Rule-driven rural multi-energy complementary distributed energy technology coupling optimization method

Abstract

The invention discloses a rule-driven rural multi-energy complementary distributed energy technology coupling optimization method, which comprises the following steps of performing quantitative evaluation on rural localized primary energy endowment and terminal multi-element energy demand characteristics, and generating standardized resource endowment data and energy demand data; constructing a rule engine based on a forward chain reasoning generative system and a hierarchical rule, inputting resource endowment data and energy consumption demand data into the rule engine, and generating a multi-energy complementary preliminary technology coupling scheme; and verifying the feasibility of the preliminary technical coupling scheme, and outputting a final technical coupling scheme and an analysis result. The method has the beneficial effects that in a rural energy system planning stage, based on rural polymorphic resource endowment and energy consumption requirements, a coupling suggestion scheme of a multi-category distributed energy technology is output through an intelligent rule engine, automatic generation of a technology coupling scheme is realized, the planning efficiency is remarkably improved, and carbon emission and energy consumption cost are reduced.

Description

Technical Field

[0001] This invention relates to the field of distributed energy system planning, and more particularly to a rule-driven method for coupling and optimizing multi-energy complementary distributed energy technologies in rural areas. Background Technology

[0002] Existing rural energy planning has long relied on an experience-driven design paradigm, which has revealed systemic flaws in practical applications.

[0003] First, current planning relies excessively on fixed combinations such as "photovoltaics + batteries" or "wind power + diesel engines," neglecting the spatiotemporal fluctuations in rural resource endowments. For example, when photovoltaic systems are mechanically applied in areas rich in solar energy resources, biogas complementary systems are not designed in conjunction with local biomass resources, resulting in low energy conversion efficiency. Agricultural photovoltaic projects commonly suffer from "abandoned farming due to sunlight," such as photovoltaic panels blocking sunlight leading to reduced crop yields, decreased farmer willingness to plant, and ultimately resulting in idle land resources and a surge in photovoltaic curtailment rates. More seriously, a single technological approach is insufficient to cope with resource fluctuations, especially in wind power-dependent areas where high wind curtailment rates are due to a lack of energy storage infrastructure, while geothermal resource areas lack coupled heat storage and cooling systems, resulting in insufficient utilization of geothermal energy during the non-heating season.

[0004] Secondly, existing planning models overemphasize economic indicators while neglecting environmental benefits and resource recycling needs. A typical contradiction lies in the biomass treatment process, where large amounts of straw and livestock manure are not incorporated into the energy conversion pathway but are instead directly burned in the open or landfilled. Rural straw burning not only wastes resources but also causes PM2.5 emissions to exceed standards. Simultaneously, carbon reduction targets are out of touch with rural realities; planning schemes rarely use carbon emissions per unit of energy as a hard constraint, resulting in rural coal consumption still accounting for a large proportion and pollutant emissions exceeding those in cities. Furthermore, there is a significant gap in rural waste resource utilization, and the energy utilization rate of solid waste is insufficient. For example, the existing invention patent application CN117611382A discloses a method for optimizing rural integrated energy systems that considers multi-layered collaboration and demand response. While this method constructs a multi-layered collaborative framework for rural energy, it lacks an embedded intelligent decision-making rule base and cannot dynamically output a technology coupling matrix based on resource data, still relying on subjective configuration by engineers.

[0005] Furthermore, rural energy systems require coordination of multiple dynamic constraints, including resource distribution, load fluctuations, and grid connection. However, current planning relies on manual trial and error adjustments, resulting in a prolonged solution generation cycle. For example, the load matching process requires manual calculation of the electricity / heat load ratio, but it is difficult to track the peak-valley load differences between winter greenhouse heating and summer agricultural product processing in real time, often leading to equipment capacity mismatch. Patent CN120184906A focuses on hardware optimization for photovoltaic DC access, completely ignoring multi-energy complementary integration and failing to address key technology coupling interfaces such as biomass gasification and geothermal pumps, thus limiting the applicability of the solution in rural multi-resource scenarios. Summary of the Invention

[0006] To address the aforementioned problems, this invention proposes a rule-driven coupling optimization method for rural multi-energy complementary distributed energy technologies, primarily resolving the issues raised in the background technology.

[0007] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0008] A rule-driven method for coupling optimization of rural multi-energy complementary distributed energy technologies includes the following steps:

[0009] A quantitative assessment of the primary energy endowment and diverse end-use energy demand characteristics of rural areas is conducted to generate standardized resource endowment data and energy demand data.

[0010] Based on a generative system and hierarchical rule construction engine using forward chain reasoning, the resource endowment data and energy demand data are input into the rule engine to generate a preliminary multi-energy complementary technology coupling scheme.

[0011] Verify the feasibility of the preliminary technical coupling scheme, and output the final technical coupling scheme and analysis results.

[0012] In some implementations, the quantitative evaluation process of the resource endowment data includes:

[0013] The available amount of biomass resources is quantified based on preset conditions, and the available amount of biomass resources is classified into rich, average or scarce levels according to preset thresholds.

