Multi-energy complementary zero-carbon service area construction method

Abstract

The invention discloses a multi-energy complementation zero-carbon service area construction method, and relates to the technical field of multi-energy complementation, and the construction method comprises the steps: selecting an optimal construction address based on the topographic data of a research area, evaluating the carbon emission at the optimal construction address in combination with the energy consumption prediction, and generating a zero-carbon construction scheme through the carbon emission; on the basis of the zero-carbon construction scheme and climate conditions, designing a construction scheme of the zero-carbon service area at the optimal construction address, and constructing the zero-carbon service area according to the construction scheme; the operation data of the zero-carbon service area is acquired by using a sensor, and control logic is generated based on the operation data to adjust the operation state of each energy facility so as to realize the purpose of multi-energy complementation. According to the invention, the energy utilization efficiency is improved, the carbon emission is reduced, the self-management and optimization capability of the service area is enhanced, and a new thought and technical support are provided for green development of the traffic transportation industry.

Description

Technical Field

[0001] The present invention relates to the field of multi-energy complementary technology, and in particular to a method for constructing a multi-energy complementary zero-carbon service area. Background Art

[0002] In the field of transportation, highway service areas are important infrastructure, and their energy consumption and carbon emissions cannot be ignored. Therefore, exploring a multi-energy complementary zero-carbon service area construction method is of great significance to promoting the green transformation of the transportation industry. The widespread application of clean energy utilization technologies in existing technologies, such as solar photovoltaic power generation, wind power generation and geothermal energy, provides service areas with clean and sustainable energy supply, significantly reducing dependence on fossil fuels. At the same time, the development of energy storage technology effectively solves the problems of intermittent and instability of clean energy. By storing and releasing energy, the stability and reliability of the energy supply in the service area can be ensured. The application of microgrid technology enables the service area to build an independent and intelligent power supply system to achieve local self-sufficiency and efficient configuration of energy.

[0003] However, existing technologies lack a unified smart energy management and control platform to integrate and optimize the use of various energy sources, which may lead to low energy efficiency and difficulty in dynamic monitoring and management of carbon emissions. At the same time, existing technologies often only focus on a certain stage of service area construction or operation, but ignore low-carbon design throughout the entire life cycle from planning to demolition, resulting in low-carbon construction in the early stage, but high operating and maintenance costs or carbon emissions rebound in the later stage. There is also a situation where only one or a few clean energy sources are relied upon, such as only using solar energy or wind energy. There is a lack of multi-energy complementary design, which leads to unstable energy supply under specific climatic conditions, affecting the normal operation of the service area.

[0004] At the same time, existing technologies may not fully consider the waste treatment issues in service areas, or the waste treatment systems may be inefficient, which may lead to environmental pollution and waste of resources. In addition, greening and ecological restoration are not considered in the construction of service areas, or greening measures are insufficient, resulting in low ecological environment quality and insufficient carbon sequestration capacity in service areas.

[0005] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0006] In view of the problems in the related technology, the present invention proposes a method for constructing a multi-energy complementary zero-carbon service area to overcome the above-mentioned technical problems existing in the existing related technology.

[0007] To this end, the specific technical solution adopted by the present invention is as follows: A method for constructing a multi-energy complementary zero-carbon service area, the method comprising: Select the best construction site based on the topographic data of the study area, and evaluate the carbon emissions at the best construction site in combination with the energy consumption forecast, and use the carbon emissions to generate a zero-carbon construction plan; Based on the zero-carbon construction plan and climate conditions, design a construction plan for the zero-carbon service area at the best construction location, and build the zero-carbon service area in accordance with the construction plan; Sensors are used to collect operational data of zero-carbon service areas, and control logic is generated based on the operational data to adjust the operating status of each energy facility to achieve multi-energy complementarity.

[0008] Preferably, the best construction site is selected based on the topographic data of the study area, and the carbon emissions at the best construction site are evaluated in combination with the energy consumption forecast. The zero-carbon construction plan is generated using the carbon emissions, including: The construction site selection evaluation index is generated based on the elevation data, ecological environment data, solar radiation and annual average wind speed in the study area, and the best construction site is selected by combining the superior and inferior solution distance method; Obtain the electricity and heat consumption at the best construction site, and calculate the corresponding carbon emissions at the best construction site based on consumption and energy consumption prediction technology; The carbon substitution and carbon sink amounts at the optimal construction site are predicted based on carbon emissions, and the energy configuration and layout at the optimal construction site are determined based on the carbon substitution and carbon sink amounts to obtain a zero-carbon construction plan.

[0009] Preferably, the construction site selection evaluation index is generated based on the elevation data, ecological environment data, solar radiation and annual average wind speed in the study area, and the best construction address is selected by combining the superior and inferior solution distance method, including: Use LiDAR to obtain the digital elevation model of the study area, and process the digital elevation model based on geographic information software to generate a slope map, and evaluate the development difficulty of the study area based on the slope map; Obtain ecological and environmental data in the study area, and use the ecological and environmental data as a basis to predict the ecological and environmental impact of the study area after the construction of the zero-carbon service area is completed; Evaluate the solar and wind energy resources in the study area based on solar radiation and annual average wind speed; The development difficulty, ecological environment impact, solar energy resources and wind energy resources are used as evaluation indicators, and the scaling method is used to score each parameter in the evaluation indicators; Determine the weights of the evaluation indicators, and combine the weight results with the superior and inferior solution distance method to construct a matrix to analyze the relative proximity of each construction address in the candidate area, and determine the best construction address based on the analysis results.

[0010] Preferably, the solar energy resources and wind energy resources in the research area are evaluated based on solar radiation and annual average wind speed, including: Obtain the actual and estimated sunshine hours corresponding to the study area, and analyze the solar radiation in the study area based on sunshine information and historical solar radiation data; Obtain wind condition data for the study area within a preset time period, analyze wind speed observations for each period based on the wind condition data, and use wind speed observations to calculate the annual average wind speed for the study area; The wind power density is obtained based on the annual average wind speed and the air density corresponding to the study area, and the exploitability of solar energy resources and wind energy resources in the study area is evaluated based on the radiation and wind power density.

[0011] Preferably, the weights of the evaluation indicators are determined, and the weight results are combined with the superior and inferior solution distance method to construct a matrix to analyze the relative proximity of each construction address in the candidate area. The optimal construction address is determined based on the analysis results, including: Based on the scoring results of each parameter in the evaluation index and the hierarchical analysis method, a judgment matrix is ​​generated, and the product of each row in the judgment matrix is ​​calculated, and the square root of the product result is calculated to obtain the square root value; After normalizing the square root value, the eigenvector is obtained, and the eigenvector is multiplied with the judgment matrix by matrix multiplication, and the weight of each parameter in the evaluation index is obtained based on the processing result; The superior-inferior solution distance method is used to establish a decision matrix including evaluation indicators and construction addresses, and the decision matrix is ​​standardized based on the properties of the evaluation indicators. According to the weight results, the standardized decision matrix is ​​weighted to construct a weighted normalized matrix, and the maximum column value and the minimum column value in the weighted normalized matrix are determined to obtain an ideal solution and a negative ideal solution; The distance between each construction address and the ideal solution and the negative ideal solution is calculated, and the relative proximity of each construction address is calculated based on the distance result. The relative proximity is sorted from high to low, and the construction address with the highest relative proximity is selected as the best selection result.

