A rural multi-capacity coupling micro-energy collaborative operation regulation method

Abstract

The application discloses a rural multi-energy coupling micro-energy collaborative operation regulation method, and belongs to the technical field of comprehensive energy system operation optimization. Through the construction of a double-layer closed-loop regulation architecture of "upper-layer collaborative regulation and decision-making-lower-layer real-time operation optimization", a multi-scale multi-energy complementary dynamic quantitative model is constructed, an improved NSGA-III algorithm and an entropy weight-TOPSIS multi-objective decision-making method are combined to generate an optimal real-time regulation strategy, and strategy verification is completed through annual time sequence dynamic simulation, so that multi-energy flow collaborative regulation of electricity, heat and gas is realized. The technical problems that the existing rural multi-energy coupling system has low multi-energy complementary utilization rate, weak grid-connected and off-grid scene adaptation capability, and is prone to energy abandonment and energy supply gap are solved, the renewable energy consumption rate and system operation reliability are greatly improved, the operation energy consumption and carbon emission are reduced, and the method is suitable for the normal operation regulation of various rural micro-energy systems.

Description

Technical Field

[0001] This invention belongs to the field of integrated energy system operation optimization technology, specifically relating to a method for coordinated operation and regulation of multi-energy coupled micro-energy in rural areas. Background Technology

[0002] The micro-energy sources mentioned in this invention refer to independent energy supply and consumption units such as photovoltaic, wind power, biomass biogas, thermal storage, and energy storage that are distributed in rural areas. Multiple micro-energy sources are interconnected and cooperate through multi-energy coupling to form a comprehensive energy supply cluster in rural areas.

[0003] The clean energy transformation in rural areas continues to deepen, and multi-energy coupled micro-energy systems combining photovoltaic, wind power, and biomass energy have become the core carrier for replacing traditional scattered coal-fired power supply and improving the level of clean energy use in rural areas. my country has abundant wind and solar resources, as well as straw and livestock manure biomass resources in rural areas. Multi-energy coupled micro-energy systems can effectively solve the problems of high pollution and low energy efficiency in rural energy use through complementary and coordinated operation of electricity, heat, and gas. However, the multi-energy coupled micro-energy systems currently in operation in rural areas generally suffer from lagging operation and control technologies, which seriously restricts the realization of the system's operational benefits.

[0004] Existing rural multi-energy system operation and control technologies largely adopt fixed dispatch strategies from urban integrated energy systems, resulting in significant scenario adaptation deficiencies. Urban parks have stable loads and strong grid support capabilities, allowing for control strategies primarily based on fixed-sequence dispatch. However, rural scenarios possess highly unique operational characteristics: First, biomass resources exhibit significant seasonal and time-of-day fluctuations in output, with losses during collection, storage, and transportation, along with collection radius constraints, leading to real-time dynamic changes in effective supply, making a fixed energy supply dispatch model unsuitable. Second, rural agricultural production loads are highly seasonal and volatile, with irrigation and livestock temperature control loads possessing unique flexible and adjustable characteristics. Existing control strategies fail to differentiate the dynamic adjustment potential of various rural load types, often employing rigid dispatch methods, resulting in wasted load adjustment resources. Third, rural grid-connected villages have weak distribution network terminals and large power fluctuations, while off-grid villages lack a large grid for backup. The operational reliability requirements of these two scenarios differ greatly, making a unified control strategy unsuitable for these differentiated operational needs.

[0005] Meanwhile, existing control technologies lack the ability to dynamically quantify and control multi-scale, multi-energy complementary power. They rely solely on fixed time-series experience to schedule equipment output, failing to quantify in real time the spatiotemporal complementary characteristics of photovoltaic daily output, wind power nighttime fluctuations, and continuous biomass energy supply. They also cannot dynamically match the seasonal complementary patterns of high wind and solar output in summer to irrigation and cooling loads, and high biomass and wind power supply in winter to heating loads. The flexible adjustment potential of multi-energy flow coupling and conversion has not been fully explored, resulting in the coexistence of system curtailment of solar and wind power, surplus waste of biomass gas, and local energy supply gaps, leading to low energy utilization efficiency.

[0006] Furthermore, current mainstream rural multi-energy system regulation adopts a single-layer static scheduling mode, where equipment output scheduling, load regulation, and energy storage charging and discharging control are independent of each other, failing to form a multi-objective collaborative dynamic iterative regulation mechanism. This makes it unsuitable for adapting to the temporal fluctuations of source and load throughout the 8760 hours of the year. Traditional static regulation strategies ignore the impact of intraday energy fluctuations, dynamic response of flexible loads, and changes in the temporal state of energy storage. The actual operating conditions deviate from the preset scheduling plan by more than 18%, frequently resulting in problems such as excessive operating costs, declining renewable energy absorption rates, and short-term power outages. In summary, there is an urgent need for a rural multi-energy coupled micro-energy collaborative operation and regulation method that is adapted to rural scenarios, can dynamically quantify the multi-energy complementarity characteristics, and achieves multi-objective closed-loop iteration. Summary of the Invention

[0007] This invention addresses the core shortcomings of existing rural multi-energy coupled micro-energy systems, such as poor adaptability to various operational scenarios, inability to dynamically quantify multi-energy complementarity potential, static and fixed scheduling strategies, insufficient multi-energy flow coordination, and large operational deviations. It provides a collaborative operation and control method for rural multi-energy coupled micro-energy systems. Through a complete technical chain—including dynamic source-load modeling adapted to rural scenarios, real-time quantification of multi-scale multi-energy complementarity, two-layer closed-loop collaborative control, multi-objective objective decision-making, and time-series dynamic verification—this method achieves refined, dynamic, and collaborative operation and control of electricity-heat-gas multi-energy flows in rural areas. This significantly improves the system's renewable energy absorption capacity and operational stability, while reducing system energy consumption and carbon emissions.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: This invention provides a method for the coordinated operation and regulation of multi-energy coupled micro-energy sources in rural areas, comprising the following steps: S1. Collect various renewable energy output parameters, multi-category energy load parameters, and multi-energy coupling equipment operation parameters in the target area, and construct a renewable energy dynamic output model, various load time-series identification models, and multi-energy equipment operation benefit models respectively. The renewable energy dynamic output model quantifies the time-series fluctuations of rural renewable energy and the dynamic characteristics of seasonal supply, storage, transportation and loss of biomass. The various load time-series identification models distinguish in real time the rigid demand and flexible adjustment potential of rural living, agricultural production and public service loads. The multi-energy equipment operation benefit model adapts to the real-time operation characteristics of multi-energy flow coupling conversion. S2. Based on the dynamic modeling results of step S1, calculate the intraday dynamic complementarity coefficient, seasonal source-load matching coefficient, and multi-energy complementarity coefficient in real time from three dimensions: intraday time series, seasonal time series, and multi-energy flow coupling. Couple the results to obtain the real-time comprehensive complementarity of the system. Use the dynamic quantitative result of multi-energy complementarity as a prerequisite constraint for system operation and regulation. S3. Construct a two-layer collaborative operation control and optimization model, and build a two-layer closed-loop control architecture with an upper-layer multi-energy collaborative control decision layer and a lower-layer real-time operation optimization layer. The upper layer takes the optimal multi-objective of real-time system operation economy, environmental protection, and energy supply reliability as the control objective, and dynamically outputs macro-control commands for multi-energy equipment output, energy storage charging and discharging, and load adjustment. The lower layer, based on the control commands of the upper layer, carries out hourly refined operation optimization in combination with multiple typical operation scenarios, feeds back real-time operation deviation indicators and corrects the upper-layer control constraints, and outputs the optimal real-time operation control strategy after iterative convergence. S4. The improved NSGA-III algorithm is used to solve the dual-layer collaborative operation control optimization model to obtain the multi-objective optimal control solution set. The comprehensive optimal collaborative operation control strategy that is suitable for the current working condition is selected by the entropy weight-TOPSIS objective decision-making method. S5. Conduct hourly time-series dynamic simulation verification of the optimal collaborative operation control strategy throughout the year to verify the system's operational stability, energy consumption level, and energy supply reliability under different operating conditions. After the verification is passed, output the normalized collaborative operation control scheme, the emergency control strategy for extreme operating conditions, and the system operation index analysis report.

