A green low-carbon oriented rural light storage electricity heat hydrogen multi-energy complementary system integration optimization method

Abstract

The application discloses a green low-carbon-oriented rural light-storage-electricity-heat-hydrogen multi-energy complementary system integration optimization method, and relates to the technical field of low-carbon energy, which comprises five steps: first, the rural multi-scene energy use rules are combed, an electricity-heat-hydrogen demand coupling strength model containing a low-carbon demand coefficient is constructed, and a demand coupling matrix is output; second, initial parameters of each energy unit are calculated based on the matrix; third, a multi-objective optimization model with low-carbon priority is constructed, and an optimal configuration scheme is solved; fourth, an operation strategy is output through a day-ahead-intra-day-real-time three-layer scheduling mechanism; and fifth, when the deviation of a core index exceeds 5%, the optimization model is adjusted reversely, and a closed loop is formed. The method significantly improves the adaptability, low-carbon property and stability of the system, improves the renewable energy consumption rate, reduces the whole life cycle cost, and provides efficient technical support for rural energy transformation.

Description

Technical Field

[0001] This invention relates to the field of low-carbon energy technology, specifically to an integrated optimization method for a green and low-carbon oriented rural photovoltaic-storage-electricity-thermal-hydrogen multi-energy complementary system. Background Technology

[0002] Rural energy transition has become a crucial link in promoting coordinated urban-rural development and achieving green and low-carbon development. Rural areas possess abundant renewable energy resources such as solar energy, and cover diverse energy consumption scenarios including living, planting, animal husbandry, and small-scale processing, giving them a natural advantage in developing multi-energy complementary systems of photovoltaic, energy storage, electricity, heat, and hydrogen. In recent years, the state has successively introduced a number of policies to support the development of rural distributed energy and the construction of multi-energy complementary demonstration projects, encouraging technological innovation to address rural energy consumption pain points and promote the transformation from a traditional fossil fuel-dependent energy consumption model to a low-carbon model dominated by renewable energy.

[0003] However, the development of multi-energy complementary systems in rural areas still faces many practical bottlenecks. On the one hand, rural energy consumption exhibits significant differences in scenarios and load fluctuations. In particular, after the merger of villages and residential areas, the load has shifted from decentralized to concentrated, making the coupling relationship between electricity, heat, and hydrogen loads more complex. Existing technologies mostly adopt generalized configuration schemes, ignoring the differences in energy consumption patterns and load concentration characteristics in different scenarios. This leads to a mismatch between equipment configuration and actual demand, resulting in problems of "over-configuration and waste" or "under-configuration and insufficient configuration," and a low renewable energy absorption rate. On the other hand, existing multi-energy complementary optimization methods mostly focus on economic efficiency or single energy utilization efficiency, weakening low-carbon constraints and failing to fully integrate regional carbon quota policies to build a full life-cycle carbon emission accounting system, making it difficult to meet the policy orientation and emission reduction needs of rural green development.

[0004] At the scheduling and optimization level, existing technologies mostly employ single-time-scale scheduling mechanisms, which cannot effectively cope with the random fluctuations in rural irradiance resources and energy load. The delayed scheduling response leads to insufficient system stability. Simultaneously, the optimization process is often a unidirectional "configuration-output" model, lacking a dynamic closed-loop correction mechanism. Parameter deviations generated during operation cannot be used to adjust the optimization model, making it easy for the overall performance to deviate from the design goals over long-term operation. Furthermore, most existing solutions focus on optimizing a single energy source or the complementary use of two energy sources, failing to fully explore the coupling and complementary potential of multiple energy sources such as electricity, heat, and hydrogen. The utilization efficiency of key collaborative links such as waste electricity to hydrogen production and fuel cell waste heat recovery is low, hindering the improvement of the system's overall energy efficiency.

[0005] In summary, given the shortcomings of existing technologies, such as insufficient adaptability to diverse energy use scenarios in rural areas, lack of coordination in low-carbon goals, lagging dynamic dispatch response, and lack of closed-loop optimization mechanisms, there is an urgent need to develop an integrated optimization method for a photovoltaic-storage-electricity-thermal-hydrogen multi-energy complementary system that can accurately match rural energy use characteristics, strengthen low-carbon orientation, and possess efficient dispatch and closed-loop correction capabilities, so as to provide technical support for rural energy transformation. Summary of the Invention

[0006] The purpose of this invention is to provide an integrated optimization method for a green and low-carbon rural photovoltaic-storage-electricity-thermal-hydrogen multi-energy complementary system. By constructing an electricity-thermal-hydrogen demand coupling strength model to adapt to the diverse energy consumption scenarios and load agglomeration characteristics in rural areas, and taking low-carbon priority multi-objective optimization as the core, combined with a three-layer dynamic scheduling and closed-loop iterative correction mechanism, it achieves efficient complementarity of electricity, heat and hydrogen multi-energy. Compared with existing technologies, it significantly improves adaptability, low carbon emissions, stability and comprehensive energy efficiency, while reducing the total life cycle cost.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] An integrated optimization method for a green and low-carbon rural photovoltaic-storage-electricity-thermal-hydrogen multi-energy complementary system includes the following steps:

[0009] S1: Analyze the energy consumption patterns of rural life, planting, breeding, and small-scale processing scenarios, construct an electric-thermal-hydrogen demand coupling strength model with electric load, heat load, and hydrogen load as the core, and output a demand coupling matrix that reflects the degree of energy consumption correlation between different scenarios.

[0010] S2: Based on the demand coupling matrix, combined with the maximum demand coupling intensity, irradiance resources and energy storage charging and discharging efficiency at each time of the day, calculate the photovoltaic array capacity, energy storage equipment capacity, electrothermal conversion unit parameters and hydrogen energy unit electrolyzer power, and determine the initial parameters of each energy unit.

