Rural micro-energy system configuration method and device and electronic equipment

Abstract

This invention provides a method, apparatus, and electronic device for configuring a rural micro-energy system, belonging to the field of energy utilization. It includes: acquiring a model corresponding to a rural micro-energy system covering multiple natural villages; configuring the parameters of energy consumption modules in the model using configuration parameters including energy consumption data; and taking the planned capacity of the new energy power generation module, energy conversion module, and energy storage module in the model as the solution quantity. Under preset constraints, with the optimization objective function of minimizing cost as the optimization objective, and maximizing the uncertain radius of the output of the new energy power generation module, the solution quantity is optimized and solved to determine the optimal solution. Finally, the model configuration strategy generated from the optimal solution is executed to obtain the configured rural micro-energy system. This method achieves reasonable planning of energy equipment capacity in the rural micro-energy system while reducing costs, improving energy utilization efficiency. Furthermore, the configuration scheme has strong robustness to uncertainties during operation.

Description

Technical Field

[0001] This invention relates to the field of energy utilization, and in particular to a method, apparatus and electronic equipment for configuring a rural micro-energy system. Background Technology

[0002] Rural areas possess abundant wind, solar, and biomass resources. Driven by the global energy system's transition to a low-carbon model, rural areas urgently need to leverage their resource advantages to optimize and upgrade their energy systems, achieve efficient conversion and stable utilization of rural energy, and propel rural areas towards a clean, efficient, and economical energy supply and utilization system.

[0003] However, there is currently a lack of unified planning and allocation for energy use in rural areas, resulting in problems such as mismatch between energy equipment and energy demand, and poor coordination of energy systems. This leads to low energy utilization rate, high costs, and an inability to balance the economy and reliability of energy supply, and may even affect the stability of the power grid. Summary of the Invention

[0004] This invention provides a method, apparatus, and electronic device for configuring a rural micro-energy system to address the problems of low energy utilization, high costs, and the inability to balance the economy and reliability of energy supply in rural areas.

[0005] In a first aspect, embodiments of the present invention provide a method for configuring a rural micro-energy system, comprising: Obtain the model corresponding to the rural micro-energy system, including a new energy power generation module, an energy conversion module, an energy storage module, and an energy consumption module; wherein, the rural micro-energy system is a regional energy system covering multiple natural villages; The configuration parameters are determined and used to configure the parameters of the energy consumption module; the configuration parameters include energy consumption data; the planned capacity of the new energy power generation module, the energy conversion module, and the energy storage module are taken as the variables to be solved. Under the constraints of preset conditions, with the goal of minimizing the cost objective function and maximizing the uncertain radius of the output of the new energy power generation module, the solution to be solved is optimized to determine the optimal solution; The model configuration strategy generated from the optimal solution is executed to obtain the configured rural micro-energy system.

[0006] In one possible implementation, the preset constraints include total supply and demand balance constraints, emergency energy self-sufficiency constraints within each natural village under off-grid conditions, electrical power balance constraints, thermal power balance constraints, cold power balance constraints, upper power limit constraints for the new energy power generation module, upper power limit constraints for the energy conversion module, and upper power limit constraints for the energy storage module.

[0007] In one possible implementation, the energy module configured in each natural village includes one or more of the new energy power generation module, the energy conversion module, and the energy storage module; Before determining the optimal solution by optimizing the unsolved quantity under the constraints of preset conditions, with the goal of minimizing the cost objective function and maximizing the uncertain radius of the output of the new energy power generation module, the method further includes: Based on the planned capacity, historical operating data, and rated parameters of the energy modules configured in each natural village, determine the energy supply expression for each natural village under off-grid conditions; Determine the maximum demand load for each natural village, and determine the proportion of the critical load within each natural village to the maximum demand load; Based on the energy supply expression, the maximum demand load, and the ratio corresponding to each natural village, an emergency energy self-sufficiency constraint is established for each natural village under off-grid conditions.

[0008] In one possible implementation, the cost objective function includes investment cost, operation and maintenance cost, wind and solar curtailment cost, and environmental cost; wherein, the new energy power generation module includes wind power generation module and photovoltaic power generation module; Under the constraints of preset conditions, with the goal of minimizing the cost objective function and maximizing the uncertain radius of the output of the new energy power generation module, the method further includes optimizing the solution to the unsolved quantity and determining the optimal solution before proceeding with the following steps: Based on the planned capacity, planned lifespan, and unit capacity investment cost of the wind power generation module, the photovoltaic power generation module, the energy conversion module, and the energy storage module, a first functional expression corresponding to the investment cost is obtained; Based on the output power of the wind power generation module, the photovoltaic power generation module, the energy conversion module, and the energy storage module, a second functional expression corresponding to the operation and maintenance cost is obtained; Based on the output power of the wind power generation module and the photovoltaic power generation module, the third function expression corresponding to the cost of wind and solar curtailment is obtained; Based on the electricity purchased from the power grid and the energy conversion amount of the energy conversion module, the fourth functional expression for the environmental cost is obtained; The basic cost objective function is obtained by summing the first function expression, the second function expression, the third function expression, and the fourth function expression; The cost objective function is obtained based on the basic cost objective function and the robustness level factor; wherein, the robustness level factor characterizes the allowable increase range of the basic cost objective function, and the allowable increase range is used to constrain the maximum value of the uncertainty radius.

[0009] In one possible implementation, the energy conversion module includes a biogas cogeneration model, and the planned capacity of the energy conversion module includes the planned capacity of the biogas cogeneration model; the energy consumption module includes a livestock farming energy consumption model. Before obtaining the model corresponding to the rural micro-energy system, the method further includes: Calculate the daily biogas production based on the types and scale of livestock farming and the biogas production rate of manure in the aforementioned livestock energy consumption model; Based on the daily biogas production and the planned capacity of the biogas cogeneration model, the biogas cogeneration model is constructed. The biogas cogeneration model includes models corresponding to micro gas turbines, biogas boilers, and waste heat recovery boilers. The planned capacity of the biogas cogeneration model includes the planned capacity of the micro gas turbines, biogas boilers, and waste heat recovery boilers. The micro gas turbines are used to generate electricity from biogas, the biogas boilers are used to generate heat from biogas, and the waste heat recovery boilers are used to generate heat from the high-temperature flue gas generated by the micro gas turbines.

[0010] In one possible implementation, the energy conversion module further includes an air source heat pump model and an absorption chiller model; Before obtaining the model corresponding to the rural micro-energy system, the method further includes: Based on the heating power, cooling power, power consumption, and planned capacity of the air source heat pump, the air source heat pump model is constructed. Based on the cooling capacity, thermal power, cooling efficiency, and planned capacity of the absorption chiller, a model of the absorption chiller is constructed.

[0011] In one possible implementation, the optimization objective, under preset constraints, is to minimize the cost objective function while maximizing the uncertain radius of the output of the new energy power generation module. The optimization process for determining the optimal solution includes: Obtain an initial solution set for the quantity to be solved; wherein the initial solution set includes multiple initial solutions for the quantity to be solved; Using the initial solution set as the initial population, and under the constraints of preset conditions, with the goal of minimizing the cost objective function, the solution to be solved is optimized to obtain the optimal solution.

