Agricultural microgrid consumption and dispatch methods based on crop energy consumption characteristics

Abstract

This invention discloses an agricultural microgrid consumption and dispatch method based on crop energy consumption characteristics, belonging to the field of microgrid energy management technology. The method includes the following steps: establishing a total cost function with the objective function being the minimum total cost; introducing virtual loads based on crop growth characteristics and associated energy consumption characteristics, using the specific time shifts of three types of dispatchable loads—general, special, and virtual—as decision variables; defining load dispatch boundary conditions based on grid-side frequency, voltage, waveform distortion rate, etc.; considering load time shift constraints, power balance constraints, energy storage capacity constraints, and grid interaction constraints; and finally, based on a source-grid-load-storage multi-objective collaborative optimization control method considering new energy penetration, grid power coordination, time-shifted load energy consumption characteristics, and energy storage economics, solving the microgrid economic dispatch model and determining the load dispatch scheme. This invention comprehensively considers economic efficiency, environmental protection, and agricultural characteristics, and can optimize the control of energy storage use and grid interaction.

Description

Technical Field

[0001] This invention relates to the field of microgrid energy management technology, specifically to an agricultural microgrid consumption and scheduling method based on crop energy consumption characteristics. Background Technology

[0002] With the rapid development of distributed renewable energy and energy storage technologies, microgrids, as a new type of power system, are playing an increasingly important role in solving renewable energy consumption, improving energy efficiency, and enhancing power supply reliability. However, due to the intermittency and randomness of renewable energy sources, as well as the volatility of power load, how to optimize the coordinated operation of various resources within a microgrid to achieve economical, environmentally friendly, and efficient energy management remains a key technical problem that urgently needs to be solved.

[0003] Currently, microgrid scheduling optimization methods mainly fall into the following categories: mathematical programming-based methods, heuristic algorithms, and artificial intelligence methods. Mathematical programming-based methods, including linear programming, nonlinear programming, and mixed-integer programming, are characterized by a solid theoretical foundation and high solution efficiency, but they struggle to fully consider the nonlinear characteristics of the system and the demands of multi-objective optimization. Heuristic algorithms, such as genetic algorithms and particle swarm optimization, can handle nonlinear and multi-objective problems well, but their computational efficiency is low and they are prone to getting trapped in local optima. Artificial intelligence-based methods, such as reinforcement learning and deep learning, perform well in handling complex decision-making problems, but they require large amounts of data for training and have limited interpretability.

[0004] In practical applications, the main challenges faced by microgrid dispatch include: (1) various uncertainties, such as the volatility of renewable energy output and load demand; (2) multi-objective optimization requirements, including economic efficiency, environmental protection, and reliability; (3) the coordination and control of distributed energy resources; (4) the optimal strategy for grid interaction; and (5) the optimization of energy storage equipment.

[0005] For microgrids in agricultural parks, their dispatch and management need to take into account the specific needs of agricultural production, such as supplemental lighting load and greenhouse control. Current technologies lack a smart dispatch method for microgrids that can comprehensively consider various distributed energy sources, energy storage devices, and agricultural characteristics, particularly lacking optimized control over the number of energy storage device cycles and effective limitations on the number of grid interactions. Furthermore, existing technologies generally use real-time electricity prices as a direct external input to the system, lacking a mechanism for dynamically adjusting prices based on load levels and rates of change. This makes the system unable to effectively respond to energy supply and demand conditions on the grid side, reducing the economic efficiency of dispatch.

[0006] Therefore, there is an urgent need for a smart load dispatching method for microgrids that can comprehensively consider economic efficiency, environmental protection, and agricultural characteristics, and can optimize the control of energy storage use and grid interaction. Summary of the Invention

[0007] This invention addresses the problem of overly simplistic solutions in existing technologies by providing a significantly different approach. It primarily offers a method for agricultural microgrid consumption and scheduling based on crop energy consumption characteristics. This method comprehensively considers multiple objectives, including grid interaction costs, equipment operation and maintenance costs, equipment investment and depreciation costs, pollution emission costs, and limitations on the number of grid-energy storage interactions. Through iterative optimization, it continuously improves the scheduling scheme, achieving economical, environmentally friendly, and stable operation of the microgrid system.