[0011] S3: Based on the initial parameters of the unit, construct a multi-objective coupled optimization model that prioritizes low carbon emissions while taking into account economic and renewable energy consumption. Calculate the carbon emissions and costs of the equipment throughout its entire life cycle, incorporate initial investment, operation and maintenance costs and carbon trading revenue, clarify equipment constraints, and solve to determine the optimal configuration scheme for each unit.

[0012] S4: Through a three-layer scheduling mechanism of 24-hour output planning, 15-minute rolling correction within the day, and 1-minute real-time response control, the optimal operating strategy for each unit is output.

[0013] S5: Based on the simulation platform, input the actual meteorological data and load data of the rural area to simulate the system operation and verify the three major indicators of carbon emission reduction rate, comprehensive cost saving rate and renewable energy consumption rate; calculate the parameter deviation between the actual operating parameters and the optimal operating strategy in S4; when the deviation of any indicator exceeds 5%, input the reverse input to S3 to adjust the multi-objective optimization model and form a closed-loop optimization.

[0014] Further, step S1 specifically involves analyzing the energy consumption patterns of four core scenarios: rural life, planting, breeding, and small-scale processing. Combined with the characteristics of load agglomeration after village mergers, a coupling strength model for electricity, heat, and hydrogen demand is constructed. The coupling strength index of this model uses three types of loads—electricity, heat, and hydrogen—as core values, assigning weights according to rural energy consumption priorities. Electricity consumption has the highest weight, followed by hydrogen load due to the specificity of the scenario, while heat load weight is adapted to actual demand allocation. Simultaneously, the model integrates real-time load and rated load limits for each scenario, and incorporates a low-carbon demand coefficient linked to regional carbon quotas. When carbon quotas are tight, the coefficient is adjusted upwards to strengthen low-carbon constraints, outputting a demand coupling matrix reflecting the close correlation between energy consumption across different scenarios.

[0015] Furthermore, step S2 specifically involves using the demand coupling matrix output in step S1 as a basis, and combining the maximum demand coupling intensity, irradiance resources, and energy storage charging and discharging efficiency for each time period of the day, to calculate the photovoltaic array capacity, energy storage equipment capacity, electrothermal conversion unit parameters, and hydrogen energy unit electrolyzer power.

[0016] The photovoltaic array capacity is calculated by combining the maximum demand coupling strength, total electrical load, local irradiance resources, and overall efficiency of photovoltaic equipment throughout the day, with the addition of a curtailment rate correction coefficient set according to the demand coupling fluctuation amplitude.

[0017] The capacity of the energy storage device is calculated by taking the difference between the total electrical load corresponding to the average demand coupling strength in each time period and the real-time output of the photovoltaic as the core, combined with the energy storage charging and discharging efficiency and the allowable charge state range.

[0018] The parameters of the electrothermal conversion unit are calculated by combining the thermal power of the electric boiler with the coupling strength of thermal demand, the maximum heat load and the equipment efficiency.

[0019] The power of the hydrogen energy unit electrolyzer is calculated based on the hydrogen load demand, combined with the coupling strength of hydrogen demand, the total hydrogen load, the lower calorific value of hydrogen, and the hydrogen production efficiency of the electrolyzer.

[0020] Furthermore, step S3, based on the initial unit parameters from step S2, constructs a multi-objective optimization model and clarifies the constraints, optimizing and determining the final specification parameters of each unit. This specifically includes the following steps:

[0021] S31: Set core optimization goals: Prioritize low carbon emissions, while taking into account economic development and renewable energy consumption, and establish three major goals: minimize carbon emissions throughout the entire life cycle, minimize the cost throughout the entire life cycle, and maximize the renewable energy consumption rate.

[0022] S32: Target Quantification Accounting: Carbon emission accounting covers the entire life cycle of equipment, cost accounting includes initial investment, operation and maintenance costs and carbon trading revenue, and the absorption rate accounting deducts the curtailed portion to quantify the utilization efficiency of renewable energy;

[0023] S33: Define clear constraints: Set equipment parameter ranges, start-up and ramp-up rate limits, establish electrical balance and thermal balance constraints, and incorporate carbon emission quotas and grid connection standards constraints.

[0024] S34: Optimization parameter solution: Based on the set objectives and constraints, the final specification parameters of each energy unit are obtained to form the optimal configuration scheme.

[0025] Furthermore, in step S4, efforts are made to formulate the optimal configuration scheme based on step S3, and the rolling correction and real-time response control within the day are adjusted based on the demand coupling strength of step S1.

[0026] Furthermore, the daytime scheduling is based on the optimization model of step S3, which is solved by typical scenario sets of sunny days, cloudy days, weekdays and holidays. The output plan of each unit is formulated 24 hours in advance as the intraday scheduling benchmark. The intraday rolling update updates the demand coupling strength of step S1 every 15 minutes and corrects the output plan by combining the robust factor constructed by the demand coupling strength, irradiance and load fluctuation standard deviation. The real-time control dynamically adjusts the control command every minute according to the change in demand coupling strength, and finally outputs the optimal operating strategy of each unit.

[0027] Furthermore, step S5 specifically includes the following steps:

[0028] S51: Building a simulation platform: Build a simulation platform based on MATLAB or Simulink, input actual rural meteorological data and load data, and simulate the system operation status;

[0029] S52: Core Indicator Verification: Verify the actual performance of the three core indicators—carbon emission reduction rate, overall cost saving rate, and renewable energy consumption rate—and test the feasibility of the optimization strategy.

[0030] S53: Deviation Calculation: Calculate the difference between the actual operating parameters obtained from the simulation and the optimal parameters in step S4;

[0031] S54: Dynamic Iterative Update: When the deviation of any indicator exceeds 5%, the multi-objective optimization model of iterative update step S3 is immediately started, and the iteration step size is adjusted according to the deviation.