[0012] In one possible implementation, the step of using the initial solution set as the initial population, and under the constraints of preset conditions, optimizing the unsolved quantity with the goal of minimizing the cost objective function to obtain the optimal solution, includes: The cost objective function value corresponding to each initial solution in the initial population is calculated; Based on the cost objective function value corresponding to each initial solution, the initial solutions in the initial population are sorted in a non-dominated manner to obtain the non-dominated sorting result, and the iteration number corresponding to the current non-dominated sorting result is recorded; wherein, the initial solutions in the non-dominated sorting result are divided into the same or different levels, each level includes one or more initial solutions, the initial solutions in the same level do not dominate each other, and the initial solutions in the later level are dominated by the initial solutions in the previous level. Under the constraints of the preset conditions, with the goal of minimizing the function value of the cost objective function, the optimal solution is obtained based on the non-dominated sorting results and the number of iterations.

[0013] Secondly, embodiments of the present invention provide a rural micro-energy system configuration device, the device comprising: The acquisition unit is used to acquire the model corresponding to the rural micro-energy system, including a new energy power generation module, an energy conversion module, an energy storage module, and an energy consumption module; wherein, the rural micro-energy system is a regional energy system covering multiple natural villages; The first processing unit is used to determine configuration parameters, which are used to configure the parameters of the energy consumption module; the configuration parameters include energy consumption data. The second processing unit is used to take the planned capacity of the new energy power generation module, the energy conversion module and the energy storage module as the quantity to be solved; The solution unit is used to optimize and solve the unsolved quantities under the constraints of preset conditions, with the goal of minimizing the cost objective function and maximizing the uncertain radius of the output of the new energy power generation module, and to determine the optimal solution. An execution unit is used to execute the model configuration strategy generated from the optimal solution to obtain the configured rural micro-energy system.

[0014] Thirdly, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect or any possible implementation thereof.

[0015] This invention provides a method, apparatus, and electronic device for configuring a rural micro-energy system. First, a model corresponding to the rural micro-energy system is obtained, including a new energy power generation module, an energy conversion module, an energy storage module, and an energy consumption module, covering multiple natural villages. Then, configuration parameters including energy consumption data are used to configure the parameters of the energy consumption module in the model, so that the system configuration meets the actual energy needs of rural areas. The planned capacity of the new energy power generation module, energy conversion module, and energy storage module in the model is used as the solution to be solved. Under preset constraints, the optimization objective is to minimize the cost objective function while maximizing the uncertain radius of the output of the new energy power generation module. The solution to be solved is optimized to determine the optimal solution. Finally, the model configuration strategy generated from the optimal solution is executed to obtain the configured rural micro-energy system. This invention enables the rational planning of energy equipment capacity in rural micro-energy systems while reducing costs. It can effectively improve energy utilization, meet actual needs, achieve reasonable energy supply, and balance the economy and reliability of energy supply. It also contributes to the stable operation of the power grid. Furthermore, the uncertainty of the output of new energy power generation modules is considered in the optimization solution process, making the configuration scheme more robust to uncertainties in the operation process. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of a rural micro-energy system provided in an embodiment of the present invention; Figure 2 This is a flowchart illustrating the implementation of the rural micro-energy system configuration method provided in this embodiment of the invention; Figure 3 This is a flowchart illustrating the implementation of another rural micro-energy system configuration method provided in this embodiment of the invention; Figure 4 This is a schematic diagram of the rural micro-energy system configuration device provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0018] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings. Invention Overview The inventors have discovered that rural areas urgently need to leverage their own resource advantages to promote the systematic upgrading of their energy systems. However, there is currently a lack of unified planning and configuration for energy utilization in rural areas, resulting in problems such as mismatch between energy equipment and energy demand, and poor coordination of the energy system. This leads to low energy utilization, unstable energy supply, high costs, and even affects the stability of the power grid, failing to balance the economy and reliability of energy supply.

[0020] To improve rural energy efficiency, reduce costs, and balance the economics and reliability of energy supply, this invention, in its implementation, acquires a rural micro-energy system model covering multiple villages, configures energy consumption module parameters using energy consumption data to meet actual rural energy needs, sets the planned capacity of energy-related equipment as the solution, and optimizes the solution under preset constraints with cost minimization as the objective. After obtaining the optimal solution, the corresponding configuration strategy is executed. This method can rationally plan energy equipment capacity, reduce costs, improve energy efficiency, balance the economics and reliability of energy supply, and ensure the stable operation of the power grid.

[0021] To make the objectives, technical solutions, and advantages of the present invention clearer, specific embodiments will be described below in conjunction with the accompanying drawings.

[0022] Figure 1 A schematic diagram of a rural micro-energy system provided in an embodiment of the present invention, such as... Figure 1 As shown, rural energy load includes electrical load, heat load, and cooling load. It mainly covers electric vehicles, agricultural product storage, rural residents' daily life, agricultural planting, and livestock breeding. Among these, electric vehicles utilize vehicle-to-grid (V2G) charging stations to interact bidirectionally with the power grid, participating as flexible loads in optimized grid dispatch.

[0023] This invention utilizes photovoltaic and wind turbines to effectively leverage the abundant photovoltaic and wind power resources in rural areas to supplement the electricity load; it also develops and utilizes biomass resources such as livestock and poultry manure generated from animal husbandry: after biomass resources are fermented to produce biogas, the resulting biogas is used to generate electricity through a micro gas turbine and heat through a biogas boiler, and the high-temperature flue gas is reused through a waste heat boiler; an absorption chiller is driven by heat energy to produce cold energy, realizing "heat to cold"; and an air source heat pump is driven by electricity to provide cold and heat energy.

[0024] In this embodiment of the invention, photovoltaic / wind power generation is used, and biogas is produced from biomass resources to supplement energy. Electricity / heat / cooling is distributed on demand through various devices, forming a closed loop of "energy production-conversion-consumption-recycling" in rural areas, which is both green and environmentally friendly, and can also reduce energy costs.

[0025] Figure 2 The implementation flowchart of the rural micro-energy system configuration method provided in the embodiments of the present invention is described in detail below: Step 201: Obtain the model corresponding to the rural micro-energy system, including a new energy power generation module, an energy conversion module, an energy storage module, and an energy consumption module; wherein, the rural micro-energy system is a regional energy system covering multiple natural villages.

[0026] For example, this embodiment pre-constructs a rural micro-energy system covering multiple natural villages, such as... Figure 1 As shown. The system was then modeled to obtain the corresponding model.

[0027] In one example, this embodiment equates the electric vehicle's power battery to an electrochemical energy storage device. The planned capacity corresponding to the energy storage module includes the rated charging power of the electric vehicle charging station and the number of charging stations.

