Collaborative optimization design method for coupling building system and renewable energy system

Abstract

The invention discloses a building system and renewable energy system collaborative optimization method, and aims to realize collaborative optimization of a building system and an energy system in zero-carbon building design. According to the method, an integrated numerical model is constructed based on the mechanism of energy flow, conversion and dissipation among multiple systems of building, enclosure structure, energy supply and energy storage, and building design parameters, building enclosure structure performance, a renewable energy system and an energy storage system are coupled from the overall level. By introducing a continuous decision variable and a discrete decision variable and adopting a multi-target heuristic intelligent optimization algorithm, unified modeling and collaborative solving of different types of design parameters are realized. According to the method, on the basis of considering double targets of carbon emission and economic cost, the balance and global optimal solution of the carbon emission and the economic cost can be obtained, the defect that a traditional method is limited to a single link, a single system and single target local improvement is overcome, and a new theoretical basis and practical reference are provided for architects and engineers to carry out low-carbon building design.

Description

Technical Field

[0001] This invention belongs to the field of building energy-saving design and renewable energy utilization technology, and in particular relates to a collaborative optimization design method that couples building systems and renewable energy systems. Background Technology

[0002] Total building carbon emissions are a function of carbon emission intensity, building space, usage time, system energy efficiency, and carbon emission factors of different energy sources. They are influenced by multiple parameters, including outdoor climate, building form, spatial volume, building envelope performance, indoor environmental parameters, electromechanical energy systems, and operating modes.

[0003] In the current system of building energy conservation and HVAC design codes, the mainstream approach to building energy conservation and carbon reduction is primarily forward design. During the building design phase, this involves reducing building load mainly through the application of high-performance thermal insulation systems and climate-adaptive design technologies. In the energy system design phase, renewable energy sources such as solar power are introduced as supplementary energy sources to undertake some carbon reduction tasks. This approach generally follows the "most unfavorable condition" as the design boundary, as exemplified by GB50736 "Code for Design of Heating, Ventilation and Air Conditioning of Civil Buildings" and the "Practical Heating and Air Conditioning Design Manual." This approach typically treats the building system and energy system as two independent entities, designing them separately. This results in a situation where, once the building system is designed and constructed, its carbon emission level throughout its entire life cycle is difficult to fundamentally change, exhibiting a "funnel effect."

[0004] In recent years, academic research and engineering exploration have increasingly focused on the innovative design of zero-energy and zero-carbon buildings. These can be broadly categorized into three methods: the first is the sequential design method, whose basic principle is "building system priority, energy system supplementation"; the second is the reverse design method, whose basic principle is "energy system priority, building system guarantee"; and the third is the piecewise optimization design method, whose basic principle is to decompose the overall optimization problem into multiple sub-problems and optimize each sub-problem separately. However, because the collaborative design of building and energy systems is a typical complex time-varying system with multiple design variables, multiple systems, and nonlinearity, the key elements of the integration and coordination of various subsystems ultimately affect the overall performance of the system. Therefore, solving zero-carbon building design based on traditional methods encounters difficulties in the following aspects: First, the sequential optimization design method fails to consider the nonlinear coupling relationship between the building system and the energy system, and cannot provide the best matching design between the building system and the energy system from a global perspective. At the same time, because the thermal design of the building system, such as the building envelope, is strictly designed according to the code limits, the efficient utilization of renewable energy by the building itself is ignored.

[0005] Second, the reverse design method assumes that the energy system’s use of renewable energy is constrained by the building’s usable skin. The design of the renewable energy system is simplified using the steady-state method, while renewable energy sources such as solar energy have intermittent and random characteristics, and supply and demand have dynamic characteristics.

[0006] Third, segmented optimization design decomposes the overall optimization problem into multiple sub-problems, each of which is then optimized locally. However, the collaborative design of building and energy systems is essentially a complex, time-varying system with multiple variables, multiple systems, and strong nonlinearity. Without a clear objective for system integration and collaboration, the carbon reduction responsibilities of each link and subsystem are difficult to define, leading to insufficient overall performance.

[0007] Therefore, how to break through traditional design thinking, fully consider the interaction between building systems and energy systems in the design of building systems and energy systems, and establish a collaborative optimization design method for building systems and renewable energy systems with carbon emission reduction as the core orientation, based on the energy flow and absorption process and the dynamic matching relationship between renewable energy supply and building energy demand, has become a key technical problem that urgently needs to be solved in the field of zero-carbon buildings. Summary of the Invention

[0008] The purpose of this invention is to overcome the shortcomings of existing foundations and methods. Based on the gradual flow and consumption of energy, and using mathematical models of both "buildings and their renewable energy systems," a new optimization design concept for building and renewable energy systems is proposed. Accordingly, a new collaborative design method for building and renewable energy systems is proposed to coordinate the trade-offs between emission reduction responsibilities and economic cost objectives at each stage. This method overcomes the problems of traditional methods, such as the fragmented modeling of building and energy systems, and the failure to fully reflect their multivariable, multi-stage, and strongly nonlinear coupling relationships. This invention introduces a global collaborative optimization approach, treating building shape, thermal performance of the building envelope, renewable energy supply characteristics, energy storage, and energy consumption processes as a whole for collaborative modeling and optimization, thereby achieving multi-stage, multi-objective, and multi-parameter coupled high-efficiency carbon emission reduction in buildings.