[0014] Wind energy resource levels are classified based on average wind power density;

[0015] Solar energy resources are classified based on the average annual total solar radiation and peak sunshine hours.

[0016] The exploitable capacity of hydropower resources is assessed using theoretical hydropower output, and the exploitable capacity of the hydropower resources is classified into different levels.

[0017] Geothermal resources are classified according to the temperature of the geothermal fluid.

[0018] In some implementations, the hierarchical rules include three types: resource assessment rules, technology coupling rules, and economic and environmental protection rules. When the rule engine is running, the rules are executed in the basic order of resource assessment rules, technology coupling rules, and economic and environmental protection rules. When a conflict is triggered in the basic order, the priority of economic and environmental protection rules is raised to the highest level, and the priority of technology coupling rules is raised to the lowest level. For conflicts of the same type of rules, the principle of recent use priority is adopted.

[0019] In some implementations, the resource assessment rules incorporate the resource endowment data and further include:

[0020] Calculate the resource fluctuation coefficients for various primary energy sources and label the volatility of the corresponding primary energy sources;

[0021] Assess the complementarity of wind and solar energy resources;

[0022] Calculate the synergy coefficients among various primary energy sources.

[0023] In some embodiments, the technology coupling rules include primary energy conversion / conversion technology coupling rules, secondary energy conversion technology coupling rules, and energy storage technology coupling rules, wherein,

[0024] The primary energy conversion / conversion technology coupling rules are based on the energy characteristics of the resource endowment data, and add the technologies that need to be coupled in combination with preset configuration rules;

[0025] The secondary energy conversion technology coupling rule is based on the conversion characteristics of the energy demand data, and adds the technologies that need to be coupled by combining preset configuration rules;

[0026] The energy storage technology coupling rules are based on the existing energy storage configuration of the system, and add technologies that need to be coupled by combining preset configuration rules.

[0027] In some implementations, the economic and environmental protection rules include economic constraint rules, environmental constraint rules, and comprehensive economic and environmental protection scoring rules.

[0028] In some implementations, the preliminary technology coupling scheme includes a list of energy technologies, a system topology suggestion, and economic and environmental indicators. The system topology suggestion includes: the rule engine creates a topology graph during the reasoning process, inputs the selected coupling technologies into the topology graph, and records the connection relationships between each coupling technology. When a technology is enabled, a new node is created in the topology graph, and directed edges are automatically established between the nodes according to the input and output types of the technologies, integrating them into a coherent directed graph to describe the energy flow and material flow paths of the entire multi-energy complementary system.

[0029] The beneficial effects of this invention are as follows: In the planning stage of rural energy systems, based on the multi-form resource endowment (biomass, wind energy, solar energy, hydropower, geothermal energy) and energy demand (cold, heat, electricity, gas, hydrogen) of rural areas, a smart rule engine outputs coupling suggestions for multiple types of distributed energy technologies, including energy conversion technology and energy storage technology, thereby realizing the automatic generation of technology coupling schemes, significantly improving planning efficiency, and reducing carbon emissions and energy costs. Attached Figure Description

[0030] Figure 1 This is an architecture diagram of the rule-driven rural multi-energy complementary distributed energy technology coupling optimization method disclosed in an embodiment of the present invention;

[0031] Figure 2 This is a schematic diagram of the layered structure of the rule engine disclosed in an embodiment of the present invention;

[0032] Figure 3 This is a schematic diagram of the system topology disclosed as an application example of the present invention. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of this invention clearer and more explicit, the content of this invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to this invention are shown in the accompanying drawings, not all of them.

[0034] Example

[0035] This embodiment proposes a rule-driven optimization method for multi-energy complementary distributed energy technology coupling in rural areas. The core innovation lies in constructing a hierarchical rule base and combining resource assessment, technology coupling, and economic and environmental rules to achieve automatic generation of technology coupling schemes.

[0036] like Figure 1 As shown (corresponding to step 1) Figure 1 In the resource-demand analysis layer, step 2 corresponds to Figure 1 In the rule base engine layer, step 3 corresponds to Figure 1 The solution verification layer and solution output layer in the system include the following steps:

[0037] Step 1: Quantitatively assess the local primary energy endowment and diverse end-user energy demand characteristics of rural areas to generate standardized resource endowment data and energy demand data.

[0038] The resource endowment and energy demand data acquired in Step 1 form the data foundation and core input of this method. Its function is to conduct refined collection and quantitative assessment of localized primary energy endowment and diverse end-user energy demands in rural areas, providing standardized and computable data input for the rule engine. This layer transforms the original spatiotemporal distribution data into assessment indicators with clear physical meaning and technical guidance value by establishing a multi-dimensional resource and demand quantification model.