[0032] Furthermore, the electricity-heat-hydrogen demand coupling strength model constructed in step S1 is as follows:

[0033] ;

[0034] In the formula, Let be the coupling strength of the requirements between scene i and scene j at time t. , , The weights for electricity, heat, and hydrogen demand are respectively. Let t be the electrical load of scenario i at time t. Let t be the heat load of scenario j. Let t be the hydrogen load of scenario k at time t. , , These are the rated values ​​for electrical, thermal, and hydrogen loads, respectively. Let t be the low-carbon demand coefficient at time t, which is linked to the regional carbon quota and has a value of 0.8-1.2.

[0035] Furthermore, the multiple scenarios mentioned in step S1 include rural life scenarios, agricultural planting scenarios, breeding scenarios, and small-scale processing scenarios, with each scenario having a weight. , , The values ​​were determined using the analytic hierarchy process (AHP), and all values ​​ranged from 0.2 to 0.5, and satisfied the following conditions: .

[0036] Furthermore, step S5 verifies the system's operational deviation through simulation. When any indicator deviation exceeds 5%, iterative optimization is triggered, and the iteration step size is dynamically adjusted according to the deviation size, ranging from 0.1 to 0.3.

[0037] This method starts with the energy consumption patterns of four major scenarios: rural life, planting, animal husbandry, and small-scale processing. Combined with the load agglomeration characteristics after village consolidation, it constructs an electricity-heat-hydrogen demand coupling strength model. By quantifying the correlation between the three types of loads, a demand coupling matrix is ​​generated. Simultaneously, a low-carbon demand coefficient linked to regional carbon quotas is incorporated, establishing a "low-carbon priority" energy constraint from the source. Based on this matrix, and combined with the maximum daily coupling strength, irradiance resources, and energy storage efficiency, the initial parameters of photovoltaic, energy storage, electrothermal conversion, and hydrogen energy units are accurately calculated to ensure an initial match between equipment capacity and scenario energy demand.

[0038] A multi-objective coupled optimization model is then constructed, with minimizing carbon emissions throughout the entire life cycle as the primary objective, while also considering cost control and improving renewable energy integration rates. This model incorporates equipment investment, operation and maintenance costs, and carbon trading revenue calculations, and solves for the optimal configuration scheme through constraints such as electricity-heat balance and carbon emission quotas. To address the dynamic fluctuations in rural irradiance and load, a three-tiered scheduling mechanism is adopted, consisting of 24-hour day-ahead planning, 15-minute intraday rolling correction, and 1-minute real-time response, outputting the optimal operating strategy for each unit.

[0039] Finally, the actual operation of the system was simulated through a simulation platform to verify the three core indicators of carbon emission reduction rate, cost saving rate and absorption rate, and to calculate the parameter deviation between the actual operation and the scheduling strategy. When the deviation exceeds 5%, the iteration step size is dynamically adjusted according to the deviation size, and the multi-objective optimization model is updated in reverse to form a closed-loop mechanism of demand modeling-parameter adaptation-optimization scheduling-verification correction, so as to continuously ensure that the system meets the comprehensive performance standards in the dimensions of low carbon, economy and efficiency.

[0040] Compared with the prior art, the beneficial effects of the present invention are:

[0041] This invention constructs an electrothermal hydrogen demand coupling strength model to quantify the energy correlation in different scenarios. It combines load agglomeration characteristics with low-carbon demand coefficient to optimize parameter matching, so that equipment configuration is highly matched with the actual energy demand in rural areas, avoiding over-matching waste or under-matching.

[0042] This invention prioritizes minimizing carbon emissions throughout the entire life cycle, integrates carbon trading revenue accounting with carbon emission quota constraints, and simultaneously takes into account cost control and the improvement of renewable energy consumption rate, so as to achieve a synergistic effect of low-carbon, economic and efficient three-dimensional goals, which is more in line with the policy orientation of green development in rural areas.

[0043] This invention adopts a three-layer scheduling mechanism of day-ahead planning, intraday correction, and real-time response, combined with robust factor dynamic adjustment, 15-minute rolling update of demand coupling strength, and 1-minute instantaneous response. Compared with existing technologies, it has a more timely response and a more stable scheduling strategy, effectively reducing the impact of fluctuations on system operation.

[0044] This invention employs a closed-loop logic of simulation verification, deviation calculation, and iterative update. When the deviation of core indicators exceeds 5%, the iteration step size is dynamically adjusted, and the multi-objective model is optimized in reverse. This ensures that the system continuously adapts to actual working conditions during long-term operation, and provides better overall performance stability than existing technologies.

[0045] This invention fully taps the complementary potential of surplus electricity to produce hydrogen and the utilization of waste heat from fuel cells through energy balance constraints and multi-unit collaborative scheduling. The renewable energy absorption rate and overall energy efficiency are significantly improved compared with existing technologies, while reducing the overall cost of the entire life cycle. Attached Figure Description

[0046] Figure 1 This is a flowchart of an integrated optimization method for a green and low-carbon rural photovoltaic-storage-electricity-thermal-hydrogen multi-energy complementary system according to the present invention. Detailed Implementation

[0047] The technical solutions of the present invention will now be described in detail with reference to the accompanying drawings.

[0048] like Figure 1 As shown, an integrated optimization method for a green and low-carbon rural photovoltaic-storage-electricity-thermal-hydrogen multi-energy complementary system includes the following steps:

[0049] S1: Analyze the energy consumption patterns of rural life, planting, breeding, and small-scale processing scenarios, construct an electric-thermal-hydrogen demand coupling strength model with electric load, heat load, and hydrogen load as the core, and output a demand coupling matrix that reflects the degree of energy consumption correlation between different scenarios.

