A multi-energy complementary optimization design method for a negative-carbon ecological building

Abstract

The application belongs to the technical field of building energy, and discloses a multi-energy complementary optimization design method for a negative carbon ecological building; comprising constructing a time-of-day output characteristic map of a photovoltaic module and a light-heat collector at a building site scale according to environmental monitoring data and building geometric data of a target building site; extracting an output space-time correlation mode of the photovoltaic module and the light-heat collector based on the time-of-day output characteristic map; constructing a spatial arrangement scheme according to a matching relationship between the output space-time correlation mode and building energy load demand, and combining the available area constraint of the target building site; evaluating the negative carbon performance index of the building according to the spatial arrangement scheme and the carbon emission of the whole life cycle of the building; and outputting a multi-energy complementary optimization design scheme of the building site; the application realizes the true negative carbon target of the building from building materials to the whole life cycle, and significantly improves the energy utilization efficiency and carbon emission reduction stability under full climate and full frequency conditions.

Description

Technical Field

[0001] This invention relates to the field of building energy technology, and more specifically, to a multi-energy complementary optimization design method for negative carbon ecological buildings. Background Technology

[0002] In practical engineering applications of carbon-negative ecological buildings, the complex spatiotemporal coupling characteristics between photovoltaic power generation and solar thermal collection systems, exhibiting multi-scale nonlinear behavior that varies with climate conditions and load demands, are a key bottleneck restricting the overall optimization effect of multi-energy complementary systems. Existing technologies generally employ static capacity allocation or single-time-scale analysis methods, neglecting the significant differences in the output correlation between photovoltaic and solar thermal systems under different seasons, time periods, and meteorological conditions, leading to the failure of optimization strategies under complex operating conditions. Particularly in scenarios such as demonstration buildings, large public buildings, and park projects with high-precision carbon-negative requirements, when the system faces frequent load fluctuations and meteorological changes, capacity configuration parameters calibrated based on a single optimization objective cannot adapt to dynamic supply and demand matching characteristics, resulting in energy waste, insufficient supply, or even system imbalance. Traditional empirical allocation and sub-item optimization methods cannot capture the intrinsic correlation between the synergistic effects between multi-energy systems and building load and environmental parameters, leading to severe deterioration in energy utilization efficiency under alternating meteorological conditions and complex load demands.

[0003] Meanwhile, due to the lack of dynamic prediction models for multi-timescale output characteristics and load matching, the system cannot anticipate and optimize energy supply and demand imbalances during seasonal transitions and extreme weather events. This results in significant energy supply fluctuations and decreased matching in high-dynamic environments such as smart buildings and green parks. Furthermore, existing technologies fail to comprehensively consider the coupled optimization of building space constraints and energy quality tiered utilization. Especially under complex building geometries, the multi-objective conflict arising from the interaction between space allocation and energy quality optimization leads to a sharp decline in overall efficiency. Single-dimensional optimization strategies cannot simultaneously address the different needs of maximizing capacity and optimizing quality matching under space-constrained conditions, making it difficult for the system to balance investment economics and long-term environmental benefits.

[0004] In view of this, the present invention proposes a multi-energy complementary optimization design method for negative carbon ecological buildings to solve the above problems. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art and to achieve the above objectives, the present invention provides the following technical solution:

[0006] A multi-energy complementary optimization design method for negative carbon ecological buildings includes:

[0007] Acquire environmental monitoring data and building geometry data of the target building site. The environmental monitoring data includes the distribution of solar irradiance, ambient temperature field, and surface reflectance at the site scale. The building geometry data includes the spatial layout coordinates of the building complex, the surface orientation parameters of individual buildings, and the optical characteristic parameters of the building envelope.

[0008] Based on environmental monitoring data and building geometry data, hourly output characteristic maps of photovoltaic modules and solar thermal collectors at the building site scale are constructed; the hourly output characteristic maps include the time-series distribution of power generation of photovoltaic modules and the time-series distribution of heat collection power of solar thermal collectors.

[0009] Based on the multi-timescale evolution of the power generation distribution of photovoltaic modules and the heat collection power distribution of solar thermal collectors, respectively, the spatiotemporal correlation patterns of the output of photovoltaic modules and solar thermal collectors are extracted.

[0010] Based on the matching relationship between the spatiotemporal correlation pattern of power output and the building energy load demand, a multi-energy complementary optimization model is constructed, and a spatial arrangement scheme for photovoltaic modules and solar thermal collectors is designed in combination with the available area constraints of the target building site.

[0011] Based on the spatial layout plan, the annual energy output and the carbon emissions of the building throughout its entire life cycle are estimated, and the negative carbon performance index of the building is evaluated accordingly. Based on the negative carbon performance index, a multi-energy complementary optimization design scheme for the building site is output.

[0012] Furthermore, the construction process of the hourly output feature map includes:

[0013] The building site is divided into spatial grid units, and the three-dimensional coordinates and normal vector direction of each spatial grid unit are recorded;

[0014] Based on solar irradiance distribution and building geometry data, the hourly solar direct irradiance and sky diffuse irradiance received by each spatial grid unit throughout the year are obtained.

[0015] The ground reflection irradiance gain and the secondary reflection irradiance gain of adjacent building surfaces are obtained based on the surface reflectivity distribution and the optical characteristic parameters of the building envelope, and are recorded as the environmental reflection irradiance correction.

[0016] The total irradiance received by each spatial grid cell is obtained by superimposing the direct solar irradiance received, the sky diffuse irradiance received, and the environmental reflected irradiance correction.

[0017] The hourly conversion efficiency of photovoltaic modules and solar thermal collectors in each spatial grid cell was calculated based on the total irradiance received and the ambient temperature field distribution.

[0018] Based on the hourly conversion efficiency and total irradiance received, the time-series distribution of photovoltaic power generation and solar thermal power collection in each spatial grid cell is calculated and summarized to obtain the hourly output characteristic map.

[0019] Furthermore, the extraction process of the spatiotemporal correlation pattern of output includes:

[0020] The time-series distributions of power generation and heat collection power are respectively decomposed into hourly fluctuation components, daily fluctuation components, and seasonal trend components.

[0021] The correlation coefficients of hourly fluctuation components of power generation time series and heat collection time series within a preset sliding window are analyzed to obtain hourly output fluctuation correlation curves.

[0022] The peak and trough times of the day are extracted from the daily fluctuation components of the power generation time series and the heat collection power time series, respectively. The peak time offset and trough time offset of the two are calculated, and the daily output peak-trough correlation matrix is ​​constructed based on their corresponding statistical characteristics.

[0023] The average output ratio of photovoltaic modules and solar thermal collectors in different seasons is obtained based on the seasonal trend components. The seasonal complementary interval and seasonal competition interval are identified according to the seasonal variation law of the average output ratio, and recorded as the seasonal output complementarity characteristics.

[0024] Based on the output fluctuation correlation curve, the output peak-valley correlation matrix, and the output complementarity characteristics, a spatiotemporal correlation model of output is constructed. The spatiotemporal correlation model of output takes the time scale as the dimension and the correlation quantification index as the attribute value.

[0025] Furthermore, the process of constructing a multi-energy complementary optimization model includes:

[0026] The system obtains the annual hourly electricity and heat load demand of the target building and classifies the electricity and heat load demand by energy grade. The energy grade classification results include high-grade energy demand, medium-temperature heat demand, and low-temperature heat demand.

[0027] The synchronous fluctuation probability distribution of photovoltaic power generation and solar thermal collector power is calculated based on the power output fluctuation correlation curve and denoted as the power output synergy degree.

