High-density street canyon photovoltaic layout parameter determination method based on double critical thresholds

Abstract

The invention discloses a high-density street canyon photovoltaic layout parameter determination method based on double critical thresholds, and the method comprises the steps: generating a plurality of scene samples in an established photovoltaic-street canyon design space, and defining a design variable set; obtaining an artificial sensible heat pulse sequence according to the obtained historical use data of the electric appliance; simulating each simulation scene sample according to the photovoltaic dynamic coefficient and the artificial sensible heat pulse sequence to obtain a plurality of simulation data; calculating a core evaluation index of each simulation scene sample according to the simulation data; constructing and solving a response curved surface model taking the design variable sets as input and the core evaluation indexes as output to obtain a double critical threshold, and obtaining a target design map according to the double critical threshold and the core evaluation indexes of all the design variable sets; and determining an optimal core evaluation index in the target design map according to a preset demand, and taking a parameter in the design variable set corresponding to the optimal core evaluation index as a photovoltaic layout parameter.

Description

Technical Field

[0001] This invention belongs to the field of green building and urban microclimate technology, specifically involving a method for determining the parameters of high-density street valley photovoltaic deployment based on dual critical thresholds. Background Technology

[0002] Distributed photovoltaic (PV) power, as an important form of clean energy utilization, has become one of the core technological pathways for achieving building carbon reduction during urban renewal due to its advantages of flexible deployment and local consumption. Old urban areas are generally characterized by high-density residential development, resulting in numerous narrow street-valley spaces (such as urban villages and old neighborhoods). These areas have high building density and large population concentration, limiting the potential for energy-saving retrofits of existing buildings. Therefore, the large-scale deployment of distributed PV power has become a key approach to improving regional energy self-sufficiency and reducing carbon emissions.

[0003] However, the unique spatial form of high-density urban valleys (large height-to-width ratio, poor ventilation, and complex sunlight conditions) coupled with high-intensity human activities (such as continuous heat dissipation from air conditioning units and concentrated energy consumption) presents a dual contradiction between "carbon reduction benefits" and "thermal environmental impact" for photovoltaic (PV) deployment. Existing PV deployment design methods primarily focus on maximizing power generation efficiency, failing to adequately adapt to the complex environmental characteristics of high-density urban valleys. This has led to a series of key problems in practical applications, severely hindering the sustainable promotion of PV technology in urban renewal.

[0004] First, existing designs generally overlook the thermal environmental side effects of photovoltaic modules. During the photoelectric conversion process, photovoltaic modules generate a significant amount of waste heat. Simultaneously, the reflective properties of their surface materials to long-wave radiation alter the radiative heat exchange balance within the street canyon. In the enclosed space of high-density, narrow street canyons, waste heat is difficult to dissipate quickly, and long-wave radiation is easily reflected and superimposed multiple times between building interfaces, readily exacerbating the local heat island effect and leading to a significant increase in temperature within the street canyon.

[0005] Secondly, there is a lack of methods for coupled analysis of photovoltaics, building energy consumption, and street valley microclimate. Existing simulation calculations often treat these three factors separately, failing to consider the closed-loop correlation effect in high-density street valleys: "human-caused heat dissipation (such as continuous heat dissipation from air conditioning units, cooking heat, etc.) - photovoltaic waste heat emission - natural heat dissipation from the street valley." In reality, human-caused heat dissipation and photovoltaic waste heat have spatiotemporal superposition characteristics, and the two together affect the street valley microclimate. Their coupling effect directly affects the comprehensive benefits of photovoltaic deployment. Separate analysis inevitably leads to design results deviating from actual operating conditions.

[0006] Third, there is a lack of quantitative trade-off tools and clear design thresholds. While photovoltaic (PV) modules can reduce building surface temperatures through shading, resulting in cooling benefits, their waste heat emissions can also lead to warming effects. The competitive relationship between these two effects varies significantly depending on the streetscape (e.g., aspect ratio, orientation, building layout). Current technology cannot provide designers with quantitative judgment criteria, making it difficult to determine the dominant critical condition for these two effects. This often leads to excessive PV deployment in pursuit of power generation and carbon reduction benefits, ultimately sacrificing indoor and outdoor thermal comfort. In some cases, the increased ambient temperature can even cause a reverse increase in air conditioning load, thus weakening the overall energy-saving effect.

