A rural building group-oriented unified specification photovoltaic energy storage module configuration method

Abstract

The application discloses a unified specification photovoltaic energy storage module configuration method for rural building groups. The available roof area, non-empty air conditioning load, air conditioning response data and comfort parameters of each building, as well as meteorological data, real-time electricity price signals and dynamic carbon responsibility signals are obtained. The photovoltaic module specification, energy storage energy module specification and energy storage power module specification shared by the building group are uniformly determined, and the capacity difference of each building is represented by non-negative integer module quantity. Based on the determined capacity, annual operation evaluation is performed to coordinate photovoltaic output, energy storage charging and discharging, grid purchase and sale of electricity and air conditioning set temperature adjustment. The total life cycle cost including hardware cost, deployment related soft cost and annual operation cost is constructed, and optimization is performed under the constraints of roof area, energy storage power-energy consistency and operation feasibility to output the shared module specification and the module quantity of each building. The application can take into account equipment standardization, building difference adaptation and annual operation feasibility.

Description

Technical Field

[0001] This invention belongs to the field of distributed energy planning technology for rural building complexes, specifically relating to a method for configuring photovoltaic energy storage modules of uniform specifications for rural building complexes. Background Technology

[0002] With the promotion of distributed photovoltaic (PV) and electrochemical energy storage devices in rural areas, utilizing residential rooftops to install PV power generation equipment and coordinating PV output, building load, and grid interaction through energy storage devices has become a way to improve the energy structure of rural buildings. Unlike individual public buildings or centralized industrial parks, rural residential building complexes typically consist of a large number of spatially dispersed buildings with relatively independent property rights. These buildings vary significantly in terms of available roof area, roof orientation, building geometry, resident population, electrical load, and equipment installation conditions. If the PV and energy storage capacity is determined solely based on the aggregated load or total installed capacity of the building complex, the resulting system-level capacity is difficult to directly allocate to each building, potentially leading to situations where some buildings cannot accommodate PV installations on their roofs, some buildings have insufficient capacity, or equipment utilization is low.

[0003] Existing technology CN109510224B discloses a method for capacity configuration and operation optimization of photovoltaic energy storage and distributed energy. This method first configures the capacity of energy conversion equipment and determines its operating output curve based on thermal load, electrical load, and basic data of the distributed energy system. Then, it forms a net electrical load curve based on the electrical load and the operating output curve, thereby optimizing the rated power, rated capacity, and charging / discharging strategy of the battery energy storage system within the photovoltaic energy storage system. Its planning objects mainly include the rated capacity of equipment and the system operating status of regional integrated energy systems. It is suitable for analyzing the energy relationships between the power grid, heating grid, gas grid, and energy conversion equipment, but it does not further address the differences in roof conditions, load characteristics, and installation conditions among buildings within a building complex. The regional-level photovoltaic capacity and energy storage capacity obtained by this method usually still need to be decomposed to specific buildings after planning is completed. During the decomposition process, situations may arise where the total system capacity is feasible, but the building-level installation is not.

[0004] The electricity consumption of air conditioning in rural residential buildings is affected not only by outdoor weather conditions but also by the thermal performance of the building envelope, building area, indoor set temperature, and residents' comfort needs. Even under the same weather conditions, different buildings may exhibit significantly different air conditioning power responses and adjustable ranges. Existing integrated energy allocation methods typically treat air conditioning, heat pumps, or thermal loads as given loads or general energy conversion links, failing to establish the response relationship between each building's set temperature and air conditioning power consumption, and neglecting to use building-level thermal comfort indicators as operational constraints. In this context, the adjustability of air conditioning loads is difficult to accurately participate in the evaluation of photovoltaic integration and energy storage operation, and there is a possibility that some buildings may exceed permissible indoor comfort levels by reducing operating costs.

[0005] Current photovoltaic (PV) energy storage capacity planning typically optimizes PV installed capacity, energy storage capacity, and energy storage power capacity as continuous variables. While continuous capacity increases the degree of freedom in solving mathematical models, the PV modules, battery clusters, energy storage cabinets, and power conversion equipment actually procured and used in construction all have fixed rated specifications. The planned continuous capacity often cannot directly correspond to existing product specifications, requiring rounding, replacement, or recombination after planning. When each building in a building complex receives a dedicated set of continuous capacity, it can result in a large number of PV specifications, energy storage capacity specifications, and energy storage power specifications, leading to numerous different building-level configuration combinations, increasing design review and implementation adjustment work.

[0006] Furthermore, existing methods typically use total system capacity or rated equipment capacity as the primary output, lacking a clear correspondence between the shared equipment specifications of a building complex and the actual number of devices in each building. After obtaining continuous capacity, planners need to discretize the capacity based on the product catalog and redetermine the number of devices based on the roof area, inverter capacity, and energy storage power requirements of each building. Different personnel using different rounding or matching methods may arrive at different implementation schemes, and the reconfigured capacity, operating costs, and photovoltaic grid integration levels may deviate from the original planning results.

[0007] Current photovoltaic energy storage plans rarely address the modular relationship between equipment energy capacity and power capacity simultaneously. If energy storage capacity and power capacity are determined independently as continuous variables, or if only simple upper and lower limits are applied, the resulting power-energy ratio may be difficult to correspond to the actual combination structure of energy storage cabinets, battery clusters, and converters. With a large number of buildings, subsequent equipment matching may result in situations where the energy storage battery capacity meets requirements but the power conversion equipment is insufficient, or where power equipment is redundant but battery capacity utilization is low.

[0008] In summary, existing technologies for photovoltaic energy storage planning in rural residential building complexes still face several technical challenges. These include difficulty in translating system-level continuous capacity into building-level equipment quantities; inadequate representation of differences between buildings; lack of direct constraints between rooftop installation conditions and equipment capacity; insufficient consideration of air conditioning response and thermal comfort boundaries; susceptibility to simplification of typical periods in annual operational evaluation; and the failure to incorporate procurement, construction, and maintenance burdens caused by the number and configuration combinations of equipment into planning costs. These issues result in significant costs associated with aligning planning outcomes with actual equipment procurement, building installation, and long-term operation and maintenance. Summary of the Invention

[0009] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a method for configuring photovoltaic energy storage modules with uniform specifications for rural building complexes.

[0010] The objective of this invention can be achieved through the following technical solutions: This invention provides a method for configuring standardized photovoltaic energy storage modules for rural building complexes, comprising the following steps: At the level of the rural building complex, the specifications of photovoltaic modules, energy storage modules, and energy storage power modules for use by all buildings are uniformly determined. For each building, the number of photovoltaic modules, energy storage modules, and energy storage power modules are determined separately, with each module number being a non-negative integer. The specifications of the photovoltaic modules, energy storage modules, and energy storage power modules are multiplied by the corresponding number of photovoltaic modules, energy storage modules, and energy storage power modules for the building, respectively, to jointly determine the photovoltaic installed capacity, energy storage capacity, and energy storage power capacity of each building, so that each building forms a differentiated capacity through different integer module numbers. Obtain available roof area, non-air conditioning power load, air conditioning response data and comfort parameters for each building for annual operational assessment, as well as corresponding meteorological data, real-time electricity price signals and dynamic carbon liability signals; The photovoltaic installed capacity, energy storage capacity, and energy storage power capacity are used as the annual operating boundaries for each building. An annual operating assessment is performed based on the annual operating boundaries to coordinate photovoltaic output, energy storage charging and discharging, grid power purchase and sale, and air conditioning temperature setting. Under thermal comfort constraints, the annual operating cost and operating performance indicators are obtained. Based on the annual operating costs and operating performance indicators, determine whether the candidate module specifications and the combination of quantities of each building module meet the annual operating feasibility constraints, and feed back the judgment results for the retention or elimination of candidate combinations. For candidate combinations that meet the annual operational feasibility constraints, the total life cycle cost of the building complex is constructed based on hardware costs, fixed deployment costs, scale-related deployment costs determined by the total number of various modules, specification / configuration complexity costs determined by the number of different photovoltaic module specifications, the number of different energy storage module specifications, the number of different energy storage power module specifications, and the number of different building-level module combinations, as well as the annual operational costs. The final module specifications and combinations of building module quantities are then selected and determined with the goal of minimizing the total life cycle cost of the building complex. Output the final module specifications and the combination of quantities for each building module.

[0011] Furthermore, the shared module specifications and the number of modules in each building together determine the actual capacity of each building, specifically including: For the For each building, multiplying the specifications of the photovoltaic modules, energy storage modules, and power storage modules by the corresponding number of photovoltaic modules, energy storage modules, and power storage modules, respectively, yields: in, Indicates the first The photovoltaic installed capacity of each building; Indicates the first Energy storage capacity of a building; Indicates the first Energy storage capacity of each building; , and They represent the first The number of photovoltaic modules, energy storage modules, and energy storage power modules of each building, and all of them are non-negative integers; , and These represent the specifications of the photovoltaic modules, energy storage modules, and power storage modules used in the rural building complex, respectively. The photovoltaic installed capacity, energy storage capacity, and power storage capacity serve as the annual operating boundaries for each building in the annual operating assessment of the corresponding candidate combination, and the candidate combination is used to determine whether it enters the final selection based on the results of the annual operating assessment.