[0028] Design the coordination constraints for the corresponding configuration capacity of photovoltaic modules and solar thermal collectors based on the output coordination degree;

[0029] Based on the energy grade classification results and hourly output characteristic maps, a collaborative objective function for energy grade cascade utilization is constructed, and a multi-energy complementary optimization model is established in combination with collaborative constraints. The optimization variables of the multi-energy complementary optimization model are the total installed capacity of photovoltaic modules, the total installed capacity of solar thermal collectors, and the configuration capacity of energy storage systems.

[0030] Furthermore, the design process for the spatial layout scheme includes:

[0031] Based on the optimization results of the multi-energy complementary optimization model, the target values ​​of the total installed capacity of photovoltaic modules and solar thermal collectors are obtained respectively.

[0032] Energy potential is assessed for each spatial grid cell, and the spatial grid cells are ranked based on the energy potential assessment results to form a spatial grid cell priority sequence.

[0033] Based on the power generation time series and heat collection time series of each spatial grid cell in the hourly power output characteristic map, calculate the energy output efficiency ratio of photovoltaic modules and solar thermal collectors in the corresponding spatial grid cells;

[0034] Based on the priority sequence of spatial grid units and the energy output efficiency ratio, a greedy algorithm is used to allocate energy types to spatial grid units. The installation tilt angle and installation azimuth angle of the photovoltaic modules or solar thermal collectors allocated in the spatial grid units are optimized according to the building surface orientation parameters and total irradiance received, resulting in a spatial layout scheme.

[0035] Furthermore, the process of assessing a building's negative carbon performance indicators includes:

[0036] Calculate the annual power generation of photovoltaic modules and the annual heat collection of solar thermal collectors based on the spatial layout plan;

[0037] The annual power generation and annual heat collection are converted into carbon emission reductions for electricity and carbon emission reductions for heat, respectively, and the annual carbon offset is calculated based on these.

[0038] Obtain the total life-cycle carbon emissions of the target building; accumulate the annual carbon offsets year by year and compare them with the total life-cycle carbon emissions;

[0039] When the cumulative annual result first exceeds the total life-cycle carbon emissions, the year in which the cumulative result is recorded is taken as the time when carbon balance is achieved.

[0040] Furthermore, the process of obtaining direct solar radiation received includes:

[0041] A solar position calculation model was established, and the solar altitude angle and solar azimuth angle were calculated hourly throughout the year in combination with the spatial layout coordinates of the building complex; and the incident direction vector of sunlight in each spatial grid cell was determined based on it.

[0042] Starting from the center point of the spatial grid cell, ray tracing is performed in the opposite direction of the incident direction vector to determine whether the ray intersects with the surface of the adjacent building.

[0043] If an intersection point exists, the spatial grid cell is determined to be in a shading state at the corresponding time, and the amount of direct solar irradiance received is recorded as zero; if no intersection point exists, the spatial grid cell is determined to be in an unshading state at the corresponding time, and the amount of direct solar irradiance received at the corresponding time is calculated based on the solar irradiance distribution, the incident direction vector, and the normal vector direction of the corresponding spatial grid cell.

[0044] Furthermore, the method for constructing the output peak-valley correlation matrix includes:

[0045] The time-series distributions of power generation and solar thermal power are segmented according to natural days to obtain the daily power generation curves and daily solar thermal power collection curves for each natural day.

[0046] The peak and valley points of the photovoltaic daily power generation curve and the solar thermal daily collector power curve are extracted respectively, and the times corresponding to the peak and valley points are recorded as the daily peak time and daily valley time.

[0047] The time difference between the peak times of the photovoltaic module and the solar thermal collector is obtained and recorded as the daily peak time offset; and the time difference between the valley times of the photovoltaic module and the solar thermal collector is obtained simultaneously and recorded as the daily valley time offset.

[0048] Statistical analysis was performed on the daily peak time offset and daily trough time offset throughout the year, and a daily output peak-trough correlation matrix was constructed based on the statistical analysis results.

[0049] Furthermore, the process of constructing the synergistic objective function for energy grade cascade utilization includes:

[0050] The power generation time series distribution is compared with the power load demand of the target building hourly to obtain the supply and demand deviation, and the direct matching degree is calculated based on it.

[0051] Based on the time-series distribution of solar thermal power and thermal load demand, an optimization subproblem is constructed with the goal of maximizing the weighted utilization rate of solar thermal power. This subproblem is then solved to obtain the allocation ratio between medium-temperature and low-temperature thermal demand. Based on the allocation ratio, the priority supply rate and degraded utilization rate corresponding to medium-temperature and low-temperature thermal demand are calculated respectively. The priority supply rate is defined as the ratio of the amount of solar thermal power supplied to medium-temperature thermal demand to the total amount of solar thermal power. The degraded utilization rate is defined as the ratio of the amount of solar thermal power supplied to low-temperature thermal demand to the total amount of solar thermal power.

[0052] Furthermore, a collaborative objective function for the tiered utilization of energy quality is constructed with the optimization objectives of maximizing the priority supply rate, maximizing the direct matching degree, and minimizing the degraded utilization rate.

[0053] Furthermore, the process of obtaining the multi-energy complementary optimization design scheme includes:

[0054] The comprehensive evaluation index of negative carbon performance is calculated based on the time to achieve carbon balance and the annual carbon offset amount.

[0055] Set a threshold for negative carbon performance; when the comprehensive evaluation index of negative carbon performance is not less than the threshold for negative carbon performance, the design scheme is deemed to meet the negative carbon target requirements; generate and output a complete multi-energy complementary optimization design scheme.

[0056] The technical effects and advantages of the multi-energy complementary optimization design method for negative carbon ecological buildings proposed in this invention are as follows:

[0057] This invention can maintain good optimized configuration performance across the entire time scale, improve energy matching accuracy and system stability under dynamic climate conditions, reduce supply and demand imbalance errors and fluctuations in energy conversion processes in multi-energy complementary systems, make photovoltaic and solar thermal synergistic operation more stable and efficient, effectively extend equipment life and improve overall energy utilization rate; especially in building projects with high precision carbon negative requirements and large capacity energy demand, it enhances the system's ability to resist climate fluctuations and load changes, enabling multi-energy complementary systems to maintain stable and reliable performance output in complex and ever-changing operating environments. Attached Figure Description

[0058] Figure 1 This is a schematic diagram of a multi-energy complementary optimization design method for a negative carbon ecological building according to the present invention. Detailed Implementation

[0059] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] Example 1

[0061] Please see Figure 1 This invention provides a multi-energy complementary optimization design method for negative carbon ecological buildings, including:

[0062] The process involves acquiring environmental monitoring data and building geometry data for the target building site. Environmental monitoring data includes key parameters such as solar irradiance distribution, ambient temperature field distribution, and surface reflectance distribution at the site scale. This data is collected through multiple sources, including published information from meteorological stations, radiation monitoring instruments, and remote sensing image analysis. Building geometry data includes the spatial layout coordinates of the building complex, surface orientation parameters of individual buildings, and optical characteristic parameters of the building envelope. This data is extracted from BIM (Building Information Modeling) or architectural drawings. Environmental monitoring data provides a climatic basis for energy potential assessment, while building geometry data provides physical constraints for spatial layout design. Together, they form the data foundation for multi-energy complementary optimization design. The data acquisition process considers a balance between temporal and spatial resolution. Solar irradiance distribution uses hourly data covering the entire year, ambient temperature field distribution synchronously records temperature variation patterns, and surface reflectance distribution obtains the reflectance characteristics of different surface materials through zoned measurements. This high-quality foundational data ensures the accuracy and reliability of subsequent modeling and analysis.

