Evaluation method, system, equipment and medium for cold and heat storage system architecture

Abstract

The invention relates to the technical field of energy storage system selection, and discloses an evaluation method, system, equipment and medium for a cold and hot storage system architecture, and the method comprises the steps: determining a plurality of evaluation index factors based on space demand, economy, energy efficiency and environmental performance; determining the model selection of each unit of a plurality of architecture types, and calculating the energy consumption of each unit model selection; calculating an evaluation index value of each evaluation index factor based on the energy consumption; constructing a hierarchical structure model; on the basis of the hierarchical structure model, combined weights of all the evaluation index factors are determined through an analytic hierarchy process and an entropy evaluation method, and the combined weights are dynamically optimized to obtain the final weight of each evaluation index factor; and based on the final weight of each evaluation index factor and a TOPSIS comprehensive evaluation method, determining the relative closeness of each architecture type, and taking the architecture type corresponding to the highest relative closeness as the optimal architecture type. The method provides a powerful basis for project investment decision-making.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage system selection, and in particular to an evaluation method, system, equipment and medium for a cold and hot dual storage system architecture. Background Art

[0002] With the substantial increase in the proportion of renewable energy sources such as wind and solar power with fluctuating power generation, higher requirements are placed on the flexible adjustment capabilities of buildings, and the setting of energy storage systems has become particularly important.

[0003] Traditional cooling and heating architectures often utilize a combination of chillers and boilers. Only the cooling source consumes electricity, subject to peak-valley electricity price fluctuations. The heating source uses gas, a constant price throughout the day with no peaks or valleys, so typically only cold storage devices are used. However, with the growing trend toward full electrification, boilers are being replaced by heat pumps. Heat pumps, like chillers, use electricity as their energy source, offering new potential for exploiting peak-valley electricity price fluctuations and shaving peak loads.

[0004] In the past, when comparing energy storage solutions, the economics of single-storage architectures were often considered, focusing solely on the operating costs of the energy storage system's ability to store cold during the cooling season or heat during the heating season. These comparisons were relatively limited in terms of the solutions and dimensions. In a world of refined design, the choice of a dual-storage architecture is particularly critical. The HVAC industry urgently needs to address the issue of how to comprehensively evaluate different dual-storage architectures from multiple perspectives, including space requirements, cost-effectiveness, energy efficiency, and environmental considerations, while balancing client needs with technical and economic feasibility, to determine the optimal architecture for different application scenarios. Summary of the Invention

[0005] In response to the shortcomings and defects of the existing technology, the present invention provides an evaluation method, system, equipment and medium for a dual-storage cold and hot storage system architecture, providing designers with an optimal dual-storage cold and hot storage architecture solution that takes into account the owner's needs and technical and economic efficiency.

[0006] The present invention is achieved through the following technical solutions: An evaluation method for a dual-storage cold and hot storage system architecture, the evaluation method comprising: Determine several evaluation index factors based on space demand, economy, energy efficiency and environment; Determine the type of units for each of several architecture types included in the dual-storage cooling and heating system, and calculate the energy consumption and initial investment for each type of unit; Calculating the evaluation index value of each evaluation index factor based on the energy consumption and initial investment of the unit selection corresponding to each architecture type; Constructing a hierarchical structure model based on the evaluation index factor, the plurality of architecture types and the optimal architecture type; Based on the hierarchical structure model, determining the combined weights of all the evaluation index factors by using the hierarchical analysis method and the entropy method respectively, and dynamically optimizing the combined weights to obtain the final weight of each evaluation index factor; The relative closeness of each of the architecture types is determined based on the final weight of each of the evaluation index factors and the TOPSIS comprehensive evaluation method, and the architecture type corresponding to the highest relative closeness is taken as the optimal architecture type.

[0007] As an optimization, based on the hierarchical structure model, the specific process of determining the combined weights of all the evaluation index factors in each of the architecture types by using the hierarchical analysis method and the entropy method is as follows: Calculating the weight of each evaluation index factor by using the hierarchical analysis method based on the hierarchical structure model, and using the weight that passes the consistency test as the first weight of the evaluation index factor; Calculating the weight of each evaluation index factor of each architecture type by an entropy method based on the hierarchical structure model to obtain a second weight of the evaluation index factor of each architecture type; The first weight and the second weight of each evaluation index factor are assigned corresponding linear weighting coefficients, thereby obtaining a combined weight of each evaluation index factor of each architecture type.

[0008] As an optimization, the hierarchical model calculates the weight of each evaluation index factor by using the hierarchical analysis method, and uses the weight that passes the consistency test as the first weight of the evaluation index factor. The specific process is as follows: The “1-9 scale method” is used to assign values to the importance of each evaluation index factor and construct an index judgment matrix: Calculating the geometric mean of each column of the indicator judgment matrix, and normalizing the geometric mean to obtain the initial weight of each evaluation indicator factor; Calculating the maximum eigenvalue of the index judgment matrix based on the initial weight of each evaluation index factor; Calculate the actual consistency index based on the maximum characteristic root CI , and based on the random consistency index RI Calculate the consistency ratio CR , ; Determine whether the consistency ratio is less than 0.1. If so, it is considered that the indicator judgment matrix passes the consistency test. The initial weight corresponding to the indicator judgment matrix is the first weight, wherein the first j The first weight of the evaluation index factor is expressed as ; Otherwise, return to the first step and readjust the indicator judgment matrix.

[0009] As an optimization, the weight of each evaluation index factor of each architecture type is calculated by the entropy method based on the hierarchical structure model. The specific process of obtaining the second weight of the evaluation index factor of each architecture type is as follows: Collect the values of each evaluation indicator for each architecture type to construct the original data matrix X， ,in, For the i The first architecture type j The evaluation index value, m is the number of architecture types, n is the number of evaluation index factors; The original data matrix is standardized to obtain a standardized index data matrix Y , ,in, For the i The first architecture type j The standardized value of the evaluation index factor; The second weight of each evaluation index factor is determined by entropy method based on the standardized index data matrix, wherein the j The second weight of the evaluation index factor The calculation formula is: ; ; ; ; in, For the j Under the evaluation index factor i The proportion of the standard value of each architecture type in the evaluation index factor; For the j The entropy value of the evaluation index factor; For the j Information redundancy of the evaluation index factors; For the j The entropy weight of the evaluation index factor, that is, j The second weight of the evaluation index factor.

