A capacity design method for an energy storage air conditioning system covering the whole cycle

Abstract

This invention discloses a capacity design method for energy storage air conditioning systems covering the entire life cycle. The method includes constructing a daily weather-load coupled feature vector, inputting a Gaussian kernel-based probabilistic clustering model to identify operating scenarios, obtaining operating scenario features and typical hourly load curves, calculating the building's virtual energy storage capacity, calculating the weighted design load based on the operating scenario features and typical hourly load curves, calculating the initial physical energy storage volume based on the energy storage device's material parameters, matching the geometric features of the energy storage device and calculating the annual average heat loss rate, correcting the initial physical energy storage volume to obtain the corrected physical energy storage volume, constructing a multi-objective function for full-cycle energy storage cost and availability, and performing collaborative optimization to output the design capacity configuration. This method can balance construction costs, operating costs, and energy availability throughout the entire life cycle, outputting a capacity configuration scheme that meets the differentiated needs of users, providing a theoretical basis and practical engineering tool for the refined design of energy storage air conditioning systems.

Description

Technical Field

[0001] This invention relates to the field of energy-saving optimization technology for air conditioning systems, and in particular to a capacity design method for an energy storage air conditioning system covering the entire life cycle. Background Technology

[0002] Energy storage air conditioning systems achieve peak-shifting and valley-filling of power load by storing cold / heat during off-peak hours and releasing it during peak hours. This is an important technical means to reduce building operating energy consumption and alleviate the power grid's peak-hour supply pressure. Reasonable capacity configuration can not only significantly reduce users' operating electricity costs, but also reduce the installed capacity requirement of cooling units during the day. It has important engineering value and social significance for alleviating the peak load pressure of the power grid, improving the overall efficiency of the power system, and promoting the consumption of renewable energy.

[0003] Existing energy storage air conditioning system capacity design methods have significant technical limitations: First, traditional methods typically calculate based on static hourly load curves for summer design days, failing to consider the dynamic characteristics of meteorological-load coupling and unable to reflect the real-time coupling effect between the energy storage device and hourly ambient temperature changes throughout the year. Furthermore, existing technologies do not consider the "virtual energy storage" potential of building structures, leading to over-configuration of physical energy storage device capacity. Simultaneously, the capacity design of key equipment such as the refrigeration unit, energy storage device, and heat exchanger is relatively independent, lacking multi-objective collaborative optimization of life-cycle cost and system cooling availability, easily resulting in wasted initial investment or insufficient cooling reliability under extreme conditions. Therefore, this invention proposes a full-cycle energy storage air conditioning system capacity design method. This method extracts the weighted design load, introduces virtual energy storage in the building to reduce the weighted design load, calculates the annual dynamic heat loss rate to correct the initial physical energy storage volume, and finally constructs a multi-objective function for life-cycle energy storage cost and availability, collaboratively optimizing the output design capacity configuration. This method breaks through the limitations of traditional static design, realizes the synergistic utilization of virtual energy storage and physical energy storage, the refined correction of heat loss, and the overall consideration of economic efficiency and reliability throughout the entire life cycle, significantly improving the scientificity and accuracy of energy storage air conditioning system capacity design. Summary of the Invention

[0004] The purpose of this invention is to provide a capacity design method for an energy storage air conditioning system that covers the entire life cycle.

[0005] To achieve the above objectives, the present invention is implemented according to the following technical solution: This invention includes the following steps: The reference data of the building to be designed is obtained to construct a daily weather-load coupled feature vector. The input is a probabilistic clustering model based on Gaussian kernel to identify the operation scenario and obtain the operation scenario features and typical hourly load curves. The virtual energy storage capacity of the building is calculated based on the building information, and the weighted design load is calculated by combining the characteristics of the operating scenario and typical hourly load curves. The initial physical energy storage volume is calculated based on the weighted design load and the material parameters of the energy storage device. The geometric characteristics of the energy storage device are matched based on the initial physical energy storage volume. The annual average heat loss rate is calculated by combining the hourly meteorological environmental data throughout the year. The modified physical energy storage volume is obtained by correcting the initial physical energy storage volume based on the annual average heat loss rate. A fixed input set and decision variable set are determined, and a multi-objective function for the full-cycle energy storage cost and availability is constructed. The design capacity configuration is then output through collaborative optimization. The reference data includes hourly meteorological environmental data throughout the year, air conditioning load data, building information, and time-of-use electricity pricing structure; the building information includes thermal property parameters of the building envelope and geometric parameters of the indoor space. The operational scenario features include the prior probabilities, mean vectors, and covariance matrices of various scenarios; The building information includes interior space geometric parameters and building envelope thermal properties. The geometric features of the matching energy storage device include the surface area to volume ratio and the comprehensive heat transfer coefficient of the insulation layer; The fixed input set includes the modified physical energy storage capacity, weighted design load, and time-of-use pricing structure. The set of decision variables includes the capacity of the refrigeration unit, the area of ​​the heat exchanger, and the volume of the designed energy storage device.