[0028] Step 202: Determine the configuration parameters. The configuration parameters are used to configure the parameters of the energy consumption module; the configuration parameters include energy consumption data.

[0029] For example, this embodiment can determine the future energy demand of multiple natural villages based on the historical energy consumption data and energy consumption growth data of each natural village, that is, the configuration parameters of energy consumption data, and then use the energy consumption data to configure the parameters of the model energy consumption module.

[0030] In one example, the configuration parameters include data on electrical load demand, heat load demand, and cooling load demand during the target planning period.

[0031] Step 203: The planned capacity of the new energy power generation module, energy conversion module and energy storage module is taken as the quantity to be solved.

[0032] For example, in order to rationally plan the capacity of the new energy power generation module, energy conversion module and energy storage module in the rural micro-energy system, this embodiment needs to optimize and solve them as variables to be solved.

[0033] In one example, the planned capacity of the new energy power generation module may include the number of new energy power generation devices and their rated power; the planned capacity of the energy storage module may include the number of charging piles and the rated charging power of electric vehicles; the planned capacity of the energy conversion module may include the planned capacity of the micro gas turbine output, the planned capacity of the biogas boiler, the planned capacity of the air source heat pump, and the planned capacity of the absorption chiller.

[0034] Step 204: Under the constraints of the preset constraints, with the goal of minimizing the cost objective function and maximizing the uncertain radius of the output of the new energy power generation module, optimize the solution to determine the optimal solution.

[0035] This embodiment is based on information gap decision theory (IGDT) to handle the uncertainty of the output of new energy power generation modules within the planning period.

[0036] For example, the preset constraints may include power upper limit constraints for each component in the rural micro-energy system, power balance constraints for electricity, heat and cold energy in the system, energy complementarity and coordination constraints between multiple natural villages, and emergency energy self-sufficiency constraints for each natural village in the off-grid state.

[0037] In one example, the basic cost objective function is composed of the overall equipment purchase cost, operation and maintenance cost, cost of the portion of renewable energy generation that is not consumed (cost of wind and solar curtailment), and environmental cost of the rural micro-energy system. Meanwhile, under the risk-avoidance IGDT strategy, this embodiment takes into account the adverse effects of the uncertainty of renewable energy module output and allows the total cost to increase to a certain extent. That is, the cost objective function is obtained by multiplying (1+π) with the basic cost objective function, where π is the robustness level factor. The robustness of the planning scheme can be adjusted by changing the value of π.

[0038] In one feasible implementation, this embodiment can use the planned capacity of the new energy power generation module, energy conversion module, and energy storage module as decision variables, the cost objective function as the objective function, and use multi-objective optimization algorithms, such as particle swarm optimization and genetic algorithms, to determine the optimal solution.

[0039] Step 205: Execute the model configuration strategy generated from the optimal solution to obtain the configured rural micro-energy system.

[0040] For example, after obtaining the optimal solution, this embodiment generates a corresponding model configuration strategy and configures the rural micro-energy system according to the model configuration strategy.

[0041] In one feasible implementation, this embodiment can run the configured rural micro-energy system and verify its economy and reliability. After the verification is passed, the actual purchase and installation can be carried out according to the optimal solution.

[0042] In summary, this invention first obtains a model corresponding to a rural micro-energy system, including a new energy power generation module, an energy conversion module, an energy storage module, and an energy consumption module, covering multiple natural villages. Then, it uses configuration parameters including energy consumption data to configure the parameters of the energy consumption module in the model, ensuring the system configuration meets the actual energy needs of rural areas. The planned capacity of the new energy power generation module, energy conversion module, and energy storage module in the model is used as the solution to be solved. Under preset constraints, the optimization objective is to minimize the cost objective function while maximizing the uncertainty radius of the new energy power generation module's output. The solution to be solved is optimized to determine the optimal solution. Finally, the model configuration strategy generated from the optimal solution is executed to obtain the configured rural micro-energy system. This invention can achieve reasonable planning of energy equipment capacity in rural micro-energy systems while reducing costs, effectively improving energy utilization and achieving reasonable energy supply that meets actual needs. It achieves a balance between the economy and reliability of energy supply, while also contributing to the stable operation of the power grid. Furthermore, the uncertainty of the new energy power generation module's output is considered during the optimization process, making the configuration scheme highly robust to uncertainties during operation.

[0043] In one feasible implementation, before configuring a rural micro-energy system, the present invention first constructs the corresponding model, constraints, and cost objective function for the rural micro-energy system, as detailed below: The rural micro-energy system is a regional energy system covering multiple natural villages. The model includes a new energy power generation module, an energy conversion module, an energy storage module, and an energy consumption module.

[0044] The new energy power generation module includes a wind power generation module and a photovoltaic power generation module. The wind power generation module is a model corresponding to distributed wind turbines, and the photovoltaic power generation module is a model corresponding to distributed photovoltaic power generation.

[0045] In one example, the output of a distributed wind turbine depends primarily on the wind speed at the turbine hub. Considering the difference between the wind speed measurement altitude and the hub height, the wind speed measurement is first approximated to the hub height: (1) In the formula, v(t), v ref (t) represents the wind speed at hub height and the wind speed at the measuring point, respectively, H and H ref These represent the hub height and the wind measurement point height, respectively, and α is the surface roughness coefficient, which is 0.22 in this embodiment.

[0046] In actual operation, the maximum output value of the wind turbine at each moment can be expressed as a piecewise linear function in the following form: (2) In the formula, Pw,rate V represents the maximum output of a single wind turbine at time t and the rated power of a single wind turbine, respectively. in and v out These are the wind speeds at the fan's entry and exit points, respectively, v r This is the rated wind speed.

[0047] Distributed photovoltaic power generation depends on irradiance, temperature, and photovoltaic panel area, and can be expressed as: (3) In the formula, P pv,rate These represent the maximum output and rated power of a single photovoltaic panel at time t, respectively. pv For photovoltaic energy conversion efficiency, A(t), A s These represent the irradiance and rated irradiance at the location of the photovoltaic power plant, respectively, α pv T is the power temperature coefficient. pv (t), T stc These represent the actual photovoltaic module temperature and the photovoltaic module temperature under standard test conditions, respectively.

[0048] In one example, the energy conversion module includes a biogas cogeneration model, an air source heat pump model, and an absorption chiller model; the energy consumption module includes a livestock farming energy consumption model.

[0049] The biogas cogeneration model includes models corresponding to micro gas turbines, biogas boilers, and waste heat recovery boilers. The planned capacity of the energy conversion module includes the planned capacity of the biogas cogeneration model.

[0050] The biogas cogeneration model includes models corresponding to micro gas turbines, biogas boilers, and waste heat recovery boilers. The planned capacity of the biogas cogeneration model includes the planned capacity of micro gas turbines, biogas boilers, and waste heat recovery boilers. Micro gas turbines are used to generate electricity from biogas, biogas boilers are used to generate heat from biogas, and waste heat recovery boilers are used to generate heat from the high-temperature flue gas generated by micro gas turbines.