[0010] As an optimization, the combined weight is expressed as W , , , For the j The combined weight of the evaluation index factors; is the linear weighting coefficient, , For thej The first weight of the evaluation index factor, For the j The second weight of the evaluation index factor, n is the number of evaluation index factors.

[0011] As an optimization, the specific process of dynamically optimizing the combined weights to obtain the final weights of each evaluation index factor in each architecture type is as follows: Calculate the peak-valley electricity price ratio correction factor based on peak electricity price and peak-valley electricity price , and calculate the energy storage rate correction factor based on the energy storage device storage capacity and the total cumulative load on the design day ; Based on the peak-valley electricity price ratio correction factor The combined weight of the evaluation index factor for operating costs is adjusted, and at the same time, based on the energy storage rate correction factor Adjust the weight of the portfolio of initial investment for the evaluation index factor; Based on the adjusted weights of initial investment and operating costs, the weights of all evaluation index factors other than the initial investment and operating costs are calculated to obtain the final weight. The calculation formula of the final weight is: ; ; ; ; ; in, Represents the final weight of the i-th evaluation index factor; 、 are the weights of the adjusted initial investment and operating expenses respectively; 、 are the combined weights of initial investment and operating expenses before adjustment; is the peak-valley electricity price ratio correction factor adjustment coefficient; is the peak electricity price, in yuan / kWh; The off-peak electricity price is in yuan / kWh; is the adjustment coefficient of the energy storage rate correction factor; The energy stored in the energy storage device is in kWh; It is the total cumulative load of the design day, in kWh.

[0012] As an optimization, the specific process of determining the relative closeness of each architecture type based on the final weight of each evaluation index factor and the TOPSIS comprehensive evaluation method is as follows: Calculate the indicator weighting matrix based on the final weightsZ , Z The calculation formula is: ; in, is the standardized indicator data matrix; For the j The final weight of the evaluation index factor; m is the number of architecture types; n is the number of evaluation index factors; Based on the indicator weighting matrix Z Calculate the positive and negative ideal distances for each of the architecture types using the following formula: ; ; ; ; in, For the j The evaluation index factor is a positive ideal solution; is a negative ideal solution; Indicates the i The positive ideal distance for each architecture type, Indicates the i Negative ideal distance for each architecture type, Indicates the first i The first structural type j The product of the evaluation index value of each evaluation index factor and the final weight; The relative closeness of each architecture type is calculated based on the positive and negative ideal distances of each architecture type. The calculation formula for the relative closeness of the i-th architecture type is: .

[0013] The present invention also discloses an evaluation system for a cold and hot dual storage system architecture, which is used to execute the evaluation method for the cold and hot dual storage system architecture, comprising: Evaluation index factor determination module, used to determine several evaluation index factors based on space demand, economy, energy efficiency, and environment; The selection module is used to determine the selection of various units of several architecture types included in the dual-storage cooling and heating system; A calculation module, used to calculate the energy consumption and initial investment of each type of unit selected; An evaluation index value calculation module, configured to calculate the evaluation index value of each evaluation index factor based on the energy consumption and initial investment of the unit selection corresponding to each of the architecture types; A hierarchical structure model construction module, configured to construct a hierarchical structure model based on the evaluation index factors, the plurality of architecture types, and the optimal architecture type; A final weight calculation module is used to determine the combined weights of all the evaluation index factors based on the hierarchical structure model by using the hierarchical analysis method and the entropy method, and dynamically optimize the combined weights to obtain the final weight of each evaluation index factor; An evaluation module is used to determine the relative closeness of each of the architecture types based on the final weight of each of the evaluation index factors and the TOPSIS comprehensive evaluation method, and to take the architecture type corresponding to the highest relative closeness as the optimal architecture type.

[0014] The present invention also discloses an electronic device comprising at least one processor and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute an evaluation method for a cold and hot dual storage system architecture as described above.

[0015] The present invention also discloses a storage medium storing a computer program, which implements the aforementioned evaluation method for a cold and hot dual storage system architecture when executed by a processor.

[0016] Compared with the prior art, the present invention has the following advantages and beneficial effects: 1. Based on the climatic conditions of the project location, peak-valley electricity price policies, building load characteristics and other conditions, appropriate evaluation indicators are selected from multiple dimensions such as space requirements, economy, energy efficiency and environmental factors. A combination of subjective and objective methods is adopted, and the basic weights of different indicators are comprehensively determined through the combined weighting method of the analytic hierarchy process and the entropy method. The combined weights are dynamically optimized by introducing the peak-valley electricity price ratio correction factor and the energy storage rate correction factor. Finally, the TOPSIS multi-attribute comprehensive evaluation method is used to evaluate the comprehensive benefits of each architecture, and the comprehensive scores of various dual-storage air-conditioning system architectures for different application scenarios are obtained.

[0017] 2. Based on the evaluation indicators of various dual-storage air conditioning architectures, key calculation results such as space requirements, economy, energy efficiency, and environmental indicators are given, and a comprehensive score is given for the architecture. The highest-ranked architecture is the most suitable dual-storage air conditioning system architecture for this application scenario, providing a strong basis for project investment decisions, helping decision makers fully understand the project's economic characteristics and make scientific and reasonable investment choices. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings: Figure 1 This is a flow chart of a method for evaluating a cold and hot dual storage system architecture according to the present invention; Figure 2 is a structural diagram of the hierarchical structure model in the present invention; Figure 3 This is a flow chart of using the hierarchical analysis method to calculate the first weight of each evaluation index factor in the present invention; Figure 4 This is a flow chart of using the entropy method to calculate the second weight of each evaluation index factor in the present invention; Figure 5 Flowchart for dynamic optimization of portfolio weights; Figure 6 This is a flow chart of using the TOPSIS comprehensive evaluation method to obtain the optimal architecture type in the present invention. DETAILED DESCRIPTION

[0019] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with examples and drawings. The exemplary embodiments of the present invention and their descriptions are only used to explain the present invention and are not intended to limit the present invention.

[0020] This paper combines the Analytic Hierarchy Process (AHP) with the Entropy Method (EMP) to determine the combined weights. This method is then integrated with the TOPSIS method for suitability analysis of the architecture of a dual-storage air conditioning system. The AHP method allows for subjective importance scoring based on the focus of each project, enabling targeted subjective judgment of each project. Furthermore, the EMP method standardizes the evaluation criteria to achieve objective judgment. Finally, the TOPSIS method uses the dynamically optimized weights derived from these subjective and objective factors for a comprehensive evaluation, ensuring the accuracy of the selected optimal architecture.