[0007] Therefore, there is an urgent need for a method to determine the parameters of high-density street valley photovoltaic deployment based on dual critical thresholds, in order to solve the problems existing in the current technology. Summary of the Invention

[0008] In view of this, the present invention provides a method for determining the high-density street valley photovoltaic deployment parameters based on dual critical thresholds, in order to solve the above-mentioned problems existing in the prior art.

[0009] To achieve the above objectives, this invention provides a method for determining high-density street valley photovoltaic deployment parameters based on dual critical thresholds, comprising the following steps: S1: Establish a photovoltaic-street valley design space based on the acquired street valley data, generate several scene samples in the photovoltaic-street valley design space, and define a set of design variables; S2: Determine the photovoltaic dynamic coefficient and obtain the artificial sensible heat pulse sequence based on the acquired historical usage data of electrical appliances; among which, the photovoltaic dynamic coefficient includes: shortwave shading coefficient, longwave re-radiation coefficient and waste heat emission coefficient; S3: Using a simulation platform, each simulated scenario sample in the photovoltaic-street valley design space is simulated based on the photovoltaic dynamic coefficient and the artificial sensible heat pulse sequence to obtain several simulation data. S4: Calculate the core evaluation indicators for each simulated scenario sample under typical weather days based on several simulation data; among them, the core evaluation indicators include: net temperature rise at the pedestrian level of the sample. Daily cooling load difference ; S5: Based on the radial basis function-Kriging algorithm, construct and solve the response surface model with the design variable set as input and the core evaluation index as output to obtain the double critical threshold, and obtain the target design spectrum according to the double critical threshold and the core evaluation index of all design variable sets. S6: Determine the optimal core evaluation index in the target design map based on the preset requirements, and use the parameters in the design variable set corresponding to the optimal core evaluation index as photovoltaic deployment parameters.

[0010] As an embodiment of the present invention, step S1 specifically includes: A parametric geometric model is established based on the acquired street valley data to obtain the photovoltaic-street valley design space; Based on Latin hypercube sampling, several simulated scenario samples are generated in the photovoltaic-street valley design space, and a set of design variables is defined as follows: In the formula, Represents the set of design variables. Indicates the height-to-width ratio of the street valley. Indicates the direction of the street. Indicates the tilt angle of the photovoltaic module. This indicates the ratio of photovoltaic module row spacing to building height. This represents the photovoltaic rooftop coverage rate; where each set of design variables represents a sample of a simulation scenario.

[0011] As an embodiment of the present invention, step S2 includes: Determine the photovoltaic dynamic coefficient; among which, the shortwave shading coefficient As shown below: In the formula, This indicates the transmittance of a photovoltaic module. For non-transparent photovoltaic modules, The value is 1; for semi-transparent photovoltaic modules, it is determined based on the light transmittance properties of the material. Waste heat emission coefficient As shown below: In the formula, This indicates the solar radiation absorption rate of the photovoltaic surface. express Photoelectric conversion efficiency at any given time This indicates the photoelectric conversion efficiency under standard test conditions. Indicates the power temperature coefficient. express The temperature of the photovoltaic cells at any given time. Indicates reference temperature; Longwave re-radiation coefficient As shown below: In the formula, Indicates the long-wave emissivity of the photovoltaic backsheet or surface. This represents the angle coefficient of the photovoltaic module with respect to the surface of the valley.

[0012] As an embodiment of the present invention, step S2 further includes: Historical usage data of electrical appliances obtained; Based on logistic regression and Markov chain, a probability model of appliance usage is constructed according to historical appliance usage data. The artificial sensible heat pulse sequence was obtained based on the electrical appliance usage probability model.

[0013] As an embodiment of the present invention, step S4 specifically includes: Based on several simulation data, the simulation time for each simulated scenario sample under a typical weather day was calculated. The instantaneous indicators are as follows: In the formula, Indicates in The cooling effect caused by photovoltaic shading at any time Indicates in The temperature increase caused by the waste heat from photovoltaic power generation at any given time. Indicates in Building cooling load power at any given time Indicates in The building cooling load power of a non-PV-based layout at any given time; For instantaneous indicators and Statistical processing was performed: all simulated times within a typical weather day were taken. The average value is used as the net temperature rise of the pedestrian layer for this sample. For all simulated moments within a typical meteorological day The daily cooling load difference of this sample is obtained by summing the results. .