[0063] Based on environmental monitoring data and building geometry data, hourly output characteristic maps of photovoltaic modules and solar thermal collectors at the building site scale were constructed. These hourly output characteristic maps are a core tool for energy system performance evaluation, comprising the time-series distribution of photovoltaic module power generation and the time-series distribution of solar thermal collector heat collection power. The map construction employed a refined modeling method, comprehensively considering the multi-path propagation of solar radiation, the dynamic changes in building shading, and the impact of ambient temperature on conversion efficiency, forming a complete energy output prediction model. The hourly output characteristic maps not only provide an assessment of energy output capacity at various spatial locations but also reveal the output variation patterns at different times and in different seasons, providing spatiotemporal data support for multi-energy complementary optimization. The construction process used a gridded processing method, discretizing the continuous building surface into computable units, ensuring both computational accuracy and controlling computational complexity.

[0064] Based on the multi-timescale evolution patterns of the power generation distribution of photovoltaic (PV) modules and the heat collection power distribution of solar thermal (CTP) collectors, this study extracts the spatiotemporal correlation patterns of PV module and CTP collector outputs. These output spatiotemporal correlation patterns are key features revealing the synergistic relationship between PV and CTP systems. Through multi-timescale decomposition techniques, complex time-series data is broken down into three levels: hourly fluctuations, daily peaks and valleys, and seasonal trends. The complementarity and competitiveness of the two energy forms are then systematically analyzed. The extraction of these output spatiotemporal correlation patterns overcomes the limitations of traditional single-timescale analysis, capturing correlation characteristics across different time dimensions and providing a scientific basis for capacity configuration optimization. The analysis process employs time series decomposition, statistical correlation analysis, and pattern recognition techniques to transform abstract time-series relationships into quantifiable correlation indicators, ensuring the relevance and effectiveness of subsequent optimization models.

[0065] Based on the spatiotemporal correlation pattern of power output and the matching relationship between building energy load demand, a multi-energy complementary optimization model is constructed. The core objectives of this model are to maximize the matching degree of energy supply and demand and optimize the tiered utilization of energy quality. It comprehensively considers the synergistic constraints of photovoltaic power generation and solar thermal collectors, establishing a multi-objective optimization framework. The model is constructed based on energy quality theory, classifying electricity and heat load demands according to energy quality, and designing a tiered utilization strategy that prioritizes high-quality energy supply, rationally allocates medium-temperature heat, and degrades low-temperature heat. The variables in the optimization model include the total installed capacity of photovoltaic modules, the total installed capacity of solar thermal collectors, and the configured capacity of the energy storage system. Constraints cover multiple dimensions, including available site area, power output synergy, and load supply reliability. The model solves using an intelligent optimization algorithm, which can quickly search for the global optimal solution in a multi-dimensional constraint space, providing capacity configuration guidance for spatial layout design.

[0066] Based on the available area constraints of the target building site and the solution results of the multi-energy complementary optimization model, a spatial layout scheme for photovoltaic modules and solar thermal collectors was designed. This spatial layout scheme is a crucial step in transforming the optimization results into engineering implementation guidance. Through energy potential assessment, priority ranking, and a greedy allocation algorithm, a rational layout of the photovoltaic and solar thermal systems was achieved. The design process first assesses the energy output efficiency of each spatial grid unit, comprehensively considering irradiance conditions, shading effects, and installation convenience to form a spatial priority sequence. Then, based on the energy output efficiency ratio of photovoltaic and solar thermal, a greedy algorithm is used to gradually allocate the energy type of each unit. Finally, the installation tilt angle and azimuth angle of each unit are optimized and adjusted to maximize energy output efficiency. This scheme not only meets the total installed capacity requirements but also achieves efficient utilization of spatial resources, providing clear technical parameters and layout drawings for engineering implementation.

[0067] Based on the spatial layout plan, the annual energy output and the building's total life-cycle carbon emissions are estimated, and the building's negative carbon performance index is assessed. Based on the negative carbon performance index, a multi-energy complementary optimization design scheme for the building site is output. The negative carbon performance index is the core standard for measuring the environmental benefits of a building, including two key indicators: annual carbon offset and carbon balance achievement time. The assessment process first calculates the annual power generation of the photovoltaic system and the annual heat collection of the solar thermal system based on hourly output characteristic maps and the spatial layout plan. Then, by using carbon emission factors, energy output is converted into electricity carbon emission reduction and thermal carbon emission reduction, and the annual carbon offset is obtained. Next, the total life-cycle carbon emissions of the target building from building material production, construction to operation and maintenance are obtained. Finally, by accumulating the carbon offset year by year and comparing it with the total life-cycle carbon emissions, the carbon balance achievement time is determined. This assessment method adopts a life-cycle perspective, transcending the limitations of traditional methods that only focus on carbon emissions during the operational period, and can objectively reflect the true environmental benefits of a building.

[0068] In this embodiment of the invention, the detailed implementation steps for constructing the hourly output characteristic map include:

[0069] The building site is divided into spatial grid cells, and the three-dimensional coordinates and normal vector direction of each spatial grid cell are recorded. Spatial meshing is a fundamental operation for achieving refined modeling. By discretizing the continuous building surface into regular computational units, complex geometric problems become computable. The meshing process employs an adaptive meshing strategy based on the geometric characteristics of the building surface, using a denser mesh for curved areas and a sparser mesh for planar areas, balancing computational accuracy and efficiency. The size of the spatial grid cells is determined based on the minimum installation area of ​​photovoltaic or solar thermal equipment, ensuring accurate capture of shading boundaries and gradient changes in irradiance distribution. Each spatial grid cell records the three-dimensional coordinates of its center point and the direction of its surface normal vector; these geometric parameters provide the basic data for subsequent irradiance calculations and shading assessments. The meshing results form a digital representation model of the building site, laying the geometric foundation for the assessment of the energy potential of the entire site. It should be noted that the spatial grid cells collected in this application only include the building surface areas within the building site where photovoltaic modules or solar thermal collectors can be installed.

[0070] Based on solar irradiance distribution and building geometry data, the hourly solar direct irradiance and sky diffuse irradiance received by each spatial grid unit throughout the year are obtained. Solar radiation is the energy source of photovoltaic and solar thermal systems, and accurately calculating the radiation received by each spatial grid unit is the core of power output prediction. The calculation process is divided into two parts: solar direct irradiance received and sky diffuse irradiance received. For solar direct irradiance calculation, a solar position model is first established, and the solar altitude angle and azimuth angle are calculated based on the site's latitude and longitude, date, and time to determine the incident direction of sunlight. Then, the ray tracing method is used to determine whether the spatial grid unit is blocked by adjacent buildings. If it is blocked, the direct irradiance is zero; if it is not blocked, the solar direct irradiance received is calculated according to the cosine law of the incident angle. For sky diffuse irradiance calculation, an isotropic sky model or the Perez model is used to calculate the diffuse irradiance received based on the sky diffuse irradiance, surface tilt angle, and visible sky ratio. This decomposition calculation method accurately reflects the physical nature of solar radiation and ensures the scientific nature of the irradiance calculation.

[0071] Based on the distribution of surface reflectivity and the optical characteristics of building envelopes, the ground-reflected irradiance gain received by each spatial grid cell and the secondary-reflected irradiance gain from adjacent building surfaces are obtained and recorded as the environmental reflected irradiance correction. Environmental reflection is a significant source of radiation gain in urban built environments, especially in areas with high reflectivity surfaces or dense glass curtain wall buildings. The calculation process uses the radiance method. First, ground reflection is calculated: based on the surface reflectivity and the total irradiance received by the ground, the radiance of the ground reflection is calculated. Then, based on the tilt angle and ground-viewable angle of the spatial grid cell, the ground-reflected irradiance gain is calculated. The secondary building reflection calculation traverses adjacent building surfaces, calculating the radiance and geometric angle factor (representing the proportion of irradiance reflected by adjacent building surfaces received by the corresponding spatial grid cell), and summing them to obtain the secondary reflected irradiance gain. The sum of the two gain values ​​constitutes the environmental reflected irradiance correction, which significantly improves the completeness of irradiance calculation, especially the accuracy of irradiance prediction for vertical or inclined surfaces.