[0021] Next, the process of the present invention is described in detail.

[0022] This embodiment 1 provides an evaluation method for a cold and hot dual storage system architecture, such as Figure 1 As shown, the evaluation method includes: S1. Determine several evaluation index factors based on space demand, economy, energy efficiency and environment.

[0023] There are many evaluation index factors based on space demand, economy, energy efficiency and environment, such as initial investment and operating costs based on economy, operating energy efficiency ratio based on energy efficiency, carbon emissions based on environment and floor area based on space demand. Of course, other evaluation index factors can also be designed, which will not be elaborated here.

[0024] S2. Determine the type of units of several architecture types included in the cold and hot dual storage system, and calculate the energy consumption and initial investment of each type of unit.

[0025] 1. Determine the architecture type and the form of cold and hot sources: Based on literature research, manufacturer research, and project case analysis, and analysis of the characteristics of projects with dual cold and hot storage requirements, four typical cold and hot storage system architectures suitable for civil building use are proposed. The four architectures are composed of different energy storage devices. To ensure efficient energy utilization, stable system operation, and meet the cold and hot demand under different working conditions, the cold and hot source forms must be adjusted according to the characteristics of the energy storage devices equipped in each architecture. The cold and hot source forms corresponding to the four architectures are shown in Table 1: Table 1 Typical cold and heat storage system architecture types and cold and heat source forms

[0026] Because chillers are far more energy efficient than air-source heat pumps in the summer, chillers are used for cold storage and direct supply of cooling water. Air-source heat pumps have low environmental requirements, flexible installation, and are widely applicable in residential, commercial, and school settings. They are also easy to select, so in the winter, air-source heat pumps are used for low-temperature heat storage and direct supply of heat (air-source heat pumps only provide heating, not cooling). Architecture 2 requires high-temperature heat storage, so it uses an electric boiler for heat storage. The air-source heat pump is only used for direct supply during the daytime heating period, and does not store heat during the nighttime off-peak period. The electric boiler is only used for heat storage during the nighttime off-peak period and is shut down during the daytime heating period.

[0027] 2. Determine the unit selection: a) Water storage chiller model: The water storage chiller is a conventional chiller. The actual capacity of the chiller needs to meet the following two conditions: ① The water reservoir is fully filled during the energy storage period; ② The water reservoir capacity plus the cumulative amount directly supplied by the host meet the cumulative load of the day. The calculation formula is as follows: ; Where: is the actual capacity of the conventional chiller, kW; is the cooling capacity in summer, kWh; is the cumulative cooling load for the summer design day, kWh; is the storage hours, h; is the duration of building use during the day, h.

[0028] b) Ice storage host model: The ice storage unit is a dual-mode unit, including ice-making and cooling modes. It switches to ice-making mode during cold storage and switches to cooling mode during direct supply. In order to fill the ice storage tank during the energy storage period, the required cooling capacity of the dual-mode chiller in ice-making mode is calculated as follows: ; Where: The cooling capacity of the dual-mode chiller in ice-making mode, kW.

[0029] There is a ratio relationship between the cooling capacity of the dual-mode chiller under the cooling condition and the ice-making condition. The calculation formula is as follows: ; Similarly, the ice storage tank capacity plus the cumulative direct supply of the dual-mode main engine cooling condition meets the design daily cumulative load. The cooling capacity of the dual-mode chiller cooling condition is calculated as follows: ; Therefore, the actual capacity of the dual-mode chiller needs to meet the above two conditions at the same time, namely: ; Where: is the cooling capacity of the dual-mode chiller under cooling mode, kW; The ice-making coefficient is the ratio of the cooling capacity of the dual-mode chiller in the ice-making mode to the cooling capacity in the cooling mode, which is generally 0.6~0.75.

[0030] c) Low-temperature water thermal storage and phase change thermal storage host model: In both low-temperature water thermal storage and phase-change thermal storage air conditioning systems, air source heat pumps are used as heat sources. The actual capacity of the air source heat pump needs to take into account the heat released by the heat storage device during the energy storage period and the direct heat supply of the air source heat pump during the daytime use period of the building to ensure sufficient heat supply in winter. At the same time, the air source heat pump needs to consider defrost correction and temperature correction in winter. The calculation formula is as follows: ; Where: is the actual capacity of the air source heat pump in the low-temperature water thermal storage and phase change thermal storage systems, kW; Heat storage for winter, kWh; is the cumulative heat load of the winter design day, kWh; It is a comprehensive correction factor that takes into account defrost correction and temperature correction.

[0031] d) High-temperature water thermal storage host model: Since the conventional outlet water temperature of air source heat pumps usually does not exceed 60℃, and high temperature water heat storage may require a heat storage temperature of more than 90℃, which is difficult for conventional air source heat pumps to meet, high temperature water heat storage requires the use of electric boilers to store heat at night, and the remaining heat supply during the day is directly supplemented by the air source heat pump. The capacity of the electric boiler needs to meet the heat release within the storage hour, and the capacity of the air source heat pump needs to meet the direct heat supply during the daytime use period of the building. The calculation formula is as follows: ; ; Where: is the actual capacity of the air source heat pump in the high-temperature water thermal storage system, kW; is the actual capacity of the electric boiler, kW; It is a comprehensive correction factor that takes into account defrost correction and temperature correction.

[0032] e) Host capacity correction: Due to the fluctuation of the building's daytime load, the host does not operate at full capacity at all times. Therefore, the host capacity needs to be corrected. First, a trial calculation is performed according to the host selection formula described in a) to d), and the calculated capacity is substituted into the design day. Then, 24-hour hourly energy distribution is performed to obtain the actual cumulative direct supply of the host on the design day. If the stored energy plus the actual cumulative direct supply of the host on the design day is less than the cumulative load on the design day, the host capacity is increased until the actual cumulative direct supply on the design day is exactly equal to the cumulative load on the design day. At this time, the host capacity correction is completed.

[0033] 3. Calculate energy consumption: Energy storage devices store energy during off-peak hours, leveraging lower off-peak electricity prices and reducing operating costs. Energy is released during daytime hours to meet the building's heating and cooling needs. Overloaded mainframes are prioritized, followed by peak loads, and finally, demand during parity periods. Any remaining energy is directly supplied by the mainframe, maximizing off-peak electricity utilization and reducing costs.