[0014] As an embodiment of the present invention, step S5 specifically includes: Based on the radial basis function-Kriging algorithm, a response surface model with the design variable set as input and the core evaluation index as output is constructed and solved to obtain a dual critical threshold; the response surface model is shown below: The dual critical thresholds include: a microclimate critical threshold and an energy consumption critical threshold; when solving the model, let respectively... and The critical thresholds for microclimate and energy consumption were obtained. An initial design map is constructed based on dual critical thresholds. The core evaluation indicators of all design variable sets are mapped to four regions of the initial design map to obtain the target design map. The construction of the initial design map involves... The area will be designated as a win-win zone. The area is used as the first consideration zone. The area is designated as the second trade-off zone; The area is designated as the deterioration zone.

[0015] As an embodiment of the present invention, step S6 specifically includes: The comprehensive benefit objective function value of the core evaluation indicators corresponding to each set of design variables in the win-win zone of the target design map is calculated as follows: In the formula, and All represent preset weights; The core evaluation index with the largest comprehensive benefit objective function value is selected as the optimal core evaluation index. The parameters of the design variable set corresponding to the optimal core evaluation index are used as photovoltaic deployment parameters, and the photovoltaic modules are deployed in the target high-density street valleys according to the photovoltaic deployment parameters.

[0016] As an embodiment of the present invention, when mapping the core evaluation indicators to the initial design map, the net temperature rise of the pedestrian layer is mapped to the critical threshold of the microclimate, and the daily cooling load difference is mapped to the critical threshold of energy consumption.

[0017] In addition, the present invention also provides a high-density street valley photovoltaic deployment parameter determination system based on dual critical thresholds, comprising: The sample generation module is used to establish a photovoltaic-street valley design space based on the acquired street valley data, generate several scene samples in the photovoltaic-street valley design space, and define a set of design variables. The data prediction module is used to determine the photovoltaic dynamic coefficient and obtain the artificial sensible heat pulse sequence based on the acquired historical usage data of electrical appliances. The photovoltaic dynamic coefficient includes: shortwave shading coefficient, longwave re-radiation coefficient, and waste heat emission coefficient. The simulation module is used to simulate each simulated scenario sample in the photovoltaic-street valley design space through a simulation platform, based on the photovoltaic dynamic coefficient and the artificial sensible heat pulse sequence, and obtain a number of simulation data. The index calculation module is used to calculate the core evaluation indexes for each simulated scenario sample under typical weather days based on several simulation data. Among these, the core evaluation indexes include: the net temperature rise at the pedestrian level of the sample. Daily cooling load difference ; The graph construction module is used to construct and solve the response surface model with the design variable set as input and the core evaluation index as output based on the radial basis function-Kriging algorithm to obtain the double critical threshold, and obtain the target design graph based on the double critical threshold and the core evaluation index of all design variable sets. The parameter determination module is used to determine the optimal core evaluation index in the target design map according to preset requirements, and to use the parameters in the design variable set corresponding to the optimal core evaluation index as photovoltaic deployment parameters.

[0018] In addition, the present invention also provides a computer-readable storage medium for storing computer instructions, which, when executed by a processor, complete the steps described in a method for determining high-density street valley photovoltaic deployment parameters based on dual critical thresholds.

[0019] The beneficial effects of this invention are as follows: In high-density street valley scenarios, the game relationship between photovoltaic shading and waste heat emission is quantified by "dual critical thresholds", filling the design blind spot; the introduction of artificial sensible heat pulse sequence and photovoltaic dynamic coefficient makes the simulation results closer to the complex actual thermal environment of urban villages than traditional single energy consumption simulation.

[0020] Other advantages, objectives, and features of the invention will be set forth in the following description and will be apparent to those skilled in the art in some respects, or may be learned by practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0021] To make the objectives, technical solutions, and beneficial effects of this invention clearer, the following figures are provided for illustration: Figure 1 This is a flowchart illustrating a method for determining high-density photovoltaic deployment parameters in rural valleys based on dual critical thresholds, according to the present invention. Figure 2 This is a schematic diagram of the physical model of a method for determining high-density street valley photovoltaic deployment parameters based on dual critical thresholds according to the present invention.