[0009] To achieve the above objectives, the present invention employs the following technical solution: 1. A collaborative optimization design method for coupling building systems and renewable energy systems includes: Step 1: Establish a building indoor air heat balance model to obtain the indoor air temperature under natural working conditions. The calculation depends on solar radiation heat gain and the air temperature on the inner surface of the building envelope. The building geometric parameters and the window-to-wall ratio of each orientation are continuous decision variables. Step 2: Correct the solar heat gain coefficient of the transparent enclosure structure based on the incident angle correction method, store the corrected parameters in the window type database for automatic updating according to the window type selection, and calculate the solar radiation heat gain of the transparent enclosure structure; where the window type is a discrete decision variable. Step 3: Divide the wall into temperature nodes along the thickness direction, establish the thermal balance differential equation for each node, and calculate the air temperature on the inner surface of the building envelope. The thickness of the wall insulation layer is a continuous decision variable in the thermal balance differential equation. Step 4: Input the results of Step 2 and Step 3 into the indoor air heat balance model established in Step 1 to obtain the indoor air temperature under natural working conditions, and calculate the hourly load of the building based on the difference between this temperature and the set comfort temperature. Step 5: Input the building's hourly load into the renewable energy system configuration and operation model to achieve joint simulation calculation of the building and the renewable energy system, where photovoltaic capacity, energy storage capacity and installation parameters are continuous decision variables for optimization design; Step 6: Construct a collaborative design method for building systems and renewable energy systems based on intelligent heuristic optimization algorithms. The outer loop takes the total life cycle cost and operating carbon emission intensity as optimization objectives, and calls the inner loop modules (steps 1 to 5) to evaluate and iterate the candidate decision variables, obtaining the optimal design solution set that satisfies different performance trade-offs under the constraints.

[0010] 2. The area of ​​the transparent building envelope in each orientation in the building's indoor air thermal balance model in Step 1. Non-transparent enclosure area The dimensions of the building are determined based on its geometric parameters and window-to-wall ratio, including length and width. Compared to windows and walls facing different directions All of these are decision variables for optimization design.

[0011] 3. Step Two: First, determine the solar heat gain coefficient of the transparent enclosure structure under different radiation conditions based on the incident angle correction method. , and The corrected solar heat gain coefficient is stored as a parameter in the window type database so that it can be automatically inherited and updated according to the window type selection. Then, the solar radiation heat gain of direct, scattered and ground reflected radiation on the transparent enclosure structure is calculated based on the corrected solar heat gain coefficient, so as to obtain the total solar radiation heat gain of the building's transparent enclosure structure.

[0012] In the formula: For orientation The heat received by the outer window from solar radiation at time t, in W; , , These represent the solar radiation intensity at time t: direct, scattered, and ground-reflected solar radiation, respectively, in W / m². , , This corresponds to the solar heat gain coefficient for the three types of radiation components.

[0013] 4. Step 3: Divide the wall into temperature nodes along its thickness direction and establish a layer-by-layer thermal equilibrium differential equation. The thermal equilibrium differential equation includes the material thickness parameters of each layer of the wall. The thickness of the insulation layer is used as a decision variable for optimization design.

[0014] 5. The calculation of the hourly heat load of the building described in step four is based on the difference between the indoor air temperature under natural operating conditions and the set comfort temperature. When the indoor temperature under natural operating conditions is lower than the set temperature, the heat load of the building at that moment is calculated. This load takes into account the heat transfer of the non-transparent building envelope. Heat transfer in transparent enclosure structures Air infiltration heat transfer and solar radiation heat gain compensation ;

[0015] 6. The renewable energy system configuration and operation model described in step five includes performance calculation modules for equipment such as photovoltaic arrays, solar collectors, energy storage batteries, hot water storage tanks, and heat pump systems. The modeling comprehensively considers photovoltaic-storage synergy, solar collector-storage-thermal synergy, and electrothermal conversion to achieve dynamic coupling of multiple energy sources. The energy operation strategy maximizes the utilization of renewable energy, with any shortfall supplemented by traditional energy sources. The photovoltaic installed capacity... and installation parameters , Solar collector installation area and installation parameters , Energy storage battery capacity and hot water storage tank capacity These are the main decision variables.

[0016] 7. The optimization objectives described in step six include: minimizing the total life-cycle cost of the building, including the construction cost of the building envelope. Renewable energy system equipment costs and operation and maintenance costs The calculation is as follows:

[0017] In the formula: The building's enclosure structure consists of the east, south, west, and north walls, and the roof. m =5; The unit volume cost of the building envelope for each orientation is RMB / m. 3 ; The unit area cost of various types of exterior windows is RMB / m². 2 ; For the number of times the insulation material can be replaced, ; The life cycle of non-insulating materials; M is the building life cycle, in years; The discount rate is %.

[0018]

[0019] In the formula: M The system lifespan is in years; For the first e The capacity of the equipment (such as installed capacity, area, volume, etc.); For the first e The initial unit investment cost of this type of equipment, in yuan / W or yuan / m²; For the first e Repair rate of this type of equipment (a percentage of the original investment), % For the first e The initial investment for this type of equipment was [amount in yuan]. m For the first renewable energy system project m Year; The discount rate is %; The replacement cost is [amount] yuan. The residual value of the equipment (a certain percentage of the original investment). The unit price of electricity purchased is yuan / Wh; The purchase price for heating is yuan / Wh; For a moment t Electricity purchased from the grid, in Wh; For a moment t Heat purchased from the district heating network, Wh.