[0034] The energy consumption of the chiller (conventional / dual mode) will vary with different operating conditions when it is directly supplying energy or operating with an energy storage device. COP As the outdoor environmental parameters change, they need to be corrected hourly, as shown in the following formula: ; ; ; Where: for i The cooling performance coefficient of the chiller when directly supplying water at the time; is the cooling coefficient under standard working conditions when the chiller is directly supplied; for i The cooling performance coefficient of the chiller during cold storage at the time; The cooling coefficient of the chiller under standard operating conditions when storing cold; for i The performance coefficient correction factor of the chiller at the time is, in order to simplify the calculation, only the influence of the cooling water temperature is considered; for i The cooling water supply temperature at the moment, ℃, is taken based on the outdoor hourly wet-bulb temperature and the rated approximation of the cooling tower (4℃) to simplify the calculation; 、 、 、 They are the correction curve coefficients of the chiller respectively. The correction curve is obtained by fitting the sample parameters of products from mainstream equipment manufacturers.

[0035] The hourly energy consumption of the chiller's cold storage is calculated as follows: ; Where: for i Energy consumption of chiller for cold storage at each moment, kW; for i Cooling capacity of the chiller at any given moment, kW.

[0036] The hourly energy consumption of direct supply chillers is calculated as follows: ; Where: for i Energy consumption of chiller when directly supplying water, kW; for i Cooling capacity of the chiller directly supplied at any given moment, kW.

[0037] The hourly energy consumption of the cooling tower is calculated as follows: ; Where: for i Hourly energy consumption of cooling tower, kW; is the cooling tower power consumption per unit cooling capacity, kW / kW.

[0038] Heat pump COP It will also change with the changes in outdoor environmental parameters and needs to be corrected hourly, as shown in the following formula: ; ; ; Where: fori Corrected heating performance coefficient under the direct supply condition of air source heat pump at the moment; Rated heating performance coefficient for air source heat pump direct supply conditions; for i Corrected heating performance coefficient of the air source heat pump under thermal storage conditions at the time; is the rated heating performance coefficient of the air source heat pump under thermal storage conditions; for i The correction factor of the heating performance coefficient of the air source heat pump at the moment. To simplify the calculation, only the influence of the outdoor temperature is considered; for i Outdoor dry-bulb temperature at the time, ℃; 、 、 、 They are the correction curve coefficients of air source heat pumps respectively. The correction curve is obtained by fitting the product sample parameters of mainstream equipment manufacturers.

[0039] The energy consumption of air source heat pump when storing heat is calculated as follows: ; Where: for i Energy consumption of air source heat pump thermal storage at each moment, kW; for i Heat storage capacity of air source heat pump at each moment, kW.

[0040] The energy consumption of air source heat pump direct supply is calculated as follows: ; Where: for i Energy consumption of direct supply of air source heat pump at any moment, kW; for i Air source heat pump directly supplies heat at all times, kW.

[0041] The hourly energy consumption of an electric boiler during heat storage is calculated as follows: ; Where: for i Thermal energy consumption of electric boiler at each moment, kW; for i The heat storage capacity of electric boiler at any moment, kW; is the heating performance coefficient (heating efficiency) of the electric boiler.

[0042] The hourly energy consumption of the water pump is calculated as follows: ; ; ; ; ; ; ; ; ; ; ; ; The hourly energy consumption of the water pump in the cooling season is calculated as follows: Water cooling model: ; Ice storage model: ; The hourly energy consumption of the water pump in the heating season is calculated as follows: ; Where: for i Energy consumption of circulating chilled water pump at all times, kW; is the power consumption of the circulating chiller pump per unit cooling capacity, kW / kW; for i Energy consumption of chilled water pump at each moment, kW; The cooling water pump power consumption per unit cooling capacity, kW / kW; for i The cooling capacity released by the energy storage device at any moment, kW; for i Energy consumption of ice storage pump at each moment, kW; The power consumption of ice storage pump per unit cooling capacity, kW / kW; for i Cooling capacity of the chiller when directly supplied, kW; for i Energy consumption of ice melting pump at each moment, kW; is the power consumption of ice melting pump per unit cooling capacity, kW / kW; for i The cooling capacity of the chiller at any moment, kW; for i Hourly energy consumption of cooling water pump, kW; is the cooling water pump power consumption per unit cooling capacity, kW / kW; for i Hourly energy consumption of chilled water pump, kW; is the chilled water pump power consumption per unit cooling capacity, kW / kW; for i Energy consumption of direct-supply cold water pump of chiller at any moment, kW; is the chiller pump power consumption per unit cooling capacity, kW / kW; for i Energy consumption of the chiller's cold storage water pump at any given moment, kW; for i Energy consumption of hot water pump for constant circulation, kW; The power consumption of the circulating hot water pump per unit heating capacity, kW / kW; for i Heat storage capacity of the host (air source heat pump, electric boiler) at the moment, kW; for i Energy consumption of hot water pump at each moment, kW; The power consumption of the heat pump per unit heating capacity, kW / kW; for i The amount of heat released by the energy storage device at any moment, kW; for i Energy consumption of hot water pump supplied by air source heat pump at any moment, kW; The power consumption of the hot water pump per unit heating capacity, kW / kW; for i The amount of heat released by the direct air source heat pump at any moment, kW; for i Energy consumption of the heat storage hot water pump of the main engine (air source heat pump, electric boiler) at any moment, kW; for i Energy consumption of water pump in cooling season with water storage, kW; for i Energy consumption of water pump in ice storage cooling season, kW; for i Energy consumption of water pump in heating season, kW.

[0043] The sum of energy consumption at each moment is: ; Where: For the system i Energy consumption at each moment, kW; Here, it means that there can only be one kind of energy consumption at the same time, that is, or. When it is a water storage cooling system, the energy consumption of the water pump in the cooling season is When it is an ice storage system, the energy consumption of the water pump in the cooling season is .

[0044] In summary, the energy consumption model can be used to calculate the energy consumption of each architecture for 8760 hours of operation throughout the year.

[0045] S3. Calculate the evaluation index value of each evaluation index factor based on the energy consumption and / or initial investment of the selected unit corresponding to each architecture type.

[0046] Taking the aforementioned five evaluation index factors (initial investment, operating costs, floor space, operating energy efficiency ratio and carbon emissions) as an example, the evaluation index values of the five evaluation index factors are calculated based on the energy consumption generated by the units corresponding to different architecture types. That is to say, assuming that there are five evaluation index factors, then each architecture type has five evaluation index values.