[0072] The total solar radiation received, including direct solar radiation received, sky diffuse solar radiation received, and environmental reflected solar radiation correction, is superimposed to obtain the total solar radiation received for each spatial grid cell. Total solar radiation received is a direct input parameter for energy system output calculation, comprehensively reflecting the radiative energy acquisition capability of the spatial grid cell. The superposition process uses a simple linear addition method to form a complete spatiotemporal radiation distribution dataset. The calculation results of total solar radiation received intuitively demonstrate the solar energy resource endowment of the building site, providing a quantitative basis for energy system site selection and capacity configuration.

[0073] Based on the total irradiance received and the ambient temperature field distribution, the hourly conversion efficiency of the photovoltaic module and the solar thermal collector in each spatial grid cell was calculated. Conversion efficiency is a key parameter connecting irradiance input and energy output, directly determining the system's energy production capacity. The calculation process considered the effect of temperature on conversion efficiency: the conversion efficiency of the photovoltaic module decreases with increasing temperature, and the calculation formula is as follows:

[0074] ;in, Let be the photovoltaic conversion efficiency at time t. This is the reference efficiency under standard test conditions. The temperature degradation coefficient is obtained from the photovoltaic module's equipment specifications. Let be the battery temperature at time t. The reference temperature is used; the battery temperature is calculated using a heat transfer model based on ambient temperature, irradiance, and installation method; the conversion efficiency of the solar thermal collector considers optical efficiency and heat loss, and is calculated using an efficiency curve model. The dynamic calculation of hourly conversion efficiency overcomes the shortcomings of the traditional constant efficiency assumption, significantly improving the accuracy of output prediction.

[0075] Based on hourly conversion efficiency and total irradiance received, the time-series distributions of photovoltaic power generation and solar thermal power collection in each spatial grid cell are calculated and summarized to obtain an hourly output characteristic map. Output calculation is the final step in energy system performance evaluation, converting irradiance and efficiency into actual energy output predictions; the calculation formula is:

[0076] ; ;

[0077] in, and These are photovoltaic power generation and solar thermal power collection, respectively. The area of ​​a spatial grid cell (m²) For photothermal heat collection efficiency; This represents the total amount of radiation received at time t;

[0078] The calculation process is performed independently for each spatial grid cell and each time step, forming two-dimensional power distribution data in space and time. When summarizing, the data is stored in matrix form, with rows representing spatial grid cells, columns representing time steps, and values ​​representing the corresponding power output. The hourly power output characteristic map not only provides energy output prediction for the entire site and all time periods, but also visualizes the spatiotemporal distribution patterns of power output through data visualization, providing intuitive technical support for design decisions.

[0079] It should be noted that the formula for calculating the solar thermal collector efficiency is:

[0080] In the formula, and Representing optical efficiency and total heat loss coefficient, respectively, obtained by fitting historical efficiency curves, where the historical efficiency curves are... As the x-axis, with The vertical axis is denoted as ; the intercept of the historical efficiency curve is . The absolute value of the slope is ; This indicates the temperature of the working medium in the solar thermal collector at time t. The ambient temperature at time t is determined by the distribution of the ambient temperature field.

[0081] In this embodiment of the invention, the extraction process of the spatiotemporal correlation pattern of output includes:

[0082] The time-series distributions of power generation and thermal power were decomposed into hourly, daily, and seasonal trend components, respectively. Time series decomposition is an effective method for revealing characteristics at multiple time scales. By decomposing complex time-series data into components of different frequencies, short-term fluctuations, medium-term cycles, and long-term trends can be analyzed separately. The decomposition process employs the Seasonal Trend Decomposition (STL) method, which has good robustness and flexibility and can handle time-varying seasonal characteristics. The decomposition algorithm first uses Locally Weighted Regression (LOESS) to extract the seasonal trend component, with the window length set to one seasonal cycle, reflecting the long-term pattern of seasonal changes. Then, the trend component is subtracted from the original series, and daily cycle analysis is performed on the residuals to extract the daily fluctuation component, capturing the diurnal cycle pattern. Finally, the remaining part is the hourly fluctuation component, reflecting short-term random fluctuations and weather disturbances. The decomposition results are three independent time series, each corresponding to the power output characteristics at different time scales, providing a clear analytical object for subsequent correlation analysis.

[0083] The cross-correlation coefficients of hourly fluctuation components of photovoltaic (PV) and solar thermal power generation time series within a preset sliding window are analyzed to obtain hourly output fluctuation correlation curves. Hourly correlation reflects the synergistic or counterbalancing relationship between PV and solar thermal systems on a short timescale, providing important guidance for energy storage system configuration and real-time scheduling strategies. The analysis process employs sliding window cross-correlation calculations with a window length of 6 hours to capture correlation changes on a half-day scale. The window slides for 1 hour at a time to ensure temporal continuity. Within each sliding window, the Pearson correlation coefficients of PV and solar thermal fluctuation components are calculated to obtain the correlation index for the corresponding time period. A positive correlation coefficient indicates synchronous fluctuations (competitive relationship), while a negative coefficient indicates opposite fluctuations (complementary relationship), and the absolute value reflects the correlation strength. The annual calculation results form hourly output fluctuation correlation curves, with time on the horizontal axis and correlation coefficients on the vertical axis. The curve shape reveals the time-varying pattern of the correlation, providing a basis for the design of time-varying compensation strategies.

[0084] The peak and trough times of the daily power generation and solar thermal power time series components are extracted separately. The peak and trough time offsets are calculated, and a daily peak-valley correlation matrix is ​​constructed based on their corresponding statistical characteristics. The daily peak-valley correlation reflects the power generation time series relationship between the photovoltaic and solar thermal systems within a daily cycle, providing guidance for day-ahead scheduling and load matching optimization. The extraction process first segments the daily fluctuation components by natural day to obtain daily curves. Then, peak and trough values ​​are detected for each daily curve. The first derivative zero-point method is used to identify extreme points, and the peak and trough times are recorded. The time offset is calculated based on these values ​​using the following formula: ; In the formula, , , and These represent the times of the daily peak and the times of the daily trough corresponding to the power generation time series and the thermal collector time series, respectively. and These represent the peak time offset and the trough time offset, respectively. Statistical analysis is performed on the offset data throughout the year to calculate the mean, variance, and frequency of offset occurrence in different time intervals, constructing a daily output peak-trough correlation matrix. The column dimensions of the matrix represent the time offset intervals, and the element values ​​of the matrix represent the probability weight of the peak-trough type occurring in that time offset interval.

[0085] Based on seasonal trend components, the average output ratio of photovoltaic (PV) modules and solar thermal collectors is calculated for each season. Seasonal complementary and competitive intervals are identified based on the seasonal variation of the average output ratio, and these are recorded as seasonal output complementarity characteristics. Seasonal complementarity reflects the output balance between PV and solar thermal systems over a long timescale, providing strategic guidance for annual capacity allocation and seasonal energy storage design. The calculation process first divides the year into four seasons. Based on hourly output fluctuation correlation curves, the average PV output and average solar thermal output for each season are calculated, and then the output ratio between them is calculated. Seasonal variations in the ratio reveal differences in system characteristics: PV is dominant in seasons with higher ratios, and solar thermal is dominant in seasons with lower ratios. Seasonal complementary intervals are identified by setting thresholds (e.g., a ratio > 1.5 indicates a PV-dominant season, and < 0.67 indicates a solar thermal-dominant season), providing a basis for capacity allocation. This long-term analysis overcomes the interference of short-term fluctuations and reveals the essential differences in system characteristics.

