Integrated optimization method for solar photo-thermal photovoltaic zero-carbon building energy system of single building

Abstract

The invention belongs to the technical field of building energy saving, and discloses an integrated optimization method for a solar photo-thermal photovoltaic zero-carbon building energy system of a single building, which combines meteorological data and a time-sharing zoning strategy to construct a physical model, makes up for the defect that traditional constant-temperature load calculation neglects personnel flow and functional area difference, improves the thermal load calculation accuracy, and improves the calculation efficiency. Equipment type selection redundancy and initial investment waste are avoided; through collaborative design of a sunlight room, an active energy system and a heat storage technology, coupling matching of passive parameters and active parameters is achieved, the problem of active and passive design splitting is solved, and the whole life cycle cost is reduced; a double-objective optimization function is combined with an improved algorithm with price sensitivity, a market price fluctuation factor is introduced, collaborative optimization of the whole life cycle cost and carbon emission is achieved, scheme robustness is guaranteed, meanwhile, through zero-carbon constraint screening, it is ensured that the building really achieves the zero-carbon operation target, and finally a parameter list with the economic optimum and zero-carbon standard reaching is output. And reliable technical support is provided for zero-carbon design of single buildings in plateau areas.

Description

Technical Field

[0001] This invention belongs to the field of building energy conservation technology, specifically a method for integrating and optimizing the energy system of a single building's solar thermal photovoltaic zero-carbon building. Background Technology

[0002] With the acceleration of the energy transition, achieving zero carbon emissions for individual buildings has become a key aspect of this process, and the design and optimization of individual buildings in plateau regions is an important research direction.

[0003] Currently, the design and optimization of zero-carbon emission single buildings in plateau regions face many obstacles. Existing technologies are difficult to adapt to their zero-carbon development needs, which restricts the achievement of zero-carbon goals.

[0004] Current design and optimization for zero-carbon emission single-building structures in plateau regions employ traditional design processes and conventional modeling and calculation methods. The design process generally follows a sequence of first determining building envelope parameters (such as insulation thickness and window-to-wall ratio), and then selecting HVAC equipment, without comprehensively considering the relationship between passive parameters and active systems (collector area, photovoltaic capacity). When modeling load calculations, the standard is based on 24-hour constant temperature throughout the building, without considering the building's occupant mobility and functional area differences (such as master bedrooms and auxiliary rooms). In the zero-carbon design phase, there is a lack of real-time closed-loop calculations where carbon emission reduction is greater than or equal to carbon emission reduction, and the impact of market price fluctuations in photovoltaics and building materials on the design is not assessed.

[0005] The core problem with current technical solutions is the failure to establish a collaborative optimization system adapted to the zero-carbon emission requirements of individual buildings in plateau regions. It also fails to fully consider the coupling relationship between passive parameters and active systems, the dynamic differences in building usage, and the quantitative requirements and economic robustness of zero-carbon operation. This directly leads to excessively high building life-cycle costs, redundant HVAC equipment selection resulting in wasted initial investment, and the completed buildings may not truly achieve zero-carbon operation. Furthermore, the lack of consideration for market price fluctuations makes the solutions economically fragile, making it difficult to guarantee the stable achievement of zero-carbon goals. Summary of the Invention

[0006] This invention provides an integrated optimization method for the energy system of a single building using solar thermal photovoltaic zero-carbon energy systems. It solves the problems of excessively high building life-cycle costs, redundant HVAC equipment selection leading to wasted initial investment, and the inability of completed buildings to truly achieve zero-carbon operation, or the fragility of the scheme due to the failure to consider market price fluctuations, making it difficult to ensure the stable achievement of zero-carbon goals.

[0007] To achieve the above objectives, the present invention provides the following technical solution: The integrated optimization method for single-building solar thermal photovoltaic zero-carbon building energy systems includes: By combining meteorological data, and based on time-sharing and zone-based strategies and differentiated temperature control logic, the heat load is reconstructed to build a physical model of a single building; Based on the physical model of a single building, complete the collaborative design of the sunroom, active energy system, and thermal storage technology, and obtain the building's passive and active parameters. A dual-objective optimization function for life-cycle cost and life-cycle carbon emissions is established. An improved non-dominated sorting genetic algorithm with price sensitivity is used to find the optimal combination of parameters. The passive parameters of the building and the active system parameters are used as decision variables. Market price fluctuation factors are introduced to screen the economically optimal combination of parameters. Select the optimal combination of parameters that satisfies the zero-carbon constraint and has the lowest total life-cycle cost, and output a list of building and equipment parameters.