[0047] Core indicator calculation model: a) Calculate initial investment and floor space: The initial investment consists of various equipment and site-related costs, including the main unit cost, energy storage device cost, cooling tower cost, plate heat exchanger cost, machine room cost, water pump cost, commercial land cost, and power capacity expansion cost. The calculation formula is as follows: ; Where: is the total initial investment, ten thousand yuan; 、 The unit prices of the main unit (conventional / dual-mode chiller, air source heat pump, electric boiler, and supporting water pump) and cooling tower are RMB / kW; 、 、 The economic value unit price of the energy storage device, plate heat exchanger, and equipment space is RMB / m 2 ; The unit price for increasing the power capacity of the computer room is RMB / KVA; is the capacity of the main unit (conventional / dual-mode chiller, air source heat pump, electric boiler), kW; is the chiller capacity (conventional / dual-duty chiller), kW; 、 are the floor space of the energy storage device and the entire system, m 2 ; is the heat transfer area of the plate heat exchanger, m 2 ; The maximum power of the host, KVA.

[0048] In terms of space requirements, it is necessary to measure the amount of building space required for the layout of each component of different architecture systems, as well as their adaptability to different building structures, to avoid space limitations that affect system installation and use. The floor space required for each architecture system is calculated using the following formula: ; Where: is the total area of the system, m 2; 、 They are the outdoor commercial floor area coefficient and indoor machine room floor area coefficient of the main unit (conventional / dual-mode chiller, air source heat pump, electric boiler, and supporting water pump), m 2 / kW; 、 are the floor space coefficients of the energy storage device room and the plate heat exchanger room, m 2 / kW; is the energy storage capacity of the energy storage device, kW.

[0049] The energy storage device here refers only to the device for storing cold and heat in the cold and heat dual storage system.

[0050] b) Calculate annual operating costs, energy efficiency ratio, and carbon emissions: Based on the hourly electricity price and hourly energy consumption, the hourly electricity fee can be obtained. Then, the annual operating electricity fee can be obtained by summing the electricity fees for 8760 hours in a year, as shown in the following formula: ; Where: The electricity cost for the whole year is RMB 10,000; for i The unit price of electricity at that moment is RMB / kWh.

[0051] The operating energy efficiency ratio reflects the energy-saving performance of the system during actual operation. The evaluation indicators include the summer operating energy efficiency ratio and the winter operating energy efficiency ratio. The calculation formula for the annual operating energy efficiency ratio is as follows: ; ; ; Where: is the energy efficiency ratio for summer operation; is the system's summer cooling capacity, kWh; is the system's summer power consumption, kWh; is the winter operation energy efficiency ratio; is the system's winter heating capacity, kWh; is the system's winter power consumption, kWh; It is the annual energy efficiency ratio of the system.

[0052] This paper is based on the background of full electrification. There is no equipment that directly burns fossil fuels. Therefore, only the indirect carbon emissions generated by the equipment consuming grid electricity are calculated. The calculation formula is as follows: ; Where: is the annual indirect carbon emissions, tCO2; is the carbon emission coefficient of electricity.

[0053] The above core indicators involve four dimensions: economy, energy efficiency, environmental protection, and space requirements. They evaluate the optimal dual-storage air-conditioning system architecture for a project from multiple dimensions, making the final architecture ranking scientific and reasonable.

[0054] After obtaining the evaluation index values, the evaluation method of the present invention is described in detail below.

[0055] The evaluation method of this invention uses a combination of subjective and objective evaluation methods, combining the analytic hierarchy process and the entropy method to determine the combined weights of various evaluation factors (such as space requirements, system operating energy efficiency, operating costs, initial investment, and carbon emissions). It introduces a "peak-to-valley electricity price ratio correction factor" and a "energy storage rate correction factor" to dynamically optimize the combined weights. Finally, the TOPSIS method is used to comprehensively evaluate each architecture and analyze its suitability in different application scenarios. The specific process is as follows: S4. Constructing a hierarchical structure model based on the evaluation index factors, the plurality of architecture types, and the optimal architecture type.

[0056] In the hierarchical structure model of the present invention, the target layer is the optimal cooling and heating dual storage air conditioning system architecture, the solution layer is the various architecture models, and the criterion layer is various indicators used to evaluate air conditioning energy storage projects. Common core indicators include: operating costs, initial investment, annual operating energy efficiency ratio, carbon emissions, and floor space. These indicators involve economy, environment, energy efficiency, and space requirements. Designers can also supplement them according to project requirements, such as Figure 2 shown.

[0057] S5. Based on the hierarchical structure model, determine the combined weights of all the evaluation index factors by using the hierarchical analysis method and the entropy method respectively, and dynamically optimize the combined weights to obtain the final weight of each evaluation index factor.

[0058] The specific process of step S5 is: S5.1. Calculate the weight of each evaluation index factor based on the hierarchical structure model using the hierarchical analysis method, and use the weight that passes the consistency test as the first weight of the evaluation index factor.

[0059] like Figure 3 As shown, the specific process of step S5.1 is: S5.1.1. Use the "1-9 scale method" to assign a value to the importance of each evaluation index factor and construct an index judgment matrix.

[0060] The AHP approach can reflect the needs of property owners to a certain extent, but the weighting of indicators is easily influenced by subjective opinions. First, indicators at the same level must be compared pairwise and assigned values based on their importance. A 1-9 scale is typically used to determine the importance of an element, as shown in Table 2. Table 2 Meaning of judgment matrix scale

[0061] Use the 1-9 scale method to assign a value to the importance of each indicator and construct a judgment matrix A , as shown below: ; in, It represents the importance of the i-th evaluation index factor relative to the j-th evaluation index factor, and n is the number of evaluation index factors.

[0062] S5.1.2. Calculate the geometric mean of each column of the indicator judgment matrix, and normalize the geometric mean to obtain the initial weight of each evaluation indicator factor.

[0063] Calculate the geometric mean of each column of the judgment matrix as follows: ; Where: is the geometric mean of each column; Will Normalization is performed to obtain the indicator weight vector, and the calculation formula is as follows: ; Where: For the j The weight of the hierarchical analysis method of the jth evaluation index factor, that is, the initial weight of the jth evaluation index factor.

[0064] S5.1.3. Calculate the maximum eigenvalue of the index judgment matrix based on the initial weight of each evaluation index factor.