[0022] Figure 3 This is a schematic diagram of a module for determining the parameters of a high-density street valley photovoltaic deployment system based on dual critical thresholds according to the present invention. Detailed Implementation

[0023] like Figures 1-2 As shown, this invention provides a method for determining high-density street valley photovoltaic deployment parameters based on dual critical thresholds, comprising the following steps: S1: Establish a photovoltaic-street valley design space based on the acquired street valley data, generate several scene samples in the photovoltaic-street valley design space, and define a set of design variables; S2: Determine the photovoltaic dynamic coefficient and obtain the artificial sensible heat pulse sequence based on the acquired historical usage data of electrical appliances; among which, the photovoltaic dynamic coefficient includes: shortwave shading coefficient, longwave re-radiation coefficient and waste heat emission coefficient; S3: Using a simulation platform, each simulated scenario sample in the photovoltaic-street valley design space is simulated based on the photovoltaic dynamic coefficient and the artificial sensible heat pulse sequence to obtain several simulation data. S4: Calculate the core evaluation indicators for each simulated scenario sample under a typical weather day based on several simulation data; among them, the core evaluation indicators include: the net temperature rise at the pedestrian level of the sample. Daily cooling load difference ; S5: Based on the radial basis function-Kriging algorithm, construct and solve the response surface model with the design variable set as input and the core evaluation index as output to obtain the double critical threshold, and obtain the target design spectrum according to the double critical threshold and the core evaluation index of all design variable sets. S6: Determine the optimal core evaluation index in the target design map based on the preset requirements, and use the parameters in the design variable set corresponding to the optimal core evaluation index as photovoltaic deployment parameters.

[0024] Specifically, step S1 is as follows: A parametric geometric model is established based on the acquired street valley data to obtain the photovoltaic-street valley design space; Based on Latin hypercube sampling, several simulated scenario samples are generated in the photovoltaic-street valley design space, and a set of design variables is defined as follows: In the formula, Represents the set of design variables. Indicates the height-to-width ratio of the street valley. Indicates the direction of the street. Indicates the tilt angle of the photovoltaic module. This indicates the ratio of photovoltaic module row spacing to building height. This represents the photovoltaic rooftop coverage rate; where each set of design variables represents a sample of a simulation scenario.

[0025] Step S2 includes: Determine the photovoltaic dynamic coefficient; among which, the shortwave shading coefficient As shown below: In the formula, This indicates the transmittance of a photovoltaic module. For non-transparent photovoltaic modules, The value is 1; for semi-transparent photovoltaic modules, it is determined based on the light transmittance properties of the material. Waste heat emission coefficient As shown below: In the formula, This indicates the solar radiation absorption rate of the photovoltaic surface. express Photoelectric conversion efficiency at any given time This indicates the photoelectric conversion efficiency under standard test conditions. Indicates the power temperature coefficient. express The temperature of the photovoltaic cells at any given time. Indicates reference temperature; Longwave re-radiation coefficient As shown below: In the formula, Indicates the long-wave emissivity of the photovoltaic backsheet or surface. This represents the angle coefficient of the photovoltaic module with respect to the surface of the valley.

[0026] Step S2 further includes: Historical usage data of electrical appliances obtained; Based on logistic regression and Markov chain, a probability model of appliance usage is constructed according to historical appliance usage data. The artificial sensible heat pulse sequence was obtained based on the electrical appliance usage probability model.

[0027] Specifically, step S4 is as follows: Based on several simulation data, the simulation time for each simulated scenario sample under a typical weather day was calculated. The instantaneous indicators are as follows: In the formula, Indicates in The cooling effect caused by photovoltaic shading at any time Indicates in The temperature increase caused by the waste heat from photovoltaic power at any given time. Indicates in Building cooling load power at any given time Indicates in The building cooling load power of a non-PV-based layout at any given time; For instantaneous indicators and Statistical processing was performed: all simulated times within a typical weather day were taken. The average value is used as the net temperature rise of the pedestrian layer for this sample. For all simulated moments within a typical meteorological day The daily cooling load difference of this sample is obtained by summing the results. .

[0028] Specifically, step S5 is as follows: Based on the radial basis function-Kriging algorithm, a response surface model with the design variable set as input and the core evaluation index as output is constructed and solved to obtain a dual critical threshold; the response surface model is shown below: The dual critical thresholds include: a microclimate critical threshold and an energy consumption critical threshold; when solving the model, let respectively... and The critical thresholds for microclimate and energy consumption were obtained. An initial design map is constructed based on dual critical thresholds. The core evaluation indicators of all design variable sets are mapped to four regions of the initial design map to obtain the target design map. The construction of the initial design map involves... The area will be designated as a win-win zone. The area is used as the first consideration zone. The area is designated as the second trade-off zone; The area is designated as the deterioration zone.

[0029] Specifically, step S6 is as follows: The comprehensive benefit objective function value of the core evaluation indicators corresponding to each set of design variables in the win-win zone of the target design map is calculated as follows: In the formula, and All represent preset weights; The core evaluation index with the largest comprehensive benefit objective function value is selected as the optimal core evaluation index. The parameters of the design variable set corresponding to the optimal core evaluation index are used as photovoltaic deployment parameters, and the photovoltaic modules are deployed in the target high-density street valleys according to the photovoltaic deployment parameters.

