irrigation system, characterized in that: The following steps are also included; According to the indicators of agricultural microgrid, pumped storage power station and irrigation system, establish agricultural microgrid model, pumped storage power station model and irrigation system model; Taking the minimization of the operation cost of the agricultural microgrid as the optimization goal, a joint dispatch model is established with the dispatch output of photovoltaic generators, wind generators and gas turbines as decision variables. Based on the envelope constraint, the uncertainty of the dispatch output of photovoltaic generators and wind turbines is described, and the interval uncertainty model of renewable energy output is obtained. A robust optimization model based on information gap decision theory is further established. By combining the agricultural microgrid model, pumped storage power station model, irrigation system model, joint scheduling model and robust optimization model, the joint scheduling strategy is solved.

2. A method for jointly dispatching an agricultural microgrid and a pumped storage power station according to claim 1, characterized in that: In the agricultural microgrid model, when the photovoltaic generator set and the wind generator set generate insufficient electricity, the gas turbine will make up for the power shortage; when the photovoltaic generator set and the wind generator set generate excess electricity, the excess electricity will be stored in the pumped-storage power station; when the storage capacity of the pumped-storage power station reaches its upper limit, the excess electricity will be discarded.

3. The method for jointly dispatching an agricultural microgrid and a pumped storage power station according to claim 1, characterized in that: The pumped storage power station model includes a turbine and a water pump, and the water-to-electricity conversion equation of the turbine and the water pump is: Where P t PS,HT , P t PS,WP are the power output of the turbine and the power consumed by the pump respectively; a PS , b PS , c PS are the water-to-electricity conversion coefficients of turbines and pumps; is the flow rate through the turbine at time t; is the flow rate through the pump at time t.

4. The method for jointly dispatching an agricultural microgrid and a pumped storage power station according to claim 1, characterized in that: The calculation expression of the power consumption load of the irrigation system is: Where P t IS The equivalent electrical energy generated by the irrigation system; IS , b IS , c IS is the equivalent power generation coefficient of the irrigation system; The flow rate of the irrigation system.

5. The method for jointly dispatching an agricultural microgrid and a pumped storage power station according to claim 1, characterized in that: The objective function of the joint scheduling model is: minC=C RE +C MT +θ PS C PS,HT +(1-θ PS )C PS,WP +C CUR Where, C is the operating cost of the agricultural microgrid system; C RE is the operating cost of renewable energy units; C MT is the operating cost of the micro gas turbine; C PS,HT is the turbine startup cost; C PS,WP is the pump startup cost; C CUR is the cost of wind and solar power abandonment; θ PS is a Boolean variable representing the working state of a small pumped storage power station. When the value is 1, the turbine is working, and when the value is 0, the pump is working; α PV , α WT are the cost coefficients of photovoltaic and wind turbines respectively; P t PV , P t WT are the dispatching outputs of photovoltaic and wind turbines at time t respectively; a MT 、b MT 、c MT are the cost coefficients of micro gas turbines; P t MT is the dispatching output of the micro gas turbine at time t; α PV,CUR , α WT,CUR are the penalty cost coefficients for abandoned solar power and abandoned wind power respectively; P t PV,CUR , P t WT,CUR They are the amount of abandoned solar power and the amount of abandoned wind power respectively.

6. A method for jointly dispatching an agricultural microgrid and a pumped storage power station according to claim 5, characterized in that: The joint dispatch model includes power balance constraints, renewable energy unit operation constraints, upper and lower water pool flow rate constraints, upper and lower water pool capacity constraints, upper and lower water pool initial and final capacity constraints, irrigation flow rate constraints and irrigation system water volume constraints; The calculation expression of the electric power balance constraint is: P t load =P t PV +P t WT +P t MT +P t PS,HT -P t PS,WP +P IS Where P t load is the system power load demand at time t; The calculation expression of the renewable energy unit operation constraint is: Where P t PV,pre , P t WT,pre are the predicted output power of photovoltaic and wind turbines at time t respectively; The calculation expression of the flow velocity constraint of the upper and lower pools is: Where V UP,min 、V UP,max V are the minimum and maximum flow rates for draining or pumping water from the upper pool, respectively; LOW,min 、V LOW,max V are the minimum and maximum flow rates for draining or pumping water from the lower pool, respectively; t UP 、V t LOW are the flow velocities of the upper and lower pools at time t respectively; The calculation expression of the upper and lower water tank capacity constraint is: In the formula, Q UP,max , Q UP,min is the upper and lower limits of the water tank capacity; Q LOW,max , Q LOW,min The upper and lower limits of the water tank capacity; The calculation expression of the real-time capacity constraint of the upper and lower water tanks is: In the formula, is the amount of water taken from the river by the upper pool at time t; The calculation expression of the initial and final capacity constraints of the upper and lower pools is: In the formula, are the initial and final water volumes of the upper pool respectively; are the initial and final water volumes of the lower pool respectively; Q UP,NO , Q LOW,NO The water volume is specified for the upper and lower pools respectively; The calculation expression of the irrigation flow rate constraint is: In IS,min ≤V t IS ≤V IS,max Where V t IS is the flow rate of the irrigation system at time t; V IS,max 、V IS,min The upper and lower limits of the flow rate of the irrigation system; The calculation expression of the water quantity constraint of the irrigation system is: In the formula, Q IS,total is the total water consumption of the irrigation system.

7. The method for jointly dispatching an agricultural microgrid and a pumped storage power station according to claim 1, characterized in that: The calculation expression of the robust optimization model is: In the formula, is the predicted value of the uncertain parameter; η is the deviation coefficient, that is, the uncertainty fluctuation coefficient; U(η,X) is the fluctuation range of the uncertainty parameter. The weighted sum is used to normalize multiple uncertain parameters into a comprehensive uncertain parameter for solution. The specific model of the uncertain parameter is: Where η PV , η WT are the predicted power fluctuation amplitudes of the uncertain parameters of photovoltaic and wind turbines, λ PV , WT are the weights of the power fluctuation amplitude predicted by the uncertain parameters of photovoltaic and wind turbines, respectively.

8. The method for jointly dispatching an agricultural microgrid and a pumped storage power station according to claim 1, characterized in that: The robust optimization model includes a risk aversion model, and the calculation expression of the risk aversion model is: Where: P t PV,true , P t WT,true are the actual output powers of the uncertain parameters of photovoltaic and wind turbines respectively; C0 is the optimal dispatching cost of the deterministic model; ξ is the avoidance coefficient.

9. A combined dispatching device for an agricultural microgrid and a pumped storage power station, comprising a memory, a processor, and a program stored in the memory, characterized in that: When the processor executes the program, it implements a joint scheduling method of an agricultural microgrid and a pumped-storage power station as described in any one of claims 1-8.

10. A storage medium having a program stored thereon, characterized in that: When the program is executed, a method for jointly dispatching an agricultural microgrid and a pumped-storage power station as described in any one of claims 1 to 8 is implemented.

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