[0008] Preferably, the time-sharing partitioning strategy specifically includes: The space is divided into a primary energy-consuming space and an auxiliary energy-consuming space. The time is divided into a daytime active activity period and a nighttime sleep period. The temperature control logic sets a comfortable temperature of 18-20℃ for the primary energy-consuming space and a freeze-proof temperature of 15-16℃ for the auxiliary energy-consuming space.

[0009] Preferably, the decision variables include insulation thickness, window-to-wall ratio, collector area, water tank volume, photovoltaic capacity, and sunroom dimensions, and the decision variables are encoded using real numbers.

[0010] Preferably, in establishing a dual-objective optimization function for total life cycle cost and total life cycle carbon emissions, a constraint handling mechanism is introduced, setting a zero-carbon quantification criterion that carbon emission reduction is greater than or equal to carbon emission, and eliminating parameters that do not meet the zero-carbon quantification criterion.

[0011] Preferably, the LINMAP or TOPSIS method is used to select the scheme that satisfies the zero-carbon constraint and has the lowest life-cycle cost from the optimal parameter combinations.

[0012] Preferably, before designing a sunroom, a determination is made as to whether a sunroom is needed, specifically: The determination of a sunny space includes both physical basis determination and economic determination, among which: The physical basis determination includes at least two of the following: climate resources, site conditions, building function, and structural form; and the climate resources must be compatible with Class I / Class II solar energy resource zones. The economic assessment is conducted by calculating the difference between the cost of the sunroom and the revenue from the reduction of active equipment, comparing the marginal energy-saving benefits of the sunroom and wall insulation, and dynamically determining whether to configure a sunroom in conjunction with price sensitivity analysis.

[0013] A single-building solar thermal photovoltaic zero-carbon building energy system integration and optimization system, including: Model building module: Used to combine meteorological data and reconstruct the physical model of a single building by using time-sharing and zone-based strategies and differentiated temperature control logic to reconstruct the heat load. Parameter acquisition module: used to complete the collaborative design of the sunroom, active energy system, and thermal storage technology based on the physical model of a single building, and to acquire the building's passive and active parameters; The optimal solution module is used to establish a dual-objective optimization function for life-cycle cost and life-cycle carbon emissions. It uses a price-sensitive improved non-dominated sorting genetic algorithm to find the optimal combination of parameters, with building passive parameters and active system parameters as decision variables, and introduces market price fluctuation factors to screen the economically optimal combination of parameters. Output module: Used to select the solution that meets the zero carbon constraint and has the lowest life cycle cost from the optimal combination of parameters, and output a list of building and equipment parameters.

[0014] A computer device includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of an integrated optimization method for a single-building solar thermal photovoltaic zero-carbon building energy system.

[0015] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of an integrated optimization method for a single-building solar thermal photovoltaic zero-carbon building energy system.

[0016] A computer program product includes a computer program, characterized in that, when executed by a processor, the computer program implements the steps of an integrated optimization method for a single-building solar thermal photovoltaic zero-carbon building energy system.