[0065] Perform consistency test on the obtained indicator judgment matrix and calculate the maximum characteristic root of the indicator judgment matrix. The calculation formula is as follows: ; represents the maximum eigenvalue of the indicator judgment matrix, and A is the indicator judgment matrix.

[0066] S5.1.4. Calculate the actual consistency index based on the maximum characteristic root CI , and based on the random consistency index RI Calculate the consistency ratio CR , ; ; Look up the table to get the random consistency index RI As shown in Table 3; Table 3

[0067] S5.1.5. Determine whether the consistency ratio is less than 0.1. If so, the indicator judgment matrix is considered to have passed the consistency test. The initial weight corresponding to the indicator judgment matrix is the first weight, where the first weight of the j-th evaluation index factor is expressed as Otherwise, return to the first step and readjust the indicator judgment matrix to make it have satisfactory consistency.

[0068] S5.2. Calculate the weight of each evaluation index factor of each architecture type using an entropy method based on the hierarchical structure model to obtain a second weight of the evaluation index factor of each architecture type.

[0069] The entropy method is an objective weighting method based on information theory. It is primarily used to determine the weight of each indicator in a multi-indicator comprehensive evaluation. It analyzes the degree of dispersion of the indicator data distribution to determine the weight of each indicator in the comprehensive evaluation. Its core concept is that the greater the dispersion of the indicator data, the more information it contains, and therefore a higher weight should be assigned. Information entropy is a measure of the degree of disorder in a system. A higher entropy value indicates a more uniform data distribution and a lower information utility value.

[0070] Next, the application of the entropy method in the present invention is specifically introduced. Figure 4 shown.

[0071] S5.2.1. Collect the values of each evaluation indicator for each architecture type to construct the original data matrix X， ,in, For the i The first j The evaluation index value, m is the number of architecture types, n is the number of evaluation index factors.

[0072] S5.2.2. Standardize the original data matrix to obtain a standardized index data matrix Y , ,in, For the i The first architecture type j The standardized value of the evaluation index factor.

[0073] In other words, in order to eliminate the influence of different indicator dimensions and orders of magnitude, the original data matrix needs to be standardized. For positive indicators, the larger the indicator value, the better. The calculation is as follows: ; For negative indicators, the smaller the indicator value, the better. The calculation is as follows: ; After standardization, the standardized indicator data matrix is obtained, which is calculated as follows: ; Where: is the standardized indicator data matrix; For the i The first j The standardized value of an indicator.

[0074] S5.2.3. Determine the second weight of each evaluation index factor by entropy method based on the standardized index data matrix, wherein the second weight of the jth evaluation index factor is The calculation formula is: ; ; ; ; in, For the j Under the evaluation index factor i The standard value of each architecture type accounts for the proportion of the evaluation index factor; Note: If =0, Need to be corrected to a minimum value (such as 10 -6 ) to avoid calculation errors.

[0075] For the j The entropy value of the evaluation index factor; ,when hour, .

[0076] For the j The information redundancy of the evaluation index factors (also known as the coefficient of variation, reflecting the importance of the index); For the j The entropy weight of the evaluation index factor, that is, j The second weight of the evaluation index factor.

[0077] S5.3. Assign corresponding linear weighting coefficients to the first weight and the second weight of each evaluation index factor, thereby obtaining a combined weight of each evaluation index factor of each architecture type.

[0078] The combined weights of each indicator determined by combining the hierarchical analysis method and the entropy method can comprehensively consider subjective and objective factors, and the combined weights of each evaluation indicator factor are composed of a set of W Expressed as: , , For the j The combined weight of the evaluation index factors; is the linear weighting coefficient, , For the j The first weight of the evaluation index factor, For the j The second weight of the evaluation index factor, n is the number of evaluation index factors.

[0079] This reflects the emphasis placed on subjective and objective weights. The value can be determined based on specific circumstances and needs. For example, when subjective information is reliable and important, a larger value can be chosen; when data is abundant and objective factors are more important, a smaller value can be chosen. Optimization methods can also be used to determine the optimal value, ensuring that the combined weights better reflect the actual situation.

[0080] S5.4. Dynamically optimize the combined weight to obtain the final weight of each evaluation index factor.

[0081] like Figure 5 As shown, the specific process is: S5.4.1. Calculate the peak-valley price ratio correction factor based on peak-period electricity prices and peak-valley electricity prices , and calculate the energy storage rate correction factor based on the energy storage device storage capacity and the total cumulative load on the design day ; S5.4.2. Based on the peak-valley electricity price ratio correction factor The combined weight of the evaluation index factor for operating costs is adjusted, and at the same time, based on the energy storage rate correction factor Adjust the weight of the portfolio of initial investment for the evaluation index factor; In the engineering practice of air conditioning energy storage systems, electricity pricing policies play a decisive role in their economic viability. Different projects have significantly different adapted electricity pricing policies due to their geographical location, electricity usage type, and time of day. Traditional system evaluation methods often ignore the sensitive impact of electricity price fluctuations on system economics when considering air conditioning energy storage systems, resulting in significant deviations between economic evaluation results and actual operating conditions. To accurately quantify the specific impact of electricity pricing policies on the dual-storage system architecture, this patent introduces a peak-to-valley electricity price ratio correction factor.

[0082] ; ; Where: is the correction factor for the peak-valley electricity price ratio; The peak-valley electricity price ratio correction factor adjustment coefficient is determined based on expert experience or regression analysis of historical project data; is the peak electricity price, RMB / kWh; is the off-peak electricity price, RMB / kWh; The weight after adjustment for operating expenses; is the unadjusted operating cost weight.

[0083] Electricity pricing policies are crucial for energy storage systems, but traditional evaluation methods may not consider the impact of electricity price differences on economic efficiency. This paper creates a "peak-to-valley electricity price ratio correction factor" to adjust the weight of operating costs based on electricity price differences at the project location. This factor is used to quantify the impact of electricity pricing policies on the economic efficiency of dual-storage cold and hot storage systems, amplify the impact on operating costs in peak-to-valley ratio scenarios, and address the issue of architecture selection bias caused by the inability of static weights to distinguish electricity price sensitivity.