[0039] In one example, resource endowment data requires at least biomass resource abundance, wind speed, light intensity, hydrological data, and geothermal temperature as a quantitative basis before assessment. The quantitative assessment process for resource endowment data includes:

[0040] Step 101: Quantify the available biomass resources based on preset conditions, and classify the available biomass resources into abundant, moderate, or scarce levels according to preset thresholds. Specifically, the available biomass resources... Calculated using the following formula:

[0041]

[0042] in, , For the first The planting area and yield per unit area of ​​similar crops, , , For the first The number of livestock and poultry in stock, the average daily manure production, and the manure collection coefficient.

[0043] Ultimately, according to The value range can be divided into three usable levels: "abundant", "average", and "scarce" by setting a preset threshold.

[0044] Step 102: Classify wind energy resources based on average wind power density. Specifically, wind energy resource level (wind energy resource potential) is determined by average wind power density. Characterization, calculated based on collected wind speed data:

[0045]

[0046] in, air density, The number of wind speed sampling points. For the first Wind speed values ​​at each sampling point; based on IEC standards and local wind conditions, They are divided into different grades, such as >400 W / m² is "excellent", 200~400 W / m² is "good", and <200 W / m² is "poor".

[0047] Step 103: Classify solar energy resource levels based on average annual total solar radiation and peak sunshine hours. Specifically, the solar energy resource level is determined by average annual total solar radiation. and peak sunshine hours Joint assessment:

[0048]

[0049]

[0050] in, This represents the number of months for which data was collected (usually 12). For the first The total solar radiation of the month; according to or The numerical range is classified according to the national light resource zoning standards.

[0051] Step 104: Assess the exploitable capacity of hydropower resources using theoretical hydropower output and classify the exploitable capacity of hydropower resources into levels. Specifically, for small-scale hydropower resources, theoretical hydropower output is used. Conduct an assessment:

[0052]

[0053] in, The density of water, It is the acceleration due to gravity. For traffic, This refers to the net water head (drop). Considering the available technological resources, an efficiency coefficient needs to be introduced. Based on the technologically exploitable capacity value Assess development value:

[0054]

[0055] Step 105: Classify geothermal resources based on geothermal fluid temperature. Specifically, the geothermal resource level is determined based on the geothermal fluid temperature. Classification: High temperature (>150°C): suitable for power generation; Medium temperature (90°C~150°C): suitable for power generation or direct heating; Low temperature (<90°C): suitable for direct use (heating, greenhouses, aquaculture);

[0056] At the same time, the thermal power of the geothermal field needs to be assessed. potential:

[0057]

[0058] in, For the density of geothermal fluid, This refers to the specific heat capacity of the geothermal fluid. This represents the flow rate of geothermal fluid. The outlet temperature of the production well. This refers to the reinjection temperature.

[0059] Finally, using the key parameters and theoretical models obtained in steps 101-105, resource time-series data is dynamically generated to support subsequent rule engine parsing.

[0060] In one example, energy demand data requires at least cooling, heating, electricity, gas, and hydrogen load curves as a basis for quantification before assessment. In the early stages of rural planning or in areas with weak infrastructure, hourly load data is often unavailable. In such cases, a level-based load estimation and modeling strategy can be employed to ensure the completeness of the rule engine's input. The quantitative assessment process for energy demand data includes:

[0061] Collect hourly load data for the whole year or typical days (quarters) to form load curves. ,in It represents load types such as cold, heat, electricity, gas, and hydrogen.

[0062] Step 106: First, extract key feature parameters from the load curve. These key feature parameters include annual maximum load, annual minimum load, annual average load, and load factor, providing input for capacity matching rules.

[0063] Maximum annual load:

[0064]

[0065] Annual minimum load:

[0066]

[0067] Annual average load:

[0068]

[0069] Load factor:

[0070]

[0071] Analyze the coupling relationships between different loads, such as the thermoelectric ratio:

[0072]

[0073] Step 107: Demand restructuring based on limited data and rural energy consumption characteristics:

[0074] Annual total load estimates based on macroeconomic data, such as inversely estimating annual total demand using available macroeconomic data (e.g., electricity bills, annual fuel consumption, population / industry size):

[0075] For the total annual demand of electricity load :

[0076]

[0077] in, For the number of households, Average annual electricity consumption per household For farmland area, Agricultural electricity intensity (e.g., kWh / mu·year). For the number of processing plants, This represents the annual electricity consumption of a single plant.

[0078] Based on the total annual heat load demand :

[0079]

[0080] in, For heating building area, This refers to the number of heating degree-days (°C·day). For heat load per unit area, For building insulation coefficient, For population, The average hot water consumption per person per day (kWh / person·day).

[0081] Based on the total annual cooling load demand :

[0082]

[0083] in, For the cooling area, Cooling days (°C·day) are the number of days the equipment is cooled. For cooling load per unit area, , For cargo density and specific heat capacity, For cold storage volume, This refers to the temperature difference between the inside and outside of the warehouse.

[0084] For the total annual gas load demand :

[0085]

[0086] in, Annual energy consumption for cooking For stove efficiency, It is a low-calorific-value gas. To meet the heat requirements of processing.