[0086] Based on the output fluctuation correlation curve, the output peak-valley correlation matrix, and the output complementarity characteristics, a spatiotemporal output correlation model is constructed. This model uses time scale as the dimension and correlation quantification indicators as attribute values. The spatiotemporal output correlation model is a comprehensive expression of multi-timescale analysis results, establishing a mapping relationship between time scales and correlation characteristics, providing a comprehensive characteristic description for multi-energy complementarity optimization. The construction process adopts a hierarchical data structure. The first layer is the time scale dimension, including hourly, daily, and seasonal levels. The second layer contains the correlation quantification indicators corresponding to each time scale: hourly levels use cross-correlation coefficients, daily levels use peak-valley offset statistics, and seasonal levels use output ratios and complementary interval identifiers. It not only includes quantitative correlation data but also extracts qualitative complementary / competitive relationship discrimination, providing rich constraint information and a basis for constructing objective functions for subsequent optimization models. The spatiotemporal output correlation model overcomes the limitations of single-time-scale analysis, achieving a systematic integration of multi-scale characteristics.

[0087] In this embodiment of the invention, the detailed implementation steps for constructing the multi-energy complementary optimization model include:

[0088] The system acquires the hourly electricity and heat load demands of the target building throughout the year and classifies these demands by energy grade. Energy grade classification is an energy quality evaluation method based on the second law of thermodynamics, categorizing demands at different temperature levels and energy forms according to their effective energy (Effective Energy Value), providing a theoretical basis for energy cascade utilization. The classification process first obtains hourly load data for the entire year using building energy consumption simulation software or historical data. Then, it classifies the loads by grade: high-grade energy demand includes electricity loads (lighting, appliances, power, etc.), with an Effective Energy Value close to 1; medium-temperature heat demand includes domestic hot water and heating demand, with an Effective Energy Value of approximately 0.15-0.25; and low-temperature heat demand includes floor radiant heating and fresh air preheating, with an Effective Energy Value below 0.1. The classification results form three categories of load time-series data, which are recorded separately. , and This provides demand-side data for subsequent quality matching optimization; this classification method based on energy quality analysis scientifically reflects the differences in energy quality and provides optimization directions for reducing energy loss and improving energy utilization efficiency.

[0089] Based on the output fluctuation correlation curve, the probability distribution of synchronous fluctuations in photovoltaic power generation and solar thermal power collection is calculated and denoted as the output synergy degree. The output synergy degree is a comprehensive indicator quantifying the collaborative working capability of the two systems, reflecting the degree of consistency in output changes within the same time period. It is of great significance for capacity allocation optimization and system stability assurance. The calculation process first involves threshold segmentation of the output fluctuation correlation curve. Periods with correlation coefficients greater than a preset synergy threshold are marked as highly synergistic, those within the preset threshold are marked as moderately synergistic, and those less than the threshold are marked as lowly synergistic. Then, the time proportion of each synergy level is statistically analyzed to form a synergy degree probability distribution. The synchronous fluctuation probability is defined as the combined proportion of highly synergistic and moderately synergistic periods, reflecting the proportion of time during which the two systems can stably collaborate. A high output synergy degree indicates that the two systems have similar output characteristics, which can easily lead to competition, requiring coordination through capacity allocation adjustments or energy storage buffering. A low synergy degree indicates strong complementarity, allowing for an appropriate increase in the total installed capacity. This indicator provides a quantitative basis for capacity configuration constraint design.

[0090] The design incorporates synergistic constraints on the corresponding capacity configurations of photovoltaic modules and solar thermal collectors based on output synergy. These constraints ensure the stable operation of the multi-energy system by limiting the capacity allocation and preventing energy waste or insufficient supply due to excessive competition. The constraint design is based on the statistical characteristics of output synergy. When the synergy exceeds a preset synergistic output threshold (high competition), the capacity ratio of photovoltaic and solar thermal collectors is limited to a preset synergistic ratio range to avoid oversupply during the same period. When the synergy is not greater than the preset synergistic output threshold (high complementarity), greater flexibility in capacity allocation is allowed to fully utilize complementary characteristics. This synergistic constraint transforms abstract correlation analysis results into specific capacity allocation limits, ensuring the rationality and practicality of the optimization model.

[0091] Based on the energy grade classification results and hourly output characteristic maps, a collaborative objective function for the tiered utilization of energy grades is constructed. This objective function is the core of the multi-energy complementary optimization model, aiming to maximize the supply-demand matching degree and optimize the grade utilization efficiency, reflecting the economic principles of energy system design. The construction process first calculates the direct matching degree of electricity by comparing the photovoltaic power generation time series with the electricity load demand hourly, calculating the supply-demand deviation, and then calculating the direct matching degree, defined as the proportion of times when the supply-demand difference is less than a preset matching threshold. Then, based on the photovoltaic thermal collector power time series and thermal load demand, and with the objective of maximizing the weighted average utilization rate, a thermal allocation optimization sub-problem is constructed. By solving the thermal allocation optimization sub-problem, the supply allocation ratio of medium-temperature and low-temperature thermal demand is obtained, and the priority supply rate and... The definition of the degraded utilization rate is as follows: the priority supply rate is the ratio of the supply of solar thermal power to medium-temperature heat demand to the total solar thermal power; the degraded utilization rate is the ratio of the supply of solar thermal power to low-temperature heat demand to the total solar thermal power. A collaborative objective function for tiered energy quality utilization is constructed with the optimization goals of maximizing the priority supply rate, maximizing the direct matching degree, and minimizing the degraded utilization rate. This objective function embodies the optimization principles of prioritizing electricity for self-use, tiered utilization of heat, and minimizing degraded utilization, and meets the technical requirements for efficient energy system utilization.

[0092] A multi-energy complementary optimization model is established by combining synergistic constraints with the synergistic objective function of energy grade cascade utilization. The complete multi-energy complementary optimization model consists of three parts: objective function, constraints, and optimization variables, forming a systematic mathematical optimization problem. Optimization variables include the total installed capacity of photovoltaic modules, the total installed capacity of solar thermal collectors, and the configured capacity of energy storage systems (including electrical and thermal energy storage). In addition to synergistic constraints, the constraints include: site availability constraints (i.e., the sum of the areas corresponding to the total installed capacity in the optimization variables must be less than or equal to the usable area of ​​the building surface), energy supply reliability constraints (i.e., the supply guarantee rate of the building load must not be lower than the expected demand), and equipment capacity non-negativity constraints (i.e., the mathematical boundary constraint that all optimization variables related to the equipment installation scale must have values ​​greater than or equal to 0). The model solution process uses a genetic algorithm (GA) to search for the global optimum in a multi-dimensional nonlinear constraint space. The solution outputs the optimal capacity configuration scheme, providing total quantity indicators for spatial layout design.

[0093] In this embodiment of the invention, the detailed implementation steps for obtaining the spatial layout scheme include:

[0094] Based on the optimization results of the multi-energy complementary optimization model, the target values ​​for the total installed capacity of photovoltaic modules and solar thermal collectors are obtained respectively. These target values ​​serve as the overall control indicators for spatial layout design, clearly defining the total scale of the photovoltaic and solar thermal systems to be deployed within the building site. The acquisition process directly reads the optimal solution from the optimization model's solution, extracting the total photovoltaic and solar thermal capacities. To facilitate spatial allocation calculations, the target values ​​for the total capacity are converted into area target values. Based on module efficiency and installed capacity per unit area, the required photovoltaic and solar thermal areas are calculated. These target values ​​serve as input constraints for the spatial allocation algorithm, ensuring that the final layout scheme meets the capacity configuration requirements determined by the optimization model, thus achieving an effective connection between system optimization and spatial implementation.