[0009] Preferably, the dynamic output modeling of renewable energy in step S1 specifically includes: Real-time collection of hourly solar irradiance and ambient temperature data in the target area; construction of a dynamic photovoltaic power output model including temperature efficiency decay correction; real-time correction of photovoltaic module power output efficiency deviation; real-time collection of hourly wind speed and wind direction data; construction of a dynamic wind turbine power output model based on wind turbine cut-in, rated, and cut-out wind speed thresholds; adapting to the random fluctuation characteristics of wind power. The dynamic survey of the seasonal and time-specific output and real-time collectable amount of straw and livestock manure biomass resources in the region, combined with a maximum collection radius of 3-5km and a dynamic loss coefficient of 5% due to mold, constructs a dynamic supply model for biomass resources. Simultaneously, it establishes dynamic constraints on the annual total amount of biomass, seasonal and time-specific supply constraints, and dynamic cost constraints on biomass collection and storage, and quantifies the effective supply boundary of rural biomass energy in real time.

[0010] Preferably, the various load time series identification and modeling in step S1 specifically includes: The real-time energy load of the target area is divided into three categories: residential energy consumption, agricultural production energy consumption, and public service energy consumption. Each category is further subdivided into real-time electricity, heat, and cooling loads. Based on the measured data from households and the time sequence of agricultural production, a dynamic load curve for each hour throughout the year is generated. Real-time definition of the dynamic adjustment attributes of various loads: domestic heating and cooling loads are dynamically adjustable flexible loads, agricultural irrigation loads are shiftable flexible loads with adjustable time periods from 8:00 to 18:00, aquaculture temperature control loads are flexible loads that can be interrupted for a short time, and public service loads are non-adjustable rigid loads. Set dynamic quantitative control constraints for flexible agricultural loads: the total daily electricity consumption of the shiftable irrigation load remains constant and is shifted only within the preset adjustable time period; the maximum daily interruption duration of the interruptable aquaculture temperature control load does not exceed 2 hours and the power of a single interruption does not exceed 30% of the rated temperature control load.

[0011] Preferably, the multi-functional equipment operation benefit modeling in step S1 specifically includes: Construct a real-time operating efficiency model for distributed photovoltaic, small wind turbine, biomass gasification cogeneration unit, electric boiler, air source heat pump, electric energy storage, thermal energy storage, biomass gas storage tank, and grid connection point interaction device, and match the photovoltaic temperature decay efficiency, biomass gasification real-time efficiency, cogeneration dynamic heat-to-power ratio, heat pump real-time COP (coefficient of performance), and energy storage charging and discharging real-time efficiency parameters in real time. Establish a full life cycle operating cost model for equipment, and integrate discount rate, equipment lifespan, and residual value rate through capital recovery coefficient to dynamically convert equipment maintenance losses, operating energy consumption, and equipment depreciation costs into real-time operating costs, matching the economic accounting logic of upper-level multi-objective control.

[0012] Preferably, the multi-energy complementary dynamic quantization result in step S2 specifically includes: Based on the intraday temporal complementary characteristics of photovoltaic daytime power output, wind power nighttime power output, and biomass continuous and stable power output, the intraday complementary coefficient is calculated in real time to quantify the hourly multi-energy power output complementarity. Based on the seasonal temporal characteristics of matching high photovoltaic output with peak agricultural irrigation and cooling loads in summer and high wind power and biomass supply with peak heating loads in winter, the seasonal matching coefficient is calculated to quantify the degree of dynamic matching between source and load on a seasonal scale. Based on the operating characteristics of multi-energy flow coupling conversion and mutual substitution, including electricity, heat, and gas, the energy complementarity coefficient is calculated to quantify the real-time energy conversion and load substitution capabilities of multi-energy coupling equipment. By coupling intraday complementarity coefficient, seasonal matching coefficient, and energy complementarity coefficient with adaptive weighting coefficient, the real-time comprehensive complementarity of the system is obtained. The weighting coefficient is dynamically adjusted according to the resource endowment of rural areas and real-time load conditions.

[0013] Preferably, the multi-objective control objectives of the upper-level multi-energy collaborative control decision layer in step S3 include: Objective 1: To minimize the annualized total cost of real-time, full lifecycle operation of the system, including real-time equipment maintenance costs, real-time biomass collection, storage and transportation costs, real-time grid interaction net costs, real-time energy shortage penalty costs, and real-time carbon emission costs; Objective 2: To achieve the highest real-time renewable energy consumption rate in the system, a consumption rate calculation model is constructed based on the ratio of real-time energy loss due to hourly curtailment of solar, wind, and biomass gas to the total real-time theoretical energy output of renewable energy. Objective 3: To achieve the highest real-time energy supply reliability of the system, an energy supply reliability calculation model is constructed based on the ratio of the total real-time energy shortage to the total real-time energy demand of the region.

[0014] Preferably, the lower-level real-time running optimization layer in step S3 specifically includes: The system includes pre-defined typical operating scenarios for spring, summer, autumn and winter, as well as extreme high and low temperatures and continuous rainy days. In grid-connected scenarios, a new operating scenario for distribution network power fluctuations has been added, and in off-grid scenarios, a new extreme operating scenario without grid backup has been added. With the optimization goals of minimizing daily real-time operating costs and minimizing hourly energy curtailment rate in typical scenarios, we refine the hourly real-time output of each device, the hourly charging and discharging power of energy storage, the hourly dynamic adjustment of flexible loads, and the hourly interactive power of the power grid. The superimposed dynamic control constraints for flexible loads ensure that the total daily electricity consumption of loads that can be shifted remains constant, and that the adjustment range and duration of interruptible loads do not exceed safety limits.

[0015] Preferably, step S4 specifically includes: An improved NSGA-III algorithm incorporating adaptive crossover mutation probability and elite retention strategy is used to solve the high-dimensional multi-objective two-layer collaborative operation and control model, obtaining a uniformly distributed multi-objective Pareto optimal control solution set. By using the entropy weight method based on the dispersion of each control target value in the Pareto solution set, the weights of the three major control targets of operation economy, environmental protection and energy supply reliability are objectively calculated, thus avoiding subjective weighting bias. The proximity between each Pareto control solution and the positive and negative ideal solutions is calculated using the TOPSIS method. The control scheme corresponding to the maximum proximity is selected as the comprehensive optimal coordinated operation control strategy for the current operating condition.