[0008] The technical solution adopted by the present invention to solve the above-mentioned technical problems is as follows:

[0009] The agricultural microgrid consumption and dispatch method based on crop energy consumption characteristics includes the following steps:

[0010] S1. Establish the objective function: Based on the microgrid system, establish the total cost function. The objective function is to minimize the total cost. ;

[0011] S2. Determine decision variables: Use the specific time shifts of three types of dispatchable loads—general, special, and virtual—as decision variables. Special loads are loads related to crop growth characteristics and associated energy consumption characteristics, while virtual loads are dynamic loads generated based on specific dispatch schemes for special loads.

[0012] S3. Define the dispatch boundary conditions: Considering grid-side constraints, define the load dispatch boundary conditions;

[0013] S4. Determine system constraints: including load time shift constraints, power balance constraints, energy storage capacity constraints, and grid interaction constraints;

[0014] S5. Model Construction: Based on steps S1-S4, a microgrid economic dispatch model is constructed.

[0015] S6. Solution: Based on the source-grid-load-storage multi-objective collaborative optimization control method that considers new energy penetration, grid power coordination, time-shifted load energy consumption characteristics, and energy storage economics, solve the microgrid economic dispatch model and determine the load dispatch scheme.

[0016] Specifically, in step S1, the total cost function for:

[0017] (1)

[0018] Where T represents time in hours; F1 represents grid interaction cost; F2 represents operation and maintenance cost; F3 represents investment depreciation cost; F4 represents pollution emission cost; and F5 represents system interaction and energy storage lifespan cost.

[0019] Furthermore, the expression for the grid interaction cost is:

[0020] (2)

[0021] in, For real-time electricity prices, Let t be the power exchange between the power grids at time t.

[0022] The expression for operation and maintenance costs is:

[0023] (3)

[0024] in, The operating and maintenance cost of the i-th type of equipment is... Let be the real-time power of the i-th device.

[0025] The expression for investment depreciation cost is:

[0026] (4)

[0027] in, Let be the initial investment cost of the i-th type of equipment. For interest rate, Let be the service life of the i-th type of equipment.

[0028] The expression for the cost of pollution emissions is:

[0029] (5)

[0030] in, The value of the j-th pollutant gas produced by the i-th type of equipment. The cost of environmental pollution caused by the j-th polluting gas, Let be the real-time power of the i-th device.

[0031] Furthermore, based on the limitation on the number of grid interactions and energy storage charging and discharging interactions, the number of grid interactions is introduced. Number of interactions with energy storage Establish system interaction and energy storage lifetime cost function The expression is:

[0032] (6)

[0033] (7)

[0034] (8)

[0035] (9)

[0036] in, To limit the cost of the power grid, For energy storage cycle costs, Costs associated with energy storage capacity degradation; It is the linear influence coefficient of power grid interaction. It is the secondary influence coefficient of power grid interaction. This represents the number of interactions with the power grid. This is the initial cost of energy storage equipment. This refers to the maximum cycle life of the energy storage device. This refers to the number of energy storage interactions. It is the coefficient of influence of energy storage cycle on capacity degradation. It is the influence coefficient of power grid interaction on system degradation. It is the reference number of power grid interactions.

[0037] Furthermore, the real-time electricity price calculation method is as follows: the base price is set according to valley period, normal period, and peak period; the normal and peak period prices are adjusted in real time based on load level and load change rate, and the calculation formula is as follows:

[0038] (10)

[0039] in, The real-time electricity price at time t The base electricity price at time t. The load level influence coefficient. The load change rate influence coefficient. Let be the load factor at time t. Let be the load change rate factor at time t.

[0040] Specifically, in step S2, based on the energy consumption characteristics associated with crop growth features in the agricultural park, an active power correction coefficient is introduced, and the expression for the virtual load is:

[0041] (11)

[0042] in, It is the total virtual load. It is the correction coefficient under the i-th supplementary lighting strategy. is the load amount under the i-th supplemental lighting strategy, and m is the number of special load categories, m≥2.

[0043] Specifically, in step S3, the grid-side constraints are: based on frequency, voltage, waveform distortion rate, and combined with local real-time market grid policies, the load dispatch boundary conditions are defined.