[0020] The goal is to minimize the carbon emission intensity of building operation, where carbon emission intensity is the carbon emissions generated by conventional energy sources during the building operation phase, excluding renewable energy systems.

[0021] In the formula: Carbon emission factor for electricity; It is a thermal carbon emission factor.

[0022] 8. The decision variables mentioned in step six include: (1) Continuous variables, including building shape parameters, building envelope design parameters, energy supply system capacity and installation parameters, and energy storage system capacity parameters;

[0023] In the formula: ; (2) Discrete variables, including window types for each orientation, with the set of available window types for each orientation being: For each window type Provided by the window type library database.

[0024]

[0025] In the formula: The heat transfer coefficient of glass; It is based on the angle of solar incidence. Corrected solar heat gain coefficient; To characterize each orientation from the candidate set In the decision of selecting only one window type, a 0-1 selection variable is introduced. When facing Select the first When choosing a window type Otherwise, it is 0;

[0026] During optimization, a window type is "selected" from the window type database, and then the parameters of that window type (U value, SHGC, cost, etc.) are automatically inherited as equivalent parameters for that orientation.

[0027]

[0028] 9. The optimization described in step six satisfies the following constraints: (1) Electric power balance constraint: at any time t Photovoltaic power generation Power purchased by the power grid Energy storage battery charging and discharging power , With building load Heat pump power and excess power The two sides satisfy the power balance relationship; solar thermal collectors Hot water storage tank and external heat sources Towards building heat load During the heating process, a thermal balance relationship should be maintained;

[0029] (2) Upper and lower limits of design parameters: The design parameters of each device shall be within the preset upper and lower limits; (3) Constraints of energy storage system: The charging and discharging power of the energy storage battery shall not exceed the rated capacity, and its state of charge shall be within the specified limits. Keep within the allowable range; the charging and discharging power of the hot water storage tank shall not exceed the design value, and its heat storage state shall also be kept within the allowable range to ensure the safe and efficient operation of the energy storage system.

[0030]

[0031] 10. The optimization method described in step six is ​​a multi-objective intelligent heuristic optimization method, preferably a non-dominated sorting genetic algorithm NSGA-II, including the following steps: generating an initial population within the feasible range of decision variables; calculating the fitness of individuals in the population, wherein the fitness function is the lifecycle cost. Carbon emission intensity of building operation The population is sorted non-dominated based on fitness values ​​and crowding is calculated to maintain solution diversity. Genetic operators such as selection, crossover, and mutation are used to perform evolutionary operations on the population to generate new individuals. Parent and offspring individuals are merged and sorted non-dominated, and high-quality solutions are preferentially retained according to rank and crowding to form a new generation of population. The above steps are repeated to calculate fitness, sort non-dominated, perform genetic operations, and update the population until the preset number of iterations is reached or the convergence criterion is met, at which point the optimization terminates. The optimization method employs a dual-layer coupling structure of outer-loop optimization and inner-loop simulation to achieve full-process calculation. The outer loop is an optimization module based on a multi-objective heuristic intelligent optimization algorithm, which generates candidate solutions and iteratively evolves them with the building's life-cycle cost and operating carbon emission intensity as objectives. The inner loop is an energy flow simulation module, which calculates the building's hourly energy flow under given decision variables and feeds the results back to the outer loop for fitness evaluation and optimization search. After multiple iterations, the Pareto optimal design solution set for the building system and the renewable energy system is obtained.

[0032] Compared with the prior art, the present invention has the following technical features: (1) In the collaborative design of building systems and renewable energy systems, this invention fully considers the interaction between the two. Based on the gradual flow, dissipation, and energy consumption of energy, it establishes an integrated numerical model covering building design, building envelope, energy supply system, and energy storage system. Compared with the insufficient integration, fragmented variable processing, and lack of flexibility in some existing commercial software in optimization modeling, this invention achieves coupling and unified modeling of each subsystem through full-code programming, and can flexibly adjust design parameters and operating strategies.

[0033] (2) This invention regards the optimization design of buildings and their renewable energy systems as a whole with multiple systems and multiple objectives, breaking through the limitations of existing methods that only optimize from a single system or local indicators. It can achieve global collaborative optimization in multiple aspects of building design, building envelope and renewable energy utilization, and take into account the balance between carbon emissions and economic costs, thereby obtaining the global optimal solution.

[0034] (3) This invention incorporates both continuous and discrete variables (window database) during the optimization process and achieves unified modeling and collaborative solution through intelligent optimization algorithms. This effectively overcomes the problem that existing methods cannot solve different types of variables in a unified manner, and improves the applicability and accuracy of optimization solutions. Attached Figure Description

[0035] Figure 1 A schematic diagram illustrating energy exchange and flow in buildings, building envelopes, and renewable energy systems; Figure 2 A schematic diagram of a framework for the coordinated optimization of buildings, building envelopes, and renewable energy systems; Figure 3 A flowchart for a method of co-optimizing building, building envelope and renewable energy systems; Figure 4 An iterative solution process for calculating indoor air temperature in buildings under natural working conditions; Figure 5 This is a flowchart for solving optimization problems of buildings, building envelopes, and renewable energy systems based on multi-objective heuristic intelligent optimization algorithms. Detailed Implementation