[0050] S2: Based on the demand coupling matrix, combined with the maximum demand coupling intensity, irradiance resources and energy storage charging and discharging efficiency at each time of the day, calculate the photovoltaic array capacity, energy storage equipment capacity, electrothermal conversion unit parameters and hydrogen energy unit electrolyzer power, and determine the initial parameters of each energy unit.

[0051] S3: Based on the initial parameters of the unit, construct a multi-objective coupled optimization model that prioritizes low carbon emissions while taking into account economic and renewable energy consumption. Calculate the carbon emissions and costs of the equipment throughout its entire life cycle, incorporate initial investment, operation and maintenance costs and carbon trading revenue, clarify equipment constraints, and solve to determine the optimal configuration scheme for each unit.

[0052] S4: Through a three-layer scheduling mechanism of 24-hour output planning, 15-minute rolling correction within the day, and 1-minute real-time response control, the optimal operating strategy for each unit is output.

[0053] S5: Based on the simulation platform, input the actual meteorological data and load data of the rural area to simulate the system operation and verify the three major indicators of carbon emission reduction rate, comprehensive cost saving rate and renewable energy consumption rate; calculate the parameter deviation between the actual operating parameters and the optimal operating strategy in S4; when the deviation of any indicator exceeds 5%, input the reverse input to S3 to adjust the multi-objective optimization model and form a closed-loop optimization.

[0054] Further, step S1 specifically involves analyzing the energy consumption patterns of four core scenarios: rural life, planting, breeding, and small-scale processing. Combined with the characteristics of load agglomeration after village mergers, a coupling strength model for electricity, heat, and hydrogen demand is constructed. The coupling strength index of this model uses three types of loads—electricity, heat, and hydrogen—as core values, assigning weights according to rural energy consumption priorities. Electricity consumption has the highest weight, followed by hydrogen load due to the specificity of the scenario, while heat load weight is adapted to actual demand allocation. Simultaneously, the model integrates real-time load and rated load limits for each scenario, and incorporates a low-carbon demand coefficient linked to regional carbon quotas. When carbon quotas are tight, the coefficient is adjusted upwards to strengthen low-carbon constraints, outputting a demand coupling matrix reflecting the close correlation between energy consumption across different scenarios.

[0055] Furthermore, step S2 specifically involves using the demand coupling matrix output in step S1 as a basis, and combining the maximum demand coupling intensity, irradiance resources, and energy storage charging and discharging efficiency for each time period of the day, to calculate the photovoltaic array capacity, energy storage equipment capacity, electrothermal conversion unit parameters, and hydrogen energy unit electrolyzer power.

[0056] The photovoltaic array capacity is calculated by combining the maximum demand coupling strength, total electrical load, local irradiance resources, and overall efficiency of photovoltaic equipment throughout the day, with the addition of a curtailment rate correction coefficient set according to the demand coupling fluctuation amplitude.

[0057] The capacity of the energy storage device is calculated by taking the difference between the total electrical load corresponding to the average demand coupling strength in each time period and the real-time output of the photovoltaic as the core, combined with the energy storage charging and discharging efficiency and the allowable charge state range.

[0058] The parameters of the electrothermal conversion unit are calculated by combining the thermal power of the electric boiler with the coupling strength of thermal demand, the maximum heat load and the equipment efficiency.

[0059] The power of the hydrogen energy unit electrolyzer is calculated based on the hydrogen load demand, combined with the coupling strength of hydrogen demand, the total hydrogen load, the lower calorific value of hydrogen, and the hydrogen production efficiency of the electrolyzer.

[0060] Furthermore, step S3, based on the initial unit parameters from step S2, constructs a multi-objective optimization model and clarifies the constraints, optimizing and determining the final specification parameters of each unit. This specifically includes the following steps:

[0061] S31: Set core optimization goals: Prioritize low carbon emissions, while taking into account economic development and renewable energy consumption, and establish three major goals: minimize carbon emissions throughout the entire life cycle, minimize the cost throughout the entire life cycle, and maximize the renewable energy consumption rate.

[0062] S32: Target Quantification Accounting: Carbon emission accounting covers the entire life cycle of equipment, cost accounting includes initial investment, operation and maintenance costs and carbon trading revenue, and the absorption rate accounting deducts the curtailed portion to quantify the utilization efficiency of renewable energy;

[0063] S33: Define clear constraints: Set equipment parameter ranges, start-up and ramp-up rate limits, establish electrical balance and thermal balance constraints, and incorporate carbon emission quotas and grid connection standards constraints.

[0064] S34: Optimization parameter solution: Based on the set objectives and constraints, the final specification parameters of each energy unit are obtained to form the optimal configuration scheme.

[0065] Furthermore, in step S4, efforts are made to formulate the optimal configuration scheme based on step S3, and the rolling correction and real-time response control within the day are adjusted based on the demand coupling strength of step S1.

[0066] Furthermore, the daytime scheduling is based on the optimization model of step S3, which is solved by typical scenario sets of sunny days, cloudy days, weekdays and holidays. The output plan of each unit is formulated 24 hours in advance as the intraday scheduling benchmark. The intraday rolling update updates the demand coupling strength of step S1 every 15 minutes and corrects the output plan by combining the robust factor constructed by the demand coupling strength, irradiance and load fluctuation standard deviation. The real-time control dynamically adjusts the control command every minute according to the change in demand coupling strength, and finally outputs the optimal operating strategy of each unit.

[0067] Furthermore, step S5 specifically includes the following steps:

[0068] S51: Building a simulation platform: Build a simulation platform based on MATLAB or Simulink, input actual rural meteorological data and load data, and simulate the system operation status;

[0069] S52: Core Indicator Verification: Verify the actual performance of the three core indicators—carbon emission reduction rate, overall cost saving rate, and renewable energy consumption rate—and test the feasibility of the optimization strategy.