[0095] Energy potential is assessed for each spatial grid cell, and the cells are ranked based on the assessment results to form a priority sequence. Energy potential assessment is a crucial step in identifying high-value placement locations, providing a priority basis for spatial allocation by comprehensively evaluating the energy output capacity of each grid cell. The assessment process employs a multi-indicator comprehensive scoring method, with key indicators including: annual total irradiance received, percentage of time spent in shade, and surface accessibility (i.e., installation and maintenance difficulty). After standardizing each indicator, a weighted sum is obtained to obtain a comprehensive score. The cells are then ranked from highest to lowest score to form a priority sequence. This ranking ensures that limited site resources are prioritized for locations with the highest energy output benefits, improving overall system efficiency.

[0096] Based on the power generation and heat collection time series of each spatial grid cell in the hourly power output characteristic map, the energy output efficiency ratio of photovoltaic modules and solar thermal collectors in the corresponding spatial grid cells is calculated. The energy output efficiency ratio is a key decision-making basis for determining whether photovoltaic or solar thermal power should be deployed in each spatial grid cell, reflecting the relative advantages of the two technologies at a specific location. The calculation process first calculates the annual photovoltaic power generation and annual solar thermal heat collection for each spatial grid cell by summing the hourly power time series. Then, according to the energy value conversion, electricity and heat are uniformly converted into economic value or energy value, and the ratio between the two is used as the efficiency ratio. An efficiency ratio greater than 1 indicates that the location is more suitable for photovoltaic deployment, and less than 1 indicates that solar thermal is more suitable. This index comprehensively considers location characteristics, technical characteristics, and energy value, providing a scientific decision-making basis for intelligent allocation algorithms.

[0097] Based on the priority sequence of spatial grid cells and the energy output efficiency ratio, a greedy algorithm is used to allocate energy types to spatial grid cells. The installation tilt angle and azimuth angle of the photovoltaic modules or solar thermal collectors allocated in the spatial grid cells are optimized according to the building surface orientation parameters and total irradiance received, resulting in a spatial layout scheme. The energy type allocation adopts a greedy strategy, processing each spatial grid cell sequentially according to the priority sequence: if the energy output efficiency ratio of the spatial grid cell is greater than 1 and there is sufficient remaining photovoltaic allocation area, it is allocated as photovoltaic; otherwise, if there is sufficient remaining solar thermal allocation area, it is allocated as solar thermal. Solar and thermal radiation; until the area target value is reached or all units are traversed; the algorithm is computationally efficient and can quickly generate allocation schemes that meet constraints; after allocation, the installation angle of each unit is optimized: for units fixed to the building surface, the tilt angle is determined by the orientation of the building surface; for units with adjustable installation (such as roof), the optimal tilt angle and azimuth angle are determined by traversal calculation or optimization algorithm to maximize the total annual radiation received; finally, a complete spatial layout scheme is output, including detailed parameters such as energy type, installation capacity, tilt angle and azimuth angle of each spatial grid unit, which can be directly used for engineering design and construction guidance.

[0098] In this embodiment of the invention, the detailed implementation steps for assessing the building's negative carbon performance indicators based on the estimated annual energy output and building life-cycle carbon emissions according to the spatial layout scheme include:

[0099] The annual power generation of photovoltaic modules and the annual heat collection of solar thermal collectors are calculated based on the spatial layout scheme. Annual energy output is the fundamental data for evaluating system performance and environmental benefits, reflecting the actual energy production capacity of the multi-energy complementary system. The calculation process is based on hourly power output characteristic maps and the spatial layout scheme. For the spatial grid units of the allocated photovoltaic systems, their power generation time series are extracted and converted according to the unit area, and then accumulated to obtain the total power generation time series of the system. Finally, hourly summation is performed throughout the year to obtain the annual power generation. The calculation process for solar thermal heat collection is similar, yielding the annual heat collection amount. To improve calculation accuracy, system loss factors need to be considered simultaneously, including inverter efficiency, line loss, and dust shading loss of photovoltaic systems; pipe heat loss and heat storage loss of solar thermal systems; and the corrected net annual output provides accurate input for subsequent carbon emission reduction calculations.

[0100] Annual power generation and annual heat collection are converted into carbon emission reductions for electricity and heat generation, respectively, and the annual carbon offset is calculated based on these. Carbon offset is a core indicator for quantifying the system's environmental benefits, reflecting the emission reduction contribution of renewable energy replacing fossil fuels. The conversion process uses the carbon emission factor method, and the formula for calculating carbon emission reductions for electricity is as follows: ;in, For carbon emission reductions in the power sector, The carbon emission factor for the power grid is determined based on the local power structure. The calculation of thermal carbon emission reduction based on the carbon emissions from replacing conventional fossil fuel heating needs to consider the type of alternative energy source, but for some alternative energy types, conversion efficiency needs to be considered; for example, the formula for calculating the thermal carbon emission reduction from replacing gas-fired boilers is:

[0101] ;in, For thermal carbon emission reduction, As a carbon emission factor of gas, Boiler efficiency is the unit of measurement. The annual carbon offset is the sum of these two values, and this indicator visually demonstrates the system's annual emission reduction benefits.

[0102] The process involves obtaining the target building's total life-cycle carbon emissions. Total life-cycle carbon emissions are a comprehensive indicator for assessing a building's overall environmental impact, encompassing carbon emissions throughout the entire process from building material production, construction, operation, and demolition / recycling. The process employs a life-cycle assessment (LCA) approach, calculated in stages: carbon emissions during the building material production stage are obtained from building material lists and carbon emission databases, primarily including the embodied carbon from high-carbon building materials such as steel, cement, and glass; carbon emissions during the construction stage include energy consumption of construction machinery and transportation emissions; carbon emissions during the operation stage are calculated based on annual energy consumption simulations for heating, cooling, and lighting, multiplied by the building's lifespan (typically 50 years) and a carbon emission factor; carbon emissions during the demolition stage are relatively small, accounting for approximately 0.01-0.02% of the total; the total life-cycle carbon emissions are calculated by summing the carbon emissions from each stage, providing a benchmark for calculating the carbon balance achievement timeline.

[0103] The annual carbon offset is accumulated year by year and compared with the total life-cycle carbon emissions. When the accumulated result first exceeds the total life-cycle carbon emissions, the year of accumulation is recorded and designated as the carbon balance achievement time. The carbon balance achievement time is a key time indicator for assessing a building's negative carbon performance, reflecting the inflection point in a building's transformation from a carbon source to a carbon sink. The calculation process uses an iterative accumulation method, setting the year counter n=1 and initializing the accumulated carbon offset to zero. Each iteration calculates the sum of the initial accumulated carbon offset and the annual carbon offset, determining if it is greater than or equal to the total life-cycle carbon emissions. If it is, the current year n is recorded as the carbon balance achievement time; otherwise, the iteration continues. This indicator visually demonstrates the time cycle for a building to achieve environmental benefit balance and serves as an important reference for investment decisions and policy formulation. If the carbon balance achievement time is less than half the building's service life, it indicates good negative carbon performance; if it is close to or exceeds the service life, the design scheme needs to be optimized to improve emission reduction capabilities.

[0104] In this embodiment of the invention, the detailed implementation steps for obtaining the amount of direct solar irradiance received include:

[0105] A solar position calculation model was established, and the solar altitude angle and solar azimuth angle were calculated hourly throughout the year based on the spatial layout coordinates of the building complex. The incident direction vector of sunlight in each spatial grid cell was then determined based on this model. Solar position calculation is a fundamental step in irradiance analysis. Astronomical algorithms are used to determine the sun's position in the celestial coordinate system, providing a geometric basis for subsequent shading assessments and irradiance calculations. The calculation model employs NREL's Solar Position Algorithm (SPA), with input parameters including site latitude and longitude, altitude, date, and time. The calculation process first calculates the Julian days, then the solar declination angle, hour angle, and solar altitude angle. Sun azimuth The incident direction vector is converted to a rectangular coordinate system based on the solar angle. This vector describes the direction of sunlight propagation, providing a unified incident direction reference for each spatial grid cell; hourly calculations throughout the year (8760 times) form a time series of solar positions, providing complete geometric conditions for subsequent irradiance calculations.