[0016] Preferably, the timing dynamic simulation verification indicators in step S5 include: Operational economic indicators: annualized operating cost over the entire system lifecycle, unit energy supply operating cost, and average annual maintenance loss cost; Environmental performance indicators: real-time renewable energy consumption rate, annual dynamic carbon emission reduction, and dynamic replacement of bulk coal / diesel; Operational reliability indicators: real-time system load failure rate, real-time grid voltage qualification rate, and continuous stable operation time of the multi-energy system; If the deviation between the simulation operation index and the control target index is less than 1%, the control strategy is deemed to have passed the verification and is solidified as a normalized operation strategy; if the deviation exceeds the standard, return to step S3 to correct the control constraint parameters and iterate and optimize the control strategy again.

[0017] Preferably, dynamic simulation verification includes: In the scenario of grid-connected administrative villages: retain the two-way dynamic interaction constraints of the distribution network, weaken the weight of energy shortage penalty, prioritize the core control objectives of operation economy and efficient consumption of renewable energy, and dynamically balance grid interaction and local multi-energy operation; Off-grid administrative village scenario: Remove distribution network interaction constraints, significantly increase the weight of energy shortage penalty, extend the simulation control time for extreme rainy and low temperature conditions, and prioritize ensuring the stability of continuous power supply and operational reliability of the system. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the energy flow topology of the multi-energy coupled micro-energy system adapted to rural scenarios according to the present invention; Figure 2 This is a flowchart illustrating the overall process of the rural multi-energy coupling micro-energy coordinated operation and control method of the present invention. Figure 3 This is a schematic diagram of the dual-layer collaborative closed-loop operation control architecture and iterative mechanism of the present invention; Figure 4 This is a curve showing the dynamic matching and control of multi-source output and load of the multi-energy system on a typical winter day according to the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be described below with reference to the accompanying drawings. The described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without innovative effort are within the scope of protection of the present invention.

[0020] The method for coordinated operation and regulation of multi-energy coupled micro-energy sources in rural areas includes the following steps: 1. Basic Data Acquisition and Multi-dimensional Modeling Methods 1.1 Resource Data Acquisition and Modeling Collect meteorological and biomass resource data for the target rural areas: Solar energy resources: Obtain hourly solar irradiance and ambient temperature data for the past 10 years in the local area, establish a photovoltaic power output model, output hourly photovoltaic power generation, and consider the impact of temperature on the degradation of photovoltaic module efficiency; Wind energy resources: Obtain hourly wind speed and direction data for the past 10 years in the local area, establish a wind turbine output model based on the wind turbine power curve, and consider the cut-in wind speed, rated wind speed and cut-out wind speed limits; Biomass resources: The survey area includes the types and annual yield of straw, the scale of livestock and poultry farming and the amount of manure discharged, and the amount of biomass that can be collected in each season of spring, summer, autumn and winter. Combined with the collection radius and collection cost, a biomass resource supply model is established, taking into account actual factors such as straw mold and loss rate.

[0021] Three types of standardized mathematical constraints are established: first, a constraint on the total amount of biomass that can be collected annually in the region, limiting the total amount of raw materials to the upper limit of land and aquaculture output; second, a constraint on biomass supply by season, setting the upper limit of available raw materials for each season according to the output ratio of the four seasons, and introducing a 5% mold loss coefficient to convert the effective gas production raw materials; and third, a formula for calculating the cost of biomass collection, storage and transportation, where the cost increases linearly with the collection radius and a hard constraint of a maximum collection radius of 3-5km is set, fully quantifying the biomass supply boundary and ensuring that the model is reproducible.

[0022] 1.2 Load Data Acquisition and Modeling The energy load of the target area is classified and statistically analyzed into three main categories: residential energy consumption, agricultural production energy consumption, and public service energy consumption. Each category is further subdivided into electrical load, heat load, and cooling load. Domestic energy consumption: lighting, home appliances, cooking, heating, cooling, etc., of which the heating and cooling loads are adjustable flexible loads; Energy consumption for agricultural production includes irrigation, agricultural product processing, and temperature control for aquaculture. Irrigation load is a flexible load that can be shifted, while temperature control load for aquaculture is a flexible load that can be interrupted. Energy consumption for public services: The electricity, heat and cooling loads of public facilities such as village committees, health clinics and schools are mainly rigid loads.

[0023] Based on door-to-door surveys and statistical data, an hourly load curve of 8760 hours throughout the year was generated to distinguish the adjustable potential and response constraints of rigid and flexible loads.

[0024] Mathematical constraints are added for the two types of flexible agricultural loads. The total daily electricity consumption of the shiftable irrigation load is limited to a constant, and the adjustable window is fixed at 8:00-18:00. The interruptible aquaculture temperature control load is set with a maximum daily interruption duration of 2 hours and a single interruption power not exceeding 30% of the rated temperature control load. This provides clear adjustable parameter inputs for lower-level operation optimization and completes the load modeling logic.

[0025] 1.3 Modeling of Energy Supply Equipment The core equipment units of the rural multi-energy coupling micro-power system include: distributed photovoltaic modules, small wind turbines, biomass gasification cogeneration units, electric boilers (electric to heat conversion), air source heat pumps, lithium battery energy storage devices, water thermal storage / phase change thermal storage devices, biomass gas storage tanks, and grid connection interface devices.

[0026] Establish a techno-economic model for each type of equipment: Output / efficiency model: describes the relationship between the input energy and output energy of a device, such as photovoltaic photoelectric conversion efficiency, biomass gasification efficiency, cogeneration heat-to-power ratio, heat pump COP, etc. Cost model: includes initial investment cost per unit capacity, operation and maintenance cost per unit of power generation, equipment lifespan, and residual value rate at the end of the period.

[0027] The initial investment in equipment is uniformly converted into an annualized cost using a capital recovery coefficient. The formula integrates the discount rate, the equipment's useful life, and the residual value rate at the end of the period, thus amortizing the one-time construction investment to each year. The conversion result directly corresponds to the investment item of the annualized total cost (GC) over the entire life cycle in the upper-level optimization objective, realizing a one-to-one correspondence between the equipment economic model and the calculation logic of the upper-level optimization objective, and eliminating the problem of disconnect between the cost calculation before and after.

[0028] Step 2: Quantification of multi-scale multi-energy complementary characteristics From the perspectives of time and energy, the complementary potential and source-load matching degree of various energy sources within the system are quantified, providing a quantitative basis for subsequent planning. 2.1 Complementarity of Time Scales Solar power output is concentrated during the day, wind power output accounts for a high proportion at night, and biomass energy can provide stable and continuous output; these three forms a natural temporal complementarity within a day. (Definition of the daily complementarity coefficient follows.) The closer the value is to 1, the stronger the intraday complementarity: in: t: Intraday time period number, with a value range of t=1,2,…,24. The day is divided into 24 calculation periods in hours and used for intraday time series energy balance calculation; : Real-time active power generation of distributed photovoltaic units in hour t; : Real-time active power generation of the small wind power unit in hour t; : Real-time active power generation of the biomass cogeneration CHP unit in hour t; : Total comprehensive power load of the park in hour t, including the superimposed power of three types of loads: residential energy load, agricultural production energy load, and public service energy load.