[0012] Furthermore, the annual operational assessment specifically includes: Divide a year into multiple daily running segments arranged in chronological order, and for each daily running segment, establish a mixed integer linear programming subproblem with an hour as the time step; The energy storage state of charge at the end of the previous day's operating period is used as the energy storage state of charge at the start of the next day's operating period, and the energy storage state of charge between adjacent time steps for each building is updated according to the following formula: in, and They represent the first The building in time step and time step The energy storage state of charge; and They represent the first The building in time step The energy storage charging power and energy storage discharging power; and These represent the energy storage charging efficiency and the energy storage discharging efficiency, respectively. Indicates the length of the time step; The mixed-integer linear programming subproblems are solved sequentially according to the time order of the daily running segments, and the solution results of each daily running segment are summarized to obtain the annual running cost and running performance indicators.

[0013] Furthermore, the processing of the air conditioning response data and thermal comfort constraints specifically includes: A building-hour-set temperature response table is generated in advance through building energy consumption simulation, and then... The building in time step The indoor set temperature is used to set interpolation weights for adjacent temperature nodes that satisfy normalization constraints and SOS2 adjacency constraints of a second type of special ordered set. The second type of special ordered set is used to limit the interpolation weights of at most two adjacent temperature nodes at the same time step to non-zero. This transforms the nonlinear relationship between the set temperature and air conditioning power consumption into a piecewise linear form that can be handled by a mixed-integer linear programming solver, resulting in the air conditioning power consumption. in, Indicates the first The building in time step The power consumption of the air conditioner; This indicates the set of temperature response nodes; Indicates the first The building in time step For the SOS2 interpolation weights for each set temperature response node; This indicates the first item in the building-hour-set temperature response table. The building in time step and the The power consumption of the air conditioner corresponding to each set temperature response node; For the same indoor set temperature, interpolation weights that satisfy normalization constraints and SOS2 adjacency constraints are set for adjacent comfort response nodes, and a prediction dissatisfaction percentage constraint is applied: in, Indicates the first The building in time step The percentage of unsatisfactory predictions; Represents the set of comfort response nodes; Indicates the first The building in time step For the SOS2 interpolation weights for each comfort response node; Indicates the first The percentage of predicted dissatisfaction corresponding to each comfort response node; This indicates the upper limit of the allowed percentage of unsatisfactory forecasts.

[0014] Furthermore, the coordination of photovoltaic power output, energy storage charging and discharging, and grid power purchase and sale specifically includes: Based on the meteorological data and the photovoltaic installed capacity of each building, the photovoltaic output boundary for each time step is determined, and the non-air conditioning power load and air conditioning power of each building are combined into the total power load of the building. Regarding the first The building in time step Set the grid power purchase capacity, grid power sales capacity, energy storage charging capacity, and energy storage discharging capacity, and apply grid interaction relationships and building-side power balance relationships: in, Indicates the first The building in time step Net power exchanged with the power grid; and They represent the first The building in time step The power purchased and sold by the power grid; Indicates the first The building in time step Total building electrical load; Indicates the first The building in time step Photovoltaic power output; and They represent the first The building in time step The energy storage charging power and energy storage discharging power; The grid's electricity purchase cost and sales revenue are calculated based on the real-time electricity price signal, and the corresponding operational performance indicators are calculated based on the dynamic carbon responsibility signal.

[0015] Furthermore, the total lifecycle cost of the building complex is determined based on the module specifications, the quantity of each building module, and their annual operating results in the candidate configuration scheme, using the following formula: in, This represents the total lifecycle cost of a building complex converted to the same planning reference point. This represents the hardware cost determined based on the equipment specifications and the number of each building module in the candidate configuration scheme; Indicates fixed deployment cost; This represents the scale-related deployment cost determined based on the total number of various modules; This represents the cost of specification / configuration complexity determined by the combination of different equipment specifications and quantities and different building-level module quantities; This represents the planned operating cost calculated based on the annual operating assessment results. Indicates replacement cost; Indicates operation and maintenance costs; This represents the revenue from electricity sales by the power grid; This indicates residual value deduction; The scale-related deployment cost is determined based on the total number of various modules, using the following formula: in, , and These represent the unit-scale deployment cost coefficients for photovoltaic modules, energy storage modules, and energy storage power modules, respectively. , and These represent the number of photovoltaic modules, energy storage modules, and power storage modules of the i-th building, respectively. This refers to the collection of buildings within the rural architectural complex.

[0016] Furthermore, the aforementioned The quantity is determined based on the combination of different equipment specifications and different building-level module quantities, using the following formula: in, , and These represent the number of different photovoltaic module specifications, different energy storage module specifications, and different energy storage power module specifications in the candidate configuration schemes, respectively. This represents the number of different building-level module combinations obtained after deduplicating the triplets consisting of the number of photovoltaic modules, energy storage modules, and energy storage power modules in each building. , , and These represent the unit complexity cost coefficients for the corresponding quantities; when the rural building complex adopts a single shared specification for each type of equipment... , and All are 1.

[0017] Furthermore, the roof can be constrained by area, as follows: in, Indicates the first The number of photovoltaic modules in each building; This indicates the area occupied by a single photovoltaic module corresponding to the photovoltaic module specifications; Indicates the first The available roof area of ​​each building; The energy storage power-energy consistency constraint is expressed as: in, and They represent the first The energy storage capacity and power storage capacity of each building; and These represent the lower and upper limits of the permissible ratio of energy storage power to energy storage capacity, respectively.

[0018] Furthermore, the optimization search for the module specifications and the number of modules in each building specifically includes: Candidate decisions are generated using random key encoding and then decoded to obtain the specifications of photovoltaic modules, energy storage modules, and power storage modules for use in the rural building complex, as well as the number of modules for each building. Feasibility repair is performed on the number of modules that do not meet the constraints of rooftop usable area, energy storage power-energy consistency, or preset engineering capacity after decoding. First, a representative period assessment or annual screening assessment is used to conduct a preliminary screening of the candidate configuration schemes that have undergone feasibility repair. Then, the candidate configuration schemes selected in the preliminary screening are subjected to an annual 8760-hour operation assessment. The candidate configuration schemes that have completed the operation evaluation, along with their annual operating costs and performance indicators, are cached, and the operation evaluation is avoided by repeatedly performing the same candidate configuration schemes based on the cached results. The shared module specifications and the number of building modules for each candidate configuration scheme are converted into the actual capacity of each building. The actual capacity is used as the annual operation boundary to obtain the annual operation results. The annual operation feasibility is judged and the total life cycle cost of the building group is calculated based on the annual operation results. Candidate configuration schemes are retained or eliminated based on the judgment and calculation results, and subsequent searches are updated until the final combination of shared module specifications and the number of building modules is obtained.

[0019] Furthermore, the output of the unified specification photovoltaic energy storage module configuration scheme specifically includes: Output the building complex shared module specification list, the module quantity list for each building, the photovoltaic installed capacity, energy storage capacity and energy storage power capacity of each building, the building-level module quantity combination list, and the annual operational feasibility evaluation results; The annual operational feasibility evaluation results include grid power purchase, grid power sales, photovoltaic power generation, air conditioning power consumption, energy storage status of charge, predicted unsatisfactory percentage, and annual operating costs. The building complex shared module specification list and the list of quantities of each building module are used for procurement, construction configuration, and operation and maintenance management.

[0020] Compared with the prior art, the present invention has the following advantages: (1) Existing technologies typically determine the continuous capacity of photovoltaic and energy storage separately for each building, which easily leads to a large number of building-specific specifications, increasing the complexity of procurement batches, equipment models, construction configurations, and subsequent spare parts management. This invention uniformly determines the specifications of photovoltaic modules, energy storage modules, and power storage modules at the building group level, transforming the procurement and maintenance objects from a large number of non-standard specifications to a small number of shared standard specifications. This facilitates batch procurement, unified construction, spare parts replacement, and centralized maintenance, and reduces the implementation risks caused by mixed models or mismatched parameters.

[0021] (2) Existing technologies, if they directly adopt a uniform capacity, are difficult to adapt to the differences in roof area, load level, and air conditioning response among different buildings. This invention sets the number of photovoltaic modules, energy storage modules, and power storage modules in non-negative integer form for each building, and determines the actual capacity by combining the uniform module specifications with the building-level module quantity. This maintains the uniformity of equipment specifications while preserving the capacity differences between buildings, avoiding capacity redundancy in low-load buildings or insufficient capacity in high-load buildings. The integer module quantity can also directly correspond to the actual procurement and installation units, reducing the work of re-verifying the continuous capacity results after rounding.