[0051] This embodiment calculates the daily biogas production based on the types and scale of livestock farming and the biogas production rate of manure in the livestock energy consumption model; and constructs a biogas cogeneration model based on the daily biogas production and the planned capacity of the biogas cogeneration model.

[0052] In one example, biogas is a biomass resource unique to rural areas. With the current trend of intensive and large-scale livestock farming in rural areas, large-scale farms generate large amounts of livestock and poultry manure daily. Utilizing this manure for constant-temperature fermentation to produce biogas can not only meet part of the fuel needs for power generation and heat production, but also reduce environmental pollution. First, calculate the daily biogas production based on the type and scale of livestock farming: (4) (5) In the formula, Let B be the daily manure production of the b type of livestock and poultry. b For the scale of breeding, M b V represents the daily manure production per head of livestock or poultry. met Total daily biogas production The biogas production rate of the b type of livestock and poultry manure.

[0053] The biogas produced can generate electricity through a micro gas turbine or heat through a biogas boiler. A waste heat boiler can collect the high-temperature flue gas generated by the micro gas turbine to supplement the heat output. The biogas cogeneration unit model can be represented as follows: (6) (7) (8) (9) (10) In the formula, Q mt (t), P mt (t) represents the thermal power and electrical power output of the micro gas turbine, respectively, and η represents the output of the micro gas turbine. mt,e η hl These represent the power generation efficiency and heat loss coefficient of the micro gas turbine, respectively. bb (t), Q whr (t) represents the thermal power of the biogas boiler and the waste heat recovery boiler, respectively. mt (t) and V bb (t) represents the biogas consumption of the micro gas turbine and the biogas boiler, respectively, θ cal η represents the calorific value of biogas. bb For the thermal efficiency of biogas boilers, η whr,r η whr,h These refer to the heat recovery efficiency and thermal efficiency of the waste heat boiler, respectively.

[0054] In one example, the energy conversion module also includes an air source heat pump model and an absorption chiller model; in this embodiment, an air source heat pump model is constructed based on the heating power, cooling power, power consumption, and planned capacity of the air source heat pump.

[0055] In one example, air source heat pumps, characterized by high energy efficiency, can replace traditional electric heating or cooling equipment, achieving good heating or cooling effects with very low energy consumption. They are currently widely used in rural areas. The air source heat pump model can be represented as: (11) (12) (13) (14) In the formula, Q ashp (t), C ashp (t), P ashp,h (t), P ashp,c (t) represents the heating and cooling power and corresponding power consumption of the air source heat pump, respectively, and η represents the power consumption of the air source heat pump. ashp,h η ashp,c These are the heating and cooling coefficients of the air source heat pump, respectively. This indicates the planned capacity of the air source heat pump.

[0056] This embodiment constructs an absorption chiller model based on the cooling capacity, thermal power, cooling efficiency, and planned capacity of the absorption chiller.

[0057] Absorption chillers use naturally occurring water or ammonia as refrigerants to convert heat energy into cold energy. They are characterized by simple structure, safety, reliability, and easy installation, and help alleviate the electrical load pressure caused by high cooling demand. This embodiment uses an absorption chiller as an effective supplement to air-source heat pump refrigeration; its mathematical model is as follows. (15) (16) In the formula, C ac (t), Q ac (t), η ac These are the cooling capacity, thermal power, and cooling efficiency of an absorption chiller. This indicates the planned capacity of the absorption chiller.

[0058] In this embodiment, the energy storage module includes a model corresponding to an electric vehicle charging pile.

[0059] With the improvement of living standards in rural areas and the vigorous promotion of policies to encourage electric vehicles in rural areas, the average number of electric vehicles per household in rural areas has been increasing year by year. The charging load brought about by large-scale grid connection has posed a challenge to the power grid. On the other hand, with the help of V2G charging piles, electric vehicles can achieve bidirectional power regulation with the power grid, providing support for peak shaving of the power grid. To characterize the charging load, a model of electric vehicle travel patterns is first established. According to existing statistics, the arrival and departure times of electric vehicles follow a normal distribution: (17) In the formula, t arr / dep μ is the time it takes for an EV to arrive at / leave a charging station. arr / dep and σ arr / dep These represent the mean and standard deviation, respectively. The daily mileage of electric vehicles follows a log-normal distribution.

[0060] (18) In the formula, d represents the daily driving range of the EV, and μ d and σ d The mean and standard deviation of daily mileage.

[0061] Based on the daily driving mileage, the initial SOC at the charging moment can be expressed as: (19) In the formula, θ ev E represents the average power consumption per kilometer of an EV. ev This refers to the rated capacity of the EV battery. The power battery of an electric vehicle can be considered equivalent to an electrochemical energy storage device. Therefore, referencing the energy storage device model, the charging process of an electric vehicle during grid connection can be described as a dynamically updated process as follows: (20) (twenty one) (twenty two) (twenty three) (twenty four) In the formula, S i (t) represents the SOC of the i-th EV at time t, S lb This is the lower limit of SOC. and Let be the battery energy of the i-th EV and the power interacting with the grid, respectively. u is the rated charge / discharge power of the EV. i (t) is a Boolean variable representing whether the EV is connected to the grid, η ch / dis For charge / discharge efficiency, N pile P represents the number of charging stations. sta This refers to the total power of the entire charging station.

[0062] In one feasible implementation, the energy consumption module includes an aquaculture energy consumption model and a planting model. Since temperature is a key factor in environmental control for facility agriculture and large-scale aquaculture, artificial control of the temperature environment can be achieved by using indoor heating, ventilation, and cooling equipment. The aquaculture energy consumption model can be replaced by a heating, ventilation, and air conditioning (HVAC) load model for a livestock farm, and the planting model can be replaced by a HVAC load model for a greenhouse.

[0063] The HVAC heat load model for greenhouses and farms can be expressed as: (25) In the formula, T inQ(t) represents the indoor temperature at time t. sup (t) and Q loss (t) represents the heat power provided by the heating equipment and the heat power lost through heat transfer between indoors and outdoors, respectively. sup (t) represents the supplementary cooling power, C m δ is the specific heat capacity of air, m is the mass of indoor air, and δ is the specific heat capacity of air. loss denoted as the heat transfer coefficient, and S is the surface area of ​​the indoor-outdoor heat exchange zone.

[0064] In this embodiment, the preset constraints include total supply and demand balance constraints, emergency energy self-sufficiency constraints in each natural village under off-grid conditions, power balance constraints, thermal power balance constraints, cold power balance constraints, power upper limit constraints for new energy power generation modules, power upper limit constraints for energy conversion modules, and power upper limit constraints for energy storage modules.

[0065] Preset constraints can be categorized into equality constraints and inequality constraints based on their type.

[0066] The equality constraints include power balance equations for electricity, heat, and cooling. This embodiment does not consider the reverse power flow between the point of common coupling (PCC) and the power grid; that is, the microgrid can only purchase electricity from the grid.