[0029] The daily complementarity coefficient of this invention The range of values ​​is ,when When the temperature approaches 1, the total supply-demand imbalance throughout the day is extremely small, and the multi-source (wind, solar, and biomass) exhibits excellent intraday temporal complementarity. When the electricity level approaches zero, there is a severe mismatch between power supply and demand throughout the day, and the effect of multi-source complementarity is extremely poor.

[0030] 2.2 Complementarity of Seasonal Time Scales Summer brings strong solar radiation and high photovoltaic power output, corresponding to peak electricity loads for agricultural irrigation and cooling. Winter brings high wind speeds and high wind power output, and abundant straw resources and strong biomass supply capacity after the autumn harvest, corresponding to peak heating loads. A seasonal matching coefficient is defined. This measures the degree of matching between energy supply and load demand on a seasonal scale. In the formula, For the total renewable energy output in season s, This represents the total energy demand for the s-th season.

[0031] 2.3 Complementarity in the Energy Dimension Electricity, heat, and gas are three energy sources that can be converted and substituted for each other through coupling devices. For example, electricity can be converted into heat energy through electric boilers and heat pumps, while biomass gas can be used to generate electricity or provide direct heat. An energy complementarity coefficient is defined. This refers to the proportion of the load that the multi-energy conversion satisfies to the total load, reflecting the flexible adjustment capability of multi-energy flow coupling.

[0032] Combining the above three coefficients, we obtain the overall system complementarity: in This is a weighting coefficient, which can be adjusted according to regional characteristics.

[0033] Step 3: Construct a two-layer collaborative planning optimization model A two-layer collaborative architecture of "upper-layer capacity planning - lower-layer operation optimization" is constructed to achieve closed-loop iteration of capacity configuration and operation strategies, and avoid the disconnect between planning and operation.

[0034] 3.1 Upper layer: Capacity configuration planning layer The upper level aims to optimize the overall benefits of the system throughout its entire life cycle and optimizes the rated capacity of each energy supply device. (1) Decision variables These correspond to: rated capacity of photovoltaic power, rated capacity of wind turbines, installed capacity of biomass cogeneration, rated power of electric energy storage, rated capacity of electric energy storage, rated power of thermal energy storage, rated capacity of thermal energy storage, volume of gas storage tanks, capacity of electric boilers, and capacity of heat pumps.

[0035] (2) The optimization objective adopts multi-objective optimization, taking into account economy, environmental protection and reliability: Objective 1: Lowest total lifecycle cost in: The initial investment annualized cost is converted into an annuity based on the equipment's lifespan using a capital recovery factor. Annual maintenance costs are positively correlated with the annual output of each piece of equipment. Annual biomass collection, transportation, and processing costs; : The net value of annual electricity purchase cost minus annual electricity sales revenue; Annual energy shortage penalty costs reflect the loss of energy supply reliability; Annual carbon emission cost is calculated based on the carbon emissions from purchased electricity and fossil fuel consumption.

[0036] Objective 2: Highest renewable energy integration rate In the formula, The total energy of curtailed solar, wind, and biomass gas throughout the year. This represents the theoretical total electricity generation and heat production of renewable energy throughout the year.

[0037] Objective 3: Highest energy supply reliability In the formula, The total energy required for power and heat shortages throughout the year. This represents the total energy demand for the entire year.

[0038] (3) Constraints ① Resource constraints Annual biomass consumption ≤ Total annual collectable biomass in the region; Biomass consumption in each season is less than or equal to the corresponding season's collectable biomass mass. Photovoltaic installation capacity ≤ available installation area of ​​the region × installed capacity per unit area; The installed capacity of the fan is less than or equal to the number of fan locations that can be installed in the area × the capacity of a single unit.

[0039] ② Power balance constraints Power balance: Thermal power balance: Gas power balance: ③ Equipment operating constraints Upper and lower limits of output for each piece of equipment: , The rated capacity of the equipment; Energy storage constraints: State of charge (SOC) upper and lower limits constraints, charge and discharge efficiency constraints, and depth of charge / discharge constraints. Climbing constraints: Limitations on the output ramping rate of equipment such as biomass generator sets and boilers.

[0040] ④ Distribution network interaction constraints Maximum power limit for grid connection points: ; Grid connection point voltage deviation constraint: The voltage deviation shall be within ±7% of the rated voltage.

[0041] ⑤ Biomass collection constraints: The biomass mass obtainable within the collection radius R is positively correlated with distance, and the collection cost increases with the increase of radius, thus satisfying the maximum collection radius constraint.

[0042] 3.2 Lower Layer: Execution Strategy Optimization Layer For the capacity configuration scheme output by the upper layer, typical scenarios throughout the year (typical days in spring, summer, autumn and winter, extreme low temperature / extreme high temperature days, and continuous cloudy and rainy days) are selected for hourly operation optimization to verify the operation performance of the capacity scheme and to feed back the operation indicators to the upper layer. (1) The optimization objective is to minimize the operating cost and the energy curtailment rate on typical days: (2) Decision variables: hourly output of each device, hourly charging and discharging power of energy storage, hourly adjustment of flexible load, and hourly interaction power with the power grid.

[0043] (3) The constraints are consistent with the power balance constraints and equipment operation constraints of the upper layer, with the addition of flexible load adjustment constraints: Sliding load: The total daily electricity consumption remains unchanged, and it can be shifted within an adjustable time period; Interruptible load: The maximum daily interruption duration and total interruption amount do not exceed the limit.

[0044] 3.3 Two-layer iteration mechanism The daily operating cost, energy curtailment rate, and energy shortage rate obtained from the lower-level operation optimization are converted into annual indicators and fed back to the upper level to correct the calculated values ​​of the upper-level objective function. This process is iterated repeatedly until the relative deviation of the total lifecycle cost between two consecutive capacity configurations is less than 1%, at which point convergence is determined, and the current capacity configuration and corresponding operating strategy are output.

[0045] Step 4: Solving the multi-objective model and deciding on the optimal solution 4.1 Multi-objective intelligent algorithm solution An improved NSGA-III algorithm is used to solve the above-mentioned bi-level multi-objective programming model. An adaptive crossover mutation probability and an elite retention strategy are introduced to improve the solution efficiency and uniformity of solution set distribution in high-dimensional decision space, and finally obtain the Pareto optimal solution set.

[0046] 4.2 Decision on the Overall Optimal Solution Entropy weight-TOPSIS method for decision-making: Entropy weight method: Based on the dispersion of each objective value in the Pareto solution set, the weights of the three optimization objectives are objectively calculated to avoid the bias of subjective weighting; TOPSIS method: Calculate the distance between each Pareto solution and the positive ideal solution and the negative ideal solution to obtain the proximity score, and select the solution with the largest proximity score as the comprehensive optimal planning scheme.

[0047] Step 5: Timing Simulation Verification and Scheme Output For the selected optimal solution, input hourly resource data and load data for 8760 hours throughout the year, and perform a full-year time-series simulation to verify the following core indicators: Economic indicators: total life cycle cost, unit energy cost, static investment payback period; Environmental indicators: renewable energy integration rate, annual carbon emission reduction, and amount of coal replacement; Reliability indicators: load failure rate, voltage qualification rate, and continuous power supply duration.

[0048] After successful verification, the final system configuration scheme, typical daily operation strategy, and indicator analysis report will be output.