[0012] Preferably, obtaining the electricity and heat consumption at the optimal construction location, and calculating the corresponding carbon emissions at the optimal construction location based on the consumption and energy consumption prediction technology includes: The building area and land load indicators corresponding to the optimal construction address are used to predict electricity consumption, and the carbon emissions generated by electricity are obtained based on electricity consumption and electricity emission factors; Analyze the heat consumption of the best construction site based on the building heating index, heating area conversion coefficient and building area, and obtain the carbon emissions generated by heat based on the heat use emission factor and heat consumption; The corresponding carbon emissions at the optimal construction site are calculated based on the carbon emissions generated by electricity and heat.

[0013] Preferably, the carbon substitution and carbon sink amount at the optimal construction site are predicted based on the carbon emissions, and the energy configuration and layout at the optimal construction site are determined according to the carbon substitution and carbon sink amount to obtain a zero-carbon construction plan, which includes: Based on carbon emissions, obtain the annual power generation generated by installing photovoltaics and wind turbines at the best construction site, and calculate the carbon substitution amount based on the annual power generation and power emission factors; Obtain greening area and carbon sink factors to analyze the carbon removal and carbon offset of green plants at the best construction site, and analyze the carbon sink based on the carbon removal and carbon offset; The carbon emission reduction rate at the best construction site is calculated based on the carbon substitution amount and the carbon sink amount, and the renewable energy at the best construction site is determined according to the carbon emission reduction rate to obtain the energy configuration type; The layout and capacity of energy facilities are planned based on the energy configuration type, the construction needs and building functions are clarified by combining the spatial layout and slope map at the optimal construction address, and the functional areas are divided to obtain a zero-carbon construction plan.

[0014] Preferably, the calculation formula for the carbon substitution amount is: ; The calculation formula for carbon sequestration is: ; The calculation formula for carbon emission reduction rate is: ; In the formula, R r represents the amount of carbon substitution, R g represents the carbon sink, R represents the carbon emission reduction rate, AD r represents the annual power generation, EF e represents the electricity emission factor, S g Indicates the green area at the best construction address, EF g represents the carbon sink factor, E e represents the carbon emissions of electricity, E h Indicates carbon emissions from heat generation.

[0015] Preferably, using sensors to collect the operating data of the zero-carbon service area, and generating control logic based on the operating data to adjust the operating status of each energy facility to achieve the purpose of multi-energy complementarity includes: Use sensors to obtain usage data, traffic flow and environmental data of each energy facility in the zero-carbon service area to form operational data, and transmit the operational data to the data center for analysis; Develop intelligent dispatching strategies and control logic based on data analysis results, transmit the control logic to the Internet of Things, and use the Internet of Things to optimize and adjust the operating status of various energy facilities; Optimize the scheduling and control strategies based on the real-time operation feedback of each energy facility and the changes in the external environment of the zero-carbon service area to achieve the goal of multi-energy complementarity.

[0016] Preferably, the usage data of each energy facility includes carbon inventory data, carbon footprint data and emission warning data; Carbon inventory data is the carbon emission data generated by purchased electricity, purchased heat and purchased fossil energy of each energy facility; Carbon footprint data refers to the carbon emissions generated by each device in each energy facility; The emission warning data is the comparison result between the real-time carbon emission data and the historical carbon emission data and carbon emission standards of each energy facility.

[0017] The beneficial effects of the present invention are: 1. The present invention realizes the efficient, intelligent and low-carbon energy utilization of service areas through the comprehensive application of multi-energy complementary clean energy, intelligent energy management and control, and low-carbon design and construction methods throughout the entire life cycle, thereby improving energy utilization efficiency, reducing carbon emissions, and enhancing the self-management and optimization capabilities of service areas, providing new ideas and technical support for the green development of the transportation industry.

[0018] 2. The present invention integrates a variety of clean energy technologies, energy-saving design methods and intelligent management and control measures to achieve low-carbon construction and sustainable zero-carbon operation of service areas, thereby solving the problems of high carbon emissions, low energy utilization efficiency and lack of effective multi-energy complementary systems in the construction and operation of highway service areas in the prior art.

[0019] 3. The present invention emphasizes low-carbon design and construction methods throughout the entire life cycle, ensuring that low-carbon measures are taken at all stages from planning, design, construction to operation to achieve long-term low-carbon operation of the service area. At the same time, by introducing intelligent management and control operations, real-time monitoring and optimal scheduling of various energy uses in the service area are achieved, thereby improving energy utilization efficiency and reducing carbon emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 It is a flow chart of a method for constructing a multi-energy complementary zero-carbon service area according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention and are mainly used to illustrate the embodiments. They can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, ordinary technicians in this field should be able to understand other possible implementation methods and advantages of the present invention.

[0023] According to an embodiment of the present invention, a method for constructing a multi-energy complementary zero-carbon service area is provided.

[0024] It should be explained that a zero-carbon service area refers to a service area that ensures that the use function of the service area is fully met, and that the net greenhouse gas emissions during the operation phase reach or are equal to or less than zero by taking energy-saving and consumption-reducing measures, utilizing renewable resources, and leveraging the carbon sink function of vegetation. According to this definition, the boundaries of a zero-carbon service area can be clearly divided into three aspects: (1) time boundary, that is, the operation cycle is limited to one natural year; (2) spatial boundary, which is based on the actual land area occupied by the service area; (3) carbon emissions during the operation period must be less than or equal to zero. This setting is based on the consideration that the carbon emissions of the service area during the operation period account for 60%-80% of its carbon emissions over its entire life cycle. Therefore, the realization of zero-carbon service areas on highways focuses on carbon emission control during their operation phase. In order to better achieve this goal, it is necessary to comprehensively consider all stages of the service area from planning, design, construction to operation and maintenance, and strictly control all dimensions such as source, process and operation and maintenance management, so as to ultimately achieve the goal of zero-carbon service areas on highways.

[0025] The present invention is further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to the method for constructing a multi-energy complementary zero-carbon service area according to an embodiment of the present invention, the construction method includes: Step S1, selecting the best construction site based on the terrain data of the study area, and evaluating the carbon emissions at the best construction site in combination with the energy consumption forecast, and using the carbon emissions to generate a zero-carbon construction plan; Step S2, based on the zero-carbon construction plan and climate conditions, design a construction plan for the zero-carbon service area at the optimal construction address, and construct the zero-carbon service area according to the construction plan; Step S3, using sensors to collect the operating data of the zero-carbon service area, and generating control logic based on the operating data to adjust the operating status of each energy facility to achieve the purpose of multi-energy complementarity.