[0067] (26) This formula represents the sum of the power output of wind turbines, photovoltaic power, and the electrical power output of micro gas turbines, plus the electricity purchased from the grid, equaling the electrical load, the power consumption of air source heat pumps, and the power of charging stations. (27) This formula means that the thermal power of the biogas boiler + the thermal power of the waste heat recovery boiler + the heating power of the air source heat pump = the heat load.

[0068] (28) This formula indicates that the cooling power of the absorption chiller + the cooling power of the air source heat pump = cooling load. The inequality constraints include upper power limits for each component within the rural microgrid: (29) (30) (31) (32) (33) In one feasible implementation, the energy modules configured in each natural village include one or more of the following: new energy power generation modules, energy conversion modules, and energy storage modules. The rural micro-energy system covers multiple natural villages. In this embodiment, energy equipment is rationally planned according to the energy usage characteristics and available resources of each natural village. For example, if natural village A has a large number of electric vehicles while other natural villages have fewer, then an electric vehicle charging station will be set up in natural village A, and multiple natural villages will share the electric vehicle charging station in natural village A. For example, if the livestock industry is relatively developed in natural village B, then a micro gas turbine, biogas boiler, and waste heat boiler can be installed in natural village B, and other natural villages will share them.

[0069] Correspondingly, based on the planned capacity, historical operating data, and rated parameters of the energy modules configured in each natural village, the energy supply expression for each natural village under off-grid conditions is determined; the maximum demand load of each natural village is determined, and the proportion of important loads in each natural village to the maximum demand load is determined; based on the energy supply expression, maximum demand load, and proportion corresponding to each natural village, an emergency energy self-sufficiency constraint is established for each natural village under off-grid conditions.

[0070] With the vigorous promotion of comprehensive rural revitalization, modern rural areas are often endowed with different functions, highlighting characteristics such as housing, agricultural production, and animal husbandry. This embodiment, based on the rural area's own industrial positioning and load characteristics, configures each natural village with several types and scales of energy equipment during the planning stage. These devices, under the state of village-wide energy interconnection, can play a complementary and synergistic role. Rural power grids are relatively weak; when a natural village is disconnected from the external power grid due to line faults or other reasons, the internal energy equipment can ensure energy supply to important loads within the village for a period of time, achieving the goal of zonal autonomy. For any natural village m∈[1,M], using ζ to represent any one of the energy forms (electricity / heat / cooling), in the case of complementary and synergistic operation, then... (34) In the formula, ζj,m(t) represents the total power output of equipment j within natural village m. This represents the total load of natural village m.

[0071] When external power is cut off, communication between villages is severed, resulting in an isolated off-grid operation. This embodiment further incorporates the zonal autonomy of each village during the off-grid period into the planning model, ensuring that planned village equipment provides energy supply for critical loads in emergency situations. This embodiment defines the energy support that various types of equipment can provide as follows: (35) (36) (37) In the formula, mean[·] represents the average value. Under regional autonomy, for any energy source, the energy supply within a single natural village can meet the village's critical load demands: (38) In the formula, The energy support provided for equipment j within the natural village m. The proportion of the important load for natural villages m.

[0072] In this embodiment, the cost objective function includes investment cost, operation and maintenance cost, wind and solar curtailment cost, and environmental cost: (39) The decision variables for the planning capacity of each piece of equipment are represented in set form as follows: (40) In the formula, and These represent the number of wind turbines and photovoltaic panels in the wind farm, respectively.

[0073] This embodiment derives the first functional expression for the investment cost based on the planned capacity, planned lifespan, and unit capacity investment cost of the wind power generation module, photovoltaic power generation module, energy conversion module, and energy storage module.

[0074] For example, the investment cost is the sum of the initial investment costs of all energy devices in multiple microgrids, and is related to the planned capacity ωj∈Ω of each device: (41) In the formula, ωj, yi, Let be the planned capacity, planned lifespan, and unit capacity investment cost of equipment j, respectively, and r be the discount rate.

[0075] Based on the output power of the wind power generation module, photovoltaic power generation module, energy conversion module, and energy storage module, the second function expression corresponding to the operation and maintenance cost is obtained.

[0076] For example, maintenance costs are related to the utilization rate of each device and can be calculated using power consumption. (42) In the formula, ypla represents the planning period calculated in the optimization, and Tk represents the number of days represented by a typical day k in a year. Let Rj,y,k(t) be the operation and maintenance cost of equipment j, and Rj,y,k(t) be the electrical / heating / cooling power of equipment j at time t on a typical day k in year y. For time-of-use pricing, Δt represents the length of a unit time period.

[0077] Based on the output power of the wind power generation module and the photovoltaic power generation module, the third function expression corresponding to the cost of wind and solar curtailment is obtained.

[0078] To encourage local consumption of new energy sources, this embodiment does not consider the back-feeding of wind and solar power to the grid: (43) In the formula, λcur is the cost coefficient for wind and solar curtailment. and These represent the actual output of wind power and photovoltaic power at time t on a typical day k in year y.

[0079] Based on the electricity purchased from the power grid and the energy conversion amount of the energy conversion module, the fourth functional expression for environmental cost is obtained.

[0080] For example, environmental cost is the difference between actual emissions and emissions reductions. The emissions calculation method is shown in equation (45), which includes the environmental costs of electricity purchased from the grid and various pollutants generated from biogas combustion. The emissions reduction calculation method is shown in equation (46), representing the reduced pollution costs of livestock and poultry manure due to biogas utilization: (44) (45) (46) In the formula, The environmental cost coefficient for pollutant l. These represent the emissions of the first type of pollutant produced per kWh of electricity and per cubic meter of biogas combustion, respectively. This refers to the daily emission of the first type of pollutant from livestock and poultry manure.

[0081] The basic cost objective function is obtained by summing the first, second, third, and fourth function expressions.

[0082] The cost objective function is derived based on the basic cost objective function and the robustness level factor; whereby the robustness level factor characterizes the allowable increase range of the basic cost objective function, and the allowable increase range is used to constrain the maximum value of the uncertainty radius.

[0083] In one example, the optimization scenario in the above modeling process is a fixed typical day. However, in reality, the output of new energy sources such as wind power and photovoltaics is highly random, and the impact of uncertainty needs to be considered. This embodiment introduces IGDT to handle uncertainty in rural micro-energy grid planning.

[0084] Therefore, this embodiment constructs an uncertainty set centered on wind and solar power output data from a typical day. The uncertainty radius is an important parameter of the uncertainty set, characterizing the degree of deviation from the typical day data and determining the size of the set. The constructed wind and solar power output uncertainty sets are as follows: (47) In the formula, Ω is an uncertain set. ψ pv and ψ w These are the uncertain radii for photovoltaic and wind power, respectively. and These are the predicted upper limits of photovoltaic (PV) and wind power output, respectively. Based on these, the uncertainty radii of both wind and PV are expressed as a whole: (48) In the formula, ψ is the overall uncertain radius, and λ pv and λ w These are the uncertainty radius weights for wind power and photovoltaic power, respectively.