[0044] Further, in step S3, for the frequency offset percentage Voltage offset percentage The calculation formula and constraint range for waveform distortion rate (THD) are as follows:

[0045] (12)

[0046] (13)

[0047] (14)

[0048] Among them, f i f represents the frequency at that moment. N Indicates the rated frequency; U i U represents the grid voltage at that moment; N Indicates the rated voltage of the power grid; A N This represents the effective value of the Nth harmonic component.

[0049] Specifically, in step S4, regarding the power balance constraints, a real-time power balance equation is established:

[0050] (15)

[0051] in, Let be the power exchange between the power grids at time t. The total load after adjustment at time t. Let be the battery power at time t. Let t be the biogas power generation capacity. Let be the photovoltaic power generation at time t. Let t be the thermoelectric conversion power at time t.

[0052] Furthermore, in step S6, the convergence criterion is: the total cost change rate η ≤ 2%, and the difference in the number of grid interactions. <1, and the difference in the number of energy storage interactions Whether <1 is true at the same time.

[0053] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0054] (1) Based on the growth characteristics of crops in agricultural parks, this invention introduces the concept of virtual load, which can optimize and adjust the load distribution according to the needs of different supplemental lighting strategies, thus meeting the needs of agricultural production and realizing the efficient use of energy.

[0055] (2) This invention innovatively proposes a limiting mechanism for the number of grid interactions and the number of energy storage interactions. By setting the corresponding cost function, the number of grid interactions and the number of energy storage charging and discharging are effectively controlled, the equipment life is extended, and the system stability is improved.

[0056] (3) The present invention introduces a real-time electricity price adjustment mechanism based on load level and load change rate, which can effectively reflect the energy supply and demand situation on the grid side and improve the economy of microgrid and large grid interaction.

[0057] (4) The present invention takes into account the influence of factors such as grid side frequency, voltage, waveform distortion rate, etc., and defines the load dispatch boundary conditions based on these factors, ensuring that the operation of the microgrid will not have an adverse effect on the large grid, thereby improving the safety and reliability of the system.

[0058] In summary, this invention comprehensively considers economic efficiency, environmental protection, and agricultural characteristics, and can optimize the control of energy storage use and grid interaction, thereby reducing the overall cost of agricultural parks.

[0059] The present invention will be explained in detail below with reference to the accompanying drawings and specific embodiments. Attached Figure Description

[0060] Figure 1 This is a flowchart illustrating the overall process of the integrated energy management optimization system of the present invention.

[0061] Figure 2 This is a logical relationship diagram regarding the dynamic generation of virtual load in this invention;

[0062] Figure 3 This is an iterative flowchart of the microgrid load scheduling calculation method of the present invention;

[0063] Figure 4 This is a comparison curve of load distribution before and after microgrid load dispatch in an embodiment of the present invention;

[0064] Figure 5 This is a power grid interaction and energy storage interaction distribution diagram in an embodiment of the present invention. Detailed Implementation

[0065] To facilitate understanding of the present invention, a more comprehensive description of the present invention will be given below with reference to the accompanying drawings, which illustrate several embodiments of the present invention. However, the present invention can be implemented in different forms and is not limited to the embodiments described in the text. Rather, these embodiments are provided to make the disclosure of the present invention more thorough and complete.

[0066] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly associated with those skilled in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0067] Example: Figure 1 As shown, the microgrid system of this invention consists of a photovoltaic power generation system, a biogas power generation system, a lithium battery energy storage system, a combined heat and power (CHP) unit, and dispatchable loads. Energy equipment includes energy storage batteries, absorption heat pumps, biogas boilers, and a power grid. Dispatchable loads are divided into three categories: general loads, special loads, and virtual loads. Special loads are loads whose energy consumption characteristics are related to crop growth characteristics, while virtual loads are dynamic loads generated based on specific dispatch schemes for special loads, used to optimize the overall operating efficiency of the system. Total cost function for:

[0068] (1)

[0069] Where T represents time in hours; F1 represents grid interaction cost (based on time-of-use pricing); F2 represents operation and maintenance cost (equipment operating cost); F3 represents investment depreciation cost (equipment depreciation); F4 represents pollution emission cost (environmental impact); and F5 represents system interaction and energy storage lifespan cost (number of interactions). , (The impact).

[0070] In this embodiment, T is set to 24, meaning a one-day period is used as the research cycle. Input the electrical load, heat load, photovoltaic power, and biogas output distribution curves for a 24-hour period in this embodiment.