[0026] In this embodiment, when selecting the best construction site based on the terrain data of the study area, and evaluating the carbon emissions at the best construction site in combination with the energy consumption forecast, and generating a zero-carbon construction plan using carbon emissions, the construction site selection evaluation index can be generated based on the elevation data, ecological environment data, solar radiation and annual average wind speed in the study area, and the best construction site can be selected in combination with the superior and inferior solution distance method; the electricity and heat consumption at the best construction site is obtained, and the corresponding carbon emissions at the best construction site are calculated based on the consumption and energy consumption prediction technology; the carbon substitution and carbon sink at the best construction site are predicted based on carbon emissions, and the energy configuration and layout at the best construction site are determined based on the carbon substitution and carbon sink to obtain a zero-carbon construction plan.

[0027] It should be noted that in the early stage of service area construction, detailed on-site investigation and feasibility analysis should be carried out to clarify the functional positioning, carbon emission targets and energy needs of the service area. Remote sensing technology should be used to analyze the topography and climate resources; collect high-precision terrain data of the study area, conduct slope and aspect analysis, identify flat or gently sloping areas suitable for construction, conduct terrain undulation analysis, and evaluate the difficulty of site development; conduct potential environmental impact analysis, collect climate data of the study area, such as temperature, precipitation, wind direction and speed, analyze the distribution of solar radiation intensity, determine the best direction and angle for installing photovoltaic panels, analyze wind speed and direction data, and evaluate the potential of wind energy resources; combine the results of topography and climate resource analysis, conduct a comprehensive evaluation of the site selection for service area construction, and determine the optimal construction plan.

[0028] In this embodiment, when generating construction site selection evaluation indicators based on elevation data, ecological environment data, solar radiation and annual average wind speed in the study area, and selecting the best construction address in combination with the superior and inferior solution distance method, laser radar can be used to obtain a digital elevation model of the study area, and the digital elevation model can be processed based on geographic information software to generate a slope map, and the development difficulty of the study area can be evaluated according to the slope map; the ecological environment data in the study area is obtained, and the ecological environment impact of the study area after the construction of the zero-carbon service area is completed is predicted based on the ecological environment data; the solar energy resources and wind energy resources in the study area are evaluated based on solar radiation and annual average wind speed; the development difficulty, ecological environment impact, solar energy resources and wind energy resources are used as evaluation indicators, and the scaling method is used to score each parameter in the evaluation indicator; the weight of the evaluation indicator is determined, and the weight result is combined with the superior and inferior solution distance method to construct a matrix to analyze the relative proximity of each construction address in the candidate area, and the best construction address is determined based on the analysis results.

[0029] It should be noted that when conducting terrain undulation analysis and evaluating the difficulty of site development, satellite remote sensing images (such as Landsat, Sentinel series, etc.) or LiDAR data can be used to obtain high-precision DEM (digital elevation model) data of the study area, and GIS (geographic information system) software can be used to process the DEM data to generate a slope map. According to the terrain undulation distribution map and the actual situation of engineering construction, the difficulty of site development can be evaluated. Generally speaking, areas with large terrain undulations have relatively high difficulty and cost of engineering construction, while areas with relatively flat terrain are conducive to the construction and operation of service areas. According to the difficulty of development, the terrain, geology, climate, construction cost, etc. are comprehensively considered to conduct an assessment and score, and the score range is set at 1 to 5 points.

[0030] At the same time, when conducting a potential ecological and environmental impact analysis and evaluating the impact of service area construction on the surrounding natural ecosystems, biodiversity, vegetation cover, etc., it is necessary to analyze the impact of vehicle emissions on air quality during the operation of the service area, including the emission of pollutants such as carbon dioxide and nitrogen oxides, consider the impact of sewage discharge and rainwater runoff in the service area on the water quality of surrounding water bodies, evaluate the impact of noise generated by vehicle traffic and personnel activities in the service area on surrounding residents and the ecological environment, analyze the changes in the original land use pattern caused by the construction of the service area, including the reduction of agricultural land and the increase of industrial land, and conduct an assessment and score based on the level or specific measurement of the impact on the ecological environment, with the score range set at 1 to 5 points.

[0031] In this embodiment, when evaluating the solar energy resources and wind energy resources in the study area based on the solar radiation and the annual average wind speed, the actual sunshine hours and the estimated sunshine hours corresponding to the study area can be obtained, and the solar radiation of the study area can be analyzed based on the sunshine information and the historical solar radiation data; the wind condition data of the study area within a preset time period is obtained, and the wind speed observation values ​​of each time period are analyzed based on the wind condition data, and the annual average wind speed of the study area is calculated using the wind speed observation values; the wind power density is obtained based on the annual average wind speed and the air density corresponding to the study area, and the exploitability of the solar energy resources and wind energy resources in the study area is evaluated based on the radiation and wind power density.

[0032] When implementing the solar energy resource and wind energy resource assessment, calculations and assessments are performed based on the exploitable solar energy resources, and scores are assigned with a score range of 1 to 5 points.

[0033] The calculation formula for solar radiation is: ; In the formula, H It represents the average annual solar radiation on the horizontal plane (unit: kWh / m2 / year). H0 represents the standard solar radiation, which is generally 1367 watts per square meter. and b It represents the empirical coefficient, which can be obtained by fitting the local actual sunshine hours, possible sunshine hours and long-term observed solar radiation data. n Indicates the actual sunshine hours (unit: hours), N Indicates the number of possible sunshine hours (usually calculated from astronomical data).

[0034] Calculate and evaluate the exploitable wind energy resources and give a score ranging from 1 to 5 points. Specifically, the wind speed observation values ​​of each period are analyzed based on the wind condition data, and the annual average wind speed of the study area is obtained using the wind speed observation values. The wind power density is obtained based on the annual average wind speed and the air density corresponding to the study area. The annual average wind speed calculation formula is: ; In the formula, Indicates the annual average wind speed (unit: m / s), υ i Indicates the wind speed observation value at each time period (unit: m / s), m Indicates the number of observation periods.

[0035] The wind power density calculation formula is: ; In the formula, P represents wind power density (unit: watts per square meter), ρ Represents the air density (unit: kg / m3), usually 1.225 kg / m3.