[0017] Compared with existing technologies, this invention has the following advantages: It provides an integrated optimization method for the energy system of a single-building solar thermal photovoltaic zero-carbon building. By combining meteorological data with time-sharing and zoning strategies to construct a physical model, it overcomes the shortcomings of traditional constant-temperature load calculations that ignore personnel flow and functional area differences, improving the accuracy of heat load calculation and avoiding redundant equipment selection and wasted initial investment. Through the coordinated design of the sunroom, active energy system, and thermal storage technology, it achieves the coupling and matching of passive and active parameters, solving the problem of separation between active and passive design and reducing the total life cycle cost. The dual-objective optimization function, combined with an improved algorithm containing price sensitivity, introduces market price fluctuation factors, achieving coordinated optimization of total life cycle cost and carbon emissions while ensuring the robustness of the solution. Simultaneously, through zero-carbon constraint screening, it ensures that the building truly achieves the goal of zero-carbon operation, ultimately outputting a list of parameters that are economically optimal and meet zero-carbon standards, providing reliable technical support for the zero-carbon design of single-building structures in plateau regions. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the energy system integration and optimization method for a single-building solar thermal photovoltaic zero-carbon building according to an embodiment of the present invention. Figure 2 This is a detailed flowchart of the energy system integration and optimization method for a single-building solar thermal photovoltaic zero-carbon building according to an embodiment of the present invention; Figure 3 This is a time-sharing partitioning strategy diagram according to an embodiment of the present invention; Figure 4 This is a block diagram of the integrated optimization system for a single-building solar thermal photovoltaic zero-carbon building energy system, as described in an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be 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. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0021] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0022] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0023] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0024] like Figure 1 As shown, this invention provides an integrated optimization method for a single-building solar thermal photovoltaic zero-carbon building energy system, comprising: S1: Combining meteorological data, the heat load is reconstructed based on time-sharing and zone-based strategies and differentiated temperature control logic to build a physical model of a single building; S2: Based on the physical model of a single building, complete the collaborative design of the sunroom, active energy system, and thermal storage technology, and obtain the building's passive and active parameters; S3: Establish a dual-objective optimization function for life-cycle cost and life-cycle carbon emissions, and use a price-sensitive improved non-dominated sorting genetic algorithm to find the optimal combination of parameters. With building passive parameters and active system parameters as decision variables, market price fluctuation factors are introduced to screen the economically optimal parameter combination. S4: Select the solution that meets the zero-carbon constraint and has the lowest life-cycle cost from the optimal combination of parameters, and output a list of building and equipment parameters.

[0025] Addressing the shortcomings of staff mobility and functional area differences, this approach improves the accuracy of heat load calculations, avoiding redundant equipment selection and wasted initial investment. Through the coordinated design of sunrooms, active energy systems, and thermal storage technologies, it achieves coupling and matching of passive and active parameters, overcoming the problem of separation between active and passive design and reducing total life-cycle costs. A dual-objective optimization function combined with an improved algorithm incorporating price sensitivity, and introducing market price fluctuation factors, achieves coordinated optimization of total life-cycle costs and carbon emissions while ensuring the robustness of the solution. Furthermore, zero-carbon constraint screening ensures that the building truly achieves zero-carbon operation goals, ultimately outputting a list of economically optimal and zero-carbon compliant parameters, providing reliable technical support for zero-carbon design of single buildings in plateau regions.

[0026] like Figure 2 As shown, the specific steps are as follows: S1: Combining meteorological data, a physical model of a single building is constructed by reconstructing the heat load based on a time-sharing and zone-based strategy and differentiated temperature control logic. The time-sharing and zone-based strategy is as follows: Figure 3 As shown.

[0027] Construct a physical model of each building and combine it with meteorological data (TMY) to implement differentiated environmental control strategies to reconstruct the heat load. : Space zoning: Divided into "primary energy-consuming spaces" (such as living room and master bedroom) and "auxiliary energy-consuming spaces" (such as storage room and corridor).

[0028] Time segmentation: Set "daytime active activity segment" and "nighttime sleep segment".

[0029] Temperature control logic: Set a comfortable temperature for the main space. (18-20℃), auxiliary space set antifreeze temperature (15-16℃).

[0030] S2: Based on the physical model of the individual building, complete the collaborative design of the sunroom, active energy system, and thermal storage technology, and obtain the building's passive and active parameters. Whether a sunroom is needed can be determined before the collaborative design of the sunroom with active energy systems and thermal storage technologies is implemented. 1. First, determine if the building needs to meet the following four physical requirements for constructing a sunroom.

[0031] Climate resources: Solar energy is abundant and heating demand is high, making it suitable only for Class I / II solar energy resource areas (such as Lhasa and Shigatse), where the average winter temperature is low. In areas with scarce solar energy, the benefits of sunlight do not outweigh the benefits.

[0032] Site requirements: Unobstructed view to the south and available land for future expansion; the sunroom must be attached to the south-facing wall. If there are tall buildings / trees obstructing the view to the south (less than 4 hours of sunlight in winter), or if the south-facing side is adjacent to a road with no setback space, it cannot be installed.

[0033] Building function: Rural and pastoral residential buildings > Public buildings. Rural and pastoral residential buildings have the highest suitability because they have functional needs for sunrooms (storage, drying, buffer zone); the suitability for stand-alone office buildings depends on their specific functions.

[0034] Structural form: The sunroom should ideally have thermally insulating walls, with the partition wall between the sunroom and the interior being a heavy-duty wall (brick / concrete / phase change wall) to retain heat. If it's an all-light steel structure without heat retention capacity, the sunroom is prone to becoming too cold at night or too hot during the day.