[0087] For the total annual demand of hydrogen load :

[0088]

[0089] in, For the number of hydrogen fuel cell vehicles, Hydrogen consumption (kg / km) Average annual mileage The energy required for energy storage For fuel cell efficiency.

[0090] Step 108, Key Parameter-Driven Time Series Generation: Dynamically generate load curves using the key feature parameters obtained in steps 106-107.

[0091] Step 2: Based on the production system and hierarchical rules of forward chain reasoning, construct a rule engine, input resource endowment data and energy demand data into the rule engine, and generate a preliminary multi-energy complementary technology coupling scheme.

[0092] like Figure 2 As shown, the hierarchical rules include three types: resource assessment rules, technology coupling rules, and economic and environmental protection rules. During the rule engine's operation, the rules are executed in the basic order of resource assessment rules, technology coupling rules, and economic and environmental protection rules (RAR→TCR→EER). When a conflict occurs in the basic order, the priority of economic and environmental protection rules (EER) is raised to the highest level, followed by resource assessment rules (RAR), and the priority of technology coupling rules is raised to the lowest level. For conflicts of the same type of rules, the principle of recent use takes precedence.

[0093] Step 201, the resource assessment rules include resource endowment data (i.e., the levels calculated and classified using formulas (1)-(7)), and also include:

[0094] Calculate the resource fluctuation coefficients for various primary energy sources and label the volatility of the corresponding primary energy sources;

[0095] Assess the complementarity of wind and solar energy resources;

[0096] Calculate the synergy coefficients among various primary energy sources.

[0097] In this embodiment, the resource assessment rules are illustrated through the following examples to further quantify the availability of resource endowments and provide priority judgment for technology matching. Specifically, these include RAR-1 to RAR-9:

[0098] RAR-1 (Rich Biomass Availability Classification):

[0099]

[0100]

[0101]

[0102] RAR-2 (Wind Power Density Classification):

[0103]

[0104]

[0105]

[0106] RAR-3 (Solar Radiation Classification):

[0107]

[0108]

[0109]

[0110] RAR-4 ​​(Determination of Hydropower Resource Developability):

[0111]

[0112]

[0113]

[0114] RAR-5 (Geothermal Fluid Temperature Classification):

[0115]

[0116]

[0117]

[0118] RAR-6 (Geothermal Sustainable Power Threshold):

[0119]

[0120]

[0121] RAR-7 (Resource Volatility Assessment):

[0122]

[0123]

[0124]

[0125] in, Resource fluctuation coefficient:

[0126]

[0127] , The mean and standard deviation of the contribution to resources. Represents resource types such as biomass, wind, solar, water, and geothermal (primarily considering wind and solar).

[0128] RAR-8 (Wind / Solar Resource Complementarity Assessment):

[0129]

[0130] RAR-9 (Multi-Resource Synergy Coefficient Calculation):

[0131]

[0132] in, The number of available resource types. For the first Resource weights (biomass 0.3, solar energy 0.25, wind energy 0.2, hydropower 0.15, geothermal energy 0.1). For the first The resource level quantification value (taken as 1.0, 0.6, and 0.3 respectively from high to low according to resource level). The complementarity enhancement coefficient (wind-solar complementarity is...) If the value is 0.2, then the value is 0.

[0133] Step 202, the technology coupling rules include primary energy conversion / switching technology coupling rules, secondary energy conversion technology coupling rules, and energy storage technology coupling rules. Based on energy type and quality, the optimal conversion and storage technologies are matched, specifically including:

[0134] Step 2021: The primary energy conversion / conversion technology coupling rules are based on the energy characteristics of resource endowment data and, in combination with preset configuration rules, add the technologies that need to be coupled. In this scheme, the primary energy conversion / conversion technology coupling rules (TCR-PEC) include the following TCR-PEC-1 to TCR-PEC-5:

[0135] TCR-PEC-1 (Biomass Resource Technology Pathway Selection):

[0136] TCR-PEC-1a (High-grade biomass technology):

[0137]

[0138]

[0139]

[0140]

[0141] in The biomass combustion temperature, Indicate whether gas is needed (when) At that time Otherwise ).

[0142] TCR-PEC-1b (Medium- and Low-Grade Biomass Technology):

[0143]

[0144]

[0145]

[0146]

[0147] in This refers to the water content of biomass.

[0148] TCR-PEC-2 (Solar Energy Utilization Technology Matching):

[0149] TCR-PEC-1a (Photovoltaic System Configuration):

[0150]

[0151] TCR-PEC-1b (Photothermal system trigger):

[0152]

[0153]

[0154] in, This represents the annual average heat load.

[0155] TCR-PEC-3 (Wind Power System Configuration):

[0156]

[0157]

[0158] TCR-PEC-4 (Small Hydropower Configuration):

[0159]

[0160]

[0161]

[0162] in, As a marker of terrain suitability, when and At that time Otherwise .