[0036] The energy flow paths and system configurations of buildings, building envelopes, and renewable energy systems involved in this invention are as follows: Figure 1 As shown. This system encompasses multiple stages, including solar energy acquisition, heat transfer, and energy storage and conversion. Based on the above system structure, the calculation principle of this invention and the relevant parameters required before calculation are as follows: Calculation principle: This invention constructs a collaborative optimization design method for buildings, building envelopes, and renewable energy systems based on a multi-objective heuristic intelligent optimization algorithm. This design method uses the building's life-cycle cost and operational carbon emission intensity as dual optimization objectives, comprehensively considering discrete and continuous design variables such as building geometry, building envelope performance, energy supply and storage system parameters, and window type selection. Under constraints, it obtains optimal design solution sets that satisfy different trade-offs, such as... Figure 2 As shown. The calculation of this invention adopts an integrated solution strategy of "inner loop energy flow numerical calculation + outer loop multi-objective intelligent optimization". The outer loop is based on a multi-objective heuristic intelligent optimization algorithm, which sets optimization parameters and calls the inner loop model. Under the given design decision variables, the inner loop performs hourly (Δt = 1 h) heat balance and energy flow calculations throughout the year, outputs the cost and carbon emission operating intensity indicators of each candidate solution, and feeds them back to the optimizer for iterative updates until the optimal solution set is obtained, such as... Figure 3 As shown. Design decision variables include, but are not limited to: building geometry and envelope parameters, energy supply system configuration and installation parameters, energy storage system parameters, and discrete selection of window types.

[0037] The convergence criterion is optimized as follows: stop when the maximum number of iterations is reached (or when the Pareto front hypervolume improvement is lower than a preset threshold), with the first criterion being the one that is met.

[0038] Parameters required before calculation: Typical meteorological year data: hourly outdoor dry-bulb temperature, total solar radiation on the horizontal surface, diffuse radiation and reflected radiation Architectural Space and Geometry: Input room volume and floor height Building Materials Library: Input the thermal conductivity, specific heat capacity, density, and thickness of each layer (excluding insulation layer) for walls and roofs facing each direction. Window type library (discrete candidates): Transparent enclosure structures of various orientations can select window types from the library. Window type parameters include U-value, SHGC based on incident angle correction, and unit cost. Energy systems: Technical and cost parameters of energy supply equipment (photovoltaics, solar collectors); technical and cost parameters of energy storage systems (thermal storage, electric storage). This invention proposes a novel collaborative optimization design method for building systems and renewable energy systems, which collaboratively optimizes building design parameters, thermal parameters of the building envelope, design parameters of the energy supply system, and design parameters of the energy storage system. Specifically, it includes the following steps: Step 1: Establish an indoor air thermal balance model for the building, including the area of ​​the transparent building envelope in each orientation. Non-transparent enclosure area Determined by the building's geometric parameters and window-to-wall ratio. Building geometric parameters (length and width) ) and the ratio of windows to walls in each direction To optimize decision variables during the design phase, coordinated design of building shape and enclosure area is achieved through linkage adjustment.

[0039]

[0040] In the formula: The heat capacity of indoor air, J / K, is determined by the air density. Specific heat capacity and room volume Decide; Indoor air temperature, °C; For the first The surface temperature of the inner surface of the building envelope (including the east, south, west, and north walls and the roof) is ℃; The heat transfer coefficient between the inner surface of the non-transparent building envelope and the indoor air, in W / (m²). 2 ·K); For the first The surface area of ​​the non-transparent enclosure structure (including the four walls on the east, south, west, and north sides and the roof); The convective heat transfer coefficient between the inner surface of the transparent building envelope and the indoor air, in W / K; For the first The area of ​​the transparent enclosure structure, m 2 ; h is the number of air exchanges. - ¹; To determine the air temperature on the inner surface of the transparent enclosure structure, the glass is considered as a quasi-steady-state heat-conducting element, i.e. , Outdoor air temperature, °C; Heat gain from internal heat sources in indoor air nodes, including heat dissipation from personnel, lighting, and equipment operation, is measured in W. .

[0041] The building's exterior walls face Total area The area of ​​the transparent enclosure structure is calculated from the building's geometric parameters (length, width, height). With non-transparent enclosure Each by Compared to windows and walls The calculation yields the following result:

[0042] Step two involves using the incident angle correction method for the solar heat gain coefficient of transparent enclosure structures to correct and calculate the direct radiation heat gain, diffuse radiation heat gain, and ground reflected radiation heat gain, respectively. The solar heat gain coefficient is then calculated. , , The parameters are derived from the window type database corresponding to the decision variables for window type selection in each orientation, and are automatically updated as the window type is selected, so as to achieve synergistic optimization of the building's transparent envelope area and the thermal performance of the window type.

[0043]

[0044] In the formula: For orientation The heat received by the outer window from solar radiation at time t, in W; The area of ​​the transparent enclosure structure facing each direction is in m². , , , respectively, represent the direct, scattered, and ground-reflected solar radiation intensities at time t, in W / m²; , , This corresponds to the solar heat gain coefficient for the three types of radiation components.

[0045] The correction for the direct radiation portion employs a correction model based on the incident angle:

[0046] The incident angle correction factor is preferably in the form of a quadratic cosine function:

[0047] Among them, parameters , , The results are obtained by least-squares fitting based on architectural glass databases (preferably LBNL WINDOW) or experimental data.