[0106] Starting from the center point of the spatial grid cell, ray tracing is performed in the opposite direction of the incident direction vector to determine whether the ray intersects with the surfaces of adjacent buildings. Occlusion detection is a crucial step in direct sunlight calculation; geometric collision detection determines whether the spatial grid cell receives direct sunlight. The ray tracing process employs a ray projection method, starting from the center point of the spatial grid cell... Starting from the opposite direction of the incident vector. The emitted rays are mathematically expressed as The ray is traversed across all building surface polygons in the scene (excluding the surface containing the spatial mesh cell), and the intersection points of the ray and the polygons are calculated. If an intersection point exists and the intersection point parameter t0 > 0 (located in the positive direction of the ray), it is determined to be occlusion; if no valid intersection point exists on any surface, it is determined to be unoccluded. To improve computational efficiency, a spatial index structure (such as an octree) is used to accelerate collision detection. The occlusion judgment results are stored as Boolean values ​​to provide a branching basis for subsequent irradiance calculations.

[0107] If an intersection point exists, the spatial grid cell is determined to be in a shaded state at the corresponding time, and the direct solar irradiance received is recorded as zero. If no intersection point exists, the spatial grid cell is determined to be in an unshaded state at the corresponding time, and the direct solar irradiance received at the corresponding time is calculated based on the solar irradiance distribution, the incident direction vector, and the normal vector direction of the corresponding spatial grid cell. Direct irradiance calculation is based on the cosine law of the incident angle, which describes the relationship between the radiation received by an inclined surface and the incident angle. For unshaded spatial grid cells, the incident angle is calculated, i.e., the angle between the incident direction of sunlight and the surface normal vector; and the direct solar irradiance received is calculated based on this angle, where the direct solar irradiance received is calculated according to the cosine value of the angle between the solar irradiance distribution, the incident direction vector, and the normal vector direction of the spatial grid cell. This calculation accurately reflects the influence of surface tilt and orientation on direct irradiance reception, providing precise radiation input for power output calculation. Hourly calculations throughout the year generate time-series data of direct irradiance for each spatial grid cell, which is an important component of the hourly power output characteristic map.

[0108] In embodiments of the present invention, the process of obtaining a multi-energy complementary optimization design scheme includes:

[0109] Based on the carbon balance achievement time and annual carbon offset, a comprehensive negative carbon performance evaluation index is calculated. This index is a quantitative indicator for measuring the environmental benefits of buildings, comprehensively reflecting both the time efficiency of achieving carbon balance and the intensity of emission reduction. The calculation process first normalizes the carbon balance achievement time, using the building's design life as a benchmark, and calculates the time efficiency factor: Time factor = 1 - (Carbon balance achievement time ÷ Building design life). Simultaneously, the emission reduction intensity factor is calculated, defined as the ratio of annual carbon offset to the building's average annual carbon emissions (the ratio of total life-cycle carbon emissions to the building's design life). The emission reduction intensity factor and the time efficiency factor are then weighted and summed to obtain the comprehensive carbon performance evaluation index. This index provides a quantitative basis for decision-making regarding project output.

[0110] A threshold for negative carbon performance is set. If the comprehensive evaluation index of negative carbon performance is less than the threshold, the design scheme is deemed not to meet the negative carbon target requirements, and the index is re-evaluated. If the comprehensive evaluation index of negative carbon performance is not less than the threshold, the design scheme is deemed to meet the negative carbon target requirements. The threshold is the dividing standard for distinguishing between qualified schemes and schemes to be optimized, and is determined comprehensively based on building type, climate region, and policy requirements. The threshold judgment provides branch control logic for subsequent processes.

[0111] When the design scheme meets the negative carbon target requirements, a complete multi-energy complementary optimization design scheme is generated and output. This scheme is a comprehensive technical document guiding project implementation, containing complete content such as design basis, system configuration, spatial layout, performance prediction, and implementation guidance. The document generation process adopts a modular organizational structure. The first part is the project overview and design basis, including basic building information, site environmental parameters, load demand characteristics, and design target settings. The second part is the multi-energy complementary system configuration scheme, detailing the total installed capacity of photovoltaic modules, the total installed capacity of solar thermal collectors, energy storage system configuration parameters, and auxiliary equipment selection, and providing technical specifications and performance parameter tables for major equipment. The third part is the detailed spatial layout design, including an energy type allocation diagram of the entire site's spatial grid units and the installation details for each area. The document includes six parts: a table of tilt and azimuth parameters, a plan view of equipment layout, and a schematic diagram of system connections; a fourth part on performance prediction and benefit analysis, providing annual power generation and heat collection forecasts, monthly energy output curves, load matching analysis results, annual carbon offsetting, and carbon balance achievement time; a fifth part on economic analysis, including initial investment estimates, annual operating costs, payback period, and net present value over the entire life cycle; and a sixth part on implementation guidance, providing construction process requirements, quality control points, commissioning and acceptance standards, and operation and maintenance management suggestions. The document uses a combination of text and graphics, with key parameters listed in tables and spatial layout presented in 3D visualization graphics to ensure that both technical and non-technical personnel can accurately understand the design scheme. Output formats include PDF technical documents and BIM model files, facilitating design briefings and construction applications.