[0071] Specifically, the expression for the power grid interaction cost is:

[0072] (2)

[0073] in, For real-time electricity prices, Let t be the power exchange between the power grids at time t.

[0074] The expression for operation and maintenance costs is:

[0075] (3)

[0076] in, The operating and maintenance cost of the i-th type of equipment is... Let be the real-time power of the i-th device.

[0077] The expression for investment depreciation cost is:

[0078] (4)

[0079] in, Let be the initial investment cost of the i-th type of equipment. For interest rate, Let be the service life of the i-th type of equipment.

[0080] The expression for the cost of pollution emissions is:

[0081] (5)

[0082] in, The value of the j-th pollutant gas produced by the i-th type of equipment. The cost of environmental pollution caused by the j-th polluting gas, Let be the real-time power of the i-th device.

[0083] The expression for system interaction and energy storage lifetime cost is:

[0084] (6)

[0085] (7)

[0086] (8)

[0087] (9)

[0088] in, To limit the cost of the power grid, For energy storage cycle costs, Costs associated with energy storage capacity degradation; It is the linear influence coefficient of power grid interaction. It is the secondary influence coefficient of power grid interaction. This represents the number of interactions with the power grid. This is the initial cost of energy storage equipment. This refers to the maximum cycle life of the energy storage device. This refers to the number of energy storage interactions. It is the coefficient of influence of energy storage cycle on capacity degradation. It is the influence coefficient of power grid interaction on system degradation. It is the reference number of power grid interactions.

[0089] Regarding the electricity pricing model, the base electricity price is set according to off-peak, normal, and peak periods; the normal and peak period electricity prices are adjusted in real time based on load levels and load change rates, and the calculation formula is as follows:

[0090] (10)

[0091] in, The real-time electricity price at time t The base electricity price at time t. The load level influence coefficient. The load change rate influence coefficient. Let be the load factor at time t. Let be the load change rate factor at time t.

[0092] The system operation is subject to the following constraints:

[0093] ① Load shifting constraint: The three types of loads can be freely dispatched, and the total load remains unchanged before and after dispatching. There are upper and lower threshold limits at each time point;

[0094] ② Power balance constraints: The power exchanged with the grid, the power generated by distributed generation, the power of energy storage, and the power of the load must meet the balance requirements;

[0095] ③ Energy storage capacity constraints: The state of charge of the battery must not exceed the set upper and lower limits. Due to load characteristics and real-time electricity pricing mechanism, there will be a specified battery capacity state at a set time point.

[0096] ④ Grid interaction constraints: The grid interaction power shall not exceed the specified limit, and there are different operating strategy requirements during peak and valley periods. The power generation of various power generation equipment shall not exceed the rated capacity of the equipment and the resource conditions.

[0097] ⑤ Grid-side constraints: These include key characteristics such as frequency, voltage, and waveform distortion rate, combined with local real-time market grid policies, to define the boundary conditions for load dispatching.

[0098] Regarding power balance constraints, a real-time power balance equation is established, which mainly includes distributed generation equipment, energy storage equipment, dispatchable loads, and grid interaction power:

[0099] (11)

[0100] in, Let be the power exchange between the power grids at time t. The total load after adjustment at time t. Let be the battery power at time t. Let t be the biogas power generation capacity. Let be the photovoltaic power generation at time t. Let t be the thermoelectric conversion power at time t.

[0101] In grid-side constraints, for the percentage of frequency offset Voltage offset percentage The calculation formula and constraint range for waveform distortion rate (THD) are as follows:

[0102] (12)

[0103] (13)

[0104] (14)

[0105] Among them, f i f represents the frequency at that moment. N Indicates the rated frequency; U i U represents the grid voltage at that moment; N Indicates the rated voltage of the power grid; A N This represents the effective value of the Nth harmonic component; in this embodiment, Take 5%, Take 2%, Take 5%.

[0106] Based on frequency offset percentage Voltage offset percentage In addition to waveform distortion rate (THD), the local real-time market grid policy is used to define the load dispatch boundary conditions.

[0107] (15)

[0108] in, The total load after adjustment at time t. and These are the defined minimum and maximum values, respectively.