[0048] Scattered radiation is corrected for using a constant: ,in These are correction factors set based on material properties. Ground reflected radiation is corrected using a constant; the ground reflected component is also corrected using a constant. , This is the ground reflection correction factor. By changing... , , , , It can be used in constructions made of different glass materials.

[0049] Step 3: Divide the exterior walls and roof into temperature nodes along the thickness direction, establish layer-by-layer thermal equilibrium differential equations, and calculate the air temperature on the inner surface of the building envelope at different orientations. The thermal equilibrium differential equations include material thickness parameters for each layer of the exterior walls and roof. The thickness of the insulation layer is used as a decision variable for optimization design.

[0050] Discretize the thickness direction of each wall as follows: n Layers, with temperature nodes arranged at the midpoint of each layer. The heat capacity of the walls on each floor The outer wall x ( x = 1, 2, ..., n The heat balance equation for the temperature node of the first floor is: (Indoor side, first floor, i.e., when...) x =1; intermediate layer, that is, when 2≤ x ≤ n -1 o'clock: the outermost layer on the outdoor side, i.e. when x = n When, calculate according to the following formula respectively.

[0051]

[0052] In the formula: The specific heat of each layer of material in the wall; The thickness of each layer of material in the wall; The density of each layer of material in the exterior wall; For the firstx Temperature of temperature nodes in the exterior wall material; For each orientation of the wall x Layer and first x +1 interlayer heat transfer coefficient; For each orientation of the wall n The heat transfer coefficient between the floor and the outdoor air; The calculation can take into account the combined air temperature of the walls facing each direction, and can be combined with the outdoor temperature and shortwave radiation utilization.

[0053] Similarly, for the roof ( y =1,2,…, m The thermal equilibrium differential equations of its temperature nodes are modeled in the same way as those of the walls.

[0054] in:

[0055] In the formula: The convective heat transfer coefficient between indoor air and the exterior wall of the first floor; The thickness of the exterior wall material on the first floor in each orientation; The thermal conductivity of the first-floor exterior wall material in each orientation; For the first n The convective heat transfer coefficient between the exterior wall and the outdoor air.

[0056] Step four: Input the calculation results from steps two and three into the building's indoor air heat balance model from step one to solve for the indoor air temperature under natural operating conditions. Based on the difference between indoor air temperature under natural conditions and the set comfort temperature, the building load on an hourly scale throughout the year is further calculated. Figure 4 The overall calculation process for steps four and five is shown, including the temperature iteration process and the calculation logic for hourly heat load.

[0057]

[0058] In the formula: For heat transfer in non-transparent enclosure structures; For heat transfer in transparent enclosure structures; Heat transfer is achieved through air infiltration.

[0059] Step 5: Input the building load into the renewable energy system configuration and operation optimization model. This model includes performance calculation modules for equipment such as photovoltaic arrays, solar collectors, energy storage batteries, hot water storage tanks, and heat pump systems. The model comprehensively considers photovoltaic-storage synergy, collector-storage-thermal synergy, and electrothermal conversion to achieve dynamic coupling of multiple energy sources. The energy operation strategy is to maximize the utilization of renewable energy, with any shortfall supplemented by traditional energy sources. The photovoltaic installed capacity... and installation parameters , Solar collector installation area and installation parameters , Energy storage battery capacity and hot water storage tank capacity These are the main decision variables.

[0060] Step Six: Taking the building's total life cycle cost and carbon emission intensity during operation as optimization objectives, and using building geometric parameters, building envelope design parameters, renewable energy system configuration parameters, and energy storage system capacity as decision variables, construct a collaborative optimization design model for the building and energy system, and solve it using an intelligent heuristic optimization method.

[0061] Furthermore, the optimization objectives include minimizing the building's total life-cycle cost, which includes the cost of the building envelope. Renewable energy system equipment costs and operation and maintenance costs The calculation is as follows:

[0062] In the formula: The building's enclosure structure consists of the east, south, west, and north walls, and the roof. m =5; The unit volume cost of the building envelope for each orientation is (yuan / m²). 3 ); Cost per unit area for various types of exterior windows (yuan / m²) 2 ); For the number of times the material is replaced, ; M represents the life cycle of non-insulating materials; M represents the building life cycle. The discount rate is %.

[0063]

[0064] In the formula: M The system lifespan is in years; For the first e The capacity of the equipment (such as installed capacity, area, volume, etc.); For the first e The initial unit investment cost of this type of equipment, in yuan / W or yuan / m²; For the first e Maintenance rate of this type of equipment (a certain percentage of the original investment); For the first e The initial investment for this type of equipment was [amount in yuan]. m For the first renewable energy system project m Year; The discount rate; The replacement cost is [amount] yuan. Equipment residual value (a percentage of the original investment); The unit price of electricity purchased is yuan / Wh; The purchase price for heating is yuan / Wh; For a moment t Electricity purchased from the grid, in Wh; For a moment t Heat purchased from the district heating network, Wh.

[0065] The goal is to minimize the carbon emission intensity of building operations, where carbon emission intensity refers to the carbon emissions generated by conventional energy sources during the building operation phase, excluding renewable energy systems.

[0066]

[0067] In the formula: Carbon emission factor for electricity; It is a thermal carbon emission factor.