[0163] TCR-PEC-5 (Geothermal Cascade Utilization):

[0164]

[0165]

[0166]

[0167] Step 2022: The secondary energy conversion technology coupling rules are based on the conversion characteristics of energy demand data and, in combination with preset configuration rules, add technologies that need to be coupled. In this scheme, the secondary energy conversion technology coupling rules (TCR-SEC) include the following TCR-SEC-1 to TCR-SEC-9:

[0168] TCR-SEC-1 (Electro-to-Heat Conversion Rule):

[0169] TCR-SEC-1a (Electric Boiler Configuration):

[0170]

[0171]

[0172] in, For daily abandoned electricity, This is the required temperature of the heat transfer medium.

[0173] TCR-SEC-1b (Heat Pump Configuration):

[0174]

[0175]

[0176] in, This refers to the temperature of the heat source.

[0177] TCR-SEC-2 (Electro-to-Hydrogen Conversion Rule):

[0178]

[0179]

[0180] in The amount of electricity wasted annually, This represents the number of days for which energy storage is required.

[0181] TCR-SEC-3 (Electric to Cooling Conversion Rule):

[0182]

[0183]

[0184] in, For the quarterly abandoned electricity, The average annual cooling load, The ambient temperature.

[0185] TCR-SEC-4 (Thermoelectric Conversion Rule):

[0186]

[0187]

[0188] in, Waste heat resources This is due to a power shortage.

[0189] TCR-SEC-5 (Hot → Cold Conversion Rule):

[0190] TCR-SEC-4a (Absorption Refrigeration Configuration):

[0191]

[0192]

[0193] TCR-SEC-4b (Adsorption-type refrigeration configuration):

[0194]

[0195]

[0196] TCR-SEC-6 (Gas-to-Electric Conversion Rule):

[0197]

[0198]

[0199] in, For daily gas production, For basic electrical load, This represents the purity of methane in the fuel gas.

[0200] TCR-SEC-7 (Gas-to-Heat Conversion Rule):

[0201]

[0202]

[0203] in, Due to the heat supply gap, It generates heat from renewable energy sources.

[0204] TCR-SEC-8 (Hydrogen → Electron Conversion Rule):

[0205]

[0206]

[0207] in, For hydrogen storage capacity, To meet the requirement of continuous power supply duration.

[0208] TCR-SEC-9 (Hydrogen → Heat Conversion Rule):

[0209]

[0210]

[0211] Step 2023: The energy storage technology coupling rules are based on the existing energy storage configuration of the system, and add technologies that need to be coupled in combination with preset configuration rules. In this scheme, the energy storage technology coupling rules (TCR-ES) include the following TCR-ES-1 to TCR-ES-9:

[0212] TCR-ES-1 (electrochemical energy storage configuration):

[0213]

[0214]

[0215] in, Daily load fluctuation rate:

[0216]

[0217] , The maximum and minimum daily electrical loads, The average annual electricity load, This represents the curtailment rate.

[0218] TCR-ES-2 (pumped storage configuration):

[0219]

[0220]

[0221] in, To reduce peak demand.

[0222] TCR-ES-3 (sensible heat storage configuration):

[0223]

[0224]

[0225] in, Daily calorie consumption This is the usable temperature rise.

[0226] TCR-ES-4 (Latent heat storage configuration):

[0227]

[0228]

[0229] TCR-ES-5 (Cold Water Storage Configuration):

[0230]

[0231]

[0232] in, To meet cooling temperature requirements, This represents the peak cooling load.

[0233] TCR-ES-6 (ice storage configuration):

[0234]

[0235]

[0236] TCR-ES-7 (Biomass Biogas Energy Storage Configuration):

[0237]

[0238]

[0239] TCR-ES-8 (High-Pressure Hydrogen Storage Configuration):

[0240]

[0241]

[0242] in, The amount of electricity wasted annually, This represents the daily hydrogen production.

[0243] TCR-ES-9 (Metal Hydride Hydrogen Storage Configuration):

[0244]

[0245]

[0246] Step 203, the economic and environmental rules, includes economic constraints, environmental constraints, and a comprehensive economic and environmental scoring system. This step mainly calculates various evaluation indicators based on the economic and environmental parameters of the selected technology to ensure that the solution meets the economic and environmental constraints.

[0247] In one example, the Economic Efficiency Constraint (EER-ECON) rule includes:

[0248] EER-ECON-1 (Investment Recovery Period Constraint):

[0249]

[0250]

[0251]

[0252] in, Investment recovery period:

[0253]

[0254] The total investment cost, To save money annually, For annual income, Annual maintenance costs To allow the maximum payback period (default 5 years). Excellent payback period (3 years is acceptable). The economic efficiency of the proposed solution is evaluated.

[0255] EER-ECON-2 (Net Present Value Constraint):

[0256]

[0257]

[0258]

[0259] in Net present value:

[0260]

[0261] For the first Annual net cash flow The discount rate (8% to 10% is acceptable for rural projects). For the project lifespan, Excellent net present value (required) ).