[0030] In mapping the core evaluation indicators to the initial design map, the net temperature rise of the pedestrian layer is mapped to the critical threshold of the microclimate, and the daily cooling load difference is mapped to the critical threshold of energy consumption.

[0031] The working principle of the above technical solution is as follows: In practice, S1 specifically involves: firstly, obtaining street valley data and historical appliance usage data through actual measurements or surveys; then, establishing a parametric geometric model based on the street valley data to construct a photovoltaic-street valley design space; subsequently, generating several simulated scene samples within this design space using the Latin hypercube sampling method, and clearly defining a set of design variables including street valley aspect ratio, street orientation, photovoltaic module tilt angle, photovoltaic module row spacing to building height ratio, and photovoltaic roof coverage rate. Each set of design variables corresponds to an independent simulated scene sample, thereby completing the coverage of different photovoltaic deployment and street valley morphology combinations, providing... Subsequent simulation analysis provides accurate and comprehensive scene inputs; by establishing a parametric geometric model to construct a photovoltaic-street valley design space, the matching degree between the design space and the actual street valley scene is improved; at the same time, by using Latin hypercube sampling to generate simulated scene samples, the uniform coverage of the value range of each design variable (street valley aspect ratio, photovoltaic deployment parameters, etc.) is ensured, avoiding the omission of key combination schemes; and the clear set of design variables makes the core influencing parameters of photovoltaic-street valley clearer, making the target of subsequent simulation analysis more focused, effectively improving the accuracy and efficiency of design space construction, and laying the foundation for the reliable development of subsequent microclimate-energy consumption coupled simulation.

[0032] S2 specifically includes: determining photovoltaic dynamic coefficients including shortwave shading coefficient, longwave re-radiation coefficient, and waste heat emission coefficient to quantify the multi-dimensional impact of photovoltaic modules on the street valley thermal environment; then, based on historical appliance usage data, fitting the prior probability of appliance activation using logistic regression, and combining Markov chain analysis to analyze the temporal transition law of appliance state to construct an appliance usage probability model; finally, outputting the minute-level on / off state of appliances through this probability model, and completing the quantification by combining the equipment's sensible heat parameters to obtain an artificial sensible heat pulse sequence; by subdividing photovoltaic dynamic coefficients such as shortwave shading and longwave re-radiation, the thermal effect characteristics of photovoltaic modules are accurately characterized, avoiding the simplification and distortion of the photovoltaic thermal impact by traditional single coefficients; the appliance usage probability model constructed by using logistic regression and Markov chain not only conforms to the historical law of appliance usage but also takes into account the temporal continuity of the state, making the model output more consistent with the actual usage scenario; the final generated artificial sensible heat pulse sequence has higher accuracy, providing reliable thermal boundary conditions for subsequent microclimate-energy consumption coupled simulation, effectively improving the authenticity and credibility of the overall simulation results; S3, specifically, refers to the simulation platform, which includes CFD and building energy consumption simulation software; among which, CFD is the k-epsilon turbulence model.

[0033] First, a collaborative simulation platform consisting of CFD (crystal-epsilon turbulence model) and building energy consumption simulation software (such as EnergyPlus) is built. Then, a two-way coupled simulation of microclimate and energy consumption is achieved through "layered solution + closed-loop iteration". The outer layer is responsible for calculating the air flow, radiation transfer and heat diffusion process in the street valley by CFD, and the photovoltaic thermal effect boundary is input as the calculation basis. The inner layer is responsible for calculating the indoor cooling load of the building by building energy consumption simulation software, and the calculated air conditioning heat exhaust power is fed back to CFD. Finally, the two exchange data in real time at a set time step (such as 1 minute) and continue to iterate until the results converge, completing the entire coupled simulation process. By leveraging a collaborative platform combining CFD and building energy consumption software, the traditional fragmented calculation model of photovoltaics, street valley microclimate, and building energy consumption is broken, accurately reproducing the closed-loop physical interaction relationship of "photovoltaic thermal effect - street valley microclimate - building cooling load - air conditioning heat dissipation". Minute-level real-time data iteration makes the simulation results more closely match the dynamic thermal environment characteristics of high-density street valleys, avoiding the distortion of results from single software simulations. At the same time, the street valley microclimate data and building cooling load data output by this process provide reliable core data sources for subsequent calculation of dual benefit indicators and critical threshold solutions, supporting the accuracy of the entire photovoltaic deployment parameter optimization process. It is worth noting that during the simulation, an artificial sensible heat pulse sequence is loaded as an unsteady-state volumetric heat source into the fluid calculation grid adjacent to the building facade; photovoltaic waste heat is coupled to the CFD flow field through a convective boundary condition, and long-wave re-radiation is loaded as a radiative heat flux term into the building surface heat balance equation.