[0068] Furthermore, the decision variables include: (1) Continuous variables, including building envelope thermal parameters, energy supply system capacity and installation parameters, and energy storage system capacity parameters.

[0069]

[0070] In the formula: ;in .

[0071] (2) Discrete / categorical variables (window types for each orientation) The set of available window types for each orientation is as follows: For each window type Provided by the window type library database.

[0072]

[0073] In the formula: The heat transfer coefficient of glass; It is based on the angle of solar incidence. Corrected solar heat gain coefficient.

[0074] To characterize each orientation from the candidate set In the decision of selecting only one window type, a 0-1 selection variable is introduced. When facing Select the first When choosing a window type Otherwise, it is 0.

[0075]

[0076] During optimization, a window type is "selected" from the window type database, and then the parameters of that window type (U value, SHGC, cost, etc.) are automatically inherited as equivalent parameters for that orientation.

[0077]

[0078] Furthermore, the optimization satisfies the following constraints: (1) Electric power balance constraint: at any time t Photovoltaic power generation Power purchased by the power grid Energy storage battery charging and discharging power , With building load Heat pump power and excess power The two sides satisfy the power balance relationship; solar thermal collectors Hot water storage tank and external heat sources Towards building heat load During the heating process, a thermal balance relationship must be maintained:

[0079] (2) Upper and lower limits of design parameters: The design parameters of each device shall be within the preset upper and lower limits; (3) Constraints of energy storage system: The energy storage system includes battery energy storage (BESS) and water storage tank (TESS), whose charging and discharging power does not exceed the rated capacity and whose energy state is kept within the allowable range.

[0080]

[0081] Furthermore, the optimization method is a multi-objective intelligent heuristic optimization method, preferably NSGA-II or other multi-objective optimization algorithms, and its specific execution flow is as follows: Figure 5 As shown, it includes the following steps: (1) Generate an initial population within the feasible range of decision variables, including building design parameters and energy system parameters; (2) Calculate the fitness of individuals in the population, the life cycle cost Carbon emission intensity of building operation As a bi-objective fitness value; (3) Sort the population by fitness values ​​and calculate crowding to maintain solution set diversity; (4) Genetic operators such as selection, crossover, and mutation are used to perform evolutionary operations on the population to generate new individuals; (5) After merging the parent and offspring individuals, perform non-dominated sorting, and prioritize the retention of high-quality solutions based on rank and crowding to form a new generation of population; (6) Repeat steps (2) to (5) to calculate fitness, non-dominated sorting, genetic operations and population update until the preset number of iterations is reached or the convergence criterion is met, then terminate the optimization and output the Pareto optimal solution set.

[0082] Example The following explanation uses a building as an example to illustrate the calculation process according to this invention.

[0083] The building used in this embodiment is a single-story residential building located in Lhasa, a region rich in solar radiation resources. The aim is to verify the applicability and effectiveness of the proposed synergistic optimization method for building, envelope, and renewable energy system. To simplify the modeling process, an idealized simplified model is used for parameter setting: no partitions, walls, or other detailed structures are installed inside the space; only the external envelope (including the four exterior walls and roof) directly exposed to the outdoor climate is retained, thus forming a closed thermal boundary condition. The interior of the building is uniformly considered as a thermally homogeneous unit, ignoring complex factors such as local thermal bridges and non-uniform heat capacity. Table 1. Values ​​of Basic Building Parameters

[0084] Table 2. Structural Layers and Material Thermophysical Properties of Building Walls and Roofs

[0085] Note: x(1) - x(9) are continuous decision variables. Table 3 Architectural Window Type Database

[0086] Note: In the optimization model, binary variables This means if facing If the window type with ID=k is selected, then ;otherwise The decision variables for each orientation window type selection are x(11), x(12), x(13), and x(14).

[0087] Table 4 Optimized Energy System Equipment Parameters

[0088] Table 5. Calculation parameters for cost and carbon emission intensity in the optimization objectives.

[0089] The outer ring is driven by a multi-objective heuristic intelligent optimization algorithm, with the optimization objectives being the minimization of the total life cycle cost and the minimization of the carbon emission intensity during operation. Optimization algorithm: Non-dominated sorting genetic algorithm (NSGA-II) is preferred, but other multi-objective heuristic intelligent algorithms can be substituted. The design decision variables include continuous variables (thermal parameters of the building envelope, capacity and installation parameters of the energy supply system, and capacity of the energy storage system), as detailed in Tables 1 and 2 (x(1)–x(10), discrete variables (window types x(11), x(12), x(13), x(14) for each orientation), photovoltaic capacity and installation parameters as decision variables x(15), x(16), x(17), collector capacity and installation parameters as decision variables x(18), x(19), x(20), and energy storage and thermal storage capacity as decision variables x(20), x(21). The set of optional window types for each orientation is as follows: For each window type The parameters are provided by the window type library database. During optimization, a window type is "selected" from the window type database, and then the parameters of that window type (U value, SHGC, cost, etc.) are automatically inherited as equivalent parameters for that orientation, as shown in Table 3.

[0090]

[0091] To characterize each orientation from the candidate set In the decision of selecting only one window type, a 0-1 selection variable is introduced. When facing Select the first When choosing a window type Otherwise, it is 0.

[0092] , The optimization algorithm initializes a population of feasible solutions in a high-dimensional design variable space and drives the population to evolve generation by generation through evolutionary operators such as selection, crossover, and mutation. During iteration, candidate solutions call the inner-loop numerical calculation model to perform hourly heat balance and energy flow calculations throughout the year, and return cost and carbon emission intensity as the basis for fitness evaluation.