[0084] The energy storage rate also has a significant impact on the investment of an energy storage project. The higher the energy storage rate, the lower the operating cost, but the larger the initial investment. Some projects prioritize cost savings and adopt higher energy storage rates. To reduce the initial investment weight of high energy storage rate projects, this patent introduces an energy storage rate correction factor. The calculation formula is as follows: ; ; Where: is the energy storage rate correction factor; The adjustment coefficient of the energy storage rate correction factor is determined based on expert experience or regression analysis of historical project data; Energy stored in the energy storage device, kWh; is the total cumulative load of the design day, kWh; is the weight after adjustment for initial investment; is the unadjusted initial investment weight.

[0085] The energy storage ratio also significantly impacts the investment in a storage project. A higher energy storage ratio reduces operating costs, but also increases the initial investment. Some projects prioritize operational savings and therefore employ higher energy storage ratios. To mitigate the initial investment weight of high-storage-ratio projects and better account for the impact of the energy storage ratio, this paper proposes an "energy storage ratio correction factor." This prevents high-storage-ratio projects from having their initial investment lowered in the rankings of certain architectures, thereby balancing long-term and short-term economics.

[0086] S5.4.3. Based on the adjusted weights of initial investment and operating costs, calculate the weights of all evaluation index factors other than the initial investment and operating costs to obtain the final weight. The calculation formula for the final weight is: ; Where: For the j The final weight of each indicator; is the combined weight of all indicators except initial investment and operating cost, where the adjusted operating cost weight is equal to , the adjusted initial investment weight is equal to . 、 Respectively, they are actually and .

[0087] ; is the set of final weights of each evaluation index factor.

[0088] S6. Determine the relative closeness of each of the architecture types based on the final weight of each evaluation index factor and the TOPSIS comprehensive evaluation method, and take the architecture type corresponding to the highest relative closeness as the optimal architecture type.

[0089] TOPSIS comprehensive evaluation method is a commonly used multi-attribute decision analysis method. First, according to the final weight , calculate the indicator weight matrix, the specific process is as follows Figure 6 shown.

[0090] S6.1. Calculate the indicator weighting matrix based on the final weight Z , Z The calculation formula is: ; in, is the standardized indicator data matrix; For the j The final weight of the evaluation index factor; m is the number of architecture types; nis the number of evaluation index factors; S6.2. Based on the indicator weighting matrix Z Calculate the positive and negative ideal distances for each of the architecture types using the following formula: ; ; ; ; in, For the j The evaluation index factor is a positive ideal solution; is a negative ideal solution; Indicates the i The positive ideal distance for each architecture type, Indicates the i Negative ideal distance for each architecture type, Indicates the first i The first structural type j The product of the evaluation index value of each evaluation index factor and the final weight; 、 This means that for positive indicators, , ; For negative indicators, , .

[0091] S6.3. Calculate the relative closeness of each architecture type based on the positive and negative ideal distances of each architecture type. The calculation formula for the relative closeness of the i-th architecture type is: .

[0092] The value of is between 0 and 1, The closer it is to 1, the closer the architecture is to the positive ideal solution, and the better its comprehensive evaluation effect; The closer it is to 0, the closer the architecture is to the negative ideal solution, and the worse its comprehensive evaluation effect is.

[0093] In summary, according to The size of the value ranks the four architecture types (the above example has four architectures, of course, other architecture types can also be set according to actual conditions). The larger the value, the higher the ranking of the architecture type, thereby screening out the optimal dual-storage air-conditioning system architecture that best suits the owner's scenario.

[0094] Example 2 discloses an evaluation system for a dual-storage cold and hot storage system architecture, which is used to execute the evaluation method for the dual-storage cold and hot storage system architecture described in Example 1, including: Evaluation index factor determination module, used to determine several evaluation index factors based on space demand, economy, energy efficiency, and environment; The selection module is used to determine the selection of various units of several architecture types included in the dual-storage cooling and heating system; A calculation module, used to calculate the energy consumption and initial investment of each type of unit selected; An evaluation index value calculation module, configured to calculate the evaluation index value of each evaluation index factor based on the energy consumption and initial investment of the unit selection corresponding to each of the architecture types; A hierarchical structure model construction module, configured to construct a hierarchical structure model based on the evaluation index factors, the plurality of architecture types, and the optimal architecture type; A final weight calculation module is used to determine the combined weights of all the evaluation index factors based on the hierarchical structure model by using the hierarchical analysis method and the entropy method, and dynamically optimize the combined weights to obtain the final weight of each evaluation index factor; An evaluation module is used to determine the relative closeness of each of the architecture types based on the final weight of each of the evaluation index factors and the TOPSIS comprehensive evaluation method, and to take the architecture type corresponding to the highest relative closeness as the optimal architecture type.

[0095] Example 3 discloses an electronic device, comprising at least one processor and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute an evaluation method for a cold and hot dual storage system architecture as described in Example 1.

[0096] Example 4 discloses a storage medium storing a computer program. When the computer program is executed by a processor, the evaluation method for a cold and hot dual storage system architecture described in Example 1 is implemented.

[0097] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for evaluating a cold and hot dual storage system architecture, characterized in that: The evaluation method includes: Determine several evaluation index factors based on space demand, economy, energy efficiency and environment; Determine the type of units for each of several architecture types included in the dual-storage cooling and heating system, and calculate the energy consumption and initial investment for each type of unit; Calculating the evaluation index value of each evaluation index factor based on the energy consumption and initial investment of the unit selection corresponding to each architecture type; Constructing a hierarchical structure model based on the evaluation index factor, the plurality of architecture types and the optimal architecture type; Based on the hierarchical structure model, determining the combined weights of all the evaluation index factors by using the hierarchical analysis method and the entropy method respectively, and dynamically optimizing the combined weights to obtain the final weight of each evaluation index factor; The relative closeness of each of the architecture types is determined based on the final weight of each of the evaluation index factors and the TOPSIS comprehensive evaluation method, and the architecture type corresponding to the highest relative closeness is taken as the optimal architecture type.

2. The evaluation method for a cold and hot dual storage system architecture according to claim 1 is characterized in that: Based on the hierarchical structure model, the specific process of determining the combined weights of all the evaluation index factors in each of the architecture types by using the hierarchical analysis method and the entropy method is as follows: Calculating the weight of each evaluation index factor by using the hierarchical analysis method based on the hierarchical structure model, and using the weight that passes the consistency test as the first weight of the evaluation index factor; Calculating the weight of each evaluation index factor of each architecture type by an entropy method based on the hierarchical structure model to obtain a second weight of the evaluation index factor of each architecture type; The first weight and the second weight of each evaluation index factor are assigned corresponding linear weighting coefficients, thereby obtaining a combined weight of each evaluation index factor of each architecture type.