[0034] S4, specifically: based on the target simulation data of each simulated scenario sample under typical weather days, calculate the net temperature rise of the pedestrian level ( (This is obtained by summing and averaging the photovoltaic shading cooling term and the waste heat heating term at all times) and the difference in daily cooling load ( The two core evaluation indicators are obtained by summing the cooling load difference between the photovoltaic and non-photovoltaic baseline conditions. By quantifying the net temperature rise of the pedestrian layer and the daily cooling load difference, the game relationship between photovoltaic shading cooling and waste heat heating is accurately depicted, filling the gap in the lack of quantitative evaluation in traditional design. It is worth noting that the typical meteorological day refers to the "typical summer meteorological day" of the target area (such as the summer solstice or summer design day).

[0035] S5, specifically, involves using the radial basis function-Kriging algorithm to construct a response surface model with the photovoltaic-street valley design variable set (street valley aspect ratio, street orientation, etc.) as input and core evaluation indicators as output; then solving this model, and based on the response surface model, calculating the... The set of design variables is used to obtain the critical threshold boundary of the microclimate; calculations are performed to make... The design variable set is used to obtain the energy consumption critical threshold boundary. The dual critical threshold is the projection of the above two boundary lines (or boundary surfaces) into the design space. That is, the zero-value isosurface formed by the solution set of design variables in the photovoltaic-street valley design space is defined as the microclimate critical threshold and the energy consumption critical threshold, respectively. Then, the response surface model is constructed with the radial basis function-Kriging algorithm to realize the accurate correlation between design variables and core evaluation indicators, and improve the fitting accuracy and reliability of the model. By solving the dual critical threshold, the balance boundary between microclimate and energy consumption is scientifically defined, effectively solving the problem of high-density street valley photovoltaic deployment. The core conflict between "carbon reduction" and "cooling" is addressed. By dividing the initial design map, the application value of different photovoltaic-valley parameter combinations is intuitively distinguished, making the boundaries of the "win-win-trade-deterioration" solution clear at a glance. Among them, the "win-win zone" directly locks in the optimal solution range that takes into account both microclimate improvement and energy saving, the "trade-trade zone" provides a clear selection direction for different engineering needs, and the "deterioration zone" helps to quickly avoid unreasonable solutions, effectively reducing the blindness of engineering decision-making. At the same time, the visualized optimization design map makes the complex parameter-benefit relationship easier to understand, greatly improving the efficiency and scientific nature of photovoltaic deployment scheme selection.

[0036] S6, specifically, when selecting the optimal core evaluation index, the comprehensive benefit objective function value is calculated and selected as the optimal index; the comprehensive benefit objective function value of the core evaluation index corresponding to each set of design variables in the win-win zone of the target design map is calculated, as shown below: In the formula, and All represent preset weights; The parameters in the set of design variables that maximize the overall benefit objective function value are selected as the photovoltaic deployment parameters.

[0037] The beneficial effects of the above technical solution are as follows: For the first time, in a high-density urban village scenario, the game relationship between photovoltaic shading and waste heat emission is quantified by "dual critical thresholds", filling the design blind spot; the introduction of artificial sensible heat pulse sequence and photovoltaic dynamic coefficient makes the simulation results closer to the complex actual thermal environment of urban villages than traditional single energy consumption simulation.