[0035] 2. Economic feasibility assessment a Sunroom Construction Costs and Revenue from Active Equipment Reduction Calculation formula:

[0036] Cost of building a sunroom (glass + steel frame + civil engineering).

[0037] , Because of the sunroom, the heat load is reduced, thus saving the initial investment in photovoltaic panels and heat pumps / collectors.

[0038] The electricity cost saved over the entire life cycle.

[0039] Conclusion: In the Lhasa area, due to the relatively transparent costs of photovoltaic and heat pump systems, a sunroom is only deemed "necessary" if its thermal buffering efficiency is high enough to significantly reduce equipment capacity. Data from the paper shows that the optimal depth is typically 1.2m-1.5m, proving that under the current cost framework, a sunroom is "worthwhile."

[0040] Sunroom vs. Wall Insulation: A Choice Choice relationship: The sunroom covers the south wall, which is equivalent to adding a layer of insulation to the south wall.

[0041] Decision logic: If the building itself already has very thick insulation (such as 200mm EPS), the marginal energy-saving benefit of the sunroom will be reduced, and it may be judged as "unnecessary".

[0042] If the wall insulation can only be thin (e.g., 100mm) due to structural limitations, then a sunroom must be provided to compensate for heat loss.

[0043] c. Price sensitivity determination (dynamic determination) Scenario A (PV is extremely cheap): If the price of PV modules falls below a certain level (e.g., <1.5 yuan / W), the algorithm may tend to "install more PV modules + direct power supply", and determine that a sunroom is not needed (because building a sunroom is more expensive than installing PV modules).

[0044] Scenario B (Conventional Prices): In the current and foreseeable future period, the passive energy-saving cost of a solar room remains lower than the active energy-saving cost, thus determining that a solar room is necessary.

[0045] The design steps are as follows: 1.1 Heat balance equation for air nodes in a solar-air interface air temperature inside the sunroom The changes are determined by solar radiation heat gain, heat loss to the outside, heat transfer to the inside, and ventilation heat exchange. Its governing equation can be expressed as: = + - -

[0046] In the formula: , Specific heat capacity and density of air; Sunroom volume (decision variable, depends on depth) ); Effective solar radiation heat gain as it enters the sunroom through the glass; Heat exchange with the room through natural ventilation holes (only in...) > (Activated at time) Heat dissipation from the building envelope of the sunroom to the outdoor environment; The sunroom conducts heat to adjacent rooms through a trombe wall.

[0047] 1.2 Effective Solar Radiation Heat Gain Model ( ) The heat gain from sunlight is the core input of the system, and the angle of incidence and the optical properties of the glass must be considered. = +

[0048] In the formula: , , These represent the intensity of direct, scattered, and ground-reflected radiation, respectively. Area of ​​the sunroom's light-receiving surface (optimization variable); : The effective transmittance-absorption product of glass and interior surfaces; Shading coefficient (needs to be introduced in summer) <1 correction).

[0049] 1.3 Coupled Heat Transfer Model of Sunroom-Indoor Space The contribution of a sunroom to indoor heating consists of two parts: conduction through the partition walls and convection through ventilation.

[0050] Unsteady heat conduction of partition walls ( ): The heat transfer of thermal storage partition walls can be calculated using either the reaction coefficient method or the finite difference method. =

[0051] This section introduces the wall heat storage concept. This reflects the phase delay characteristic.

[0052] Ventilation and convection heat transfer ( ): When the control strategy determines to open the ventilation vents: =

[0053] In the formula: Area of ​​ventilation holes; The height difference between the upper and lower ventilation openings (utilizing the chimney effect).

[0054] 2. Collaborative optimization logic of key design parameters In the patented optimization algorithm (NSGA-II), the design parameters of the sunroom are set as part of the decision variable vector X, and the optimal solution is determined by finding the minimum value of the life cycle cost (LCC).

[0055] 2.1 Geometric parameters: Depth (D) and aspect ratio Optimization variable: Depth of the sunroom .

[0056] Mechanism of influence: volume .

[0057] Increasing depth D Increased buffer space Temperature fluctuations slow down, but the heat density per unit volume decreases.

[0058] Optimization results (based on the paper's conclusions): In the Lhasa area, the recommended optimal depth is 1.2m~1.5m. At this depth, the thermal buffering efficiency between sunlight and water is [value missing]. Reaching peak:

[0059] 2.2 Thermal parameters: Glass selection ( ,SHGC) Optimization trade-offs: The higher the SHGC (Solar Heat Gain Coefficient), the better. The larger it is (the more heat it gets during the day).