[0022] (3) Existing planning models mainly use equipment investment costs and operating costs as the main evaluation criteria. The number of equipment specifications, the number of building-level configuration combinations, and the resulting design, training, inventory, and operation and maintenance costs are usually not included in the optimization process, which may lead to schemes with low book costs but heavy implementation burdens. This invention incorporates specification / configuration complexity costs into deployment-related soft costs, and together with hardware costs and annual operating costs, constitutes the total life cycle cost of the building complex. This enables the optimization process to evaluate capacity benefits and non-standardized deployment costs simultaneously, reducing the situation where new equipment specifications or configuration combinations are added only to obtain small operating benefits, and improving the engineering economy and standardization of the scheme.

[0023] (4) Existing technologies often determine capacity based on peak load, typical daily or annual utilization hours, which makes it difficult to reflect time-coupled factors such as the transfer of energy storage state of charge across time periods, changes in time-of-use electricity prices, continuous low light, and air conditioning load response. Some schemes may be feasible under typical operating conditions, but problems such as insufficient energy storage capacity, limited charging and discharging power, or inability to meet comfort constraints may occur during specific periods of the year. This invention uses the capacity formed by the configuration of candidate modules as the operating boundary, performs annual operation assessment, and coordinates photovoltaic output, energy storage charging and discharging, grid power purchase and sale, and air conditioning set temperature adjustment, thereby verifying the annual operational feasibility of the scheme during the planning stage and reducing the possibility that configurations only applicable to local operating conditions will be selected.

[0024] (5) Existing methods typically treat air conditioning load as a fixed load, which makes it difficult to reflect the impact of building thermal characteristics and temperature setting changes on power consumption and comfort. This invention coordinates air conditioning load under thermal comfort constraints based on building-hour-set temperature response data, enabling flexible loads within a certain range to participate in photovoltaic and energy storage scheduling. While ensuring residential comfort, it improves local photovoltaic consumption and load timing matching, and reduces the tendency to rely solely on increasing energy storage capacity to cope with short-term load fluctuations.

[0025] (6) When converting existing configuration results into marketable equipment, specification matching and manual rounding are often required. Rounding may disrupt roof area limitations or the matching relationship between energy storage power and energy capacity. This invention directly optimizes the number of integer modules and applies roof area constraints and energy storage power-energy consistency constraints, so that uninspired or unreasonable capacity ratio schemes are eliminated in the planning stage, reducing the conversion error between the optimization results and the actual equipment.

[0026] (7) For the complex optimization problem formed by uniform specifications, integer number of modules and annual operation evaluation, this invention reduces the amount of calculation of obviously infeasible and duplicate schemes by candidate decision coding, feasibility repair, representative period coarse screening, annual operation fine evaluation and result caching. While retaining the final annual operation verification, it improves optimization efficiency and reduces the judgment bias caused by simplifying to peak working conditions or typical daily evaluation.

[0027] (8) Existing technologies typically only output equipment capacity or economic indicators, which still require manual conversion into procurement lists and building configuration relationships. This invention outputs the specifications of shared modules for building groups, the quantity of each building module, building-level configuration combinations, and annual operational feasibility evaluation results, enabling the planning results to directly serve procurement, construction configuration, and subsequent operation and maintenance, reducing manual breakdown, duplicate calculations, and errors in building correspondence. Attached Figure Description

[0028] Figure 1 This is a flowchart of a standardized photovoltaic energy storage module configuration method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of a standardized photovoltaic energy storage module configuration method according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the unified specification module configuration system structure according to an embodiment of the present invention; Figure 4 This is a schematic diagram illustrating the relationship between the unified specification module and the number of building-level certificates in an embodiment of the present invention. Detailed Implementation

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0030] Example 1: like Figure 1 , Figure 2 As shown, this embodiment provides a method for configuring photovoltaic energy storage modules of uniform specifications for rural building clusters. Rural building clusters include multiple related but not identical rural residential buildings, each with different roof areas, geometric dimensions, thermal zone structures, building envelope thermal parameters, non-air conditioning electrical loads, air conditioning response characteristics, and resident comfort requirements. This method uses the building cluster as a unified planning object, selects shared module specifications at the building cluster level, uses integer module quantities to create capacity differences at the individual building level, and verifies the operability of candidate configuration schemes through year-round operational evaluation.

[0031] This embodiment can be executed by a configuration computing server. The configuration computing server is connected to building energy consumption databases, meteorological databases, electricity price databases, dynamic carbon liability signal databases, and equipment product databases, and includes a data preprocessing module, a building operation response module, a candidate configuration generation module, an annual operation assessment module, a cost calculation module, an optimization search module, and a result output module. These modules can be deployed on the same server, or separately on local computing nodes and cloud computing nodes. For projects with a small number of buildings, the solution can be completed on ordinary workstations; for projects with a large number of buildings, different candidate configuration schemes can be distributed to multiple computing nodes for parallel evaluation. Distributed deployment can shorten the waiting time for the annual operation assessment, while single-machine deployment facilitates data not leaving the domain and on-site verification at the project site.

[0032] It should be noted that the uniform specifications in this embodiment refer to the same rated power specifications for individual photovoltaic modules, the same rated energy specifications for individual energy storage modules, and the same rated power specifications for individual energy storage power modules used in all buildings within the same planning batch. The total capacity of each building is not required to be the same, but is determined by the shared specifications and the number of integer modules corresponding to that building. Energy storage modules can be energy units corresponding to battery clusters, racks, or standard battery cabinets, while energy storage power modules can be standard power units of bidirectional converters or power units in integrated energy storage units that can be independently connected in parallel for expansion. Setting energy storage specifications and energy storage power specifications separately helps to avoid the problem of mutual constraints between energy demand and instantaneous power demand when selecting complete sets of equipment based solely on fixed duration.

[0033] This embodiment includes the following steps: Step S1: Acquire building cluster data and external signals. Building cluster data includes the available roof area, roof orientation and slope, shading information, non-air conditioning electrical load, air conditioning response data, comfort parameters, available photovoltaic output boundary, and energy storage project parameters for each building. External signals include meteorological data, real-time electricity price signals, and dynamic carbon responsibility signals. Non-air conditioning electrical load is the base load obtained by subtracting air conditioning electricity from the building's total electricity meter load, or it can be obtained by superimposing lighting, sockets, domestic hot water, and other identifiable electricity consumption items.

[0034] In one specific implementation, the usable roof area is obtained through UAV mapping, building information modeling, satellite imagery, or on-site measurement, deducting parapet setbacks, maintenance access, fire access, roof equipment, shaded areas, and structurally non-load-bearing areas. For buildings with multiple roofs, the usable area and photovoltaic output of each roof can be calculated separately, and then roof areas capable of installing photovoltaic modules of the same specification can be combined. Using the net area after deducting uninstallable areas reduces the overestimation of the number of modules caused by relying solely on the building's projected area.

[0035] Non-air conditioning electricity load can be derived from smart meters, sub-metering of power distribution, or decomposition results of historical bills. When historical data is incomplete, it can be supplemented by interpolation of adjacent times for the same building, substitution of corresponding times for the same type of day, or normalized load curves for similar buildings. For outliers that significantly exceed the metering range or exhibit short-term fluctuations, correction can be performed using median filtering and joint identification of physical boundaries. Timestamps from different data sources are unified to the planning timeline. In this embodiment, one hour is used as a time step, with a complete year comprising 8760 time steps. In scenarios requiring reflection of short-term peak values, 30-minute or 15-minute time steps can also be used, and the annual evaluation indicators are then summarized according to the actual time steps. A finer time resolution can more accurately characterize energy storage power constraints, while hourly time resolution can significantly reduce the computational load during the building complex planning phase.

[0036] Meteorological data should include at least total horizontal solar irradiance, diffuse irradiance, outdoor dry-bulb temperature, and optional wind speed data. Meteorological data can be from a typical meteorological year in the planning area, or from measured data over several consecutive years, selecting representative years. Real-time electricity price signals are the purchase and sale prices of electricity corresponding to each time step; dynamic carbon liability signals represent the carbon liability borne for each unit of electricity obtained from the grid at a corresponding time step, and can use the regional grid average emission factor, marginal emission factor, or time-varying carbon liability coefficient provided by electricity trading settlement rules. Using the marginal emission factor better reflects the impact of energy storage charging and discharging on new power generation demand; using the average emission factor makes the data easier to obtain and suitable for early planning.