[0036] In this embodiment, when determining the weight of the evaluation index, and combining the weight result with the superior and inferior solution distance method to construct a matrix to analyze the relative proximity of each construction address in the candidate area, and determining the best construction address based on the analysis result, a judgment matrix can be generated based on the scoring results of each parameter in the evaluation index and the hierarchical analysis method, and the product of each row in the judgment matrix is ​​calculated, and the square root calculation is performed on the product result to obtain the square root value; after performing a normalization operation on the square root value, a eigenvector is obtained, and the eigenvector and the judgment matrix are subjected to matrix multiplication processing, and the weight of each parameter in the evaluation index is obtained based on the processing result; a decision matrix including the evaluation index and each construction address is established by using the superior and inferior solution distance method, and the decision matrix is ​​standardized based on the properties of the evaluation index; the standardized decision matrix is ​​weighted according to the weight result to construct a weighted normalized matrix, and the column maximum value and column minimum value in the weighted normalized matrix are determined to obtain an ideal solution and a negative ideal solution; the distance between each construction address and the ideal solution and the negative ideal solution is calculated, and the relative proximity of each construction address is calculated based on the distance result, and the relative proximity is sorted from high to low, and the construction address with the highest relative proximity is selected as the best selection result.

[0037] It should be explained that when selecting the zero-carbon service area site selection plan, AHP (analytic hierarchy process) is first used to determine the weights of the evaluation indicators (development difficulty, potential ecological and environmental impact, solar energy resources, and wind energy resources), and TOPSIS (top-to-bottom solution distance method) is used to rank these plans to select the best service area site selection plan. The specific steps are as follows: (1) Use AHP to construct a judgment matrix, calculate eigenvalues ​​and eigenvectors, and obtain the weight of each evaluation index; in the criterion layer, each criterion needs to be compared pairwise to construct a judgment matrix. In this process, the 1-5 scale method is used to quantify the relative importance of the two criteria. Considering the importance of ecological and environmental protection, the potential ecological and environmental impact is set to be higher than the development difficulty. At the same time, given that this embodiment is aimed at the site selection of the zero-carbon service area construction method, the natural endowment characteristics of new energy have a higher priority than the site selection of ordinary service areas. Therefore, the weight of solar energy resources and wind energy resources in the judgment matrix should be higher than the development difficulty and potential ecological and environmental impact. In actual operation, since the laying of photovoltaic panels is more convenient and the service area is limited, it is not suitable to install wind turbines in many cases. Therefore, solar energy resources are considered to be superior to wind energy resources. Based on the above analysis, the construction of the judgment matrix should reflect the following relationship: solar energy resources> wind energy resources> potential ecological and environmental impact> development difficulty. The specific judgment matrix diagram is shown in Table 1: Table 1 Judgment matrix diagram

[0038] Compute the product of each row: Development difficulty (1*1 / 3*1 / 5*1 / 5=1 / 75); Potential ecological and environmental impact (3*1*1 / 3*1 / 3=1 / 3); Solar energy resources (5*3*1*2=30); Wind energy resources (5*3*1 / 2*1=15 / 2).

[0039] Calculate the fourth root of the product of each row: development difficulty (0.359), potential ecological and environmental impact (0.760), solar energy resources (2.340), and wind energy resources (1.667).

[0040] Normalization: Development difficulty (0.359 / (0.359+0.760+2.340+1.667)≈0.078); Potential ecological and environmental impact (0.760 / (0.359+0.760+2.340+1.667)≈0.165); Solar energy resources (2.340 / (0.359+0.760+2.340+1.667)≈0.507); Wind energy resources (1.667 / (0.359+0.760+2.340+1.667)≈0.359).

[0041] The eigenvector (0.078, 0.165, 0.507, 0.359) and the judgment matrix are multiplied and then divided by the corresponding eigenvector element, and the sum is obtained to obtain the maximum eigenvalue. ≈4.121, calculate the confidence interval CI=( - d ) / ( d -1), where d =4 (4th-order matrix), after calculation, CI≈0.040, for the 4th-order matrix, consistency RI=0.89 (obtained by referring to the random consistency RI table), the calculated consistency ratio CR=CI / RI≈0.044, which is much less than 0.1, so it is judged that the matrix has satisfactory consistency.

[0042] Therefore, the weights of each indicator are as follows: development difficulty (0.078), potential ecological and environmental impact (0.165), solar energy resources (0.507), and wind energy resources (0.359).

[0043] (2) Use the TOPSIS method to construct a decision matrix and normalize the matrix; construct a decision matrix in which each row represents a candidate address and each column represents an evaluation criterion (i.e., indicator). Assume that m candidate addresses and 4 evaluation criteria, the decision matrix will be am ×4 matrix. Since the data of different criteria may have different dimensions and units, they need to be standardized to eliminate the impact of the dimensions. The standardization method can be selected according to the nature of the criterion (benefit type or cost type). For benefit type criteria (such as solar energy resources and wind energy resources), the following formula is used for standardization: ; For cost-based criteria (such as development difficulty and potential ecological and environmental impact), the following formula is used for standardization: ; In the formula, χ ij represents the elements in the original decision matrix, Represents the elements in the standardized decision matrix.

[0044] The weights obtained by AHP are used to weight the normalized matrix to construct a weighted normalized matrix. The standardized decision matrix is ​​multiplied by the weights to construct a weighted normalized decision matrix, as follows: ; In the formula, v ij represents the weighted normalization matrix, Indicates j The weights of the three criteria are as follows: development difficulty (0.078), potential ecological and environmental impact (0.165), solar energy resources (0.507), and wind energy resources (0.359).

[0045] (3) Determine the positive ideal solution and the negative ideal solution, that is, the maximum and minimum values ​​of each column in the weighted normalized matrix. In the weighted normalized matrix, find the maximum and minimum values ​​under each criterion to form the ideal solution and the negative ideal solution respectively; the ideal solution is the set of optimal values ​​(for benefit-based criteria) or worst values ​​(for cost-based criteria) under each criterion, while the negative ideal solution is the set of worst values ​​(for benefit-based criteria) or optimal values ​​(for cost-based criteria) under each criterion.

[0046] The specific distance calculation of each solution to the positive ideal solution and the negative ideal solution is as follows: The distance calculation formula between the construction address and the ideal solution is: ; The distance calculation formula between the construction address and the negative ideal solution is: ; Calculate the relative closeness of each solution, that is, the ratio of the negative ideal solution distance to the sum of the positive ideal solution distance and the negative ideal solution distance, as follows: The calculation formula for relative proximity is: ; In the formula, Indicates i The distance between the construction address and the ideal solution, Indicates i The distance between the construction address and the negative ideal solution, c i Indicates i The relative proximity of the construction sites, n express, and denote the ideal solution and negative ideal solution in j The value under the criterion, v ij represents the weighted normalization matrix.

[0047] The closer the relative proximity value is to 1, the closer the candidate address is to the ideal solution and the farther it is from the negative ideal solution, so it is a better choice. The candidate addresses are sorted from high to low according to the relative proximity, and the solution with the highest relative proximity is selected as the best location. The candidate addresses are sorted according to the relative proximity, and the address with the largest relative proximity is selected as the best zero-carbon service area address.