[0060] The lower the heat transfer coefficient The smaller the size (the better the insulation at night).

[0061] Patent strategy: The algorithm compared "single-frame double-glass" and "vacuum glass". The conclusion shows that, considering the decrease in photovoltaic power generation costs, using ordinary double-glazed glass (…) The LCC (Life Cycle Cost) of a solution that incorporates larger-capacity photovoltaics is superior to that of a solution that uses expensive vacuum glass.

[0062] 3. Operation control strategy The operation of the sunroom must follow a logic control based on temperature differences: Operating Condition A: Daytime Active Heat Storage Mode Triggering conditions: and .

[0063] Action: Open the upper and lower ventilation openings of the partition wall (or start the low-power circulating fan).

[0064] Energy flow: At this time, the sunroom directly bears part of the indoor heat load.

[0065] Operating Condition B: Nighttime Passive Buffer Mode Triggering conditions: or .

[0066] Action: Close all vents and pull down the nighttime insulation curtain in the sunroom (if applicable).

[0067] Physical significance: At this time, the sunroom acts as an additional air gap, which increases the overall heat transfer coefficient of the exterior wall. Revised to:

[0068] in Additional thermal resistance provided for the sunroom.

[0069] 4. Adaptation of the sunroom in the system solution: Compatible with distributed building photovoltaic arrays + electrothermal conversion equipment + energy storage + thermal storage: The sunroom bears the basic heat load and reduces peak loads.

[0070] Formula correlation: Sunlight room increases collector area Design requirements are reduced:

[0071] Design considerations: It is recommended that the partition wall be made of high heat storage heavy material (such as concrete + phase change plaster) to form a "solid-liquid dual heat storage" with the water tank.

[0072] Compatible with distributed building photovoltaic arrays + solar thermal fields + energy storage + thermal storage: The sunroom primarily serves as a heat buffer, reducing the frequency of heat pump startups at night.

[0073] Formula correlation: Sunlight increases the equivalent outdoor temperature, thus improving the heat pump COP: Depend on (If ventilation is provided in the sunroom) Design considerations: A space for photovoltaic installation must be reserved at the top of the sunroom, and the depth must be controlled to avoid obstructing the top photovoltaic panels.

[0074] Through the rigorous derivation based on the above formula, the sunroom design in this patent is no longer an empirical architectural structure, but a quantifiable, controllable, and optimizable precision energy component.

[0075] S3: Establish a dual-objective optimization function for life-cycle cost and life-cycle carbon emissions, and use a price-sensitive improved non-dominated sorting genetic algorithm to find the optimal combination of parameters. With building passive parameters and active system parameters as decision variables, market price fluctuation factors are introduced to screen the economically optimal parameter combination. Objective function: 1. Lowest life cycle cost (MinLCC): Includes initial investment, operating costs, maintenance costs, and residual value recovery.

[0076] 2. Minimum life cycle carbon emissions (MinCE): including carbon hidden in building materials, carbon from equipment production, and indirect carbon from operation.

[0077] Zero-carbon quantification criteria (constraints): ≥

[0078] Right now ( ( ) )- ≥ ( ( ) ) The design also mandates that the annual net carbon emission reduction must meet specific targets (e.g., d≥1882kg for typical apartment types).

[0079] Solving using the improved NSGA-II algorithm: Decision variables: covering all dimensions of parameters such as insulation thickness, window-to-wall ratio, collector area, water tank volume, photovoltaic capacity, and additional sunroom dimensions.

[0080] Price sensitivity analysis: Introduce market price fluctuation factors (such as ±10% of photovoltaic / window prices) into the optimization loop to analyze the sensitivity of parameters to cost changes, eliminate solutions with poor economic robustness, and output the parameter combination with the best risk resistance.

[0081] Encoding of decision variables and population initialization: Using real-coded methods, passive building parameters (such as the depth of the sunroom) are encoded. Insulation thickness ) and active system parameters (such as collector area) Photovoltaic capacity The initial population is randomly generated and mapped to a decision vector. The population size is set to N (e.g., N=50).

[0082] Fitness Evaluation: Using the dynamic load calculation model and the LCC economic model, calculate the two objective function values ​​for each individual: =min LCC (Life Cycle Cost); =min CE (carbon emissions throughout the entire life cycle).