[0037] Air conditioning response data can be generated offline from building energy consumption simulation, forming a building-hour-set temperature response table. Specifically, a building model is established for each building, including the building envelope, window-to-wall ratio, thermal zone area, internal heat gain, air conditioning equipment performance, and occupant activity levels. Multiple discrete indoor set temperatures are set at each time step, and the corresponding air conditioning power consumption and the percentage of predicted dissatisfaction are recorded. Building energy consumption simulation can use a dynamic thermal model calibrated with measured data. Another approach is to train a proxy model using historical room temperature, outdoor temperature, set temperature, and air conditioning power, and then generate a response table for preset temperature nodes. This method can use real resident operating data to correct deviations in the idealized model and is more suitable for existing residential buildings.

[0038] Comfort parameters should include at least the permissible range of indoor set temperatures and the maximum permissible percentage of predicted dissatisfaction. For situations where different residents or rooms have different comfort needs, independent comfort parameters can be set for each building, or different maximums can be set for different time periods, such as residential and non-residential periods. By varying comfort constraints with different buildings and times, excessive temperature deviations can be prevented from forming on the individual resident side due to uniform scheduling across the entire building complex.

[0039] Step S2: Construct decision variables for unified specification modules. The equipment product database stores the rated power, single-module footprint, unit price, and installation interface information of procurable photovoltaic modules; the rated energy, cycle life, and unit price of energy storage modules; and the rated power, efficiency, and unit price of energy storage power modules. A building group shares one photovoltaic module specification, one energy storage module specification, and one energy storage power module specification. For each building, the quantities of photovoltaic modules, energy storage modules, and energy storage power modules are set separately. All these quantities are non-negative integers, and it is permissible for a building to have zero quantities of a particular type of module.

[0040] In one specific implementation, the shared module specifications are selected from a discrete candidate set in the product database. For example, the candidate set of rated power for photovoltaic modules may include 0.45kWp, 0.55kWp, and 0.65kWp; the candidate set of energy storage modules may include 2.5kWh, 5kWh, and 10kWh; and the candidate set of energy storage power modules may include 1.25kW, 2.5kW, and 5kW. This discrete selection method allows the optimization results to directly correspond to market products. In another implementation, continuous specifications are first searched within an allowable range, and then mapped to the nearest standard product that meets the deviation threshold. This method facilitates the evaluation of reasonable specification ranges before the equipment catalog is finalized, but standard product mapping and feasibility verification still need to be completed before outputting the solution.

[0041] Step S3: Calculate the capacity of each building based on the shared specifications and the number of integer modules. For the first... The photovoltaic installed capacity, energy storage capacity, and energy storage power capacity of each building are expressed as follows: in, , and They represent the first The photovoltaic installed capacity, energy storage capacity, and energy storage power capacity of each building; , and These represent the number of photovoltaic modules, energy storage modules, and energy storage power modules in the building, respectively. , and These represent the specifications of the photovoltaic modules, energy storage modules, and energy storage power modules shared by the building complex. Therefore, different buildings can achieve different capacities by varying the number of modules, while the procurement and deployment are still limited to a small number of shared specifications.

[0042] In this embodiment, the upper limit for the number of photovoltaic modules is first calculated based on the available roof area, and then further tightened by considering grid connection capacity and structural load. The upper limits for the number of energy storage modules and energy storage power modules can be determined based on the building's maximum daily electricity consumption, grid connection capacity, equipment installation space, and project investment limits. Setting integer upper limits related to building conditions can narrow the search range and avoid generating candidate solutions that significantly exceed the building's actual needs.

[0043] Step S4: Perform an annual operational assessment on the candidate configuration schemes. The annual operational assessment uses the photovoltaic installed capacity, energy storage capacity, and energy storage power capacity determined by the candidate configuration schemes as the operational boundaries, coordinates photovoltaic output, energy storage charging and discharging, grid power purchase and sale, and air conditioning set temperature regulation, and calculates the annual operating cost and operational performance indicators under the premise of meeting the building's power consumption balance and thermal comfort requirements.

[0044] The available photovoltaic output at each time step can be obtained by multiplying the unit capacity photovoltaic output sequence by the installed photovoltaic capacity. When considering module temperature, the... The building in time step The photovoltaic output boundary can be expressed as: in, Indicates the actual photovoltaic power utilized; This indicates the available output coefficient per unit installed capacity; This indicates the surface irradiance of the component after taking into account roof orientation, tilt angle, and shading; Indicates standard test irradiance; Indicates the component's temperature power coefficient; and These represent the component temperature and the standard test temperature, respectively. When the actual utilized power is less than the available output, the difference is recorded as the amount of light wasted. This representation method can convert different roof orientations and shading conditions into differentiated output boundaries for modules of the same specification.

[0045] The air conditioning response is processed using SOS2 interpolation. Assume... To define the set of temperature response nodes, For the first The building in time step For the The interpolation weights for each temperature node are then used to express the air conditioner's power consumption and the indoor set temperature as follows: in, The table showing the building-hour-set temperature response is as follows: The power consumption of the air conditioner corresponding to each set temperature node; Indicates the first A set temperature node. For Applying the SOS2 adjacency constraint ensures that at most two adjacent temperature nodes have non-zero weights at any given time, thereby avoiding unreasonable interpolation across multiple temperature nodes while maintaining the linear solution structure.

[0046] The percentage of dissatisfaction can be predicted using interpolation between adjacent nodes that are consistent with the set temperature. Let... For a set of comfort response nodes, For the first The weights of each comfort response node are then: in, Indicates the first The building in time step The percentage of unsatisfactory predictions; Indicates the first The percentage of predicted dissatisfaction for each comfort response node; This indicates the upper limit allowed for the corresponding building and time period. Temperature interpolation weights and comfort interpolation weights can be kept consistent by setting a common temperature-related constraint. For projects that only have a permissible room temperature range but no PPD data, comfort constraints can be replaced with room temperature upper and lower limit constraints; PPD constraints can take into account the effects of air temperature, humidity, clothing, and metabolic rate, while room temperature constraints require fewer parameters.

[0047] The total electrical load of each building is obtained by adding the non-air conditioning electrical load to the air conditioning electrical power: in, Indicates the total electrical load of the building. This represents non-air conditioning electrical loads. By modeling air conditioning loads independently from the base loads, the timing of some electrical consumption can be adjusted using the building's thermal inertia without changing rigid loads such as lighting and sockets.

[0048] In this embodiment, the energy storage state of charge (SBC) referred to in the claims is represented by the available energy state within the energy storage, with dimensions in kWh. If a dimensionless SBC ratio is required, the available energy state can be divided by the energy storage capacity. After adopting the energy state, the update relationship between adjacent time steps is as follows: in, and These represent the available energy stored at adjacent time steps; and These represent charging power and discharging power, respectively. and These represent charging efficiency and discharging efficiency, respectively. This represents the time step. The energy storage state boundary is represented as: in, and These are the lower and upper limits for the permissible state of charge ratio. For example, they can be set to 0.10 and 0.90 to preserve a safety margin and reduce lifetime loss caused by deep charge and discharge.

[0049] To prevent simultaneous charging and discharging within the same time step, a binary state variable can be set. and and apply: When electricity prices are not negative and both charging and discharging incur losses, binary state variables can be omitted, and simultaneous charging and discharging can be suppressed through efficiency losses and small cycle costs. Omitting binary variables can improve the solution speed for large-scale building complexes; setting binary variables can ensure that the operating logic strictly holds when negative electricity prices, subsidies, or complex electricity sales rules exist.

[0050] The relationship between building-side power balance and net grid exchange is expressed as follows: in, and These represent the power purchased and the power sold, respectively. This represents the net power exchanged with the power grid. If reverse power transmission is prohibited at the project site, then... If electricity sales are permitted, mutually exclusive variables for electricity purchase and sales can be set to prevent simultaneous electricity purchase and sales within the same time step. Further constraints can be imposed on the maximum power purchase, maximum reverse power, and distribution transformer capacity at the access point to ensure the configuration meets local distribution network access conditions.

[0051] The annual operational assessment divides a year into multiple daily operating periods arranged chronologically. For each day, a mixed-integer linear programming subproblem is established with an hourly time step. The available energy storage at the end of the previous day is used as the initial state for the next day, thus preserving the impact of continuous rain, holiday loads, and cross-day electricity price changes on the energy storage state. The initial state on the first day can be set at 50% of the energy storage capacity, and a constraint is imposed at the end of the year that the final state must not be lower than the initial state, preventing the creation of a false operating cost reduction by overdrawing the year-end energy storage capacity.

[0052] In another specific implementation, a rolling daily optimization with a look-ahead window can be used. This involves solving for the current 24 hours and several subsequent hours each time, but only executing the operational decisions for the current day, and then passing the final state to the next day. The look-ahead window can reduce premature slack or overcharging near the daily boundary due to a lack of future information; solving only on a 24-hour basis results in a smaller model size, making it more suitable for stages requiring rapid screening of a large number of candidate configurations. Regardless of the method used, the final results are summarized in chronological order for the entire year.