[0109] like Figure 2 The diagram illustrates the process of dynamically generating virtual loads. First, the allocation of special loads needs to be considered. Since the introduction of virtual loads is based on the energy consumption characteristics associated with crop growth features in agricultural parks, and considering the impact of different supplemental lighting strategies on their growth characteristics, this embodiment categorizes special loads into three types. , , This represents the load under three different supplemental lighting strategies, and introduces three different active power correction coefficients. In this embodiment, the correction coefficients are... , , Using values ​​of 1.00, 1.15, and 1.20 respectively, we obtain the virtual load. :

[0110] (16)

[0111] in, It is the total virtual load. It is the correction coefficient under the i-th supplementary lighting strategy. It is the load under the i-th supplemental lighting strategy.

[0112] Once the total cost optimization function, its variables, and various constraints are determined, the basic microgrid economic dispatch model is established. This embodiment uses the YALMIP toolbox to establish a mixed-integer linear programming model, and then uses the Cplex solver to solve the model.

[0113] like Figure 3 As shown, the data is first initialized, and the first iteration of the optimization calculation is performed. In this first calculation, the initial electricity price model is used, the virtual load is 0, and the number of grid interactions and the number of energy storage interactions are the initial given values. and Using the specific time shifts of three types of schedulable loads—general, special, and virtual—as decision variables, the overall target cost function is calculated. To obtain the number of new generation power grid interactions Number of interactions with energy storage Furthermore, based on the total cost change rate η and the difference in the number of interactions between the grid and energy storage. , Convergence judgment (η≤2%) <1, (Whether <1 is true simultaneously) proceeds to the next iteration; from the second iteration onwards, the virtual load generated in the previous iteration will be applied. The calculation is performed using a real-time electricity price model, and the process is repeated until the final result is output.

[0114] In this embodiment, after obtaining the optimal scheduling scheme, a clear comparison of the 24-hour load distribution before and after the load time shift can be seen. The interaction between the power grid's power purchase and sale and the energy storage's charging and discharging can also be obtained at this time. Figure 4 , Figure 5 As shown.

[0115] Depend on Figure 4 and Figure 5As can be seen, the optimized load distribution is smoother, with a significant decrease in load during peak electricity price periods and a moderate increase in load during off-peak electricity price periods. This achieves peak shaving and valley filling while also providing some time-shifted load supplementation during the midday peak photovoltaic output period, thereby increasing the utilization rate of photovoltaic energy, reducing the number of interactions, and lowering interaction costs. The optimized battery charging and discharging strategy is coordinated with the electricity price signal using price differentiation, charging during periods of sufficient photovoltaic energy and discharging during peak electricity price periods to fully utilize the power. The optimized grid interaction power fluctuation is reduced, and the number of interactions is significantly reduced, effectively mitigating the impact on the grid. In this embodiment, after using the optimal economic dispatch scheme, the total cost decreased by 7%, while also extending the service life of the energy storage system.

[0116] In summary, the agricultural microgrid scheduling method provided by this invention comprehensively considers multiple objectives such as grid interaction costs, equipment operation and maintenance costs, equipment investment depreciation costs, pollution emission costs, and the limit on the number of interactions between the grid and energy storage. Through iterative optimization, the scheduling scheme is continuously improved to achieve the economical, environmentally friendly, and stable operation of the microgrid system. This method can be applied to agricultural park microgrid systems composed of various distributed energy sources such as photovoltaics, biogas, and energy storage.

[0117] The present invention has been described by way of example in conjunction with the accompanying drawings. Obviously, the specific implementation of the present invention is not limited to the above-described manner. Any non-substantial improvement made by adopting the inventive concept and technical solution of the present invention, or the direct application of the inventive concept and technical solution of the present invention to other occasions without modification, shall be within the protection scope of the present invention.

Claims

1. A method for agricultural microgrid consumption and dispatch based on crop energy consumption characteristics, characterized in that: Includes the following steps: S1. Establish the objective function: Based on the microgrid system, establish the total cost function. The objective function is to minimize the total cost. ; S2. Determine decision variables: Use the specific time shifts of three types of dispatchable loads—general, special, and virtual—as decision variables. Special loads are loads related to crop growth characteristics and associated energy consumption characteristics, while virtual loads are dynamic loads generated based on specific dispatch schemes for special loads. S3. Define the dispatch boundary conditions: Considering grid-side constraints, define the load dispatch boundary conditions; S4. Determine system constraints: including load time shift constraints, power balance constraints, energy storage capacity constraints, and grid interaction constraints; S5. Model Construction: Based on steps S1-S4, a microgrid economic dispatch model is constructed. S6. Solution: Solve the microgrid economic dispatch model to determine the load dispatch scheme; In step S2, based on the energy consumption characteristics associated with crop growth features in the agricultural park, an active power correction coefficient is introduced, and the expression for the virtual load is: (1) in, It is the total virtual load. It is the correction coefficient under the i-th supplementary lighting strategy. is the load amount under the i-th supplemental lighting strategy, and m is the number of special load categories, m≥2.