[0083] In this process, a constraint handling mechanism is introduced to address issues that do not meet the zero-carbon constraint (i.e., ... A penalty function is applied to the solution of ), causing it to be eliminated in the sorting.

[0084] Evolutionary cycle operation: Selection operator: Binary Tournament Selection is used.

[0085] Crossover operator: Employs simulated binary crossover (SBX), with crossover probability... =0.9.

[0086] Mutation operator: Employs polynomial mutation, with mutation probability... =1 / n (n is the number of variables).

[0087] The above operators generate a offspring population. .

[0088] Non-dominated sorting and environment selection: Implement an elite strategy and merge and By using non-dominated sorting and crowding distance calculation, the optimal N individuals are selected to form a new generation of population. .

[0089] Convergence determination and post-processing: Repeat the above steps until the maximum number of iterations is reached (e.g., Gen=300). Output the final set of Rank 1 individuals, which is the Pareto Optimal Set. Use decision-aiding methods (such as LINMAP or TOPSIS) to select the final implementation scheme from the solution set.

[0090] S4: Select the solution that meets the zero-carbon constraint and has the lowest life-cycle cost from the optimal combination of parameters, and output a list of building and equipment parameters.

[0091] Select the scheme that meets the zero carbon constraint and has the lowest LCC from the Pareto frontier, and output a list of building and equipment parameters.

[0092] Example: A single dwelling in a settlement for farmers and herdsmen in a certain area (200㎡, Class II severe cold region).

[0093] Model and strategy settings: The master bedroom and living room are designated as the main spaces (20℃ from 08:00 to 22:00, 16℃ at night); the rest are auxiliary spaces (15℃ throughout the day).

[0094] Introduce a phase change electrothermal film (phase change temperature 20℃, 25W / piece).

[0095] Optimize computation: Set up a genetic algorithm (population 60, iterations 300) and input Lhasa TMY meteorological data.

[0096] Perform price sensitivity analysis: simulate a scenario where the price of high-efficiency windows increases by 10%.

[0097] Optimal result: Passive parameters: 120mm thick EPS for exterior walls, 0.35 window-to-wall ratio for south-facing windows (adjusted to be lower due to price sensitivity analysis), and 1.2m depth for the additional sunroom.

[0098] Active parameters: 20㎡ solar collector, 1.5m³ water tank, 5.8kWp photovoltaic capacity (increased to compensate for the reduction in passive heat gain).

[0099] Synergistic effect: The phase change thermal storage layer utilizes surplus photovoltaic power to keep the indoor temperature fluctuation within ±1.5℃ at night.

[0100] Indicator verification: Carbon balance: Annual emission reduction of 2450kg - (operational emissions of 520kg + implicit carbon of 400kg) = net emission reduction of 1530kg > 0, meeting the zero carbon constraint.

[0101] Economic efficiency: LCC is reduced by 18% compared to the unoptimized solution.

[0102] like Figure 4 As shown, the present invention also provides an integrated optimization system for a single-building solar thermal photovoltaic zero-carbon building energy system, comprising: Model building module: Used to combine meteorological data and reconstruct the physical model of a single building by using time-sharing and zone-based strategies and differentiated temperature control logic to reconstruct the heat load. Parameter acquisition module: used to complete the collaborative design of the sunroom, active energy system, and thermal storage technology based on the physical model of a single building, and to acquire the building's passive and active parameters; The optimal solution module is used to establish a dual-objective optimization function for life-cycle cost and life-cycle carbon emissions. It uses a price-sensitive improved non-dominated sorting genetic algorithm to find the optimal combination of parameters, with building passive parameters and active system parameters as decision variables, and introduces market price fluctuation factors to screen the economically optimal combination of parameters. Output module: Used to select the solution that meets the zero carbon constraint and has the lowest life cycle cost from the optimal combination of parameters, and output a list of building and equipment parameters.

[0103] A computer device is provided according to an embodiment of the present invention. This computer device includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the various method embodiments described above. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the various device embodiments described above.

[0104] The computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention.

[0105] The computer device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device may include, but is not limited to, a processor and memory.

[0106] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0107] The memory can be used to store the computer program and / or module, and the processor implements various functions of the computer device by running or executing the computer program and / or module stored in the memory, and by calling the data stored in the memory.