[0085] Under the risk-averse IGDT strategy, decision-makers consider the adverse effects of uncertainty and allow total costs to increase to a certain extent. Therefore, the optimized planning scheme is conservative for the worst-case scenario of uncertain variables. The IGDT strategy model is constructed as follows: (49) In the formula, π is the robustness level factor, which can be adjusted to improve the robustness of the planning scheme by changing the value of π. The objective function value is obtained by optimizing using typical daily forecast data. It can be seen that the model has a two-layer max-finding structure, which is difficult to solve directly, as the cost increases with the decrease of wind power and photovoltaic output. Therefore, the two-layer optimization in equation (49) can be transformed into a single-layer optimization form: (50) Through optimization formula (50), the planning scheme for rural micro-energy grid is finally obtained.

[0086] Figure 3 A flowchart illustrating another method for configuring a rural micro-energy system provided in this embodiment of the invention is described in detail below: Step 301: Obtain the model corresponding to the rural micro-energy system, including a new energy power generation module, an energy conversion module, an energy storage module, and an energy consumption module; wherein, the rural micro-energy system is a regional energy system covering multiple natural villages.

[0087] For example, in this embodiment, the rural micro-energy system covers multiple natural villages, and the corresponding models include new energy power generation modules (distributed wind power generation model, distributed photovoltaic power generation model), energy conversion modules (biogas cogeneration model, air source heat pump model, absorption chiller model), energy storage modules (electric vehicle model) and energy consumption modules (breeding and planting energy consumption models).

[0088] Step 302: Determine the configuration parameters. The configuration parameters are used to configure the parameters of the energy consumption module; the configuration parameters include energy consumption data.

[0089] For example, in this embodiment, the configuration parameters are mainly energy consumption data, including the electricity, heat and cooling load requirements of each natural village during the target planning period, which can be determined through historical energy consumption data and growth forecasts.

[0090] For example, based on the scale of breeding and the planting area, the corresponding HVAC heat load and production energy consumption can be calculated, providing a basis for the configuration of energy consumption module parameters.

[0091] Step 303: The planned capacity of the new energy power generation module, energy conversion module and energy storage module is taken as the quantity to be solved.

[0092] In one example, the quantity to be solved is shown in equation (40).

[0093] Step 304: Obtain the initial solution set of the quantity to be solved; wherein, the initial solution set includes multiple initial solutions of the quantity to be solved.

[0094] For example, in this embodiment, multiple initial solutions are generated through random initialization or empirical selection, forming an initial population. Each initial solution corresponds to a combination of planned energy equipment capacities.

[0095] Step 305: Calculate the cost objective function value corresponding to each initial solution in the initial population.

[0096] For example, in this embodiment, the investment cost corresponding to each initial solution is calculated based on the planned capacity of the equipment, the planning period and the unit investment cost, as shown in Equation (41); the operation and maintenance cost corresponding to each initial solution is calculated based on the equipment output power and operation and maintenance rate, as shown in Equation (42); for the wind power and photovoltaic output that are not absorbed, the cost of wind curtailment and solar curtailment corresponding to each initial solution is calculated according to the cost coefficient, as shown in Equation (43); the environmental cost corresponding to each initial solution is calculated by combining the pollutant emissions from grid power purchase and energy conversion with the emission reduction benefits, as shown in Equation (44).

[0097] Step 306: Based on the cost objective function value corresponding to each initial solution, perform non-dominated sorting on each initial solution in the initial population to obtain the non-dominated sorting result, and record the iteration number corresponding to the current non-dominated sorting result; wherein, each initial solution in the non-dominated sorting result is divided into the same or different levels, each level includes one or more initial solutions, the initial solutions in the same level do not dominate each other, and the initial solutions in the later level in different levels are dominated by the initial solutions in the previous level.

[0098] In one example, this embodiment divides the initial population into different levels based on the cost objective function values ​​of each initial solution. Solutions at the same level do not dominate each other, meaning they cannot be superior to each other on all objectives simultaneously; solutions at later levels are dominated by solutions at earlier levels.

[0099] For example, if the investment cost and operation and maintenance cost of solution C are both lower than those of solution D, then solution C dominates solution D, and solution C belongs to a higher level.

[0100] Step 307: Under the constraints of the preset constraints, with the goal of minimizing the function value of the cost objective function, the optimal solution is obtained based on the non-dominated sorting results and the number of iterations.

[0101] For example, in this embodiment, if the number of iterations reaches a preset value, the solution at the first level of the non-dominated sorting results is output as the optimal solution. Otherwise, parent solutions are selected according to the principle of minimizing the cost objective function, and child solutions are generated through crossover and mutation, which are then merged into the initial population to continue iterative optimization.

[0102] In the process of finding the optimal solution, the solution must always meet preset constraints such as total supply and demand balance, off-grid emergency self-sufficiency, power balance, and power upper limit to ensure feasibility.

[0103] In one possible implementation, step 307 includes: If the number of iterations reaches the preset number of iterations, the solution in the first level of the non-dominated sorting result is output as the preferred solution; otherwise, according to the preset screening conditions, the parent solution is selected, and under the constraints of the preset constraints, crossover mutation is performed based on the parent solution to obtain the child solution, and the child solution is merged into the initial population as a new solution, and the merged initial population is used as the current initial population; wherein, the screening conditions are set with the minimum function value of the cost objective function as the optimization objective; the steps of non-dominated sorting of each solution in the current initial population according to the function value of the cost objective function corresponding to each solution are re-executed to obtain the non-dominated sorting result, and the number of iterations corresponding to the current non-dominated sorting result are recorded, until the preferred solution is output.

[0104] In summary, this embodiment achieves multi-energy complementarity through multi-module collaborative modeling and optimized configuration, including wind, solar, and biomass energy, effectively reducing energy waste. Targeting the characteristics of rural industries such as animal husbandry and agriculture, it integrates applicable technologies such as biogas power generation and air-source heat pumps, aligning with actual energy consumption scenarios and adapting to the unique needs of rural areas. Furthermore, through new energy consumption and environmental cost accounting, it promotes the development of rural energy systems towards low-carbon and environmentally friendly directions, reducing dependence on traditional power grids. Simultaneously, this embodiment, through the planning of a regional-level energy system covering multiple natural villages, achieves energy equipment sharing and complementary collaboration, avoiding redundant construction, improving resource utilization efficiency, and optimizing resource allocation. Moreover, this embodiment aims to minimize the entire lifecycle cost, comprehensively considering investment, operation and maintenance, energy curtailment, and environmental costs, avoiding over-configuration or inefficient operation of equipment, effectively reducing system costs. Through off-grid emergency self-sufficiency constraints, it ensures the energy supply of critical loads in natural villages during grid failures, enhancing the resilience of rural energy systems and guaranteeing energy reliability.