[0070] S53: Deviation Calculation: Calculate the difference between the actual operating parameters obtained from the simulation and the optimal parameters in step S4;

[0071] S54: Dynamic Iterative Update: When the deviation of any indicator exceeds 5%, the multi-objective optimization model of iterative update step S3 is immediately started, and the iteration step size is adjusted according to the deviation.

[0072] Furthermore, the electricity-heat-hydrogen demand coupling strength model constructed in step S1 is as follows:

[0073] ;

[0074] In the formula, Let be the coupling strength of the requirements between scene i and scene j at time t. , , The weights for electricity, heat, and hydrogen demand are respectively. Let t be the electrical load of scenario i at time t. Let t be the heat load of scenario j. Let t be the hydrogen load of scenario k at time t. , , These are the rated values ​​for electrical, thermal, and hydrogen loads, respectively. Let t be the low-carbon demand coefficient at time t, which is linked to the regional carbon quota and has a value of 0.8-1.2.

[0075] Furthermore, the multiple scenarios mentioned in step S1 include rural life scenarios, agricultural planting scenarios, breeding scenarios, and small-scale processing scenarios, with each scenario having a weight. , , The values ​​were determined using the analytic hierarchy process (AHP), and all values ​​ranged from 0.2 to 0.5, and satisfied the following conditions: .

[0076] Furthermore, step S5 verifies the system's operational deviation through simulation. When any indicator deviation exceeds 5%, iterative optimization is triggered, and the iteration step size is dynamically adjusted according to the deviation size, ranging from 0.1 to 0.3.

[0077] In this example, by covering four core scenarios—rural life, agricultural planting, livestock and poultry breeding, and small-scale processing—the system focuses on collecting hourly data on electricity load (including lighting, irrigation equipment, livestock temperature control, and processing machinery electricity), heat load (including domestic heating, livestock insulation, and processing heat), and hydrogen load (including fuel cell agricultural machinery and hydrogen for fresh storage) for each scenario.

[0078] By combining the load aggregation characteristics after the merger of villages and communities, we statistically analyzed the peak load periods, fluctuation ranges, rated upper limits, and seasonal variation patterns of various scenarios to form a multi-dimensional energy consumption database.

[0079] A weighted judgment matrix is ​​established with "energy necessity" as the target layer and electrical load, thermal load, and hydrogen load as the criteria layers, and a pairwise comparison judgment matrix is ​​constructed.

[0080] The judgment matrix is ​​solved using the eigenvalue method to obtain the electrical load weights. Heat load weight Hydrogen load weight ,satisfy and Among them, the weight of residential electricity consumption is the highest, followed by the weight of hydrogen load, and the weight of heat load is adjusted to adapt to actual demand; the weights are valid when the consistency test index CR < 0.1.

[0081] Based on the load correlation in multiple scenarios and low-carbon constraints, a dynamic coupling strength model is constructed, and its formula is as follows:

[0082]

[0083] In the formula, Let be the coupling strength of the requirements between scene i and scene j at time t. , , The weights for electricity, heat, and hydrogen demand are respectively. Let t be the electrical load of scenario i at time t. Let t be the heat load of scenario j. Let t be the hydrogen load of scenario k at time t. , , These are the rated values ​​for electrical, thermal, and hydrogen loads, respectively. Let t be the low-carbon demand coefficient at time t, which is linked to the regional carbon quota and has a value of 0.8-1.2.

[0084] Based on the above model, the coupling strength between any two scenarios throughout the entire time period is calculated. , build The demand coupling matrix, where n is the number of scenarios and the matrix elements are the real-time coupling strength values ​​of the corresponding scenario pairs, intuitively presents the energy consumption correlation characteristics of multiple scenarios.

[0085] Based on the demand coupling matrix and combined with boundary conditions such as irradiation resources and equipment efficiency, the initial parameters of the photovoltaic array, energy storage equipment, electrothermal conversion unit and hydrogen energy unit are calculated respectively.

[0086] The formula for calculating the photovoltaic array capacity is as follows:

[0087] ;

[0088] In the formula, The photovoltaic array capacity (kW); Total electrical load (kW) for the entire scenario. This represents the maximum demand coupling strength across all time periods throughout the day. This is a correction factor for the amplitude of demand coupling fluctuations (calculated based on the standard deviation of load fluctuations, with a value of 0.05-0.15). The overall efficiency of photovoltaic equipment (including module efficiency and inverter efficiency, with a value of 0.85-0.90). The local average daily radiation intensity (kWh / (m²)) 2 ·d)); This is the curtailment rate correction factor (set according to the demand coupling fluctuation range, with a value of 0.03-0.08).

[0089] The formula for calculating the capacity of energy storage equipment is:

[0090] ;

[0091] In the formula, For energy storage device capacity; This represents the average demand coupling strength over different time periods. Provide real-time power for photovoltaic systems; This is the safety margin factor (with a value of 1.1-1.2). , The energy storage charging and discharging efficiency (values ​​range from 0.9 to 0.95); , The allowable charge state range for energy storage (values ​​0.8-1.0 and 0.2-0.3).

[0092] The formula for calculating the parameters of the electrothermal conversion unit is:

[0093] ;

[0094] In the formula, The thermal power of the electrothermal conversion unit; For maximum heat load, For thermal demand coupling strength; Efficiency of the electrothermal conversion equipment (value ranges from 0.9 to 0.98).

[0095] The formula for calculating the power of a hydrogen energy unit electrolyzer is:

[0096] ;

[0097] In the formula, This refers to the power of the electrolytic cell; For total hydrogen load, The coupling strength for hydrogen demand; Hydrogen has a low calorific value; The efficiency of hydrogen production in the electrolyzer (value ranges from 0.7 to 0.85).

[0098] Based on the initial parameters, a multi-objective optimization model is constructed that prioritizes low carbon emissions while also considering economic benefits and energy consumption. The optimal equipment configuration scheme is then solved using an algorithm.