[0093] Under each candidate design solution (i.e., a set of decision variables), the inner loop performs an hourly energy consumption simulation throughout the year, with a time step Δt = 1 hour. First, an indoor air heat balance model is constructed with an open space as the basic unit (see Table 1). Combining the heat transfer of the building envelope (thermal property parameters of each layer of materials are shown in Table 2), solar radiation heat gain (related parameters are shown in Table 3), air infiltration, and internal heat sources, the indoor air temperature under natural operating conditions is calculated. Based on the difference between the indoor temperature under natural operating conditions and the set comfort temperature, the hourly load of the building is calculated. The power generation is calculated based on the hourly solar radiation intensity and photovoltaic module characteristics. The battery energy storage, charging and discharging, and efficiency loss process is described based on the state of charge model, with related parameters shown in Table 4. The energy management strategy prioritizes the use of renewable energy and energy storage systems, with the grid supplementing any shortfall. On this basis, a dual balance equation for electricity and heat is constructed to ensure the overall energy conservation of the system. Finally, the calculated building life cycle cost and building operation carbon emission intensity are used as the fitness function values ​​of the outer loop optimization (related parameters are shown in Table 5) and returned to the outer loop optimization module to determine whether energy balance, parameter constraints, and other restrictions are met. The optimization process terminates when the maximum number of evolutionary generations is reached, or when the hypervolume improvement rate of the Pareto solution set for several consecutive generations falls below a set threshold. Finally, representative design schemes are selected based on the distribution characteristics of non-dominated solutions to support multi-objective integrated design decisions for zero-carbon buildings.

Claims

1. A collaborative optimization design method for coupled building systems and renewable energy systems, characterized in that, The method includes: Step 1: Establish a building indoor air heat balance model to obtain the indoor air temperature under natural working conditions. The calculation depends on solar radiation heat gain and the air temperature on the inner surface of the building envelope. The building geometric parameters and the window-to-wall ratio of each orientation are continuous decision variables. Step 2: Correct the solar heat gain coefficient of the transparent enclosure structure based on the incident angle correction method, store the corrected parameters in the window type database for automatic updating according to the window type selection, and calculate the solar radiation heat gain of the transparent enclosure structure; where the window type is a discrete decision variable. Step 3: Divide the wall into temperature nodes along the thickness direction, establish the thermal balance differential equation for each node, and calculate the air temperature on the inner surface of the building envelope. The thickness of the wall insulation layer is a continuous decision variable in the thermal balance differential equation. Step 4: Input the results of Step 2 and Step 3 into the indoor air heat balance model established in Step 1 to obtain the indoor air temperature under natural working conditions, and calculate the hourly load of the building based on the difference between this temperature and the set comfort temperature. Step 5: Input the building's hourly load into the renewable energy system configuration and operation model to achieve joint simulation calculation of the building and the renewable energy system, where photovoltaic capacity, energy storage capacity and installation parameters are continuous decision variables for optimization design; Step 6: Construct a collaborative design method for building systems and renewable energy systems based on intelligent heuristic optimization algorithms. The outer loop takes the total life cycle cost and operating carbon emission intensity as optimization objectives, and calls the inner loop modules (steps 1 to 5) to evaluate and iterate the candidate decision variables, obtaining the optimal design solution set that satisfies different performance trade-offs under the constraints.

2. The method according to claim 1, characterized in that, The area of ​​the transparent building envelope in each orientation in the building's indoor air thermal balance model in Step 1. Non-transparent enclosure area The dimensions of the building are determined based on its geometric parameters and window-to-wall ratio, including length and width. Compared to windows and walls facing different directions All of these are decision variables for optimization design.

3. The method according to claim 1, characterized in that, Step two: First, determine the solar heat gain coefficient of the transparent enclosure structure under different radiation conditions based on the incident angle correction method. , and The corrected solar heat gain coefficient is stored as a parameter in the window type database so that it can be automatically inherited and updated according to the window type selection. Then, the solar radiation heat gain of direct, scattered and ground reflected radiation on the transparent enclosure structure is calculated based on the corrected solar heat gain coefficient, so as to obtain the total solar radiation heat gain of the building's transparent enclosure structure. In the formula: For orientation The heat received by the outer window from solar radiation at time t, in W; , , These represent the solar radiation intensity at time t: direct, scattered, and ground-reflected solar radiation, respectively, in W / m². , , This corresponds to the solar heat gain coefficient for the three types of radiation components.

4. The method according to claim 1, characterized in that, Step 3: Divide the wall into temperature nodes along its thickness direction and establish a layer-by-layer thermal equilibrium differential equation. The thermal equilibrium differential equation includes the material thickness parameters of each layer of the wall. The thickness of the insulation layer is used as a decision variable for optimization design.