3. The evaluation method for a cold and hot dual storage system architecture according to claim 2, characterized in that: The specific process of calculating the weight of each evaluation index factor by the hierarchical structure model through the hierarchical analysis method and taking the weight that passes the consistency test as the first weight of the evaluation index factor is as follows: The "1-9 scale method" is used to assign values to the importance of each evaluation index factor and construct an index judgment matrix: Calculating the geometric mean of each column of the indicator judgment matrix, and normalizing the geometric mean to obtain the initial weight of each evaluation indicator factor; Calculating the maximum eigenvalue of the index judgment matrix based on the initial weight of each evaluation index factor; Calculate the actual consistency index based on the maximum characteristic root CI , and based on the random consistency index RI Calculate the consistency ratio CR , ; Determine whether the consistency ratio is less than 0.

1. If so, it is considered that the indicator judgment matrix passes the consistency test. The initial weight corresponding to the indicator judgment matrix is the first weight, wherein the first j The first weight of the evaluation index factor is expressed as ; Otherwise, return to the first step and readjust the indicator judgment matrix.

4. The evaluation method for a cold and hot dual storage system architecture according to claim 2, characterized in that: The specific process of calculating the weight of each evaluation index factor of each architecture type by the entropy method based on the hierarchical structure model to obtain the second weight of the evaluation index factor of each architecture type is as follows: Collect the values of each evaluation indicator for each architecture type to construct the original data matrix X， ,in, For the i The first architecture type j The evaluation index value, m is the number of architecture types, n is the number of evaluation index factors; The original data matrix is standardized to obtain a standardized index data matrix Y , ,in, For the i The first j The standardized value of the evaluation index factor; The second weight of each evaluation index factor is determined by entropy method based on the standardized index data matrix, wherein the j The second weight of the evaluation index factor The calculation formula is: ； ； ； ； in, For the j Under the evaluation index factor i The proportion of the standard value of each architecture type in the evaluation index factor; For the j The entropy value of the evaluation index factor; For the j Information redundancy of the evaluation index factors; For the j The entropy weight of the evaluation index factor, that is, j The second weight of the evaluation index factor.

5. The evaluation method for a cold and hot dual storage system architecture according to claim 1 is characterized in that: The combined weight is expressed as W , , , For the j The combined weight of the evaluation index factors; is the linear weighting coefficient, , For the j The first weight of the evaluation index factor, For the j The second weight of the evaluation index factor, n is the number of evaluation index factors.

6. The evaluation method for a cold and hot dual storage system architecture according to claim 5, characterized in that: The specific process of dynamically optimizing the combined weights to obtain the final weight of each evaluation index factor in each architecture type is as follows: Calculate the peak-valley electricity price ratio correction factor based on peak electricity price and peak-valley electricity price , and calculate the energy storage rate correction factor based on the energy storage device storage capacity and the total cumulative load on the design day ; Based on the peak-valley electricity price ratio correction factor The combined weight of the evaluation index factor for operating costs is adjusted, and at the same time, based on the energy storage rate correction factor Adjust the weight of the portfolio of initial investment for the evaluation index factor; Based on the adjusted weights of initial investment and operating costs, the weights of all evaluation index factors other than the initial investment and operating costs are calculated to obtain the final weight. The calculation formula of the final weight is: ； ； ； ； ； in, Represents the final weight of the i-th evaluation index factor; 、 are the weights of the adjusted initial investment and operating expenses respectively; 、 are the combined weights of initial investment and operating expenses before adjustment; is the peak-valley electricity price ratio correction factor adjustment coefficient; is the peak electricity price, in yuan / kWh; The off-peak electricity price is in yuan / kWh; is the adjustment coefficient of the energy storage rate correction factor; The energy stored in the energy storage device is in kWh; It is the total cumulative load of the design day, in kWh.

7. The evaluation method for a cold and hot dual storage system architecture according to claim 1, characterized in that: The specific process of determining the relative closeness of each architecture type based on the final weight of each evaluation index factor and the TOPSIS comprehensive evaluation method is as follows: Calculate the indicator weighting matrix based on the final weights Z , Z The calculation formula is: ； in, is the standardized indicator data matrix; For the j The final weight of the evaluation index factor; m is the number of architecture types; n is the number of evaluation index factors; Based on the indicator weighting matrix Z Calculate the positive and negative ideal distances for each of the architecture types using the following formula: ； ； ； ； in, For the j The evaluation index factor is a positive ideal solution; is a negative ideal solution; Indicates the i The positive ideal distance for each architecture type, Indicates the i Negative ideal distance for each architecture type, Indicates the first i The first structural type j The product of the evaluation index value of each evaluation index factor and the final weight; The relative closeness of each architecture type is calculated based on the positive and negative ideal distances of each architecture type. The calculation formula for the relative closeness of the i-th architecture type is: 。 8. An evaluation system for a cold and hot dual storage system architecture, used to execute the evaluation method for a cold and hot dual storage system architecture according to any one of claims 1 to 7, characterized in that: include: Evaluation index factor determination module, used to determine several evaluation index factors based on space demand, economy, energy efficiency, and environment; The selection module is used to determine the selection of various units of several architecture types included in the dual-storage cooling and heating system; A calculation module, used to calculate the energy consumption and initial investment of each type of unit selected; An evaluation index value calculation module is used to calculate the evaluation index value of each evaluation index factor based on the energy consumption and initial investment of the unit selection corresponding to each architecture type; A hierarchical structure model construction module, configured to construct a hierarchical structure model based on the evaluation index factors, the plurality of architecture types, and the optimal architecture type; A final weight calculation module is used to determine the combined weights of all the evaluation index factors based on the hierarchical structure model by using the hierarchical analysis method and the entropy method, and dynamically optimize the combined weights to obtain the final weight of each evaluation index factor; An evaluation module is used to determine the relative closeness of each of the architecture types based on the final weight of each of the evaluation index factors and the TOPSIS comprehensive evaluation method, and to take the architecture type corresponding to the highest relative closeness as the optimal architecture type.

9. An electronic device, characterized in that: The system comprises at least one processor and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a method for evaluating a cold and hot dual storage system architecture according to any one of claims 1 to 7.

10. A storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the evaluation method of a cold and hot dual storage system architecture according to any one of claims 1 to 7 is implemented.

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