[0099] Optimize target setting

[0100] 1. The primary objective is to minimize carbon emissions throughout the entire life cycle (Cybercarbon emissions). );

[0101] 2. The secondary objective is to minimize the total lifecycle cost. );

[0102] 3. The auxiliary objective is to maximize the renewable energy integration rate. ).

[0103] The life-cycle carbon emission accounting is as follows:

[0104] ;

[0105] In the formula, (Equipment manufacturing carbon emissions) = ∑ (equipment weight × carbon emission coefficient per unit weight). (Transportation carbon emissions) = Transportation distance × Carbon emission intensity of transportation vehicle × Equipment weight; (Operational carbon emissions) = ∑ (Fossil energy consumption × Carbon emission coefficient per unit of energy consumption); (Carbon emissions from waste disposal) = Equipment recycling rate × Carbon emission coefficient from recycling and disposal.

[0106] The full life cycle cost accounting is as follows:

[0107]

[0108] In the formula, (Initial investment) = ∑ (Equipment capacity × Unit capacity cost); (Operation and maintenance costs) = Annual operation and maintenance rate × ×Lifetime period; (Carbon trading revenue) = (Benchmark carbon emissions - actual carbon emissions) × carbon trading price.

[0109] The renewable energy integration rate is calculated as follows:

[0110] ;

[0111] Among them This refers to the actual amount of electricity consumed by photovoltaic power. This represents the total photovoltaic power generation. For the actual electricity consumed by other renewable energy sources, This is the total electricity generated by other renewable energy sources.

[0112] The constraints are as follows:

[0113] Equipment parameter constraints are , , , (Upper and lower limits are set according to the site and investment budget);

[0114] The operating status constraints are: the number of equipment start-stops ≤ the maximum allowed number of start-stops per day, and the ramp rate ≤ the maximum ramp rate of the equipment.

[0115] Energy balance constraints include electrical balance and thermal balance constraints:

[0116] The electrical balance is:

[0117] ;

[0118] In the formula, The real-time output of the photovoltaic array at time t is the actual electrical power generated by the photovoltaic system. Let t be the discharge power of the energy storage device. The total electrical load power at time t represents the total power demand across all scenarios, summarizing the electricity needs of rural life, agricultural planting, and other scenarios. The charging power of the energy storage device at time t; Let t be the electrical power consumed by the electrothermal conversion unit at time t, corresponding to the electrical energy consumption of the heat load; Let t be the electrical power consumed by the hydrogen electrolyzer at time t.

[0119] The thermal equilibrium is:

[0120] ;

[0121] , The output thermal power of the electrothermal conversion unit at time t is the effective thermal energy generated by the equipment. The total heat load power at time t represents the total heat load power across all scenarios, summarizing the heat demand for scenarios such as domestic heating and livestock insulation. Let t be the system heat loss power at time t, including heat dissipation from pipes and equipment, estimated at 5%-10% of the total output heat power.

[0122] Low-carbon and grid-connection constraints are that actual carbon emissions are ≤ regional carbon quotas and grid-connected power fluctuations are ≤ grid allowable ranges.

[0123] The steps to solve for the set objectives and constraints are as follows:

[0124] Step 1, initialize the population and encode device capacity parameters ( , , , The population size is set at 100-200.

[0125] Step 2: Calculate the fitness value of each individual (corresponding to the three optimization objectives);

[0126] Step 3: Generate offspring population through selection, crossover, and mutation operations, with a crossover probability of 0.8-0.9 and a mutation probability of 0.01-0.05;

[0127] Step 4: Perform non-dominated sorting based on the reference point method to select the Pareto optimal solution;

[0128] Step 5: Terminate the iteration (if the number of iterations is ≥200 or the objective function converges), and output the compromise solution in the Pareto optimal solution set as the optimal configuration.

[0129] The final specifications of the photovoltaic array capacity, energy storage equipment capacity, electrothermal conversion unit thermal power, and hydrogen electrolyzer power are determined, and an equipment configuration list is formed.

[0130] A three-tiered scheduling mechanism of "day-ahead planning - intraday correction - real-time control" is adopted to dynamically adjust the operating status of each energy unit.

[0131] The 24-hour output planning includes a control model based on an optimized scheduling model for typical scenario sets; its algorithm support is the K-means scenario clustering algorithm.

[0132] The implementation steps are as follows

[0133] Step 1: Use the K-means algorithm to cluster historical weather and load data into four typical scenarios: sunny days, cloudy days, weekdays, and holidays.

[0134] Step 2: Solve the multi-objective optimization model for each scenario and formulate a 24-hour output plan for each unit;

[0135] Step 3: Output the daily output plan as the basis for intraday scheduling.

[0136] The intraday 15-minute rolling correction includes a robust factor correction model as the control model, and the core formula is:

[0137]

[0138] In the formula, For output correction amount, The standard deviation of irradiation and load fluctuation. The robustness coefficient (values ​​range from 0.8 to 1.2);

[0139] The implementation steps involve updating the demand coupling strength every 15 minutes. The standard deviation of irradiance and load fluctuation is calculated, and the day-ahead power output plan is corrected through a robust factor to generate optimized operation instructions for the current period.

[0140] The real-time 1-minute response control includes a control model that dynamically adjusts the demand coupling strength.

[0141] The implementation steps involve collecting real-time changes in demand coupling strength every minute. By using the MPC algorithm to predict the load and new energy output in the next 5 minutes, the energy storage charging and discharging power, the operating status of the electrothermal conversion unit, and the power of the electrolytic cell are dynamically adjusted to ensure energy balance and constraint satisfaction.

[0142] By integrating the results of the three-level scheduling, a full-time operation strategy document for each energy unit is formed, which clarifies the hourly output value, start-stop status, and control parameters.

[0143] Through simulation and deviation feedback, dynamic iteration of system optimization is achieved.