[0262] EER-ECON-3 (Cost Per kilowatt-hour constraint):

[0263]

[0264]

[0265]

[0266] in To levelize energy costs:

[0267]

[0268] , , The first Annual investment costs, operation and maintenance costs, and fuel costs. For the first Annual energy output This refers to the electricity price on the power grid.

[0269] EER-ECON-4 (Internal Rate of Return Constraint):

[0270]

[0271]

[0272] in The internal rate of return can be obtained by solving the following equation:

[0273]

[0274] This is the minimum allowable rate of return (typically 8%). This represents an excellent return (typically 15%).

[0275] EER-ECON-5 (Cost-Effectiveness Constraint):

[0276]

[0277]

[0278] in, Cost-effectiveness ratio:

[0279]

[0280] For the first Total annual income For the first Total annual cost This is the minimum allowable ratio (1.2 is acceptable for rural scenarios). The threshold for the excellent ratio (can be 1.5).

[0281] EER-ECON-6 (Operation and Maintenance Cost Constraints):

[0282]

[0283] In one example, the Environmental Efficiency and Environmental Protection (EER-ENV) rules include:

[0284] EER-ENV-1 (Carbon Emission Reduction Constraint):

[0285]

[0286]

[0287] in, Annual carbon emission reduction:

[0288]

[0289]

[0290] For the first Alternative energy sources For the first Carbon emission factors of various energy sources Carbon emissions during the construction process. For the first The carbon emissions of this material For the first The carbon emission factor of this material This represents the minimum emission reduction requirement (50 tCO2 / year for rural scenarios). To achieve the target emission reduction, The environmental friendliness of the proposed solution will be assessed.

[0291] EER-ENV-2 (Constraints for Synergistic Emission Reduction of Pollutants):

[0292]

[0293]

[0294]

[0295]

[0296] in, , , Emission reductions for PM2.5, SO2, and nitrogen oxides:

[0297]

[0298] pollutants ( The emission reduction of ) For the first Alternative energy sources For the first Pollutants from various energy sources Emission factors, , , These are the minimum emission reduction requirements for the three pollutants. This is the minimum requirement for total pollutant emission reduction.

[0299] In one example, the Economic and Environmental Efficiency Rating (EER-C) includes:

[0300]

[0301] in, Overall score for economic and environmental benefits:

[0302]

[0303] , Economic and environmental considerations are taken into account. This is the lowest acceptable rating.

[0304] Ultimately, the resulting preliminary technology coupling scheme includes a list of energy technologies, a system topology suggestion, and economic and environmental performance indicators. The system topology suggestion involves the rule engine creating a topology graph during the reasoning process, inputting the selected coupling technologies into the topology graph, and recording the connections between each coupling technology. Each time a technology is activated, a new node is created in the topology graph, and directed edges are automatically established between nodes based on the technology's input and output types, integrating them into a coherent directed graph to describe the energy and material flow paths of the entire multi-energy complementary system. More preferably, the preliminary technology coupling scheme also includes economic and environmental performance indicators (economic, environmental, and comprehensive evaluation results of the scheme). This output will serve as input to the optimization and verification layer in step 3 for verifying the technology coupling scheme.

[0305] Step 3: Verify the feasibility of the preliminary technology coupling scheme, and output the final technology coupling scheme and analysis results.

[0306] The methods used in step 3 of this solution are all mature technologies in the field of distributed energy system planning, and are not the focus of innovation in this invention. The verification layer uses mature technologies and methods, and does not involve the innovative content of this patent. The focus is on providing reliability verification for the scheme generated by the rule engine to ensure the actual feasibility of the output scheme, so it will not be elaborated further.

[0307] (1) The following conventional verification methods can be used for specific verification:

[0308] First, a multi-objective optimization algorithm: the non-dominated sorting genetic algorithm (NSGA-II) is used to optimize the techno-economic efficiency of the above preliminary technical coupling scheme;

[0309] Then, system configuration and simulation: the equipment capacity and key parameters are optimized and configured, and 8760 hours of hourly operation simulation are carried out using energy system simulation software (such as HOMER, TRNSYS, etc.);

[0310] Finally, sensitivity analysis: ±20% fluctuation analysis was performed on key parameters (energy prices, equipment costs, load growth, etc.).

[0311] (2) Verification content:

[0312] The feasibility of the following aspects will be verified:

[0313] Technical feasibility: energy supply and demand balance, equipment capacity matching, system stability, etc.

[0314] Economic rationality: investment payback period ≤ 5 years, internal rate of return ≥ 8%, etc.;

[0315] Environmental compliance: Carbon emission reduction ≥ 50 tons / year, pollutant emissions meet standards, etc.

[0316] (3) Output:

[0317] The verification layer outputs the following conclusive information:

[0318] A boolean flag indicating whether the scheme passes or fails validation;

[0319] Key performance indicators of the system (energy self-sufficiency rate, system efficiency, emission reduction effect, etc.);

[0320] Optimization suggestions (such as recommended equipment capacity configuration, recommended operation strategy optimization, etc.);

[0321] This output is directly passed to the solution output layer for final solution generation.