[0048] In this embodiment, when obtaining the electricity and heat consumption at the optimal construction address and calculating the corresponding carbon emissions at the optimal construction address based on the consumption and energy consumption prediction technology, the building area and land load indicators corresponding to the optimal construction address can be used to predict the electricity consumption, and the carbon emissions generated by electricity can be obtained based on the electricity consumption and the electricity emission factor; the heat consumption of the optimal construction address is analyzed according to the building heating index, the heating area conversion coefficient and the building area, and the carbon emissions generated by heat are obtained based on the heat use emission factor and the heat consumption; the corresponding carbon emissions at the optimal construction address are calculated based on the carbon emissions generated by electricity and the carbon emissions generated by heat.

[0049] It needs to be explained that, in combination with the energy consumption prediction method, the carbon emissions during the operation of the service area are evaluated and the appropriate zero-carbon solution is selected. The carbon emissions of the service area mainly come from multiple aspects, including the burning of fossil fuels by the service area’s own vehicles, the electricity and heat consumption during refrigeration and heating, and the fugitive emissions generated by equipment such as air conditioners and fire extinguishers. The electricity and heat emissions generated by refrigeration and heating account for as much as 80%, which is the dominant factor in the service area’s carbon emissions.

[0050] Therefore, when planning a zero-carbon service area, predicting the electricity and heat consumption of the service area and calculating the corresponding carbon emissions based on this is a key link in achieving the goal of zero-carbon operation of highway service areas. It is also necessary to calculate the carbon substitution and carbon sink amount of the service area, and calculate the carbon emission reduction rate of the service area to determine the carbon emission reduction rate indicator of the zero-carbon service area.

[0051] Among them, the power consumption AD e (kWh) is calculated as follows: ; In the formula, S e Indicates the land area or building area of ​​the service area (hm2) 2 or m 2 ), q e Indicates the land load index (kW / hm 2 or kW / m 2 , the value is taken from the "Urban Power Planning Code" (GB / T50293-2014), i Indicates the land use category. Indicates the annual maximum utilization load hours ( h The service area operates 24 hours a day throughout the year, and you can choose 24h / d*365d=8760h).

[0052] Heat consumption AD h (MJ) is calculated as follows: ; In the formula, q h Indicates the building heating index (W / m 2 , the value is taken from the "Urban Heating Planning Specifications" (GB / T51074-2015), s h Indicates building area (m 2 ), i Indicates the building type, 0.8 indicates the building heating area conversion coefficient, T Indicates the heating time ( s ).

[0053] The carbon emissions from electricity ( E e ) is calculated as follows: ; In the formula, E e Indicates the carbon emissions from electricity generation (kgCO2), AD e Indicates the predicted value of power consumption (kWh),EF e It represents the emission factor of electricity use (kgCO2 / kWh, the value is taken from the 2016 China regional power grid baseline emission factor or the latest national average carbon emission factor).

[0054] Carbon emissions from heat generation ( E h ) is calculated as follows: ; In the formula, E h Indicates the carbon emissions from heat generation (kgCO2), AD h Indicates the predicted value of electricity consumption (MJ), EF h It represents the emission factor of heat use (kgCO2 / MJ, the value represents 0.11kgCO2 / MJ).

[0055] In this embodiment, when predicting the carbon substitution and carbon sink at the optimal construction address based on the carbon emissions, and determining the energy configuration and layout at the optimal construction address based on the carbon substitution and carbon sink to obtain a zero-carbon construction plan, the annual power generation generated by installing photovoltaics and wind turbines at the optimal construction address can be obtained based on the carbon emissions, and the carbon substitution can be calculated based on the annual power generation and the electricity emission factor; the green area and the carbon sink factor are obtained to analyze the carbon removal and carbon offset status of green plants at the optimal construction address, and the carbon sink is analyzed based on the carbon removal and carbon offset status; the carbon emission reduction rate at the optimal construction address is calculated based on the carbon substitution and carbon sink, and the renewable energy at the optimal construction address is determined based on the carbon emission reduction rate to obtain the energy configuration type; the layout and capacity of energy facilities are planned based on the energy configuration type, the construction needs and building functions are clarified in combination with the spatial layout and slope map at the optimal construction address, and the functional areas are divided to obtain a zero-carbon construction plan.

[0056] Specifically, according to the annual power generation of photovoltaic and wind turbines installed during the design phase of the highway service area, the calculation formula for carbon substitution is: ; Green plants in service areas play an important role in carbon removal and carbon offset, and are an important means to increase carbon sinks in highway service areas. In the planning stage of highway zero-carbon service areas, the calculation formula for carbon sinks is: ; The calculation formula for carbon emission reduction rate is: ; In the formula, R r Indicates the amount of carbon replacement (kg / CO2), R grepresents the carbon sink, R represents the carbon emission reduction rate, AD r represents annual power generation (kWh), EF e represents the electricity emission factor (kgCO2 / kWh, the value is based on the 2016 China regional power grid baseline emission factor or the latest national average carbon emission factor), S g Represents the green area at the optimal construction address (hm 2 ), EF g represents the carbon sink factor ( t CO2 / hm 2 , the value can be 7.7 t CO2 / hm 2 , refer to IPCC Guideline for National GHGInventories), E e represents the carbon emissions of electricity, E h Indicates carbon emissions from heat generation.

[0057] Given that fugitive emissions and carbon emissions from service area vehicles account for a relatively small proportion, this calculation focuses on the carbon emissions from electricity and heat in the service area. To ensure that the service area can achieve zero-carbon operation, its carbon reduction rate is required to be R Must reach or exceed 100%.

[0058] It should be explained that in order to formulate a detailed zero-carbon service area construction plan, including spatial layout, energy system configuration and greening design, it is necessary to clarify the main functions of the service area (such as refueling, catering, rest, etc.) and user needs (such as electric vehicle charging, wireless network coverage, etc.), and reasonably divide the functional areas (such as refueling areas, parking areas, service area buildings, etc.) according to factors such as topography and traffic flow, to ensure smooth connection and efficient operation between the functional areas, analyze the renewable energy resources (such as solar energy, wind energy, etc.) where the service area is located, determine the type of energy system (such as photovoltaic power generation, wind power generation, etc.), plan the layout and capacity of energy facilities, ensure the stability and reliability of energy supply, consider the configuration of energy storage systems, and solve the problems of intermittent and instability of renewable energy; formulate a greening plan to increase the greening coverage rate of the service area, improve ecological benefits, select suitable native tree species and vegetation, enhance the adaptability and aesthetics of greening, consider innovative greening methods such as vertical greening, and improve space utilization; comprehensively consider factors such as spatial layout, energy equipment and greening design, and carry out overall coordination and optimization.