[0144] A simulation platform was built based on MATLAB / Simulink, integrating a meteorological data module (inputting local hourly irradiance and temperature data), a load module (inputting measured electrical / thermal / hydrogen load data), an equipment model module (mechanical models of photovoltaic, energy storage, electrothermal conversion, and electrolyzer), and a control module (three-layer scheduling logic).

[0145] The calculation of core indicators includes:

[0146] The carbon emission reduction rate is:

[0147] ;

[0148] In the formula, For carbon emissions from traditional energy systems, To optimize the carbon emissions of the system;

[0149] Overall cost savings rate:

[0150] ;

[0151] In the formula, For the cost of traditional energy systems, To optimize system costs;

[0152] The renewable energy integration rate is:

[0153] ;

[0154] In the formula, For renewable energy consumption rate; This refers to the actual amount of electricity consumed by photovoltaic power. This represents the total photovoltaic power generation.

[0155] For the actual electricity consumed by other renewable energy sources; This is the total electricity generated by other renewable energy sources.

[0156] Operational parameter deviation analysis, the deviation calculation formula is:

[0157] ;

[0158] In the formula, Let be the deviation rate of the k-th indicator. These are simulated measured index values. The index value corresponding to the optimal operating strategy;

[0159] Deviation judgment is when When this occurs, closed-loop optimization is triggered, and the steps are as follows:

[0160] Step 1, using the deviation rate Calculate the gradient of the objective function. ;

[0161] Step 2, setting the iteration step size (Adjust dynamically according to the magnitude of the deviation) );

[0162] Step 3, update the parameters of the multi-objective optimization model as follows:

[0163] ;

[0164] In the formula, To optimize the model parameters before and after optimization, including weights and constraint boundaries; This is the iteration step size; The calculation gradient is determined for the deviation rate.

[0165] Step 4 involves repeating steps two through four until all indicators have a deviation rate of ≤5%.

[0166] The system outputs the optimal equipment configuration and operation strategy after closed-loop iteration, forming a feasible system integration optimization solution.

[0167] The preferred embodiments of the present invention have been described in detail above with reference to the accompanying drawings. These embodiments are merely descriptions of preferred embodiments and are not intended to limit the scope or concept of the invention. The specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. Such combinations, as long as they do not violate the spirit of the present invention, should also be considered as part of this disclosure. To avoid unnecessary repetition, the present invention will not further describe the various possible combinations.

[0168] This invention is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this invention and without departing from the design idea of ​​this invention, all modifications and improvements made by those skilled in the art to the technical solutions of this invention should fall within the protection scope of this invention. The technical content for which protection is sought in this invention has been fully described in the claims.

Claims

1. A green and low-carbon oriented integrated optimization method for rural photovoltaic-storage-electricity-thermal-hydrogen multi-energy complementary systems, characterized in that, Includes the following steps: S1: Analyze the energy consumption patterns of rural life, planting, breeding, and small-scale processing scenarios, construct an electric-thermal-hydrogen demand coupling strength model with electric load, heat load, and hydrogen load as the core, and output a demand coupling matrix that reflects the degree of energy consumption correlation between different scenarios. S2: Based on the demand coupling matrix, combined with the maximum demand coupling intensity, irradiance resources and energy storage charging and discharging efficiency at each time of the day, calculate the photovoltaic array capacity, energy storage equipment capacity, electrothermal conversion unit parameters and hydrogen energy unit electrolyzer power, and determine the initial parameters of each energy unit. S3: Based on the initial parameters of the unit, construct a multi-objective coupled optimization model that prioritizes low carbon emissions while taking into account economic and renewable energy consumption. Calculate the carbon emissions and costs of the equipment throughout its entire life cycle, incorporate initial investment, operation and maintenance costs and carbon trading revenue, clarify equipment constraints, and solve to determine the optimal configuration scheme for each unit. S4: Through a three-layer scheduling mechanism of 24-hour output planning, 15-minute rolling correction within the day, and 1-minute real-time response control, the optimal operating strategy for each unit is output. S5: Based on the simulation platform, input the actual meteorological data and load data of the rural area to simulate the system operation and verify the three major indicators of carbon emission reduction rate, comprehensive cost saving rate and renewable energy consumption rate; calculate the parameter deviation between the actual operating parameters and the optimal operating strategy in S4; when the deviation of any indicator exceeds 5%, input the reverse input to S3 to adjust the multi-objective optimization model and form a closed-loop optimization.

2. The integrated optimization method for a green and low-carbon rural photovoltaic-storage-electricity-thermal-hydrogen multi-energy complementary system according to claim 1, characterized in that, Step S1 specifically involves analyzing the energy consumption patterns of four core scenarios: rural life, planting, animal husbandry, and small-scale processing. Combined with the characteristics of load agglomeration after village mergers, a coupling strength model for electricity, heat, and hydrogen demand is constructed. The model's coupling strength index uses three load categories—electricity, heat, and hydrogen—as core values, assigning weights according to rural energy consumption priorities. Electricity consumption has the highest weight, followed by hydrogen load due to the specificity of the scenario, while heat load weight is adapted to actual demand allocation. Simultaneously, the model integrates real-time load and rated load limits for each scenario, and incorporates a low-carbon demand coefficient linked to regional carbon quotas. When carbon quotas are tight, the coefficient is adjusted upwards to strengthen low-carbon constraints, outputting a demand coupling matrix reflecting the close correlation between energy consumption across different scenarios.