[0049] Example 1: Grid-connected administrative villages in the main grain-producing areas of the North China Plain This embodiment fully implements all the steps of the collaborative planning and configuration method of the present invention, and is a standard application scenario in a typical agricultural area of ​​North China.

[0050] 1.1 Step 1: Basic Data Collection and Multidimensional Modeling (1) Resource data modeling The target village is located in Dezhou City, Shandong Province, which has a temperate monsoon climate. Hourly meteorological data for the past 10 years were collected: annual average total surface irradiance is 1382 kWh / m². 2 The average annual wind speed is 3.42 m / s (at a height of 10 m). The photovoltaic output model considers temperature degradation; at nominal efficiency of 25℃, the photovoltaic efficiency decreases by approximately 8.2% during the highest midday temperature in summer. The wind turbine cut-in wind speed is 3 m / s, rated wind speed is 12 m / s, and cut-out wind speed is 25 m / s. Biomass resource survey: 680 tons of wheat straw and 635 tons of corn straw can be collected annually, totaling 1315 tons of straw. Pig farms produce 920 tons of fresh manure annually, of which 210 tons can be used as gasification feedstock after drying. A biomass collection cost model is established, with a maximum collection radius of 3 km and a unit mass collection cost of 85 yuan / t. The straw storage mold loss rate is estimated at 5%. Seasonal yield distribution: Autumn accounts for 42%, winter 28%, spring 18%, and summer 12%.

[0051] (2) Load data modeling Based on household surveys and agricultural production records, an hourly load of 8760 hours was generated throughout the year, with a total energy consumption equivalent to approximately 2.56 million kWh / year. This was categorized into three types, distinguishing between rigid and flexible energy consumption: Residential energy consumption: Annual electricity consumption is 1.286 million kWh; the winter heating season is 120 days with a total heating load of 3240 GJ; the summer cooling season is 90 days with a total cooling load of 912 GJ; of which the adjustable heating load accounts for 20% and the adjustable cooling load accounts for 15%.

[0052] Energy consumption for agricultural production: Irrigation annual electricity consumption is 458,000 kWh (can be shifted, adjustable time period is 8:00-18:00); Livestock temperature control annual electricity consumption is 523,000 kWh (can be interrupted, maximum daily interruption time is 2 hours); Grain drying annual electricity consumption is 182,000 kWh.

[0053] Energy consumption for public services: annual electricity consumption of 124,000 kWh, heating / cooling load of 308 GJ, all of which are rigid loads.

[0054] (3) Equipment technical and economic modeling Eight core equipment types were selected, including distributed photovoltaic (PV), small wind turbines, biomass gasification CHP, electric energy storage, thermal energy storage, gas storage tanks, air source heat pumps, and electric boilers. Output efficiency models and life cycle cost models were established for each type. The core parameters include: PV unit investment of 4200 yuan / kW and life cycle of 25 years; biomass CHP gasification efficiency of 72%, heat-to-power ratio of 1.3, unit investment of 8500 yuan / kW and life cycle of 15 years; and lithium battery energy storage unit investment of 1200 yuan / kWh, charge-discharge efficiency of 92% and life cycle of 10 years.

[0055] 1.2 Step 2: Quantification of Multi-scale Multi-energy Complementary Characteristics Based on the above basic data, the complementary properties are calculated according to the three types of coefficients proposed in this invention: Solar-scale complementarity: Solar complementarity coefficient of typical winter days Typical summer days Annual average This demonstrates the intraday complementary effect of photovoltaic power output during the day, wind power output at night, and stable biomass power output.

[0056] Seasonal complementarity: The ratios of energy supply to load demand in spring, summer, autumn, and winter are 0.72, 1.15, 0.93, and 0.88, respectively, yielding the seasonal matching coefficient. The high output of photovoltaic power in summer is well matched with the peak season for irrigation and cooling, and the high output of biomass / wind power in winter is well matched with the peak season for heating.

[0057] Energy complementarity: The multi-energy conversion of electricity, heat, and gas can cover 68% of the heat load and 32% of the base electrical load, with an energy complementarity coefficient of [missing value]. Take weights The overall complementarity of the system was calculated. It possesses excellent potential for multi-energy complementarity, which serves as a guiding principle for pre-planning optimization.

[0058] 1.3 Step 3: Construction of the Two-Level Collaborative Programming Optimization Model (1) Upper-level capacity configuration planning The decision variables are a 10-dimensional capacity vector: rated parameters of photovoltaic, wind turbine, CHP power generation / heating, electric energy storage power / capacity, thermal energy storage power / capacity, gas storage tank volume, heat pump, and electric boiler; the optimization objectives are the lowest life cycle cost, the highest renewable energy absorption rate, and the highest energy supply reliability; the constraints include resource constraints, three types of power balance constraints, equipment operation constraints, distribution network interaction constraints, and biomass collection constraints.

[0059] (2) Optimization of lower-level operation strategy Five typical scenarios were selected: typical days for winter heating, typical days for summer irrigation, typical days for the spring and autumn transition season, extreme low temperature days, and consecutive rainy days. The annual indicators were calculated by weighting the number of days in each season. The lower level optimized hourly equipment output, energy storage charging and discharging, flexible load response, and grid interaction power with the goal of minimizing daily operating costs and energy curtailment rate. Adjustment constraints for loads that can be shifted and interruptible were added.

[0060] (3) Two-layer iteration mechanism The lower layer converts typical daily operating costs, energy curtailment rate, and energy shortage rate into annual indicators, and feeds back to correct the calculated values ​​of the upper layer's objective function. After 12 iterations, the relative deviation between the two full life cycle costs is 0.87%, which is less than the convergence threshold of 1%, and the iteration terminates.

[0061] Step 4: Multi-objective solution and optimal solution decision An improved NSGA-III algorithm was used to solve the problem, with a population size of 60 and 100 iterations. Adaptive crossover and mutation probabilities were introduced, resulting in 28 Pareto optimal solutions. The entropy-weighted TOPSIS method was employed for decision-making: based on solution set constraints, three objective weights were calculated—life-cycle cost weight (0.42), renewable energy integration rate weight (0.35), and energy supply reliability weight (0.23). The closest approximation was calculated using TOPSIS, and the solution with the highest approximation value of 0.863 was selected as the overall optimal solution.

[0062] Step 5 of 1.5: Timing Simulation Verification and Final Configuration The optimal solution underwent hourly simulation verification for 8760 hours throughout the year. The deviation between the planned and simulated indicators was 0.92%, meeting the accuracy requirements. The final configuration and core performance are as follows: Key performance indicators: annualized cost over the entire life cycle is RMB 817,000 / year, unit energy supply cost is RMB 0.378 / kWh; renewable energy consumption rate is 96.2%; energy supply reliability is 99.92%; annual replacement of 1210t of scattered coal and annual reduction of CO2 emissions of 3130t; static investment payback period is 7.7 years.

[0063] Example 2: Administrative villages in the hilly areas of southern China that combine farming and animal husbandry This embodiment is designed to address the characteristics of high cooling load and large fluctuations in photovoltaic power during the rainy season in the double-cropping rice production areas of southern China, by adapting and adjusting the complementary quantization weights and flexible load model of the method of this invention.