[0105] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0106] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0107] Figure 4 A schematic diagram of the rural micro-energy system configuration device provided in an embodiment of the present invention is shown. For ease of explanation, only the parts related to the embodiment of the present invention are shown, and are described in detail below: like Figure 4 As shown, the rural micro-energy system configuration device includes: The acquisition unit 401 is used to acquire the model corresponding to the rural micro-energy system, including a new energy power generation module, an energy conversion module, an energy storage module, and an energy consumption module; wherein, the rural micro-energy system is a regional energy system covering multiple natural villages.

[0108] The first processing unit 402 is used to determine configuration parameters, which are used to configure the parameters of the energy consumption module; the configuration parameters include energy consumption data.

[0109] The second processing unit 403 is used to take the planned capacity of the new energy power generation module, energy conversion module and energy storage module as the quantity to be solved.

[0110] The solver unit 404 is used to optimize the solution of the quantity to be solved under the constraints of preset constraints, with the goal of minimizing the cost objective function, and to determine the optimal solution.

[0111] The execution unit 405 is used to execute the model configuration strategy generated by the optimal solution to obtain the configured rural micro-energy system.

[0112] In one possible implementation, the preset constraints include total supply and demand balance constraints, emergency energy self-sufficiency constraints within each natural village under off-grid conditions, power balance constraints, thermal power balance constraints, cold power balance constraints, upper power limit constraints for new energy power generation modules, upper power limit constraints for energy conversion modules, and upper power limit constraints for energy storage modules.

[0113] In one possible implementation, the energy modules configured in each natural village include one or more of the following: new energy power generation modules, energy conversion modules, and energy storage modules. Before the solution unit 404, the device further includes: a third processing unit, used to: determine the energy supply expression of each natural village under off-grid conditions based on the planned capacity, historical operating data, and rated parameters of the energy modules configured in each natural village; determine the maximum demand load of each natural village and the proportion of important loads in each natural village to the maximum demand load; and establish emergency energy self-sufficiency constraints for each natural village under off-grid conditions based on the energy supply expression, maximum demand load, and proportion corresponding to each natural village.

[0114] In one possible implementation, the cost objective function includes investment cost, operation and maintenance cost, wind and solar curtailment cost, and environmental cost; wherein, the new energy power generation module includes wind power generation module and photovoltaic power generation module; before the solution unit 404, the device further includes: a fourth processing unit, used to: obtain a first function expression corresponding to the investment cost based on the planned capacity, planned years, and unit capacity investment cost of the wind power generation module, photovoltaic power generation module, energy conversion module, and energy storage module; obtain a second function expression corresponding to the operation and maintenance cost based on the output power of the wind power generation module, photovoltaic power generation module, energy conversion module, and energy storage module; obtain a third function expression corresponding to the wind and solar curtailment cost based on the output power of the wind power generation module and photovoltaic power generation module; obtain a fourth function expression for the environmental cost based on the grid purchase electricity and the energy conversion amount of the energy conversion module; and sum the first, second, third, and fourth function expressions to obtain the cost objective function.

[0115] In one possible implementation, the energy conversion module includes a biogas cogeneration model, and the planned capacity of the energy conversion module includes the planned capacity of the biogas cogeneration model; the energy consumption module includes a livestock energy consumption model; before the acquisition unit 401, the device further includes: a first construction unit, used to: calculate the daily biogas production based on the livestock species, scale, and biogas production rate of manure in the livestock energy consumption model; and construct a biogas cogeneration model based on the daily biogas production and the planned capacity of the biogas cogeneration model; wherein the biogas cogeneration model includes models corresponding to a micro gas turbine, a biogas boiler, and a waste heat recovery boiler, and the planned capacity of the biogas cogeneration model includes the planned capacity of the micro gas turbine, the biogas boiler, and the waste heat recovery boiler; the micro gas turbine is used to generate electrical power from biogas, the biogas boiler uses biogas to generate thermal power, and the waste heat recovery boiler uses the high-temperature flue gas generated by the micro gas turbine to generate thermal power.

[0116] In one possible implementation, the energy conversion module further includes an air source heat pump model and an absorption chiller model; before the acquisition unit 401, the device further includes: a second construction unit, used to: construct an air source heat pump model based on the heating power, cooling power, power consumption and planned capacity of the air source heat pump; and construct an absorption chiller model based on the cooling capacity, thermal power, cooling efficiency and planned capacity of the absorption chiller.

[0117] In one possible implementation, the solving unit 404 is specifically used for: Obtain the initial solution set of the variable to be solved; wherein, the initial solution set includes multiple initial solutions of the variable to be solved.

[0118] Using the initial solution set as the initial population, and under the constraints of preset conditions, the optimization objective is to minimize the cost objective function to find the optimal solution.

[0119] In one possible implementation, the solving unit 404 is further used for: The cost objective function value corresponding to each initial solution in the initial population is calculated.

[0120] Based on the cost objective function value corresponding to each initial solution, the initial solutions in the initial population are sorted in a non-dominated manner to obtain the non-dominated sorting result, and the iteration number corresponding to the current non-dominated sorting result is recorded. In the non-dominated sorting result, each initial solution is divided into the same or different levels. Each level includes one or more initial solutions. Initial solutions in the same level do not dominate each other. In different levels, the initial solutions in the later level are dominated by the initial solutions in the previous level.

[0121] Under the constraints of the preset conditions, with the goal of minimizing the function value of the cost objective function, the optimal solution is obtained based on the non-dominated sorting results and the number of iterations.

[0122] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0123] Figure 5 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Figure 5 As shown, the electronic device 5 of this embodiment includes a processor 50 and a memory 51. The memory 51 stores a computer program 52. When the processor 50 executes the computer program 52, it implements the steps in the various method embodiments described above. Alternatively, when the processor 50 executes the computer program 52, it implements the functions of each module / unit in the various device embodiments described above.

[0124] For example, computer program 52 may be divided into one or more modules / units, which are stored in memory 51 and executed by processor 50 to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 52 in electronic device 5.

[0125] Electronic device 5 may include, but is not limited to, processor 50 and memory 51. Those skilled in the art will understand that... Figure 5 This is merely an example of electronic device 5 and does not constitute a limitation on electronic device 5. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device 5 may also include input / output devices, network access devices, buses, etc.

[0126] For the sake of simplicity and clarity, only the above-described functional modules / units are used as examples. In practical applications, the functions described above can be assigned to different functional modules / units as needed. These modules / units can be implemented in hardware, software, or a combination of both.

[0127] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not detailed or described in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Unless otherwise specified or in conflict with logic, the terminology and / or descriptions between different embodiments are consistent and can be referenced interchangeably. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0128] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for configuring a rural micro-energy system, characterized in that, The method includes: Obtain the model corresponding to the rural micro-energy system, including a new energy power generation module, an energy conversion module, an energy storage module, and an energy consumption module; wherein, the rural micro-energy system is a regional energy system covering multiple natural villages; The configuration parameters are determined and used to configure the parameters of the energy consumption module; the configuration parameters include energy consumption data; the planned capacity of the new energy power generation module, the energy conversion module, and the energy storage module are taken as the variables to be solved. Under the constraints of preset conditions, with the goal of minimizing the cost objective function and maximizing the uncertain radius of the output of the new energy power generation module, the solution to be solved is optimized to determine the optimal solution; The model configuration strategy generated from the optimal solution is executed to obtain the configured rural micro-energy system.