3. The integrated optimization method for a green and low-carbon rural photovoltaic-storage-electricity-thermal-hydrogen multi-energy complementary system according to claim 1, characterized in that, Step S2 specifically involves using the demand coupling matrix output in step S1 as a basis, and combining the maximum demand coupling intensity, irradiance resources, and energy storage charging and discharging efficiency for each time period of the day, to calculate the photovoltaic array capacity, energy storage equipment capacity, electrothermal conversion unit parameters, and hydrogen energy unit electrolyzer power. The photovoltaic array capacity is calculated by combining the maximum demand coupling strength, total electrical load, local irradiance resources, and overall efficiency of photovoltaic equipment throughout the day, with the addition of a curtailment rate correction coefficient set according to the demand coupling fluctuation amplitude. The capacity of the energy storage device is calculated by taking the difference between the total electrical load corresponding to the average demand coupling strength in each time period and the real-time output of the photovoltaic as the core, combined with the energy storage charging and discharging efficiency and the allowable charge state range. The parameters of the electrothermal conversion unit are calculated by combining the thermal power of the electric boiler with the coupling strength of thermal demand, the maximum heat load and the equipment efficiency. The power of the hydrogen energy unit electrolyzer is calculated based on the hydrogen load demand, combined with the hydrogen demand coupling strength, the total hydrogen load size, the lower calorific value of hydrogen, and the hydrogen production efficiency of the electrolyzer.

4. The integrated optimization method for a green and low-carbon rural photovoltaic-storage-electricity-thermal-hydrogen multi-energy complementary system according to claim 1, characterized in that, Step S3, based on the initial unit parameters from step S2, constructs a multi-objective optimization model and clarifies the constraints, then optimizes and determines the final specification parameters of each unit. This specifically includes the following steps: S31: Set core optimization goals: Prioritize low carbon emissions while taking into account economic factors and renewable energy consumption, and establish three major goals: minimize carbon emissions throughout the entire life cycle, minimize the cost throughout the entire life cycle, and maximize the renewable energy consumption rate. S32: Target Quantification Accounting: Carbon emission accounting covers the entire life cycle of equipment, cost accounting includes initial investment, operation and maintenance costs and carbon trading revenue, and the absorption rate accounting deducts the curtailed portion to quantify the utilization efficiency of renewable energy; S33: Define clear constraints: Set equipment parameter ranges, start-up and ramp-up rate limits, establish electrical balance and thermal balance constraints, and incorporate carbon emission quotas and grid connection standards constraints. S34: Optimization parameter solution: Based on the set objectives and constraints, the final specification parameters of each energy unit are obtained to form the optimal configuration scheme.

5. The integrated optimization method for a green and low-carbon rural photovoltaic-storage-electricity-thermal-hydrogen multi-energy complementary system according to claim 1, characterized in that, Step S4 focuses on planning the optimal configuration scheme based on step S3, and intraday rolling correction and real-time response control are adjusted based on the demand coupling strength of step S1.

6. A green and low-carbon oriented integrated optimization method for a rural photovoltaic-storage-electricity-thermal-hydrogen multi-energy complementary system according to any one of claims 1 or 5, characterized in that, The scheduling system is based on the optimization model of step S3, which is solved by typical scenarios of sunny days, cloudy days, weekdays and holidays. The output plan of each unit is formulated 24 hours in advance as the daily scheduling benchmark. The daily rolling process updates the demand coupling strength of step S1 every fifteen minutes, and combines it with a robust factor constructed from the demand coupling strength, irradiance, and load fluctuation standard deviation to correct the output plan. Real-time control dynamically adjusts control commands every minute based on changes in the coupling strength of demand, ultimately outputting the optimal operating strategy for each unit.

7. The integrated optimization method for a green and low-carbon oriented rural photovoltaic-storage-electricity-thermal-hydrogen multi-energy complementary system according to claim 1, characterized in that, Step S5 specifically includes the following steps: S51: Building a simulation platform: Build a simulation platform based on MATLAB or Simulink, input actual rural meteorological data and load data, and simulate the system operation status; S52: Core Indicator Verification: Verify the actual performance of the three core indicators—carbon emission reduction rate, overall cost saving rate, and renewable energy consumption rate—and test the feasibility of the optimization strategy. S53: Deviation Calculation: Calculate the difference between the actual operating parameters obtained from the simulation and the optimal parameters in step S4; S54: Dynamic Iterative Update: When the deviation of any indicator exceeds 5%, the multi-objective optimization model of iterative update step S3 is immediately started, and the iteration step size is adjusted according to the deviation.

8. A green and low-carbon oriented integrated optimization method for a rural photovoltaic-storage-electricity-thermal-hydrogen multi-energy complementary system according to any one of claims 1 or 2, characterized in that, The specific model of the coupling strength of the electricity-heat-hydrogen demand constructed in step S1 is as follows: ； In the formula, Let be the coupling strength of the requirements between scene i and scene j at time t. , , The weights for electricity, heat, and hydrogen demand are respectively. Let t be the electrical load of scenario i at time t. Let t be the heat load of scenario j. Let t be the hydrogen load of scenario k at time t. , , These are the rated values ​​for electrical, thermal, and hydrogen loads, respectively. The low-carbon demand coefficient at time t is linked to the regional carbon quota and ranges from 0.8 to 1.

2.

9. The integrated optimization method for a green and low-carbon oriented rural photovoltaic-storage-electricity-thermal-hydrogen multi-energy complementary system according to claim 8, characterized in that, The multiple scenarios mentioned in step S1 include rural life scenarios, agricultural planting scenarios, breeding scenarios, and small-scale processing scenarios, with each scenario having a weight. , , The values ​​were determined using the analytic hierarchy process (AHP), and all values ​​ranged from 0.2 to 0.5, and satisfied the following conditions: .

10. A green and low-carbon oriented integrated optimization method for a rural photovoltaic-storage-electricity-thermal-hydrogen multi-energy complementary system according to any one of claims 1 or 7, characterized in that, Step S5 verifies the system's operational deviation through simulation. When any indicator deviation exceeds 5%, iterative optimization is triggered. The iteration step size is dynamically adjusted according to the deviation size, ranging from 0.1 to 0.3.

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