[0108] If the modules / units integrated into the computer device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory, random access memory, electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0109] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the scope of the invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0110] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can be appropriately combined to form other embodiments that can be understood by those skilled in the art. The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A method for integrating and optimizing the energy system of a single-building solar thermal photovoltaic zero-carbon building, characterized in that, include: By combining meteorological data, and based on time-sharing and zone-based strategies and differentiated temperature control logic, the heat load is reconstructed to build a physical model of a single building; Based on the physical model of a single building, complete the collaborative design of the sunroom, active energy system, and thermal storage technology, and obtain the building's passive and active parameters. A dual-objective optimization function for life-cycle cost and life-cycle carbon emissions is established. An improved non-dominated sorting genetic algorithm with price sensitivity is used to find the optimal combination of parameters. The passive parameters of the building and the active system parameters are used as decision variables. Market price fluctuation factors are introduced to screen the economically optimal combination of parameters. Select the optimal combination of parameters that satisfies the zero-carbon constraint and has the lowest total life-cycle cost, and output a list of building and equipment parameters.

2. The method for integrating and optimizing a single-building solar thermal photovoltaic zero-carbon building energy system according to claim 1, characterized in that, The time-sharing partitioning strategy is specifically as follows: The space is divided into a primary energy-consuming space and an auxiliary energy-consuming space. The time is divided into a daytime active activity period and a nighttime sleep period. The temperature control logic sets a comfortable temperature of 18-20℃ for the primary energy-consuming space and a freeze-proof temperature of 15-16℃ for the auxiliary energy-consuming space.

3. The method for integrating and optimizing the energy system of a single-building solar thermal photovoltaic zero-carbon building according to claim 1, characterized in that, The decision variables include insulation thickness, window-to-wall ratio, collector area, water tank volume, photovoltaic capacity, and sunroom dimensions. These decision variables are encoded using real numbers.

4. The method for integrating and optimizing the energy system of a single-building solar thermal photovoltaic zero-carbon building according to claim 1, characterized in that, In establishing a dual-objective optimization function for life cycle cost and life cycle carbon emissions, a constraint handling mechanism is introduced, setting a zero-carbon quantification criterion that carbon emission reduction is greater than or equal to carbon emission, and eliminating parameters that do not meet the zero-carbon quantification criterion.

5. The method for integrating and optimizing the energy system of a single-building solar thermal photovoltaic zero-carbon building according to claim 1, characterized in that, The LINMAP or TOPSIS method is used to select the optimal combination of parameters that satisfies the zero-carbon constraint and has the lowest total life-cycle cost.

6. The method for integrating and optimizing the energy system of a single-building solar thermal photovoltaic zero-carbon building according to claim 1, characterized in that, Before designing a sunroom, it's essential to determine whether a sunroom is necessary. Specifically: The determination of a sunny space includes both physical basis determination and economic determination, among which: The physical basis determination includes at least two of the following: climate resources, site conditions, building function, and structural form; and the climate resources must be compatible with Class I / Class II solar energy resource zones. The economic assessment is conducted by calculating the difference between the cost of the sunroom and the revenue from the reduction of active equipment, comparing the marginal energy-saving benefits of the sunroom and wall insulation, and dynamically determining whether to configure a sunroom in conjunction with price sensitivity analysis.

7. A single-building solar thermal photovoltaic zero-carbon building energy system integration and optimization system, characterized in that, include: Model building module: Used to combine meteorological data and reconstruct the physical model of a single building by using time-sharing and zone-based strategies and differentiated temperature control logic to reconstruct the heat load. Parameter acquisition module: used to complete the collaborative design of the sunroom, active energy system, and thermal storage technology based on the physical model of a single building, and to acquire the building's passive and active parameters; The optimal solution module is used to establish a dual-objective optimization function for life-cycle cost and life-cycle carbon emissions. It uses a price-sensitive improved non-dominated sorting genetic algorithm to find the optimal combination of parameters, with building passive parameters and active system parameters as decision variables, and introduces market price fluctuation factors to screen the economically optimal combination of parameters. Output module: Used to select the solution that meets the zero carbon constraint and has the lowest life cycle cost from the optimal combination of parameters, and output a list of building and equipment parameters.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the single-building solar thermal photovoltaic zero-carbon building energy system integration and optimization method according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method for integrating and optimizing the energy system of a single building's solar thermal photovoltaic zero-carbon building as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method for integrating and optimizing the energy system of a single building's solar thermal photovoltaic zero-carbon building as described in any one of claims 1-6.

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