[0038] like Figure 3 As shown, the present invention also provides a high-density street valley photovoltaic deployment parameter determination system based on dual critical thresholds, comprising: The sample generation module is used to establish a photovoltaic-street valley design space based on the acquired street valley data, generate several scene samples in the photovoltaic-street valley design space, and define a set of design variables. The data prediction module is used to determine the photovoltaic dynamic coefficient and obtain the artificial sensible heat pulse sequence based on the acquired historical usage data of electrical appliances. The photovoltaic dynamic coefficient includes: shortwave shading coefficient, longwave re-radiation coefficient, and waste heat emission coefficient. The simulation module is used to simulate each simulated scenario sample in the photovoltaic-street valley design space through a simulation platform, based on the photovoltaic dynamic coefficient and the artificial sensible heat pulse sequence, and obtain a number of simulation data. The index calculation module is used to calculate the core evaluation indexes for each simulated scenario sample under typical weather days based on several simulation data. Among these, the core evaluation indexes include: the net temperature rise at the pedestrian level of the sample. Daily cooling load difference ; The graph construction module is used to construct and solve the response surface model with the design variable set as input and the core evaluation index as output based on the radial basis function-Kriging algorithm to obtain the double critical threshold, and obtain the target design graph based on the double critical threshold and the core evaluation index of all design variable sets. The parameter determination module is used to determine the optimal core evaluation index in the target design map according to preset requirements, and to use the parameters in the design variable set corresponding to the optimal core evaluation index as photovoltaic deployment parameters.

[0039] The working principle and beneficial effects of the technical solution described in the above system have been explained in detail in the technical solution described in the aforementioned method, and will not be repeated here.

[0040] In addition, the present invention also provides a computer-readable storage medium for storing computer instructions, which, when executed by a processor, complete the steps described in a method for determining high-density street valley photovoltaic deployment parameters based on dual critical thresholds.

[0041] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made to it in form and detail without departing from the scope defined by the claims of the present invention.

Claims

1. A method for determining high-density street valley photovoltaic deployment parameters based on dual critical thresholds, characterized in that, Includes the following steps: S1: Establish a photovoltaic-street valley design space based on the acquired street valley data, generate several scenario samples in the photovoltaic-street valley design space, and define a set of design variables; S2: Determine the photovoltaic dynamic coefficient and obtain the artificial sensible heat pulse sequence based on the acquired historical usage data of electrical appliances; among which, the photovoltaic dynamic coefficient includes: shortwave shading coefficient, longwave re-radiation coefficient and waste heat emission coefficient; S3: Using a simulation platform, each simulated scenario sample in the photovoltaic-street valley design space is simulated based on the photovoltaic dynamic coefficient and the artificial sensible heat pulse sequence to obtain several simulation data. S4: Calculate the core evaluation indicators for each simulated scenario sample under a typical weather day based on several simulation data; among them, the core evaluation indicators include: the net temperature rise at the pedestrian level of the sample. Daily cooling load difference ; S5: Based on the radial basis function-Kriging algorithm, construct and solve the response surface model with the design variable set as input and the core evaluation index as output to obtain the double critical threshold, and obtain the target design spectrum according to the double critical threshold and the core evaluation index of all design variable sets. S6: Determine the optimal core evaluation index in the target design map based on the preset requirements, and use the parameters in the design variable set corresponding to the optimal core evaluation index as photovoltaic deployment parameters.

2. The method for determining high-density street valley photovoltaic deployment parameters based on dual critical thresholds according to claim 1, characterized in that, Step S1 is as follows: A parametric geometric model is established based on the acquired street valley data to obtain the photovoltaic-street valley design space; Based on Latin hypercube sampling, several simulated scenario samples are generated in the photovoltaic-street valley design space, and a set of design variables is defined as follows: In the formula, Represents the set of design variables. Indicates the height-to-width ratio of the street valley. Indicates the direction of the street. Indicates the tilt angle of the photovoltaic module. This indicates the ratio of photovoltaic module row spacing to building height. This represents the photovoltaic rooftop coverage rate; where each set of design variables represents a sample of a simulation scenario.

3. The method for determining high-density street valley photovoltaic deployment parameters based on dual critical thresholds according to claim 1, characterized in that, Step S2 includes: Determine the photovoltaic dynamic coefficient; among which, the shortwave shading coefficient As shown below: In the formula, This indicates the transmittance of a photovoltaic module. For non-transparent photovoltaic modules, The value is 1; for semi-transparent photovoltaic modules, it is determined based on the light transmittance properties of the material. Waste heat emission coefficient As shown below: In the formula, This indicates the solar radiation absorption rate of the photovoltaic surface. express Photoelectric conversion efficiency at any given time This indicates the photoelectric conversion efficiency under standard test conditions. Indicates the power temperature coefficient. express The temperature of the photovoltaic cells at any given time. Indicates reference temperature; Longwave re-radiation coefficient As shown below: In the formula, Indicates the long-wave emissivity of the photovoltaic backsheet or surface. This represents the angle coefficient of the photovoltaic module with respect to the surface of the valley.