[0322] Step 3, as the final output interface of this method, has the core function of transforming the technically coupled scheme generated by the rule engine and verified by the scheme verification layer into a structured and standardized planning output, including the following output content:

[0323] (1) List of technology coupling schemes: Output the technology combinations and basic relationships in a standardized format to form the basic framework for technology integration, including the following key points:

[0324] Technology type (primary energy conversion / conversion, secondary energy conversion, energy storage), technology name, input energy form, output energy form, recommended capacity configuration (capacity, volume, power, etc., from the solution verification layer), performance parameters (mainly considering energy efficiency parameters, from the technology library).

[0325] (2) System topology description: The coupling relationship between energy flow and matter flow in the system is described using graph theory language, and the connection logic of each unit (technology) is clarified (from the rule engine layer).

[0326] (3) Economic and environmental protection indicators report: The core benefit indicators of the plan are presented in a standardized list (from the rule engine layer and plan verification).

[0327] (4) Operation strategy framework: Recommend the operation mode and scheduling strategy framework of the system (e.g., limited consumption mode of renewable energy, cogeneration mode, energy storage peak-valley arbitrage scheduling strategy, etc., from the scheme verification layer).

[0328] Application Examples

[0329] Consider a northern village with a primary agricultural and livestock farming economy. The village has a significant winter heating demand, and its existing heating system mainly relies on decentralized coal-fired boilers. Figure 3 The implementation steps are as follows:

[0330] Step S1: Construct a multi-energy complementary distributed energy technology library suitable for rural areas, involving primary energy conversion / conversion technology, secondary energy conversion / conversion technology, and energy storage technology. Each technology includes input energy form, output energy form, technical parameters, economic parameters, and environmental parameters.

[0331] Step S2: Construct a hierarchical rule base, including resource assessment rules, technology coupling rules, and economic and environmental protection rules.

[0332] Step S3: Collect rural resource endowment data and energy demand data. Assume the following data is collected from the scenario:

[0333] Resource endowment

[0334] Biomass: Annual straw production of 1200 tons, daily livestock and poultry manure production of 2.8 tons (collection coefficient taken as 0.85), according to Calculate the total utilization ;

[0335] Solar energy: Annual radiation ;

[0336] Wind energy: annual average wind power density ;

[0337] Geothermal energy: Geothermal fluid temperature Geothermal field thermal power potential .

[0338] Energy demand

[0339] Heat load: Total annual demand Peak load ;

[0340] Electricity load: Total annual demand Peak load ;

[0341] Calculate the thermoelectric ratio: .

[0342] Resource and load time-series data generated using key parameters and mature models.

[0343] Step S4: Input the above data into the rule engine layer. The rule engine layer executes the rules in the priority order of RAR→TCR→EER (to save space, rules that are not triggered are not listed):

[0344] RAR rules:

[0345] RAR-1: ;

[0346] RAR-2: ;

[0347] RAR-3: ;

[0348] RAR-5: ;

[0349] RAR-6: ;

[0350] RAR-7: , ;

[0351] RAR-8: ;

[0352] RAR-9: (Resource synergy is low).

[0353] TCR rule triggered:

[0354] TCR-PEC-1a: and ;

[0355] TCR-PEC-2a: ;

[0356] TCR-PEC-5: ;

[0357] EER rule triggering (assuming it can be estimated from economic and environmental parameters in the technology library):

[0358] EER-ECON-1: Assuming an estimate ;

[0359] EER-ECON-2: Assuming an estimate ;

[0360] EER-ECON-4: Assuming an estimate ;

[0361] EER-ENV-1: Assuming the estimate is... ;

[0362] EER-ENV-2: Assuming the estimate is ;

[0363] EER-C: Assumption , , Calculate .

[0364] The final technical solution was then proposed: "direct combustion combined heat and power + photovoltaic power generation + ground source heat pump".

[0365] Step S5: Receive the preliminary technical solution and its associated parameters output by the rule engine layer, and refine and optimize the preliminary solution through modeling, simulation, and optimization methods.

[0366] Multi-objective optimization decision-making based on the NSGA-II algorithm generates energy equipment capacity configuration schemes, for example:

[0367] Biomass direct combustion cogeneration: 200 kW (electricity) / 240 kW (heat);

[0368] Photovoltaics: 120 kWp;

[0369] Ground source heat pump: 100 kW.

[0370] HOMER software was used to conduct an 8760-hour hourly system simulation to verify the system's supply-demand balance and stability. The key performance indicators are assumed to be as follows:

[0371] Energy self-sufficiency rate: 92%;

[0372] Overall system efficiency: 78%;

[0373] Power curtailment rate: <5%.

[0374] The sensitivity of the scheme is analyzed, including the setting of biomass price (±20%), grid electricity price (±20%), and equipment investment cost (±15%). The analysis can obtain the impact of price fluctuations on the economic indicators of the scheme and verify the satisfaction of economic constraints.