[0053] No. Daily operational targets may include electricity purchase costs, electricity sales revenue, optional energy storage cycle loss costs, and comfort deviation penalties. The annual net cost of electricity purchase and sale is expressed as: in, and These represent the real-time electricity purchase price and the electricity sales price, respectively. Annual operating costs can also include variable maintenance costs related to photovoltaic power generation, energy storage throughput, or air conditioning equipment operating time. Including a small degradation cost in the energy storage throughput can prevent frequent charging and discharging that are not economically meaningful when the electricity price difference approaches the energy storage loss cost.

[0054] Dynamic carbon liability signals are used to calculate annual operational performance indicators. Taking the example of assuming carbon liability when purchasing electricity and obtaining optional credits when selling electricity, the annual dynamic carbon liability is expressed as follows: in, and These represent the dynamic carbon liability coefficients for electricity purchase and electricity sales, respectively. This represents the carbon offset coefficient for electricity sales, which can be set from 0 to 1 according to the accounting rules. This indicator can be used as an output performance indicator, or in another implementation, it can be set as an upper limit constraint or form a weighted target with the annual operating cost. Outputting carbon responsibility as a separate indicator is beneficial for comparing the time-series carbon impact of candidate schemes without changing the economic optimization objective; when set as a constraint, it can ensure that the configuration scheme achieves the predetermined low-carbon level.

[0055] Annual operational feasibility is determined by the following conditions: building-side power balance is achieved at each time step; available energy storage, charging / discharging power, and grid exchange power do not exceed limits; the percentage of unsatisfactory predictions for each residential period does not exceed the corresponding upper limit; there are no unmet loads; and the energy storage status at the end of the year meets the set end-point conditions. To identify reasons for infeasibility, non-negative relaxation variables can be set for unmet loads, comfort limits exceeding limits, and access capacity exceeding limits in the initial assessment, and a penalty coefficient much higher than the normal operating cost can be assigned. If any relaxation variable in the optimal solution exceeds the preset tolerance, the candidate configuration is marked as infeasible for the year, and the building, time period, and constraint type causing the infeasibility are recorded. This approach facilitates subsequent feasibility repairs more effectively than simply returning "no feasible solution."

[0056] Annual operational performance indicators can include grid-purchased electricity, grid-sold electricity, photovoltaic power generation, local photovoltaic consumption, curtailed solar power, air conditioning electricity consumption, energy storage charging, energy storage discharging, equivalent cycle count of energy storage, peak purchased power, predicted percentage of unsatisfactory performance exceeding limits and annual dynamic carbon liability. By simultaneously outputting cost, energy, comfort, and carbon indicators, it is possible to avoid selecting configurations with hidden operational defects based solely on a single annual cost.

[0057] Step S5: Construct the total life cycle cost target for the building complex. The total life cycle cost of the building complex discounts the costs and benefits incurred at different points in time to the same planning reference point, and is expressed as: in, This represents the total lifecycle cost of a building complex converted to the same planning reference point. This represents the hardware cost determined based on the equipment specifications and the number of each building module in the candidate configuration scheme; Indicates fixed deployment cost; This represents the scale-related deployment cost determined based on the total number of various modules; This represents the cost of specification / configuration complexity determined by the combination of different equipment specifications and quantities and different building-level module quantities; This represents the planned operating cost calculated based on the annual operating assessment results. Indicates replacement cost; Indicates operation and maintenance costs; This represents the revenue from electricity sales by the power grid; This indicates residual value deduction; Hardware costs can be calculated based on the unit price of the modules corresponding to the shared specifications and the quantity of each building module: in, , and These represent the purchase and installation unit prices for individual photovoltaic modules, energy storage modules, and energy storage power modules, respectively. This represents the cost of system balancing components such as brackets, cables, protection devices, metering equipment, and communication equipment. The cost of system balancing components can be calculated as the sum of fixed costs and capacity-related costs.

[0058] The scale-related deployment cost is determined based on the total number of various modules, using the following formula: in, , and These represent the unit-scale deployment cost coefficients for photovoltaic modules, energy storage modules, and energy storage power modules, respectively. , and These represent the number of photovoltaic modules, energy storage modules, and power storage modules of the i-th building, respectively. This refers to the collection of buildings within the rural architectural complex.

[0059] The The quantity is determined based on the combination of different equipment specifications and different building-level module quantities, using the following formula: in, , and These represent the number of different photovoltaic module specifications, different energy storage module specifications, and different energy storage power module specifications in the candidate configuration schemes, respectively. This represents the number of different building-level module combinations obtained after deduplicating the triplets consisting of the number of photovoltaic modules, energy storage modules, and energy storage power modules in each building. , , and These represent the unit complexity cost coefficients for the corresponding quantities; when the rural building complex adopts a single shared specification for each type of equipment... , and All are 1.

[0060] The unit complexity cost coefficient can be calculated based on the design, procurement, warehousing, training, commissioning, and maintenance man-hours incurred by adding a new equipment specification or configuration template in historical projects. Alternatively, it can be estimated item by item by project managers. For projects that cannot be accurately monetized, relative weights can be assigned to various complexity levels first, and then sensitivity analysis can be used to determine the stability of the optimal solution with respect to these complexity weights. By incorporating the number of combinations into the cost, the optimization search will tend to reuse existing configuration combinations when capacity and performance are similar, thereby reducing the types of construction drawings, protection settings, and spare parts lists.

[0061] Step S6: Search for candidate unified specification module configuration schemes under constraints. The available roof area constraint is expressed as: in, This indicates the area occupied by a single photovoltaic module corresponding to the selected photovoltaic module specification, and may include the area converted from module gaps and maintenance spacing; Indicates the first The available roof area of ​​a building. For multiple orientation zones, area constraints can be applied to each roof zone separately, and the sum of the number of modules in each zone can be equal to the total number of photovoltaic modules in the building.

[0062] Energy storage power-energy consistency constraints are expressed as follows: in, and These represent the lower and upper limits of the permissible ratio of energy storage power to energy storage capacity, respectively, in units of hourly. This constraint can exclude combinations of large energy capacity but insufficient power, or large power capacity but excessively short duration. For example, for energy storage primarily responsible for intraday peak shifting, the permissible range can be determined in reverse based on the rated duration of 0.5 hours to 4 hours; the specific value should be set in conjunction with the battery rate, converter capacity, and application objectives.

[0063] Preset engineering capacity constraints may also include building access point capacity, DC-to-AC ratio of photovoltaic inverters, energy storage installation space, floor load-bearing capacity, maximum number of parallel modules, number of manageable devices per communication branch, and fire compartment capacity limits. These constraints can be quickly eliminated after candidate solution decoding, or directly incorporated into the optimization model. Quick elimination is suitable for situations with complex constraint logic and a large number of candidates, while direct modeling helps the solver reduce the search space using the constraint structure.

[0064] This embodiment uses random key encoding to generate candidate decisions. Each candidate decision is represented by a random key vector with values ​​ranging from 0 (inclusive) to 1 (exclusive). The first three random keys are mapped to the candidate sets for photovoltaic module specifications, energy storage module specifications, and energy storage power module specifications, respectively. The remaining random keys are mapped to the number of the three modules in each building. If a candidate set contains... If there are 1 element, then the random key is... The corresponding candidate number can be represented as: The candidate number is determined by dividing the candidate set into equal intervals according to the random key.

[0065] For the If the maximum feasible quantity of a certain type of module in a building is... The number of modules can then be decoded as: The number of modules is determined by dividing the random key into equal intervals between zero and the upper limit of the feasible number, and taking the corresponding non-negative integer value.

[0066] Random key encoding enables continuous swarm intelligent search operators to handle discrete sizes and integer quantities, and can ensure that the same random key always gets the same configuration through fixed decoding rules.

[0067] If a candidate solution violates constraints after decoding, feasibility repair is performed. For rooftop area exceeding limits, the number of photovoltaic modules is reduced to the maximum allowable integer for the area. For energy storage power-energy ratio exceeding limits, the operation that results in the smallest expected lifecycle cost increment can be selected from among the feasible operations of reducing power modules, adding energy modules, or vice versa. For grid connection capacity exceeding limits, the number of modules that cause peak backfeeding or peak power purchase can be reduced. If the annual operational feasibility cannot be met after repair, it is marked as infeasible. Repairing based on the minimum cost increment can reduce the problem of candidate solutions being biased towards low capacity boundaries due to simple truncation.

[0068] To reduce the computational burden of the annual assessment, candidate solutions that have undergone feasibility remediation undergo a representative period assessment or an annualized screening assessment. Representative periods can consist of typical weather days, peak load days, consecutive days with lowest photovoltaic output, high electricity price days, and high carbon responsibility days, with annual weights assigned to each representative period. The annualized screening assessment can also utilize simplified energy storage strategies to quickly estimate purchased electricity, curtailed photovoltaic power, and capacity gaps. In the initial screening stage, candidate solutions with high costs and no significant capacity gaps are retained, and then a full-year 8760-hour operational assessment is performed on the selected solutions. Using multiple types of representative periods reduces the risk of overlooking extreme continuous operating conditions when using only average typical days.