4. The method for determining high-density street valley photovoltaic deployment parameters based on dual critical thresholds according to claim 1, characterized in that, Step S2 also includes: Historical usage data of electrical appliances obtained; Based on logistic regression and Markov chain, a probability model of appliance usage is constructed according to historical appliance usage data. The artificial sensible heat pulse sequence was obtained based on the electrical appliance usage probability model.

5. The method for determining high-density street valley photovoltaic deployment parameters based on dual critical thresholds according to claim 1, characterized in that, Step S4 is as follows: Based on several simulation data, the simulation time for each simulated scenario sample under a typical weather day was calculated. The instantaneous indicators are as follows: In the formula, Indicates in The cooling effect caused by photovoltaic shading at any time Indicates in The temperature increase caused by the waste heat from photovoltaic power generation at any given time. Indicates in Building cooling load power at any given time Indicates in The building cooling load power of a non-PV-based layout at any given time; For instantaneous indicators and Statistical processing was performed: all simulated times within a typical weather day were taken. The average value is used as the net temperature rise of the pedestrian layer for this sample. For all simulated moments within a typical meteorological day The daily cooling load difference of this sample is obtained by summing the results. .

6. The method for determining high-density street valley photovoltaic deployment parameters based on dual critical thresholds according to claim 1, characterized in that, Step S5 is as follows: Based on the radial basis function-Kriging algorithm, a response surface model is constructed and solved with the design variable set as input and the core evaluation index as output, to obtain a dual critical threshold; the response surface model is shown below: The dual critical thresholds include: a microclimate critical threshold and an energy consumption critical threshold; when solving the model, let respectively... and The critical thresholds for microclimate and energy consumption were obtained. An initial design map is constructed based on dual critical thresholds. The core evaluation indicators of all design variable sets are mapped to four regions of the initial design map to obtain the target design map. The construction of the initial design map involves... The area will be designated as a win-win zone. The area is used as the first consideration zone. The area is designated as the second trade-off zone; The area is designated as the deterioration zone.

7. The method for determining high-density street valley photovoltaic deployment parameters based on dual critical thresholds according to claim 1, characterized in that, Step S6 is as follows: The comprehensive benefit objective function value of the core evaluation indicators corresponding to each set of design variables in the win-win zone of the target design map is calculated as follows: In the formula, and All represent preset weights; The core evaluation index with the largest comprehensive benefit objective function value is selected as the optimal core evaluation index. The parameters of the design variable set corresponding to the optimal core evaluation index are used as photovoltaic deployment parameters, and the photovoltaic modules are deployed in the target high-density street valleys according to the photovoltaic deployment parameters.

8. The method for determining high-density street valley photovoltaic deployment parameters based on dual critical thresholds according to claim 6, characterized in that, When mapping the core evaluation indicators to the initial design map, the net temperature rise of the pedestrian layer is mapped to the critical threshold of the microclimate, and the daily cooling load difference is mapped to the critical threshold of energy consumption.

9. A system for determining parameters of high-density street valley photovoltaic deployment based on dual critical thresholds, characterized in that, include: The sample generation module is used to establish a photovoltaic-street valley design space based on the acquired street valley data, generate several scene samples in the photovoltaic-street valley design space, and define a set of design variables. The data prediction module is used to determine the photovoltaic dynamic coefficient and obtain the artificial sensible heat pulse sequence based on the acquired historical usage data of electrical appliances. The photovoltaic dynamic coefficient includes: shortwave shading coefficient, longwave re-radiation coefficient, and waste heat emission coefficient. The simulation module is used to simulate each simulated scenario sample in the photovoltaic-street valley design space through a simulation platform, based on the photovoltaic dynamic coefficient and the artificial sensible heat pulse sequence, and obtain a number of simulation data. The index calculation module is used to calculate the core evaluation indexes for each simulated scenario sample under typical weather days based on several simulation data. Among these, the core evaluation indexes include: the net temperature rise at the pedestrian level of the sample. Daily cooling load difference ; The graph construction module is used to construct and solve the response surface model with the design variable set as input and the core evaluation index as output based on the radial basis function-Kriging algorithm to obtain the double critical threshold, and obtain the target design graph based on the double critical threshold and the core evaluation index of all design variable sets. The parameter determination module is used to determine the optimal core evaluation index in the target design map according to preset requirements, and to use the parameters in the design variable set corresponding to the optimal core evaluation index as photovoltaic deployment parameters.

10. A computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the steps described in the method for determining high-density street valley photovoltaic deployment parameters based on dual critical thresholds disclosed in claims 1-8.

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