[0059] In this embodiment, based on the zero-carbon construction plan and climate conditions, a construction plan for a zero-carbon service area at the optimal construction address is designed, and when the zero-carbon service area is constructed according to the construction plan, it is divided into a design stage and an operation stage. At the same time, during the design process, various specific implementation plans of the zero-carbon service area can be designed based on the results of the planning stage, including thermal insulation, heating and ventilation, lighting, renewable energy utilization, waste resource treatment, and smart energy management and control systems, as follows: (1) Thermal insulation system: According to the regional climate conditions, high-performance thermal insulation materials are used to optimize the building envelope and reduce energy loss; (2) Heating and ventilation system: Utilize passive design principles, combine energy-efficient air conditioning systems and air purification devices to achieve indoor comfort and energy saving. Make full use of natural light and reduce the need for artificial lighting through a reasonable window-to-wall ratio and window design. At the same time, utilize wind pressure and heat pressure to design an effective natural ventilation system and reduce air conditioning energy consumption. Determine the best orientation of the building according to local climatic conditions to maximize the use of solar energy and reduce solar radiation heat gain; design reasonable shading facilities (such as awnings, eaves, etc.) to reduce the impact of direct sunlight on the building in summer; select high-performance thermal insulation materials to improve the thermal performance of the building envelope; set up insulation structures such as air interlayers or vacuum layers to reduce heat transfer; use the thermal inertia of building materials to reduce indoor temperature fluctuations through night ventilation and other methods; increase environmental humidity and air flow through reasonable landscape design and water arrangement to improve the comfort of the outdoor environment.

[0060] (3) Lighting system: fully introduce natural light, combine with intelligent lighting control system, automatically adjust lighting brightness according to actual needs, reduce energy consumption, maximize the introduction of natural light through reasonable skylight and side window design, use reflectors, light guides and other devices to guide natural light deep into the room, and arrange intelligent lighting fixtures and control equipment indoors; install environmental sensing equipment such as photosensitive sensors and human body sensors; According to the lighting needs of different time periods and different areas, formulate multi-scenario lighting strategies, set the switching logic and brightness adjustment rules between natural light and artificial lighting, integrate the intelligent lighting system with environmental sensing equipment, realize real-time data collection and transmission, and perform system debugging and optimization to ensure the accuracy and response speed of lighting control. At the same time, conduct regular inspections and maintenance of the intelligent lighting system to ensure its normal operation, and continuously optimize the lighting strategy and control logic according to the actual operating conditions.

[0061] (4) Utilization of renewable energy: deploy clean energy facilities such as solar photovoltaic panels and wind turbines to build a multi-energy complementary energy supply system, select appropriate installation locations for new energy sources, such as photovoltaic panels, determine their location and orientation to ensure that the photovoltaic panels can receive the most sunlight, such as wind energy or hydrogen energy, choose to stay away from densely populated areas to ensure a safe distance for equipment operation, consider the interfaces and communication protocols between systems, and ensure that the systems can be seamlessly integrated; According to the characteristics of renewable energy power generation and load demand, select appropriate energy storage technology, determine the capacity and power of the energy storage system to meet the imbalance between renewable energy power generation and load demand, and develop an intelligent dispatching system to monitor and predict renewable energy power generation and load demand in real time, dynamically adjust the operating status of each renewable energy system and energy storage system, achieve optimal energy allocation, and formulate a collaborative control strategy to ensure that when renewable energy power generation is insufficient or excessive, the energy storage system can respond in time to balance supply and demand. Through optimization algorithms (such as genetic algorithms, particle swarm optimization algorithms, etc.), multi-energy systems are collaboratively optimized to improve the overall efficiency and stability of the system.

[0062] (5) Waste resource treatment system: Use energy-saving and low-carbon sewage treatment and rainwater collection and utilization technologies to achieve resource recycling.

[0063] (6) Smart energy management and control system: Integrates data collection, analysis, prediction and optimization functions to achieve refined management of energy use in the service area.

[0064] At the same time, during the construction phase, construction must be carried out in accordance with the design drawings to ensure the effective implementation of various energy-saving and emission-reduction measures, and pay attention to environmental protection during the construction process: (1) Selection of green building materials: Give priority to the use of low-carbon and environmentally friendly materials, increase the proportion of prefabricated buildings, and reduce carbon emissions during the construction process.

[0065] (2) Refined construction: Use advanced construction techniques and technologies to ensure project quality and achieve energy conservation and emission reduction goals.

[0066] (3) Application of clean energy: During the construction process, priority should be given to the use of renewable energy, such as solar power, to reduce dependence on fossil energy.

[0067] (4) Environmental protection measures: Implement strict construction noise and dust control to protect the surrounding ecological environment.

[0068] In this embodiment, when using sensors to collect the operating data of the zero-carbon service area and generating control logic based on the operating data to adjust the operating status of each energy facility to achieve the purpose of multi-energy complementarity, sensors can be used to obtain the usage data, traffic flow and environmental data of each energy facility in the zero-carbon service area to form operating data, and the operating data can be transmitted to the data center for analysis; based on the data analysis results, intelligent scheduling strategies and control logic are formulated, and the control logic is transmitted to the Internet of Things, and the operating status of each energy facility is optimized and adjusted using the Internet of Things; scheduling strategies and control strategies are optimized according to the real-time operating feedback of each energy facility and changes in the external environment of the zero-carbon service area to achieve the purpose of multi-energy complementarity.

[0069] It needs to be explained that after the construction of the zero-carbon service area is completed, it will enter the operation stage. During the operation, the energy use of the service area will be monitored and managed in real time through the smart energy management and control platform to ensure that carbon emissions are controlled within the predetermined targets and continuously optimize the operation strategy.

[0070] In the smart energy management and control platform, the Internet of Things, big data, artificial intelligence and other technical means are used to realize the intelligent scheduling and optimization of energy use. Through the Internet of Things technology, sensors and equipment are installed in key locations such as the energy supply system, building environment, and traffic flow to collect data in real time. The collected data is transmitted to the data center or cloud platform for processing and analysis. According to the results of data analysis, intelligent scheduling strategies and control logic are formulated, and remote monitoring and control functions are realized through the Internet of Things technology. The energy supply system is adjusted and optimized in real time. According to the system operation feedback and external environment changes, the scheduling strategy and control logic are continuously optimized to improve the overall efficiency of the system.

[0071] It needs to be explained that, in this embodiment, the data analysis result specifically refers to the energy configuration result at the optimal construction address obtained by calculating and analyzing the data collected by the sensor. The intelligent scheduling strategy is to determine the zero-carbon plan based on the energy configuration result to obtain the energy configuration type, and generate the energy scheduling result of the service area based on the energy configuration type. The control logic is to design various specific implementation plans for the zero-carbon service area based on the energy scheduling results, control the use process such as lighting, renewable energy utilization, etc., to achieve the purpose of multi-energy complementarity.

[0072] The implementation steps of zero-carbon service area intelligent management and control include five steps, namely perception layer, communication layer, storage layer, application layer and presentation layer. The functions of zero-carbon service area intelligent management and control include operation and maintenance monitoring, carbon emission monitoring, load and output forecasting, optimized scheduling, indicator analysis, historical query, etc.