2. The agricultural microgrid consumption and dispatch method based on crop energy consumption characteristics according to claim 1, characterized in that: In step S1, the total cost function for: (2) Where T represents time in hours; F1 represents grid interaction cost; F2 represents operation and maintenance cost; F3 represents investment depreciation cost; F4 represents pollution emission cost; and F5 represents system interaction and energy storage lifespan cost.

3. The agricultural microgrid consumption and dispatch method based on crop energy consumption characteristics according to claim 2, characterized in that: The expression for the power grid interaction cost is: (3) in, For real-time electricity prices, Let t be the power exchange power between the power grids. And / or, the expression for operation and maintenance costs is: (4) in, The operating and maintenance cost of the i-th type of equipment is... Let be the real-time power of the i-th device; And / or, the expression for investment depreciation cost is: (5) in, Let be the initial investment cost of the i-th type of equipment. For interest rate, The service life of the i-th type of equipment; And / or, the expression for the cost of pollution emissions is: (6) in, The value of the j-th pollutant gas produced by the i-th type of equipment. The cost of environmental pollution caused by the j-th polluting gas, Let be the real-time power of the i-th device.

4. The agricultural microgrid consumption and dispatch method based on crop energy consumption characteristics according to claim 2, characterized in that: The expression for system interaction and energy storage lifetime cost is: (7) (8) (9) (10) in, To limit the cost of the power grid, For energy storage cycle costs, Costs associated with energy storage capacity degradation; It is the linear influence coefficient of power grid interaction. It is the secondary influence coefficient of power grid interaction. This represents the number of interactions with the power grid. This is the initial cost of energy storage equipment. This refers to the maximum cycle life of the energy storage device. This refers to the number of energy storage interactions. It is the coefficient of influence of energy storage cycle on capacity degradation. It is the influence coefficient of power grid interaction on system degradation. It is the reference number of power grid interactions.

5. The agricultural microgrid consumption and dispatch method based on crop energy consumption characteristics according to claim 3, characterized in that: The expression for real-time electricity price is: (11) in, The real-time electricity price at time t The base electricity price at time t. The load level influence coefficient. The load change rate influence coefficient. Let be the load factor at time t. Let be the load change rate factor at time t.

6. The agricultural microgrid consumption and dispatch method based on crop energy consumption characteristics according to claim 1, characterized in that: In step S3, the grid-side constraints are: based on frequency, voltage, waveform distortion rate, and local real-time market grid policies, the load dispatch boundary conditions are defined.

7. The agricultural microgrid consumption and dispatch method based on crop energy consumption characteristics according to claim 6, characterized in that: In step S3, for the frequency offset percentage Voltage offset percentage The calculation formula and constraint range for waveform distortion rate (THD) are as follows: (12) (13) (14) Among them, f i f represents the frequency at time i; N Indicates the rated frequency; U i U represents the grid voltage at time i; N Indicates the rated voltage of the power grid; A N This represents the effective value of the Nth harmonic component.

8. The agricultural microgrid consumption and dispatch method based on crop energy consumption characteristics according to claim 1, characterized in that: In step S4, regarding the power balance constraints, the real-time power balance equation is established: (15) in, Let be the power exchange between the power grids at time t. The total load after adjustment at time t. Let be the battery power at time t. Let be the biogas power generation capacity at time t. Let be the photovoltaic power generation at time t. Let t be the thermoelectric conversion power at time t.

9. The agricultural microgrid consumption and dispatch method based on crop energy consumption characteristics according to claim 1, characterized in that: In step S6, the convergence criteria are: the rate of change of total cost η ≤ 2%, and the difference in the number of grid interactions. <1, and the difference in the number of energy storage interactions Whether <1 is true at the same time.

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