[0375] Through the scheme verification layer, conclusions can be obtained regarding the technical feasibility, economic rationality, and environmental compliance of the scheme. If the scheme passes verification, no adjustments are needed. The scheme output layer then transforms the verified scheme into standardized planning outputs, including a list of technical coupling schemes as shown in Table 1, and the system topology (see appendix). Figure 3 ), and an analysis of economic and environmental indicators as shown in Table 2.

[0376] Table 1 List of Technology Coupling Schemes

[0377]

[0378] System topology (see appendix) Figure 3 ).

[0379] Table 2 Analysis of Economic and Environmental Indicators

[0380]

[0381] Recommended strategy framework:

[0382] Renewable energy priority: Photovoltaic power is prioritized for supplying ground source heat pumps and local loads, with surplus power fed into the grid;

[0383] Cogeneration scheduling: In winter, electricity is determined by heat demand, prioritizing the fulfillment of heat load; in summer, heat demand is determined by electricity demand, and excess heat can be stored or used for absorption cooling.

[0384] The above embodiments are merely illustrative of the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement it accordingly. They should not be construed as limiting the scope of protection of the present invention. All equivalent changes or modifications made based on the essence of the content of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A rule-driven coupling optimization method for rural multi-capability complementary distributed energy technologies, characterized in that, The method comprises the following steps: Quantitative assessment of the primary energy endowment and terminal multi-energy demand characteristics of rural localization is performed to generate standardized resource endowment data and energy demand data; A rule engine is constructed based on a forward chain reasoning production system and hierarchical rules, the resource endowment data and the energy demand data are input into the rule engine, and a preliminary technical coupling scheme of multi-energy complementation is generated; The feasibility of the preliminary technical coupling scheme is verified, and a final technical coupling scheme and analysis results are output.

2. The rule driven rural multi-complementary distributed energy technology coupling optimization method according to claim 1, wherein, The quantitative assessment process of the resource endowment data comprises: The available amount of biomass resources is quantified based on preset conditions, and the available amount of biomass resources is divided into rich, general or scarce levels according to preset threshold values; The wind energy resource level is divided based on average wind power density; The solar energy resource level is divided based on annual average total solar radiation and peak sunshine duration; The water energy resource development capacity is evaluated using theoretical water energy power, and the water energy resource development capacity is divided into levels; The geothermal resource level is divided according to the temperature of geothermal fluid.

3. The rule driven rural multi-complementary distributed energy technology coupling optimization method according to claim 1, characterized in that, The hierarchical rules comprise resource assessment rules, technical coupling rules and economic and environmental protection rules, and the rules are executed in the order of the resource assessment rules, the technical coupling rules and the economic and environmental protection rules during the operation of the rule engine; When the order triggers a conflict, the priority of the economic and environmental protection rules is raised to the highest, and the priority of the technical coupling rules is lowered to the lowest; For the same type of rule conflict, the principle of recent use priority is adopted.

4. The rule driven rural multi-complementary distributed energy technology coupling optimization method according to any one of claims 1-2, characterized in that, The resource assessment rules incorporate the resource endowment data, and further comprise: The resource fluctuation coefficients of various primary energies are calculated, and the fluctuation of the corresponding primary energy is marked; The complementarity of wind energy and solar energy resources is assessed; The synergy coefficients between various primary energies are calculated.

5. The rule driven rural multi-complementary distributed energy technology coupling optimization method as claimed in claim 1, wherein, The technical coupling rules comprise primary energy conversion / transfer technical coupling rules, secondary energy conversion technical coupling rules, and energy storage technical coupling rules, wherein The primary energy conversion / transfer technical coupling rules increase the technologies that need to be coupled based on the energy characteristics of the resource endowment data and in combination with preset configuration rules; The secondary energy conversion technical coupling rules increase the technologies that need to be coupled based on the conversion characteristics of the energy demand data and in combination with preset configuration rules; The energy storage technical coupling rules increase the technologies that need to be coupled based on the existing energy storage configuration of the system and in combination with preset configuration rules.

6. The rule driven rural multi-complementary distributed energy technology coupling optimization method as claimed in claim 1, wherein, The economic and environmental protection rules comprise economic constraint rules, environmental protection constraint rules, and economic and environmental protection comprehensive score rules.

7. The rule driven rural multi-complementary distributed energy technology coupling optimization method as claimed in claim 1, wherein, The preliminary technical coupling scheme comprises an energy technology list, a system topology suggestion and economic and environmental protection indicators, wherein the system topology suggestion comprises that the rule engine creates a topology graph in the reasoning process, inputs the coupling technologies screened into the topology graph, records the connection relationship between various coupling technologies, triggers the creation of a new node in the topology graph when each technology is enabled, and automatically establishes directed edges between nodes according to the input and output types of the technologies, integrates into a coherent directed graph, and is used to describe the energy flow and material flow paths of the entire multi-energy complementary system.

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