[0064] 2.1 Basic Modeling and Complementary Quantization Adaptation The target village is located in Hengyang City, Hunan Province, with a subtropical monsoon climate and an annual radiation of 1295 kWh / m². 2The average annual wind speed is 2.6 m / s; the annual harvestable biomass yields 920 tons of rice straw, 180 tons of camellia fruit shells, and 160 tons of dry livestock and poultry manure. Biomass production is concentrated in the summer and autumn harvest seasons. Load modeling adds aquaculture aeration and temperature control loads; interruptible flexible loads account for 42% of the production load; the cooling period is 120 days, with a higher proportion of cooling loads than in northern regions. Multi-scale complementarity quantification: affected by the rainy season, the daily complementarity coefficient averages [missing value]. The matching degree between double-cropping rice irrigation and summer photovoltaic power output is high, with a high seasonal matching coefficient. Energy complementarity coefficient Adjust the weights to Comprehensive complementarity .

[0065] 2.2 Bilevel Programming and Solution The lower-level typical scenario adds a continuous rainy season scenario; the upper-level constraints tighten the upper limit of summer biomass supply. After 11 iterations, the convergence was achieved with a deviation of 0.82%. In the entropy-weighted TOPSIS decision, due to the high reliability requirements of power supply during the rainy season, the power supply reliability weight is automatically increased to 0.27. The final optimal configuration is as follows: 2.3 Performance Indicators The annualized cost over the entire life cycle is 702,000 yuan / year, and the unit energy supply cost is 0.369 yuan / kWh; the renewable energy consumption rate is 95.7%; the energy supply reliability is 99.89%; it replaces 960 tons of scattered coal annually and reduces CO2 emissions by 22,480 tons annually; the static investment payback period is 7.5 years.

[0066] Example 3: Semi-agricultural and semi-pastoral administrative villages in the Northwest pastoral area This embodiment is designed for scenarios with severe cold, long heating seasons, and weak power grid support, and focuses on enhancing the reliability target and energy storage complementarity of the method of the present invention.

[0067] 3.1 Basic Modeling and Complementary Quantization Adaptation The target village is located in Xilingol League, Inner Mongolia, with a mid-latitude continental climate and an annual radiation of 1620 kWh / m². 2 The average annual wind speed is 5.1 m / s; it can collect 1450 tons of forage straw and 480 tons of dry cattle and sheep manure annually, with a biomass collection radius of 5 km; the heating season is 180 days, and heating load accounts for 62% of total energy consumption. Load modeling adds flexible load for pen temperature control, with an adjustable ratio of 30%; the distribution network is weakly connected, with a maximum power purchase limit of 150 kW, and high priority given to power supply reliability. Multi-scale complementarity quantification: photovoltaic and wind power have significant intraday peak-shaving, and the daily complementarity coefficient is [not specified]. High wind power and biomass output in winter are matched with peak heating demand, resulting in a seasonal matching coefficient. Energy complementarity coefficient Overall complementarity This is the highest among the four types of scenarios.

[0068] 3.2 Bilevel Programming and Solution The upper-level constraints include a new hard constraint on the upper limit of electricity purchase at the grid connection point; the lower-level constraints include a new extreme cold wave scenario, with the load loss penalty coefficient increased to three times the normal value. After 14 iterations, convergence was achieved with a deviation of 0.91%. In the entropy-weighted TOPSIS decision-making process, the objective weight of power supply reliability was increased to 0.31. The final optimal configuration is as follows: 3.3 Performance Indicators The annualized cost over the entire life cycle is RMB 685,000, with a unit energy supply cost of RMB 0.395 / kWh; the renewable energy consumption rate is 97.1%; the energy supply reliability is 99.85%; it replaces 1,580 tons of scattered coal annually and reduces CO2 emissions by 4,090 tons annually; the static investment payback period is 8.1 years.

[0069] Example 4: Off-grid administrative villages in remote mountainous areas of Southwest China This embodiment is designed for off-grid scenarios without large power grid coverage. The constraints and convergence criteria of the method of this invention are adapted and adjusted to ensure power supply independence.

[0070] 4.1 Basic Modeling and Complementary Quantization Adaptation The target village is located in the mountainous area of ​​Nujiang Prefecture, Yunnan Province, without access to a major power grid. The village has 118 households and 426 people; the annual irradiance is 1420 kWh / m². 2 The average annual wind speed is 2.2 m / s; the annual collectable crop straw and livestock manure dry material totals 680 tons, with a collection radius of 2 km. The load is mainly for residential electricity consumption, tea and walnut processing, and irrigation, all powered off-grid. Distribution network interaction constraints have been eliminated, but a new constraint on continuous off-grid power supply duration has been added. Multi-scale complementarity quantification: Wind power resources are scarce, and the daily complementarity coefficient... Seasonal matching coefficient Energy complementarity coefficient Overall complementarity .

[0071] 4.2 Bilevel Programming and Solution The upper layer removes the electricity sales revenue item and increases the energy shortage penalty coefficient to 5 times that of the grid-connected scenario; the lower layer extends the duration of the continuous rainy scenario to 7 days, and tightens the convergence criterion to a cost deviation of less than 0.5%. After 16 iterations, the deviation is 0.46%. In the entropy-weighted TOPSIS decision, the energy supply reliability weight reaches 0.38, the highest among all embodiments. The final optimal configuration is as follows: 4.3 Performance Indicators The annualized cost over the entire life cycle is RMB 394,000, with a unit energy supply cost of RMB 0.458 / kWh (far lower than the RMB 1.2 / kWh of traditional diesel power generation); the renewable energy consumption rate is 94.8%; the energy supply reliability is 99.72%, meeting the power supply standards for off-grid areas; it can replace 125 tons of diesel fuel annually and reduce CO2 emissions by 340 tons annually; the static investment payback period is 6.9 years.

[0072] Comparative Example The following comparative examples are all based on the basic data of administrative villages in the North China Plain in Example 1, and correspond to the three typical planning methods of the prior art, respectively, to compare and verify the technical gains of each core innovation point of the present invention.

[0073] Comparative Example 1: Single-energy power supply planning scheme (without multi-energy complementary architecture) The current mainstream rural "distributed photovoltaic + power grid + electric boiler heating" scheme does not apply the multi-scale complementary quantification method of this invention, does not construct a multi-energy coupling system, and only configures photovoltaics and electric boilers. It uses a single energy type, without wind power or biomass energy participation, and lacks energy storage and multi-energy conversion equipment. Planning method: Photovoltaic capacity is estimated based on annual electricity consumption and annual photovoltaic utilization hours; electric boilers are configured based on maximum heat load; and the single objective is to minimize annual electricity purchase cost.

[0074] Configuration results Performance indicators The annualized cost over the entire life cycle is RMB 1.126 million, with a unit energy supply cost of RMB 0.521 / kWh; the renewable energy consumption rate is 72.3%, with surplus photovoltaic power at midday unable to be consumed locally, resulting in an annual curtailment rate of 27.7%; the energy supply reliability is 99.5%, completely dependent on the main power grid, with heating and production loads completely interrupted when the grid is shut down; it replaces 420 tons of scattered coal annually, reducing CO2 emissions by 1090 tons annually; the static investment payback period is 12.4 years. Differences from this invention: Due to the lack of a multi-energy complementary architecture and quantitative design of complementarity, the energy utilization form is singular, resulting in a low consumption rate, high energy supply cost, and weak carbon emission reduction effect, with overall benefits significantly lower than the solution of this invention.