[0073] Specifically, the perception layer is the process of the platform acquiring data, and it is also the execution layer of the control platform instructions. The perception layer includes energy supply equipment, energy-consuming equipment and smart terminals in the service area. The service area equipment includes central air-conditioning systems, photovoltaic systems, charging piles, smart water meters, smart electricity meters, sewage treatment equipment, lighting systems, energy storage systems, etc.

[0074] The communication layer is the channel for device data transmission and supports two-way communication. According to the connection form of the service area equipment, it is divided into wired connection and wireless connection. Wired connection includes RS485, RS232 and Ethernet communication methods; wireless connection includes 4G / 5G, WIFI, NB-IOT, Zigbee, LoRa and Bluetooth wireless communication methods.

[0075] The storage layer is the storage layer for the operation data of the energy system. It stores the collected data and calculated data in an orderly manner, and realizes the classification and association analysis of the data for data query and business use, including relational databases, structured databases, in-memory databases and other forms.

[0076] The application layer is the core function of the management and control platform. The zero-carbon service area applications include: System monitoring: monitoring of energy system operation data, including project overview, system operation monitoring and intelligent analysis. The project overview is a display of static information and key operation data of the service area; system operation and maintenance monitoring is a detailed display of equipment operation parameters and intelligent terminal data in the service area energy system, which can be displayed in pages according to the equipment type; intelligent analysis is based on the historical operation data of the energy system, and compares and analyzes the real-time operation data to achieve online analysis of equipment energy efficiency and faults; Carbon emission monitoring: Carbon emission data monitoring of the service area energy system, including carbon inventory, carbon footprint, emission warning and carbon emission analysis. Among them, carbon inventory is the statistics of carbon emissions generated by the service area energy system's purchased electricity, purchased heat and fossil energy combustion; carbon footprint is the statistics of carbon emission data generated by various equipment and different activities in the energy system; emission warning is based on the energy system's carbon emission historical data and the industry's average emission level, and calculates and judges real-time emission data to achieve carbon emission warning for different processes; carbon emission analysis focuses on monitoring equipment and activities with high carbon emissions in the service area, uses carbon emission data and system operation energy efficiency data for comprehensive judgment, and proposes emission reduction measures for the service area; Load and output forecast: including multi-energy load forecast and photovoltaic output forecast functions. Based on the research results of load forecast and photovoltaic forecast, combined with the historical data and real-time data of energy system operation, multi-energy load forecast and photovoltaic output forecast are realized to provide support for the realization of optimized scheduling strategy.

[0077] Optimized dispatching: It is the process in which the control platform executes multiple control strategies to achieve remote optimized control of equipment. The control platform can meet the needs of low-carbon operation, off-grid seamless switching dispatching, and isolated grid operation dispatching; Indicator analysis: By storing data, it can realize the comparison of operating data, display of system operation reports and data export. The reports include daily reports, monthly reports and annual reports, which support online viewing and PDF data export. Historical query: By storing data, functions such as dispatch instruction query, statistical data query, and equipment output query can be realized.

[0078] At the same time, the display layer refers to the computer, large-screen display and mobile phone mobile display.

[0079] During the operation phase, a strict energy conservation and emission reduction management system must be established, heating, ventilation, lighting, sewage treatment and other systems must be regularly maintained and optimized, energy conservation and emission reduction goals and indicators must be clearly defined, energy use standards and specifications must be formulated, and an energy metering and statistical system must be established to monitor and record energy consumption in real time, and energy efficiency improvement measures must be formulated and implemented, such as equipment transformation and upgrading, and optimization of operating parameters. Make full use of renewable energy sources, such as photovoltaic power generation, wind power generation, etc., increase the proportion of green electricity use, carry out energy-saving and emission reduction publicity and education activities, improve energy-saving and emission reduction awareness and capabilities, establish an energy-saving and emission reduction supervision and assessment mechanism, and inspect and evaluate the energy-saving and emission reduction work of various departments and positions; formulate an energy-saving and emission reduction reward and punishment system, commend and reward individuals and departments with outstanding performance in energy-saving and emission reduction, and punish and correct violations of energy-saving and emission reduction regulations.

[0080] Ultimately, based on the feedback analysis of operational data, we continuously adjusted the energy use strategy, introduced new technologies and processes, and continuously improved the energy utilization efficiency and carbon emission control capabilities of the service area. Therefore, through the implementation of waste resource treatment systems and greening carbon fixation measures, we not only effectively improved the environmental quality of the service area and reduced environmental pollution, but also promoted ecological restoration and protection.

[0081] In summary, with the help of the above technical solutions of the present invention, the present invention realizes the efficient, intelligent and low-carbon utilization of energy in the service area through the comprehensive application of multi-energy complementary clean energy, intelligent energy management and control, and low-carbon design and construction methods throughout the life cycle, thereby improving energy utilization efficiency, reducing carbon emissions, and enhancing the self-management and optimization capabilities of the service area, providing new ideas and technical support for the green development of the transportation industry. The present invention integrates a variety of clean energy technologies, energy-saving design methods and intelligent management and control measures to achieve low-carbon construction and sustainable zero-carbon operation of service areas, and solves the problems of high carbon emissions, low energy utilization efficiency, and lack of effective multi-energy complementary systems in the construction and operation of highway service areas in the prior art. The present invention emphasizes low-carbon design and construction methods throughout the life cycle, ensuring that low-carbon measures are taken in all stages from planning, design, construction to operation to achieve long-term low-carbon operation of the service area. At the same time, by introducing intelligent management and control operations, real-time monitoring and optimized scheduling of various energy uses in the service area are realized, thereby improving energy utilization efficiency and reducing carbon emissions.

[0082] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for constructing a multi-energy complementary zero-carbon service area, characterized in that: The construction method includes: Select the best construction site based on the topographic data of the study area, and evaluate the carbon emissions at the best construction site in combination with the energy consumption forecast, and use the carbon emissions to generate a zero-carbon construction plan; Based on the zero-carbon construction plan and climate conditions, design a construction plan for the zero-carbon service area at the best construction location, and build the zero-carbon service area in accordance with the construction plan; Sensors are used to collect operational data of zero-carbon service areas, and control logic is generated based on the operational data to adjust the operating status of each energy facility to achieve multi-energy complementarity.

2. A method for constructing a multi-energy complementary zero-carbon service area according to claim 1, characterized in that: The method of selecting the best construction site based on the topographic data of the study area, and evaluating the carbon emissions at the best construction site in combination with the energy consumption forecast, and using the carbon emissions to generate a zero-carbon construction plan includes: The construction site selection evaluation index is generated based on the elevation data, ecological environment data, solar radiation and annual average wind speed in the study area, and the best construction site is selected by combining the superior and inferior solution distance method; Obtain the electricity and heat consumption at the best construction site, and calculate the corresponding carbon emissions at the best construction site based on consumption and energy consumption prediction technology; The carbon substitution and carbon sink amounts at the optimal construction site are predicted based on carbon emissions, and the energy configuration and layout at the optimal construction site are determined based on the carbon substitution and carbon sink amounts to obtain a zero-carbon construction plan.