[0112] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0113] All formulas in this manual are dimensionless and calculated numerically. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0114] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A multi-energy complementary optimization design method for negative carbon ecological buildings, characterized in that, include: Acquire environmental monitoring data and building geometry data of the target building site. The environmental monitoring data includes the distribution of solar irradiance, ambient temperature field, and surface reflectance at the site scale. The building geometry data includes the spatial layout coordinates of the building complex, the surface orientation parameters of individual buildings, and the optical characteristic parameters of the building envelope. Based on environmental monitoring data and building geometry data, hourly output characteristic maps of photovoltaic modules and solar thermal collectors at the building site scale are constructed; the hourly output characteristic maps include the time-series distribution of power generation of photovoltaic modules and the time-series distribution of heat collection power of solar thermal collectors. Based on the multi-timescale evolution of the power generation distribution of photovoltaic modules and the heat collection power distribution of solar thermal collectors, respectively, the spatiotemporal correlation patterns of the output of photovoltaic modules and solar thermal collectors are extracted. Based on the matching relationship between the spatiotemporal correlation pattern of power output and the building energy load demand, a multi-energy complementary optimization model is constructed, and a spatial arrangement scheme for photovoltaic modules and solar thermal collectors is designed in combination with the available area constraints of the target building site. Based on the spatial layout plan, the annual energy output and carbon emissions throughout the building's life cycle are estimated, and the negative carbon performance index of the building is evaluated accordingly. And output a multi-energy complementary optimization design scheme for the building site based on the negative carbon performance index; The process of constructing the hourly output feature map includes: The building site is divided into spatial grid units, and the three-dimensional coordinates and normal vector direction of each spatial grid unit are recorded; Based on solar irradiance distribution and building geometry data, the hourly solar direct irradiance and sky diffuse irradiance received by each spatial grid unit throughout the year are obtained. The ground reflection irradiance gain and the secondary reflection irradiance gain of adjacent building surfaces are obtained based on the surface reflectivity distribution and the optical characteristic parameters of the building envelope, and are recorded as the environmental reflection irradiance correction. The total irradiance received by each spatial grid cell is obtained by superimposing the direct solar irradiance received, the sky diffuse irradiance received, and the environmental reflected irradiance correction. The hourly conversion efficiency of photovoltaic modules and solar thermal collectors in each spatial grid cell is calculated based on the total irradiance received and the ambient temperature field distribution. Based on the hourly conversion efficiency and total irradiance received, the time-series distribution of photovoltaic power generation and solar thermal power collection in each spatial grid cell is calculated and summarized to obtain the hourly output characteristic map. The process of extracting the spatiotemporal correlation pattern of output includes: The time-series distributions of power generation and heat collection power are respectively decomposed into hourly fluctuation components, daily fluctuation components, and seasonal trend components. The correlation coefficients of hourly fluctuation components of power generation time series and heat collection time series within a preset sliding window are analyzed to obtain hourly output fluctuation correlation curves. The peak and trough times of the day are extracted from the daily fluctuation components of the power generation time series and the heat collection power time series, respectively. The peak time offset and trough time offset of the two are calculated, and the daily output peak-trough correlation matrix is ​​constructed based on their corresponding statistical characteristics. The average output ratio of photovoltaic modules and solar thermal collectors in different seasons is obtained based on the seasonal trend components. The seasonal complementary interval and seasonal competition interval are identified according to the seasonal variation law of the average output ratio, and recorded as the seasonal output complementarity characteristics. Based on the output fluctuation correlation curve, the output peak-valley correlation matrix, and the output complementarity characteristics, a spatiotemporal correlation model of output is constructed. The spatiotemporal correlation model of output takes the time scale as the dimension and the correlation quantification index as the attribute value. The process of constructing a multi-energy complementary optimization model includes: The system obtains the annual hourly electricity and heat load demand of the target building and classifies the electricity and heat load demand by energy grade. The energy grade classification results include high-grade energy demand, medium-temperature heat demand, and low-temperature heat demand. The synchronous fluctuation probability distribution of photovoltaic power generation and solar thermal collector power is calculated based on the power output fluctuation correlation curve and denoted as the power output synergy degree. Design the coordination constraints for the corresponding configuration capacity of photovoltaic modules and solar thermal collectors based on the output coordination degree; Based on the energy grade classification results and hourly output characteristic maps, a collaborative objective function for energy grade cascade utilization is constructed, and a multi-energy complementary optimization model is established in combination with collaborative constraints. The optimization variables of the multi-energy complementary optimization model are the total installed capacity of photovoltaic modules, the total installed capacity of solar thermal collectors, and the configuration capacity of energy storage systems.

2. The multi-energy complementary optimization design method for negative carbon ecological buildings according to claim 1, characterized in that, The design process for a spatial layout plan includes: Based on the optimization results of the multi-energy complementary optimization model, the target values ​​of the total installed capacity of photovoltaic modules and solar thermal collectors are obtained respectively. Energy potential is assessed for each spatial grid cell, and the spatial grid cells are ranked based on the energy potential assessment results to form a spatial grid cell priority sequence. Based on the power generation time series and heat collection time series of each spatial grid cell in the hourly power output characteristic map, calculate the energy output efficiency ratio of photovoltaic modules and solar thermal collectors in the corresponding spatial grid cells; Based on the priority sequence of spatial grid units and the energy output efficiency ratio, a greedy algorithm is used to allocate energy types to spatial grid units. The installation tilt angle and installation azimuth angle of the photovoltaic modules or solar thermal collectors allocated in the spatial grid units are optimized according to the building surface orientation parameters and total irradiance received, resulting in a spatial layout scheme.

3. The multi-energy complementary optimization design method for negative carbon ecological buildings according to claim 1, characterized in that, The process of assessing a building's negative carbon performance indicators includes: Calculate the annual power generation of photovoltaic modules and the annual heat collection of solar thermal collectors based on the spatial layout plan; The annual power generation and annual heat collection are converted into carbon emission reductions for electricity and carbon emission reductions for heat, respectively, and the annual carbon offset is calculated based on these. Obtain the total life-cycle carbon emissions of the target building; accumulate the annual carbon offsets year by year and compare them with the total life-cycle carbon emissions; When the cumulative annual result first exceeds the total life-cycle carbon emissions, the year in which the cumulative result is recorded is taken as the time when carbon balance is achieved.

4. The multi-energy complementary optimization design method for negative carbon ecological buildings according to claim 1, characterized in that, The process of obtaining direct solar radiation received includes: A solar position calculation model was established, and the solar altitude angle and solar azimuth angle were calculated hourly throughout the year in combination with the spatial layout coordinates of the building complex; and the incident direction vector of sunlight in each spatial grid cell was determined based on it. Starting from the center point of the spatial grid cell, ray tracing is performed in the opposite direction of the incident direction vector to determine whether the ray intersects with the surface of the adjacent building. If an intersection point exists, the spatial grid cell is determined to be in a shading state at the corresponding time, and the amount of direct solar irradiance received is recorded as zero; if no intersection point exists, the spatial grid cell is determined to be in an unshading state at the corresponding time, and the amount of direct solar irradiance received at the corresponding time is calculated based on the solar irradiance distribution, the incident direction vector, and the normal vector direction of the corresponding spatial grid cell.

5. The multi-energy complementary optimization design method for negative carbon ecological buildings according to claim 1, characterized in that, Methods for constructing the output peak-valley correlation matrix include: The time-series distributions of power generation and solar thermal power are segmented according to natural days to obtain the daily power generation curves and daily solar thermal power collection curves for each natural day. The peak and valley points of the photovoltaic daily power generation curve and the solar thermal daily collector power curve are extracted respectively, and the times corresponding to the peak and valley points are recorded as the daily peak time and daily valley time. The time difference between the peak times of the photovoltaic module and the solar thermal collector is obtained and recorded as the daily peak time offset; and the time difference between the valley times of the photovoltaic module and the solar thermal collector is obtained simultaneously and recorded as the daily valley time offset. Statistical analysis was performed on the daily peak time offset and daily trough time offset throughout the year, and a daily output peak-trough correlation matrix was constructed based on the statistical analysis results.

6. The multi-energy complementary optimization design method for negative carbon ecological buildings according to claim 1, characterized in that, The process of constructing the synergistic objective function for energy grade cascade utilization includes: The power generation time series distribution is compared with the power load demand of the target building hourly to obtain the supply and demand deviation, and the direct matching degree is calculated based on it. Based on the time-series distribution of solar thermal power and thermal load demand, an optimization subproblem is constructed with the goal of maximizing the weighted utilization rate of solar thermal power. This subproblem is then solved to obtain the allocation ratio between medium-temperature and low-temperature thermal demand. Based on the allocation ratio, the priority supply rate and degraded utilization rate corresponding to medium-temperature and low-temperature thermal demand are calculated respectively. The priority supply rate is defined as the ratio of the amount of solar thermal power supplied to medium-temperature thermal demand to the total amount of solar thermal power. The degraded utilization rate is defined as the ratio of the amount of solar thermal power supplied to low-temperature thermal demand to the total amount of solar thermal power. Furthermore, a collaborative objective function for the tiered utilization of energy quality is constructed with the optimization objectives of maximizing the priority supply rate, maximizing the direct matching degree, and minimizing the degraded utilization rate.

7. The multi-energy complementary optimization design method for negative carbon ecological buildings according to claim 1, characterized in that, The process of obtaining a multi-energy complementary optimization design scheme includes: The comprehensive evaluation index of negative carbon performance is calculated based on the time to achieve carbon balance and the annual carbon offset amount. Set a threshold for negative carbon performance; when the comprehensive evaluation index of negative carbon performance is not less than the threshold for negative carbon performance, the design scheme is deemed to meet the negative carbon target requirements; generate and output a complete multi-energy complementary optimization design scheme.

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