[0075] Comparative Example 2: Single-layer static capacity planning method (without bi-layer collaborative iteration) The conventional multi-energy system planning method is adopted, and the multi-energy system architecture is retained, but the two-layer collaborative iteration mechanism of this invention is not applied. Only the upper-layer capacity static optimization is carried out, and a fixed heuristic scheduling strategy of "photovoltaic priority, wind power second, and biomass as a backup" is adopted. No lower-layer hourly operation optimization and feedback correction are performed.

[0076] Planning process The objective function is calculated directly using the theoretical annual power generation and annual heat production, without considering intraday fluctuations and operational constraints, and without incorporating flexible load regulation capabilities, and the capacity configuration result is output in one go.

[0077] Configuration results Performance indicators The annualized cost calculated during the planning phase was 792,000 yuan / year; the actual annualized operating cost was 935,000 yuan / year, with a deviation of 18.1% between planning and operation; the renewable energy absorption rate was 88.4%, the curtailment rate was 11.6%, and insufficient energy storage capacity resulted in a large amount of surplus energy that could not be absorbed; the energy supply reliability was 99.61%, and the load failure rate was 0.39%; the static investment payback period was 9.8 years. Difference from this invention: Due to the lack of lower-level operational optimization and two-layer iterative feedback, the planning results deviated significantly from actual operating conditions, resulting in the problem of "optimal on paper, ineffective in practice," thus verifying the core value of the two-layer collaborative architecture of this invention.

[0078] Comparative Example 3: General Industrial Park Multi-functional Planning Method (without rural scenario adaptation modeling) The general model for multi-energy planning of urban industrial parks is applied, but the multi-dimensional modeling method of this invention for rural scenarios is not used: the seasonal distribution of biomass production and collection radius constraints are not considered, and the model is based on uniform supply throughout the year; the flexible load of agriculture is not distinguished, and the model is based on the peak and valley characteristics of industrial load; and rural-specific parameters such as straw loss and collection costs are not included.

[0079] Planning process Using a general industrial load curve, biomass supply has no seasonal upper limit or collection radius restriction, and planning is carried out based on the general constraints of the industrial park.

[0080] Existing problems and performance indicators The planned biomass cogeneration capacity is 300kW / 390kW, with an annual biomass demand of 1850t, exceeding the region's annual collectable capacity by 41%. In actual operation, fuel supply is severely insufficient, requiring additional coal purchases. During peak summer irrigation periods, biomass feedstock supply is inadequate, resulting in a significant power shortage. The renewable energy absorption rate is 85.7%, and the power supply reliability is 99.35%, with severe load shedding during peak agricultural production periods. Actual operating costs deviate from the planned values ​​by more than 25%, rendering the plan infeasible for engineering implementation. Difference from this invention: Due to the lack of refined modeling adapted to rural scenarios, the planning results are severely out of sync with rural resources and load characteristics, making practical application impossible. This verifies the core innovative significance of the invention's scenario adaptability.

[0081] Comprehensive comparative analysis The core technical features and performance indicators of Example 1 and the three comparative examples are summarized as follows: As can be seen from the comparison, this invention solves the three core defects of existing technologies—poor scenario adaptation, insufficient complementary mining, and disconnect between planning and operation—through a complete technical chain of "rural adaptation modeling, multi-scale complementary quantification, two-layer collaborative planning, and multi-objective scientific decision-making." It achieves significant improvements in four dimensions: economy, environmental protection, reliability, and feasibility, and has outstanding substantive features and significant technological progress.

[0082] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computers or available storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0083] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0084] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0085] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0086] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for coordinated operation and regulation of multi-energy coupled micro-energy sources in rural areas, characterized in that, Includes the following steps: S1. Collect various renewable energy output parameters, multi-category energy load parameters, and multi-energy coupling equipment operation parameters in the target area, and construct a renewable energy dynamic output model, various load time-series identification models, and multi-energy equipment operation benefit models respectively. The renewable energy dynamic output model quantifies the time-series fluctuations of rural renewable energy and the dynamic characteristics of seasonal supply, storage, transportation and loss of biomass. The various load time-series identification models distinguish in real time the rigid demand and flexible adjustment potential of rural living, agricultural production and public service loads. The multi-energy equipment operation benefit model adapts to the real-time operation characteristics of multi-energy flow coupling conversion. S2. Based on the dynamic modeling results of step S1, calculate the intraday dynamic complementarity coefficient, seasonal source-load matching coefficient, and multi-energy complementarity coefficient in real time from three dimensions: intraday time series, seasonal time series, and multi-energy flow coupling. Couple the results to obtain the real-time comprehensive complementarity of the system. Use the dynamic quantitative result of multi-energy complementarity as a prerequisite constraint for system operation and regulation. S3. Construct a two-layer collaborative operation and regulation optimization model, and build a two-layer closed-loop regulation architecture consisting of an upper multi-energy collaborative regulation decision layer and a lower real-time operation optimization layer. The upper layer takes the optimization of multiple objectives such as real-time operation economy, environmental protection and energy supply reliability as the control objectives, and dynamically outputs macro-control instructions for multi-energy equipment output, energy storage charging and discharging and load regulation; the lower layer, based on the control instructions of the upper layer, carries out hourly refined operation optimization in combination with multiple typical operation scenarios, feeds back real-time operation deviation indicators and corrects the upper layer control constraints, and outputs the optimal real-time operation control strategy after iterative convergence. S4. The improved NSGA-III algorithm is used to solve the dual-layer collaborative operation control optimization model to obtain the multi-objective optimal control solution set. The comprehensive optimal collaborative operation control strategy that is suitable for the current working condition is selected by the entropy weight-TOPSIS objective decision-making method. S5. Conduct hourly time-series dynamic simulation verification of the optimal collaborative operation control strategy throughout the year to verify the system's operational stability, energy consumption level, and energy supply reliability under different operating conditions. After the verification is passed, output the normalized collaborative operation control scheme, the emergency control strategy for extreme operating conditions, and the system operation index analysis report.

2. The method for coordinated operation and regulation of multi-energy coupled micro-energy sources in rural areas according to claim 1, characterized in that, The dynamic output modeling of renewable energy in step S1 specifically includes: Real-time collection of hourly solar irradiance and ambient temperature data in the target area; construction of a dynamic photovoltaic power output model including temperature efficiency decay correction; real-time correction of photovoltaic module power output efficiency deviation; real-time collection of hourly wind speed and wind direction data; construction of a dynamic wind turbine power output model based on wind turbine cut-in, rated, and cut-out wind speed thresholds; adapting to the random fluctuation characteristics of wind power. The dynamic survey of the seasonal and time-specific output and real-time collectable amount of straw and livestock manure biomass resources in the region, combined with a maximum collection radius of 3-5km and a dynamic loss coefficient of 5% due to mold, constructs a dynamic supply model for biomass resources. Simultaneously, it establishes dynamic constraints on the annual total amount of biomass, seasonal and time-specific supply constraints, and dynamic cost constraints on biomass collection and storage, and quantifies the effective supply boundary of rural biomass energy in real time.