2. The method for configuring a rural micro-energy system according to claim 1, characterized in that, The preset constraints include total supply and demand balance constraints, emergency energy self-sufficiency constraints for each natural village in the off-grid state, power balance constraints, thermal power balance constraints, cold power balance constraints, power upper limit constraints for the new energy power generation module, power upper limit constraints for the energy conversion module, and power upper limit constraints for the energy storage module.

3. The method for configuring a rural micro-energy system according to claim 2, characterized in that, The energy modules configured in each natural village include one or more of the new energy power generation module, the energy conversion module, and the energy storage module; Before determining the optimal solution by optimizing the unsolved quantity under the constraints of preset conditions, with the goal of minimizing the cost objective function and maximizing the uncertain radius of the output of the new energy power generation module, the method further includes: Based on the planned capacity, historical operating data, and rated parameters of the energy modules configured in each natural village, determine the energy supply expression for each natural village under off-grid conditions; Determine the maximum demand load for each natural village, and determine the proportion of the critical load within each natural village to the maximum demand load; Based on the energy supply expression, the maximum demand load, and the ratio corresponding to each natural village, an emergency energy self-sufficiency constraint is established for each natural village under off-grid conditions.

4. The method for configuring a rural micro-energy system according to any one of claims 1-3, characterized in that, The cost objective function includes investment cost, operation and maintenance cost, wind and solar curtailment cost, and environmental cost; among which, the new energy power generation module includes wind power generation module and photovoltaic power generation module; Under the constraints of preset conditions, with the goal of minimizing the cost objective function and maximizing the uncertain radius of the output of the new energy power generation module, the method further includes optimizing the solution to the unsolved quantity and determining the optimal solution before proceeding with the following steps: Based on the planned capacity, planned lifespan, and unit capacity investment cost of the wind power generation module, the photovoltaic power generation module, the energy conversion module, and the energy storage module, a first functional expression corresponding to the investment cost is obtained; Based on the output power of the wind power generation module, the photovoltaic power generation module, the energy conversion module, and the energy storage module, a second functional expression corresponding to the operation and maintenance cost is obtained; Based on the output power of the wind power generation module and the photovoltaic power generation module, the third function expression corresponding to the cost of wind and solar curtailment is obtained; Based on the electricity purchased from the power grid and the energy conversion amount of the energy conversion module, the fourth functional expression for the environmental cost is obtained; The basic cost objective function is obtained by summing the first function expression, the second function expression, the third function expression, and the fourth function expression; The cost objective function is obtained based on the basic cost objective function and the robustness level factor; wherein, the robustness level factor characterizes the allowable increase range of the basic cost objective function, and the allowable increase range is used to constrain the maximum value of the uncertainty radius.

5. The method for configuring a rural micro-energy system according to any one of claims 1-3, characterized in that, The energy conversion module includes a biogas cogeneration model, and the planned capacity of the energy conversion module includes the planned capacity of the biogas cogeneration model. The energy consumption module includes an aquaculture energy consumption model; Before obtaining the model corresponding to the rural micro-energy system, the method further includes: Calculate the daily biogas production based on the types and scale of livestock farming and the biogas production rate of manure in the aforementioned livestock energy consumption model; Based on the daily biogas production and the planned capacity of the biogas cogeneration model, the biogas cogeneration model is constructed. The biogas cogeneration model includes models corresponding to micro gas turbines, biogas boilers, and waste heat recovery boilers. The planned capacity of the biogas cogeneration model includes the planned capacity of the micro gas turbines, biogas boilers, and waste heat recovery boilers. The micro gas turbines are used to generate electricity from biogas, the biogas boilers are used to generate heat from biogas, and the waste heat recovery boilers are used to generate heat from the high-temperature flue gas generated by the micro gas turbines.

6. The method for configuring a rural micro-energy system according to claim 5, characterized in that, The energy conversion module also includes an air source heat pump model and an absorption chiller model; Before obtaining the model corresponding to the rural micro-energy system, the method further includes: Based on the heating power, cooling power, power consumption, and planned capacity of the air source heat pump, the air source heat pump model is constructed. Based on the cooling capacity, thermal power, cooling efficiency, and planned capacity of the absorption chiller, a model of the absorption chiller is constructed.

7. The method for configuring a rural micro-energy system according to any one of claims 1-3, characterized in that, Under the constraints of preset conditions, the optimization objective is to minimize the cost objective function while maximizing the uncertain radius of the output of the new energy power generation module. The optimization process involves solving for the unsolved variables to determine the optimal solution, including: Obtain an initial solution set for the quantity to be solved; wherein the initial solution set includes multiple initial solutions for the quantity to be solved; Using the initial solution set as the initial population, and under the constraints of preset conditions, with the goal of minimizing the cost objective function and maximizing the uncertain radius of the output of the new energy power generation module, the solution is optimized to obtain the optimal solution.

8. The method for configuring a rural micro-energy system according to claim 7, characterized in that, The process involves using the initial solution set as the initial population, under preset constraints, minimizing the cost objective function as the optimization objective, and simultaneously maximizing the uncertain radius of the output of the new energy power generation module to optimize the unsolved quantities and obtain the optimal solution, including: The cost objective function value corresponding to each initial solution in the initial population is calculated; Based on the cost objective function value corresponding to each initial solution, the initial solutions in the initial population are sorted in a non-dominated manner to obtain the non-dominated sorting result, and the iteration number corresponding to the current non-dominated sorting result is recorded; wherein, the initial solutions in the non-dominated sorting result are divided into the same or different levels, each level includes one or more initial solutions, the initial solutions in the same level do not dominate each other, and the initial solutions in the later level are dominated by the initial solutions in the previous level. Under the constraints of the preset conditions, with the goal of minimizing the function value of the cost objective function, the optimal solution is obtained based on the non-dominated sorting results and the number of iterations.

9. A rural micro-energy system configuration device, characterized in that, The device includes: The acquisition unit is used to acquire the model corresponding to the rural micro-energy system, including a new energy power generation module, an energy conversion module, an energy storage module, and an energy consumption module; wherein, the rural micro-energy system is a regional energy system covering multiple natural villages; The first processing unit is used to determine configuration parameters, which are used to configure the parameters of the energy consumption module; the configuration parameters include energy consumption data. The second processing unit is used to take the planned capacity of the new energy power generation module, the energy conversion module and the energy storage module as the quantity to be solved; The solution unit is used to optimize and solve the unsolved quantities under the constraints of preset conditions, with the goal of minimizing the cost objective function and maximizing the uncertain radius of the output of the new energy power generation module, and to determine the optimal solution. An execution unit is used to execute the model configuration strategy generated from the optimal solution to obtain the configured rural micro-energy system.

10. An electronic device, characterized in that, It includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the rural micro-energy system configuration method as described in any one of claims 1 to 8.

Images