3. A method for constructing a multi-energy complementary zero-carbon service area according to claim 2, characterized in that: The construction site selection evaluation index is generated based on the elevation data, ecological environment data, solar radiation and annual average wind speed in the study area, and the best construction site is selected by combining the superior and inferior solution distance method, including: Use LiDAR to obtain the digital elevation model of the study area, and process the digital elevation model based on geographic information software to generate a slope map, and evaluate the development difficulty of the study area based on the slope map; Obtain ecological and environmental data in the study area, and use the ecological and environmental data as a basis to predict the ecological and environmental impact of the study area after the construction of the zero-carbon service area is completed; Evaluate the solar and wind energy resources in the study area based on solar radiation and annual average wind speed; The development difficulty, ecological environment impact, solar energy resources and wind energy resources are used as evaluation indicators, and the scaling method is used to score each parameter in the evaluation indicators; Determine the weights of the evaluation indicators, and combine the weight results with the superior and inferior solution distance method to construct a matrix to analyze the relative proximity of each construction address in the candidate area, and determine the best construction address based on the analysis results.

4. A method for constructing a multi-energy complementary zero-carbon service area according to claim 3, characterized in that: The solar energy resources and wind energy resources in the research area based on solar radiation and annual average wind speed assessment include: Obtain the actual and estimated sunshine hours corresponding to the study area, and analyze the solar radiation in the study area based on sunshine information and historical solar radiation data; Obtain wind condition data for the study area within a preset time period, analyze wind speed observations for each period based on the wind condition data, and use wind speed observations to calculate the annual average wind speed for the study area; The wind power density is obtained based on the annual average wind speed and the air density corresponding to the study area, and the exploitability of solar energy resources and wind energy resources in the study area is evaluated based on the radiation and wind power density.

5. A method for constructing a multi-energy complementary zero-carbon service area according to claim 4, characterized in that: Determining the weight of the evaluation index, and combining the weight result with the superior and inferior solution distance method to construct a matrix to analyze the relative proximity of each construction address in the candidate area, and determining the best construction address based on the analysis result includes: Based on the scoring results of each parameter in the evaluation index and the hierarchical analysis method, a judgment matrix is ​​generated, and the product of each row in the judgment matrix is ​​calculated, and the square root of the product result is calculated to obtain the square root value; After normalizing the square root value, the eigenvector is obtained, and the eigenvector is subjected to matrix multiplication with the judgment matrix, and the weight of each parameter in the evaluation index is obtained based on the processing result; The superior-inferior solution distance method is used to establish a decision matrix including evaluation indicators and construction addresses, and the decision matrix is ​​standardized based on the properties of the evaluation indicators. According to the weight results, the standardized decision matrix is ​​weighted to construct a weighted normalized matrix, and the maximum column value and the minimum column value in the weighted normalized matrix are determined to obtain an ideal solution and a negative ideal solution; The distance between each construction address and the ideal solution and the negative ideal solution is calculated, and the relative proximity of each construction address is calculated based on the distance result. The relative proximity is sorted from high to low, and the construction address with the highest relative proximity is selected as the best selection result.

6. A method for constructing a multi-energy complementary zero-carbon service area according to claim 5, characterized in that: The obtaining of the electricity and heat consumption at the optimal construction site and the calculation of the corresponding carbon emissions at the optimal construction site based on the consumption and energy consumption prediction technology include: The building area and land load indicators corresponding to the optimal construction address are used to predict electricity consumption, and the carbon emissions generated by electricity are obtained based on electricity consumption and electricity emission factors; Analyze the heat consumption of the best construction site based on the building heating index, heating area conversion coefficient and building area, and obtain the carbon emissions generated by heat based on the heat use emission factor and heat consumption; The corresponding carbon emissions at the optimal construction site are calculated based on the carbon emissions generated by electricity and heat.

7. A method for constructing a multi-energy complementary zero-carbon service area according to claim 6, characterized in that: The method of predicting the carbon substitution and carbon sink at the optimal construction site based on carbon emissions, and determining the energy configuration and layout at the optimal construction site according to the carbon substitution and carbon sink to obtain a zero-carbon construction plan includes: Based on carbon emissions, obtain the annual power generation generated by installing photovoltaics and wind turbines at the best construction site, and calculate the carbon substitution amount based on the annual power generation and power emission factors; Obtain greening area and carbon sink factors to analyze the carbon removal and carbon offset of green plants at the best construction site, and analyze the carbon sink based on the carbon removal and carbon offset; The carbon emission reduction rate at the best construction site is calculated based on the carbon substitution amount and the carbon sink amount, and the renewable energy at the best construction site is determined according to the carbon emission reduction rate to obtain the energy configuration type; The layout and capacity of energy facilities are planned based on the energy configuration type, the construction needs and building functions are clarified by combining the spatial layout and slope map at the optimal construction address, and the functional areas are divided to obtain a zero-carbon construction plan.

8. A method for constructing a multi-energy complementary zero-carbon service area according to claim 7, characterized in that: The calculation formula of the carbon replacement amount is: ； The calculation formula of the carbon sequestration is: ； The calculation formula for the carbon emission reduction rate is: ； In the formula, R r represents the carbon substitution amount, R g represents carbon sink, R represents carbon reduction rate, AD r Indicates annual power generation, EF e represents the electricity emission factor, S g represents the green area at the optimal construction address, EF g represents the carbon sink factor, E e Represents carbon emissions from electricity, E h Indicates carbon emissions from heat generation.

9. A method for constructing a multi-energy complementary zero-carbon service area according to claim 8, characterized in that: The method of collecting the operation data of the zero-carbon service area using sensors and generating control logic based on the operation data to adjust the operation status of each energy facility to achieve the purpose of multi-energy complementation includes: Use sensors to obtain usage data, traffic flow and environmental data of each energy facility in the zero-carbon service area to form operational data, and transmit the operational data to the data center for analysis; Develop intelligent dispatching strategies and control logic based on data analysis results, transmit the control logic to the Internet of Things, and use the Internet of Things to optimize and adjust the operating status of various energy facilities; Optimize the scheduling and control strategies based on the real-time operation feedback of each energy facility and the changes in the external environment of the zero-carbon service area to achieve the goal of multi-energy complementarity.

10. A method for constructing a multi-energy complementary zero-carbon service area according to claim 9, characterized in that: The usage data of each energy facility includes carbon inventory data, carbon footprint data and emission warning data; The carbon inventory data are the carbon emission data generated by the purchased electricity, purchased heat and purchased fossil energy of each energy facility; The carbon footprint data is the carbon emission data generated by each device in each energy facility; The emission warning data is the comparison result between the real-time carbon emission data and the historical carbon emission data and carbon emission standards of each energy facility.

Images