[0069] In one specific implementation, the representative time period screening value is expressed as: in, Indicates a representative set of time periods; Indicates the first Annual weighting for representative time periods; This indicates the operating cost for that period. Indicates the amount of constraint violation; This indicates a high penalty coefficient. The coarse screening value is only used for ranking and does not replace the final annual evaluation. This two-stage evaluation method can concentrate computational resources on more promising candidate solutions while preserving the annual feasibility of the final result.

[0070] Candidate configuration schemes that have completed operational evaluations, along with their annual operating costs and performance indicators, are cached. The cache key consists of the shared photovoltaic module specification number, the shared energy storage module specification number, the shared energy storage power module specification number, and the total number of modules arranged in a fixed building sequence. When other engineering parameter versions are included, the electricity price version, meteorological data version, and comfort parameter version are also written into the cache key. If the same cache key is encountered again during the search process, the existing result is read directly to avoid duplicate solutions. Including the input data version in the cache key prevents the incorrect reuse of old results after parameter updates.

[0071] Optimization search can employ random-key-based genetic algorithms, differential evolution algorithms, or particle swarm optimization algorithms, using the total lifecycle cost of the building group as the fitness level, and imposing infeasibility penalties when annual operation becomes infeasible. The search termination criteria can include reaching the maximum number of iterations, the improvement in the optimal value over several consecutive generations being less than a preset threshold, or the computation time reaching the project's allowable limit. For projects with a small number of buildings and a limited number of candidate specifications, shared specifications can be enumerated and mixed-integer programming can be used to directly solve for the number of each building module; the enumeration method provides strong global optimality verification, while random-key search is more suitable for situations where the number of combinations grows rapidly with the building scale.

[0072] Taking a sample building complex consisting of ten rural houses as an example, the usable roof area of ​​each building can be distributed over 60m². 2 up to 180m 2 There are differences in annual non-air conditioning electricity consumption and air conditioning response between different periods. Multiple candidate values ​​can be set for photovoltaic specifications, energy storage specifications, and energy storage power specifications. The energy storage charge / discharge efficiency is set to 0.95, the allowable state of charge ratio range is set to 0.10 to 0.90, the upper limit for the predicted unsatisfactory percentage during residential periods is set to 20%, and the planning period is set to 15 years. These values ​​are only used to illustrate the calculation process and do not constitute a limitation on the scope of protection of this invention. During optimization, a set of shared specifications is first determined, then the number of three integer modules for each of the ten buildings is determined separately, and each candidate combination is sequentially subjected to preliminary screening and annual evaluation.

[0073] In the above example, if two buildings have different roof areas but similar load patterns, they can use the same combination of energy storage modules but different numbers of photovoltaic modules. If another building has a smaller roof area but a higher nighttime load, the number of photovoltaic modules can be reduced while the number of energy storage modules can be increased, while keeping the shared specifications unchanged. The resulting configuration difference is only reflected in the number of standard modules, without requiring additional dedicated equipment specifications for each building. For buildings with similar capacity requirements, the complexity and cost of specifications / configuration will encourage them to reuse the same combination of module numbers, thereby reducing the types of construction templates and protection settings.

[0074] Step S7: Output a unified specification photovoltaic energy storage module configuration scheme. The output includes a shared module specification list for the building complex, a module quantity list for each building, the photovoltaic installed capacity, energy storage capacity, and energy storage power capacity for each building, a building-level module quantity combination list, and the annual operational feasibility evaluation results. The shared module specification list must at least record the specification number, rated parameters, single module area, unit price, and interface requirements; the building module quantity list records the number of three integer modules according to the building identifier; the building-level configuration combination list categorizes identical triples and counts the corresponding building quantities.

[0075] The annual operational feasibility assessment results should include at least the grid-purchased electricity volume, grid-sold electricity volume, photovoltaic power generation, air conditioning electricity consumption, energy storage status of charge, percentage of unsatisfactory forecasts, and annual operating costs. They may also include peak power, curtailment of solar power, photovoltaic self-consumption rate, energy storage cycle count, and dynamic carbon liability. For buildings with limited constraint margins, the assessment can simultaneously output the remaining roof area, energy storage power-energy ratio margin, grid connection capacity margin, and comfort margin, allowing designers to review the results under conditions of equipment deviations, weather fluctuations, and load increases.

[0076] The output can generate an equipment procurement list, a building-module allocation list, and a construction configuration sheet. The procurement list summarizes the total number of modules according to shared specifications, the building-module allocation list specifies the number of modules to be allocated to each building, and the construction configuration sheet records the corresponding capacity, wiring method, protection parameters, and communication address range. Since all buildings use shared module specifications, spare parts of the same specification can be transferred between buildings in case of equipment failure; when capacity expansion is needed, integer modules can be added provided that the remaining roof area, energy storage ratio, and access capacity are met. This output method reduces the secondary conversion from optimized capacity to the engineering bill of materials.

[0077] In another implementation, if the project prioritizes controlling initial investment, an initial investment cap can be added in addition to the total lifecycle cost; if the project requires achieving a minimum photovoltaic self-consumption rate or a maximum dynamic carbon liability, the corresponding operational performance indicators can be set as constraints. In this case, the basic decision-making structure of shared specifications and the number of integer modules remains unchanged. By transforming project objectives into constraints rather than redefining the equipment structure, the same configuration framework can be reused under different regional electricity prices, subsidies, and carbon management requirements.

[0078] It should also be noted that the specific parameters, candidate specification sets, time steps, planning periods, and solution algorithms mentioned above are only for illustrating the implementation method of this method. In practical applications, adjustments can be made based on the scale of rural building clusters, local weather, grid connection rules, equipment catalogs, and residents' comfort needs. As long as the shared photovoltaic module specifications, energy storage module specifications, and energy storage power module specifications are determined at the building cluster level, the capacity differences are expressed using integer module quantities at the building level, and the configuration scheme is obtained by combining annual operation assessments, deployment-related soft costs, and constraint optimization, all fall within the scope of the method described in this embodiment.

[0079] Example 2: This embodiment is used to verify the annual operational feasibility and deployment effect of a unified specification photovoltaic energy storage module configuration scheme.

[0080] A cluster of 20 rural residential buildings was selected as the verification object, and a standardized modular scheme and a continuous capacity benchmark scheme were set up for comparison. Both schemes used the same building load data, meteorological data, real-time electricity price signals, dynamic carbon liability signals, and annual operation assessment models to eliminate the influence of differences in operating boundaries and external conditions on the evaluation results.

[0081] Among them, the continuous capacity benchmark scheme determines the photovoltaic installed capacity, energy storage capacity, and energy storage power capacity for each building separately, without standardizing the equipment specifications between different buildings; while the unified specification module scheme uniformly determines the photovoltaic module specifications, energy storage module specifications, and energy storage power module specifications at the building group level, and forms differentiated capacities through the integer number of modules corresponding to each building.

[0082] Evaluation metrics include total lifecycle cost of the building complex, first-year operating cost, hardware cost, deployment-related soft costs, electricity purchased from the grid, electricity sold to the grid, photovoltaic power generation, air conditioning electricity consumption, and average projected dissatisfaction percentage (PPD). These metrics are used to evaluate the economic viability, operational feasibility, energy interaction performance, and indoor thermal comfort levels of the two schemes.

[0083] Under a baseline soft cost parameter setting, the total life cycle cost of the building complex under the uniform specification modular scheme is RMB 7032.73 thousand, while the total life cycle cost of the building complex under the continuous capacity baseline scheme is RMB 7424.78 thousand. Compared with the continuous capacity baseline scheme, the total life cycle cost of the building complex under the uniform specification modular scheme is reduced by approximately 5.28%.

[0084] It should be noted that the aforementioned cost reduction does not require the standardized modular solution to outperform the continuous capacity benchmark solution in all operational indicators, such as grid power purchase, grid power sales, photovoltaic power generation, air conditioning power consumption, and average PPD. Rather, it reduces deployment-related soft costs caused by non-standard equipment selection, decentralized procurement, differentiated construction, commissioning, and subsequent operation and maintenance through standardized equipment specifications and building-level configuration combination control. Even if some operational indicators of the standardized modular solution are close to those of the continuous capacity benchmark solution, it can still achieve a lower overall life-cycle cost, provided that the operational feasibility and thermal comfort constraints of each building's annual operation are met.

[0085] In this embodiment, the continuous capacity benchmark scheme generates 20 photovoltaic capacity specifications, 20 energy storage capacity specifications, and 20 energy storage power capacity specifications for 20 buildings. The unified specification module scheme unifies the photovoltaic module specifications, energy storage module specifications, and energy storage power module specifications into one type, and expresses the capacity differences between the 20 buildings through 17 different combinations of building-level module quantities.