3. The method for coordinated operation and regulation of multi-energy coupled micro-energy sources in rural areas according to claim 1, characterized in that, Step S1, which involves the time-series identification and modeling of various loads, specifically includes: The real-time energy load of the target area is divided into three categories: residential energy consumption, agricultural production energy consumption, and public service energy consumption. Each category is further subdivided into real-time electricity, heat, and cooling loads. Based on the measured data from households and the time sequence of agricultural production, a dynamic load curve for each hour throughout the year is generated. Real-time definition of the dynamic adjustment attributes of various loads: domestic heating and cooling loads are dynamically adjustable flexible loads, agricultural irrigation loads are shiftable flexible loads with adjustable time periods from 8:00 to 18:00, aquaculture temperature control loads are flexible loads that can be interrupted for a short time, and public service loads are non-adjustable rigid loads. Set dynamic quantitative control constraints for flexible agricultural loads: the total daily electricity consumption of the shiftable irrigation load remains constant and is shifted only within the preset adjustable time period; the maximum daily interruption duration of the interruptable aquaculture temperature control load does not exceed 2 hours and the power of a single interruption does not exceed 30% of the rated temperature control load.

4. The method for coordinated operation and regulation of multi-energy coupled micro-energy sources in rural areas according to claim 1, characterized in that, Step S1, the modeling of the operational benefits of multi-energy equipment, specifically includes: Construct a real-time operating efficiency model for distributed photovoltaic, small wind turbine, biomass gasification cogeneration unit, electric boiler, air source heat pump, electric energy storage, thermal energy storage, biomass gas storage tank, and grid connection point interaction device, and match the photovoltaic temperature decay efficiency, biomass gasification real-time efficiency, cogeneration dynamic heat-to-power ratio, heat pump real-time COP, and energy storage charging and discharging real-time efficiency parameters in real time. Establish a full life cycle operating cost model for equipment, and integrate discount rate, equipment lifespan, and residual value rate through capital recovery coefficient to dynamically convert equipment maintenance losses, operating energy consumption, and equipment depreciation costs into real-time operating costs, matching the economic accounting logic of upper-level multi-objective control.

5. The method for coordinated operation and regulation of multi-energy coupled micro-energy sources in rural areas according to claim 1, characterized in that, The specific results of multi-energy complementarity dynamic quantification in step S2 include: Based on the intraday temporal complementary characteristics of photovoltaic daytime power output, wind power nighttime power output, and biomass continuous and stable power output, the intraday complementary coefficient is calculated in real time to quantify the hourly multi-energy power output complementarity. Based on the seasonal temporal characteristics of matching high photovoltaic output with peak agricultural irrigation and cooling loads in summer and high wind power and biomass supply with peak heating loads in winter, the seasonal matching coefficient is calculated to quantify the degree of dynamic matching between source and load on a seasonal scale. Based on the operating characteristics of multi-energy flow coupling conversion and mutual substitution, including electricity, heat, and gas, the energy complementarity coefficient is calculated to quantify the real-time energy conversion and load substitution capabilities of multi-energy coupling equipment. By coupling intraday complementarity coefficient, seasonal matching coefficient, and energy complementarity coefficient with adaptive weighting coefficient, the real-time comprehensive complementarity of the system is obtained. The weighting coefficient is dynamically adjusted according to the resource endowment of rural areas and real-time load conditions.

6. The method for coordinated operation and regulation of multi-energy coupled micro-energy sources in rural areas according to claim 1, characterized in that, The multi-objective control objectives of the upper-level multi-energy collaborative control decision layer in step S3 include: Objective 1: To minimize the annualized total cost of real-time, full lifecycle operation of the system, including real-time equipment maintenance costs, real-time biomass collection, storage and transportation costs, real-time grid interaction net costs, real-time energy shortage penalty costs, and real-time carbon emission costs; Objective 2: To achieve the highest real-time renewable energy consumption rate in the system, a consumption rate calculation model is constructed based on the ratio of real-time energy loss due to hourly curtailment of solar, wind, and biomass gas to the total real-time theoretical energy output of renewable energy. Objective 3: To achieve the highest real-time energy supply reliability of the system, an energy supply reliability calculation model is constructed based on the ratio of the total real-time energy shortage to the total real-time energy demand of the region.

7. The method for coordinated operation and regulation of multi-energy coupled micro-energy sources in rural areas according to claim 1, characterized in that, The lower-level real-time optimization layer in step S3 specifically includes: The system includes pre-defined typical operating scenarios for spring, summer, autumn and winter, as well as extreme high and low temperatures and continuous rainy days. In grid-connected scenarios, a new operating scenario for distribution network power fluctuations has been added, and in off-grid scenarios, a new extreme operating scenario without grid backup has been added. With the optimization goals of minimizing daily real-time operating costs and minimizing hourly energy curtailment rate in typical scenarios, we refine the hourly real-time output of each device, the hourly charging and discharging power of energy storage, the hourly dynamic adjustment of flexible loads, and the hourly interactive power of the power grid. The superimposed dynamic control constraints for flexible loads ensure that the total daily electricity consumption of loads that can be shifted remains constant, and that the adjustment range and duration of interruptible loads do not exceed safety limits.

8. The method for coordinated operation and regulation of multi-energy coupled micro-energy sources in rural areas according to claim 1, characterized in that, Step S4 specifically includes: An improved NSGA-III algorithm incorporating adaptive crossover mutation probability and elite retention strategy is used to solve the high-dimensional multi-objective two-layer cooperative operation and control model, obtaining a uniformly distributed multi-objective Pareto optimal control solution set; By using the entropy weight method based on the dispersion of each control target value in the Pareto solution set, the weights of the three major control targets of operation economy, environmental protection and energy supply reliability are objectively calculated, thus avoiding subjective weighting bias. The proximity between each Pareto control solution and the positive and negative ideal solutions is calculated using the TOPSIS method. The control scheme corresponding to the maximum proximity is selected as the comprehensive optimal coordinated operation control strategy for the current operating condition.

9. The method for coordinated operation and regulation of multi-energy coupled micro-energy sources in rural areas according to claim 1, characterized in that, The timing dynamic simulation verification indicators in step S5 include: Operational economic indicators: annualized operating cost over the entire system lifecycle, unit energy supply operating cost, and average annual maintenance loss cost; Environmental performance indicators: real-time renewable energy consumption rate, annual dynamic carbon emission reduction, and dynamic replacement of bulk coal / diesel; Operational reliability indicators: real-time system load failure rate, real-time grid voltage qualification rate, and continuous stable operation time of the multi-energy system; If the deviation between the simulation operation index and the control target index is less than 1%, the control strategy is deemed to have passed the verification and is solidified as a normalized operation strategy; if the deviation exceeds the standard, return to step S3 to correct the control constraint parameters and iterate and optimize the control strategy again.

10. The method for coordinated operation and regulation of multi-energy coupled micro-energy sources in rural areas according to claim 1, characterized in that, Dynamic simulation verification includes: In the scenario of grid-connected administrative villages: retain the two-way dynamic interaction constraints of the distribution network, weaken the weight of energy shortage penalty, prioritize the core control objectives of operation economy and efficient consumption of renewable energy, and dynamically balance grid interaction and local multi-energy operation; Off-grid administrative village scenario: Remove distribution network interaction constraints, significantly increase the weight of energy shortage penalty, extend the simulation control time for extreme rainy and low temperature conditions, and prioritize ensuring the stability of continuous power supply and operational reliability of the system.

Images