5. The method according to claim 1, characterized in that, The hourly heat load calculation for the building described in step four is based on the difference between the indoor air temperature under natural operating conditions and the set comfort temperature. When the indoor temperature under natural operating conditions is lower than the set temperature, the heat load of the building at that moment is calculated. This load takes into account the heat transfer of the non-transparent building envelope. Heat transfer in transparent enclosure structures Air infiltration heat transfer and solar radiation heat gain compensation ; （10）。 6. The method according to claim 1, characterized in that, Step 5 describes a renewable energy system configuration and operation model, which includes performance calculation modules for equipment such as photovoltaic arrays, solar collectors, energy storage batteries, hot water storage tanks, and heat pump systems. The model comprehensively considers photovoltaic-storage synergy, solar collector-storage-thermal synergy, and electrothermal conversion to achieve dynamic coupling of multiple energy sources. The energy operation strategy maximizes the utilization of renewable energy, with any shortfall supplemented by traditional energy sources. The photovoltaic installed capacity... and installation parameters , Solar collector installation area and installation parameters , Energy storage battery capacity and hot water storage tank capacity These are the main decision variables.

7. The method according to claim 1, characterized in that, The optimization objectives described in step six include: minimizing the total life-cycle cost of a building, including the construction cost of the building envelope. Renewable energy system equipment costs and operation and maintenance costs The calculation is as follows: In the formula: The building's enclosure structure consists of the east, south, west, and north walls, and the roof. m =5; The unit volume cost of the building envelope for each orientation is RMB / m. 3 ; The unit area cost of various types of exterior windows is RMB / m². 2 ; For the number of times the insulation material can be replaced, ; The life cycle of non-insulating materials; M is the building life cycle, in years; The discount rate is %; In the formula: M The system lifespan is in years; For the first e The capacity of the equipment (such as installed capacity, area, volume, etc.); For the first e The initial unit investment cost of this type of equipment, in yuan / W or yuan / m²; For the first e Repair rate of this type of equipment (a percentage of the original investment), % For the first e The initial investment for this type of equipment was [amount in yuan]. m For the first renewable energy system project m Year; The discount rate is %; The replacement cost is [amount] yuan. The residual value of the equipment (a certain percentage of the original investment). The unit price of electricity purchased is yuan / Wh; The purchase price for heating is yuan / Wh; For a moment t Electricity purchased from the grid, in Wh; For a moment t Heat purchased from the district heating network, Wh; The goal is to minimize the carbon emission intensity of building operation, where carbon emission intensity is the carbon emissions generated by conventional energy sources during the building operation phase, excluding renewable energy systems. In the formula: Carbon emission factor for electricity; It is a thermal carbon emission factor.

8. The method according to claim 1 or 7, characterized in that, The decision variables mentioned in step six include: (1) Continuous variables, including building shape parameters, building envelope design parameters, energy supply system capacity and installation parameters, and energy storage system capacity parameters; In the formula: ; (2) Discrete variables, including window types for each orientation, with the set of available window types for each orientation being: For each window type Provided by the window type library database; In the formula: The heat transfer coefficient of glass; It is based on the angle of solar incidence. Corrected solar heat gain coefficient; To characterize each orientation from the candidate set In the decision of selecting only one window type, a 0-1 selection variable is introduced. When facing Select the first When choosing a window type Otherwise, it is 0; During optimization, a window type is "selected" from the window type database, and its parameters (U-value, SHGC, cost, etc.) are automatically inherited as equivalent parameters for that orientation. （21）。 9. The method according to claim 1 or 7, characterized in that, The optimization described in step six satisfies the following constraints: (1) Electric power balance constraint: at any time t Photovoltaic power generation Power purchased by the power grid Energy storage battery charging and discharging power , With building load Heat pump power and excess power The two sides satisfy the power balance relationship; solar thermal collectors Hot water storage tank and external heat sources Towards building heat load During the heating process, a thermal balance relationship should be maintained; (2) Upper and lower limits of design parameters: The design parameters of each device shall be within the preset upper and lower limits; (3) Constraints of the energy storage system: The charging and discharging power of the energy storage battery shall not exceed the rated capacity, and its state of charge shall be kept within the allowable range; the charging and discharging power of the hot water tank shall not exceed the design value, and its state of heat storage shall also be kept within the allowable range to ensure the safe and efficient operation of the energy storage system. （24）。 10. The method according to claim 1 or 7, characterized in that, Step six describes an optimization method that is a multi-objective intelligent heuristic optimization method, preferably a non-dominated sorting genetic algorithm NSGA-II, which includes the following steps: generating an initial population within the feasible range of decision variables; calculating the fitness of individuals in the population, wherein the fitness function is the lifecycle cost. Carbon emission intensity of building operation The population is sorted non-dominated based on fitness values ​​and crowding is calculated to maintain solution diversity. Genetic operators such as selection, crossover, and mutation are used to perform evolutionary operations on the population to generate new individuals. Parent and offspring individuals are merged and sorted non-dominated, and high-quality solutions are preferentially retained according to rank and crowding to form a new generation of population. The above steps are repeated to calculate fitness, sort non-dominated, perform genetic operations, and update the population until the preset number of iterations is reached or the convergence criterion is met, at which point the optimization terminates. The optimization method employs a dual-layer coupling structure of outer-loop optimization and inner-loop simulation to achieve full-process calculation. The outer loop is an optimization module based on a multi-objective heuristic intelligent optimization algorithm, which generates candidate solutions and iteratively evolves them with the building's life-cycle cost and operating carbon emission intensity as objectives. The inner loop is an energy flow simulation module, which calculates the building's hourly energy flow under given decision variables and feeds the results back to the outer loop for fitness evaluation and optimization search. After multiple iterations, the Pareto optimal design solution set for the building system and the renewable energy system is obtained.

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