[0086] Therefore, the standardized modular solution transforms the differences in roof area, load level, and operational response between buildings from continuous differences in equipment specifications into differences in the number of modules under a shared standard module. While retaining building-level capacity adaptability, it significantly reduces the number of equipment specifications and configuration combinations. This configuration method can reduce the complexity of procurement lists, differences in construction parameters, and types of spare parts, and improve the convenience of allocating, replacing, and expanding the capacity of equipment of the same specifications between different buildings.

[0087] Annual operational assessment results show that the standardized modular solution can complete year-round operation under constraints of available roof area, energy storage power-energy consistency, energy storage state of charge, building-side power balance, and PPD comfort. Therefore, the cost reduction in this embodiment is not achieved by sacrificing operational feasibility or thermal comfort levels, but rather by reducing specifications and configuration complexity while meeting annual operational requirements.

[0088] Example 3: like Figure 3 , Figure 4As shown, this embodiment provides a unified-specification photovoltaic energy storage module configuration system for rural building clusters, used to execute the aforementioned unified-specification photovoltaic energy storage module configuration method. The system includes a data acquisition module, a unified-specification decision module, a capacity conversion module, an annual operation assessment module, a cost evaluation module, an optimization search module, and a solution output module.

[0089] The data acquisition module is used to acquire building-side data and external operational data for each building within the rural building complex. Building-side data includes the available roof area, non-air conditioning electricity load, air conditioning response data, comfort parameters, and energy storage engineering parameters for each building. External operational data includes meteorological data, real-time electricity price signals, and dynamic carbon liability signals. The data acquisition module can also perform time alignment, missing value handling, and unit standardization on data from different sources, and form an input data sequence according to the time step used in the annual operational assessment.

[0090] The unified specification decision module generates specifications for photovoltaic modules, energy storage modules, and energy storage power modules for use by the building complex, and generates the corresponding number of photovoltaic modules, energy storage modules, and power storage modules for each building. All module quantities are non-negative integers. The unified specification decision module can select shared module specifications from a preset set of candidate specifications, or generate candidate values ​​for shared module specifications within preset upper and lower engineering limits.

[0091] The capacity conversion module multiplies the shared module specifications by the integer number of modules corresponding to each building to determine the photovoltaic installed capacity, energy storage capacity, and energy storage power capacity of each building. The module also checks whether the converted photovoltaic installed capacity meets the rooftop usable area limit and whether the energy storage capacity and energy storage power capacity meet the preset proportional relationship.

[0092] The annual operation assessment module is used to coordinate photovoltaic output, energy storage charging and discharging, grid power purchase and sale, and air conditioning temperature setting and adjustment, using the photovoltaic installed capacity, energy storage energy capacity, and energy storage power capacity of each building as the operating boundary, and outputs annual operating costs and operating performance indicators.

[0093] The annual operation evaluation module may include a mixed integer linear programming solution unit, an energy storage operation constraint unit, a grid interaction constraint unit, an air conditioning response SOS2 interpolation unit, and a PPD comfort constraint unit.

[0094] The mixed-integer linear programming solution unit is used to divide the annual operating cycle into multiple daily operating segments arranged in chronological order, and to establish hourly operating subproblems for each daily operating segment. During the solution process, the energy storage state of charge at the end of the previous day is transferred to the beginning of the next day to maintain the continuity of the energy storage operating state throughout the year.

[0095] The energy storage operation constraint unit is used to set energy storage charging power constraints, energy storage discharging power constraints, energy storage energy state update constraints, upper and lower limits of state of charge constraints, and operation constraints to avoid simultaneous charging and discharging of energy storage.

[0096] The grid interaction constraint unit is used to establish the relationship between the grid power purchase, grid power sales and net grid exchange power of each building, and to establish the building-side power balance relationship in combination with photovoltaic output, total building power load and energy storage charging and discharging power.

[0097] The SOS2 interpolation unit for air conditioning response is used to perform piecewise linear interpolation on the air conditioning power consumption corresponding to adjacent set temperature nodes based on the building-hour-set temperature response table, so as to obtain the air conditioning power consumption of each building at different time steps and different set temperatures.

[0098] The PPD comfort constraint unit is used to calculate the predicted percentage of dissatisfaction based on the comfort response data corresponding to the indoor set temperature, and to constrain the predicted percentage of dissatisfaction to not exceed the allowable upper limit, thereby avoiding annual operation optimization by over-adjusting the air conditioning set temperature to reduce operating costs.

[0099] The operational performance indicators output by the annual operation assessment module may include grid electricity purchases, grid electricity sales, photovoltaic power generation, local photovoltaic consumption, air conditioning electricity consumption, energy storage charging and discharging, energy storage state of charge, average PPD, dynamic carbon responsibility indicators, and annual operating costs.

[0100] The cost evaluation module is used to calculate the total life cycle cost of a building complex based on the specifications of shared modules, the number of modules in each building, and the results of annual operational assessments. The total life cycle cost of a building complex includes hardware costs, deployment-related software costs, the discounted value of annual operating costs during the planning period, replacement costs, operation and maintenance costs, electricity sales revenue from the power grid, and residual value deductions.

[0101] Deployment-related soft costs include fixed deployment costs, scale-related soft costs that vary with deployment size, and specification / configuration complexity costs determined by the number of different equipment specifications and the number of different building-level module combinations. By setting specification / configuration complexity costs, the overall cost of candidate configuration solutions can be increased accordingly as the number of equipment specifications or building-level configuration combinations increases.

[0102] The optimization search module is used to search for uniform-specification photovoltaic energy storage module configuration schemes under constraints such as available roof area, energy storage power-energy consistency, preset engineering capacity, and annual operational feasibility. The optimization search module can use random key encoding to generate shared module specifications and the number of integer modules for each building, and perform feasibility repair on candidate schemes that do not meet the constraints.

[0103] To reduce the computational burden of the annual operational assessment, the optimization search module can first call the annual operational assessment module to perform representative period assessments or annualized screening assessments on candidate configuration schemes, and then perform an annual 8760-hour operational assessment on the screened candidate configuration schemes. For candidate configuration schemes that have completed the assessment, the optimization search module associates and caches their module specifications, number of modules, annual operating costs, and operating performance indicators to avoid the same candidate configuration schemes being solved repeatedly.

[0104] The solution output module is used to output a list of shared module specifications for the building complex, a list of module quantities for each building, the photovoltaic installed capacity, energy storage capacity, and energy storage power capacity for each building, a list of building-level module quantity combinations, and the annual operational feasibility evaluation results. The output results can be provided in the form of tabular files, database records, or a visual interface, for direct use in equipment procurement, building-level construction configuration, and subsequent operation and maintenance management.

[0105] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0106] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for configuring standardized photovoltaic energy storage modules for rural building complexes, characterized in that, Includes the following steps: At the level of the rural building complex, the specifications of photovoltaic modules, energy storage modules, and power storage modules for use by all buildings are uniformly determined. For each building, the number of photovoltaic modules, energy storage modules, and energy storage power modules are determined separately, with each module number being a non-negative integer. The specifications of the photovoltaic modules, energy storage modules, and energy storage power modules are multiplied by the corresponding number of photovoltaic modules, energy storage modules, and energy storage power modules for the building, respectively, to jointly determine the photovoltaic installed capacity, energy storage capacity, and energy storage power capacity of each building, so that each building forms a differentiated capacity through different integer module numbers. Obtain available roof area, non-air conditioning power load, air conditioning response data and comfort parameters for each building for annual operational assessment, as well as corresponding meteorological data, real-time electricity price signals and dynamic carbon liability signals; The photovoltaic installed capacity, energy storage capacity, and energy storage power capacity are used as the annual operating boundaries for each building. An annual operating assessment is performed based on the annual operating boundaries to coordinate photovoltaic output, energy storage charging and discharging, grid power purchase and sale, and air conditioning temperature setting. Under thermal comfort constraints, the annual operating cost and operating performance indicators are obtained. Based on the annual operating costs and operating performance indicators, determine whether the candidate module specifications and the combination of quantities of each building module meet the annual operating feasibility constraints, and feed back the judgment results for the retention or elimination of candidate combinations. For candidate combinations that meet the annual operational feasibility constraints, the total life cycle cost of the building complex is constructed based on hardware costs, fixed deployment costs, scale-related deployment costs determined by the total number of various modules, specification / configuration complexity costs determined by the number of different photovoltaic module specifications, the number of different energy storage module specifications, the number of different energy storage power module specifications, and the number of different building-level module combinations, as well as the annual operational costs. The final module specifications and combinations of building module quantities are then selected and determined with the goal of minimizing the total life cycle cost of the building complex. Output the final module specifications and the combination of quantities for each building module.

2. The method for configuring standardized photovoltaic energy storage modules for rural building complexes according to claim 1, characterized in that, The shared module specifications and the number of modules in each building together determine the actual capacity of each building, specifically including: For the For each building, multiplying the specifications of the photovoltaic modules, energy storage modules, and power storage modules by the corresponding number of photovoltaic modules, energy storage modules, and power storage modules, respectively, yields: in, Indicates the first The photovoltaic installed capacity of each building; Indicates the first Energy storage capacity of a building; Indicates the first Energy storage capacity of each building; , and They represent the first The number of photovoltaic modules, energy storage modules, and energy storage power modules of each building, and all of them are non-negative integers; , and These represent the specifications of the photovoltaic modules, energy storage modules, and power storage modules used in the rural building complex, respectively. The photovoltaic installed capacity, energy storage capacity, and power storage capacity serve as the annual operating boundaries for each building in the annual operating assessment of the corresponding candidate combination, and the candidate combination is used to determine whether it enters the final selection based on the results of the annual operating assessment.

3. The method for configuring standardized photovoltaic energy storage modules for rural building complexes according to claim 1, characterized in that, The annual operational assessment specifically includes: Divide a year into multiple daily running segments arranged in chronological order, and for each daily running segment, establish a mixed integer linear programming subproblem with an hour as the time step; The energy storage state of charge at the end of the previous day's operating period is used as the energy storage state of charge at the start of the next day's operating period, and the energy storage state of charge between adjacent time steps for each building is updated according to the following formula: in, and They represent the first The building in time step and time step The energy storage state of charge; and They represent the first The building in time step The energy storage charging power and energy storage discharging power; and These represent the energy storage charging efficiency and the energy storage discharging efficiency, respectively. Indicates the length of the time step; The mixed-integer linear programming subproblems are solved sequentially according to the time order of the daily running segments, and the solution results of each daily running segment are summarized to obtain the annual running cost and running performance indicators.

4. The method for configuring standardized photovoltaic energy storage modules for rural building complexes according to claim 1, characterized in that, The processing of the air conditioning response data and thermal comfort constraints specifically includes: A building-hour-set temperature response table is generated in advance through building energy consumption simulation, and then... The building in time step The indoor set temperature is used to set interpolation weights for adjacent temperature nodes that satisfy normalization constraints and SOS2 adjacency constraints of a second type of special ordered set. The second type of special ordered set is used to limit the interpolation weights of at most two adjacent temperature nodes at the same time step to non-zero. This transforms the nonlinear relationship between the set temperature and air conditioning power consumption into a piecewise linear form that can be handled by a mixed-integer linear programming solver, resulting in the air conditioning power consumption. in, Indicates the first The building in time step The power consumption of the air conditioner; This indicates the set of temperature response nodes; Indicates the first The building in time step For the SOS2 interpolation weights for each set temperature response node; This indicates the first item in the building-hour-set temperature response table. The building in time step and the The power consumption of the air conditioner corresponding to each set temperature response node; For the same indoor set temperature, interpolation weights that satisfy normalization constraints and SOS2 adjacency constraints are set for adjacent comfort response nodes, and a prediction dissatisfaction percentage constraint is applied: in, Indicates the first The building in time step The percentage of unsatisfactory predictions; Represents the set of comfort response nodes; Indicates the first The building in time step For the SOS2 interpolation weights for each comfort response node; Indicates the first The percentage of predicted dissatisfaction corresponding to each comfort response node; This indicates the upper limit of the allowed percentage of unsatisfactory forecasts.

5. The method for configuring standardized photovoltaic energy storage modules for rural building complexes according to claim 1, characterized in that, The coordination of photovoltaic power output, energy storage charging and discharging, and grid power purchase and sale specifically includes: Based on the meteorological data and the photovoltaic installed capacity of each building, the photovoltaic output boundary for each time step is determined, and the non-air conditioning power load and air conditioning power of each building are combined into the total power load of the building. Regarding the first The building in time step Set the grid power purchase capacity, grid power sales capacity, energy storage charging capacity, and energy storage discharging capacity, and apply grid interaction relationships and building-side power balance relationships: in, Indicates the first The building in time step Net power exchanged with the power grid; and They represent the first The building in time step The power purchased and sold by the power grid; Indicates the first The building in time step Total building electrical load; Indicates the first The building in time step Photovoltaic power output; and They represent the first The building in time step The energy storage charging power and energy storage discharging power; The grid's electricity purchase cost and electricity sales revenue are calculated based on the real-time electricity price signal, and the corresponding operational performance indicators are calculated based on the dynamic carbon responsibility signal.

6. The method for configuring standardized photovoltaic energy storage modules for rural building complexes according to claim 1, characterized in that, The total lifecycle cost of the building complex is determined based on the module specifications, the number of each building module, and their annual operating results in the candidate configuration schemes, using the following formula: in, This represents the total lifecycle cost of a building complex converted to the same planning reference point. This represents the hardware cost determined based on the equipment specifications and the number of each building module in the candidate configuration scheme; Indicates fixed deployment cost; This represents the scale-related deployment cost determined based on the total number of various modules; This represents the cost of specification / configuration complexity determined by the combination of different equipment specifications and quantities and different building-level module quantities; This represents the planned operating cost calculated based on the annual operating assessment results. Indicates replacement cost; Indicates operation and maintenance costs; This represents the revenue from electricity sales by the power grid; This indicates residual value deduction; The scale-related deployment cost is determined based on the total number of various modules, using the following formula: in, , and These represent the unit-scale deployment cost coefficients for photovoltaic modules, energy storage modules, and energy storage power modules, respectively. , and These represent the number of photovoltaic modules, energy storage modules, and power storage modules of the i-th building, respectively. This refers to the collection of buildings within the rural architectural complex.

7. A method for configuring standardized photovoltaic energy storage modules for rural building complexes according to claim 6, characterized in that, The The quantity is determined based on the combination of different equipment specifications and different building-level module quantities, using the following formula: in, , and These represent the number of different photovoltaic module specifications, different energy storage module specifications, and different energy storage power module specifications in the candidate configuration schemes, respectively. This represents the number of different building-level module combinations obtained after deduplicating the triplets consisting of the number of photovoltaic modules, energy storage modules, and energy storage power modules in each building. , , and These represent the unit complexity cost coefficients for the corresponding quantities; when the rural building complex adopts a single shared specification for each type of equipment... , and All are 1.

8. The method for configuring standardized photovoltaic energy storage modules for rural building complexes according to claim 1, characterized in that, The available area constraint for the roof is expressed as follows: in, Indicates the first The number of photovoltaic modules in each building; This indicates the area occupied by a single photovoltaic module corresponding to the photovoltaic module specifications; Indicates the first The available roof area of ​​each building; The energy storage power-energy consistency constraint is expressed as: in, and They represent the first The energy storage capacity and power storage capacity of each building; and These represent the lower and upper limits of the permissible ratio of energy storage power to energy storage capacity, respectively.

9. A method for configuring standardized photovoltaic energy storage modules for rural building complexes according to claim 1, characterized in that, The optimization search for the module specifications and the number of modules in each building includes: Candidate decisions are generated using random key encoding and then decoded to obtain the specifications of photovoltaic modules, energy storage modules, and power storage modules for use in the rural building complex, as well as the number of modules for each building. Feasibility repair is performed on the number of modules that do not meet the constraints of rooftop usable area, energy storage power-energy consistency, or preset engineering capacity after decoding. First, a representative period assessment or annual screening assessment is used to conduct a preliminary screening of the candidate configuration schemes that have undergone feasibility repair. Then, the candidate configuration schemes selected in the preliminary screening are subjected to an annual 8760-hour operation assessment. The candidate configuration schemes that have completed the operation evaluation, along with their annual operating costs and performance indicators, are cached, and the operation evaluation is avoided by repeatedly performing the same candidate configuration schemes based on the cached results. The shared module specifications and the number of building modules for each candidate configuration scheme are converted into the actual capacity of each building. The actual capacity is used as the annual operation boundary to obtain the annual operation results. The annual operation feasibility is judged and the total life cycle cost of the building group is calculated based on the annual operation results. Candidate configuration schemes are retained or eliminated based on the judgment and calculation results, and subsequent searches are updated until the final combination of shared module specifications and the number of building modules is obtained.

10. A method for configuring standardized photovoltaic energy storage modules for rural building complexes according to claim 1, characterized in that, The output of the unified specification photovoltaic energy storage module configuration scheme specifically includes: Output the building complex shared module specification list, the module quantity list for each building, the photovoltaic installed capacity, energy storage capacity and energy storage power capacity of each building, the building-level module quantity combination list, and the annual operational feasibility evaluation results; The annual operational feasibility evaluation results include grid power purchase, grid power sales, photovoltaic power generation, air conditioning power consumption, energy storage status of charge, predicted unsatisfactory percentage, and annual operating costs. The building complex shared module specification list and the list of quantities of each building module are used for procurement, construction configuration, and operation and maintenance management.

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