[0006] Furthermore, the method for obtaining operational scenario characteristics and typical hourly load curves includes: The system acquires hourly meteorological environmental data, air conditioning load data, building information, and time-of-use electricity price structure for the building under design throughout the year. It then calculates the daily average outdoor temperature, daily temperature fluctuation standard deviation, daily average humidity, total solar radiation, daily cumulative cooling and heating load, and the ratio of daily peak load to average load to form a daily meteorological-load coupling feature vector. A dynamic scene clustering algorithm based on Gaussian kernels is used to classify the operating days throughout the year, and a joint probability density function of weather and load is constructed, the expression of which is: ; in Let the weather-load joint probability density function be... This represents the daily weather-load coupling characteristic vector. For the number of clustering scenarios, For clustering scenario indexing, for Prior probabilities in clustering scenarios express Feature vectors in clustering scenarios around Clustering scenario mean vector The discrete distribution pattern, for Covariance matrix in clustering scenarios; The daily meteorological-load coupled feature vector is input into the meteorological-load joint probability density function to identify the annual operation scenario, obtain the operation scenario features, and extract the typical hourly load curve corresponding to the scenario.

[0007] Furthermore, the method for calculating the weighted design load includes: Calculate the indoor space volume based on the building's interior space geometric parameters. Based on the thermal properties of the building envelope, the equivalent heat capacity and allowable temperature fluctuation range of the inner walls of various building envelopes are determined, and the adjustable width of the indoor temperature comfort zone is determined. The virtual energy storage capacity of a building is calculated using the following expression: ; in For the virtual energy storage capacity of buildings, Indoor air density, The specific heat capacity of air, For the number of building envelope types, for The equivalent heat capacity of the inner wall surface of the enclosure structure. for Permissible temperature fluctuation range of the inner wall surface of the enclosure structure; Based on the building's virtual energy storage capacity, the typical hourly load curve is pre-reduced to obtain the reduced typical load curve, which is then combined with the prior probabilities of each operating scenario. and base load refrigeration unit capacity The weighted design load covering the entire life cycle is calculated using the following expression: ; ; in For weighted design load, For the number of clustering scenarios, For clustering scenario indexing, For peak protection factor, This is the peak-shifting and valley-filling demand coefficient. This is a typical hourly load curve. This is a typical load curve after reduction. For time indexing, For virtual energy storage utilization efficiency, To allow for load transfer duration, This represents the virtual energy storage availability state coefficient.

[0008] Furthermore, the method for calculating the annual average heat loss rate includes: The initial solid energy storage volume is calculated based on the weighted design load and energy storage device material parameters. The overall heat transfer coefficient and shape factor of the insulation layer are then matched to this initial solid energy storage volume, and the surface area to volume ratio is calculated. The expression is as follows: ; ; ; in For the initial physical energy storage volume, For weighted design load, To improve the efficiency of physical energy storage utilization, The density of the cold storage medium, The specific heat capacity of the cold storage medium. For usable temperature difference, The return water temperature, The lowest usable temperature, For surface area, The shape factor, The ratio of surface area to volume; The total stored energy is decomposed into usable energy and residual energy. A time-varying heat loss coefficient function is constructed using hourly meteorological and environmental data throughout the year. The annual average heat loss rate is calculated based on the time-varying heat loss coefficient function, and the expression is as follows: ; ; in The time-varying heat loss coefficient function, The overall heat transfer coefficient of the insulation layer, For time step, This is the ambient temperature correction factor. for ambient temperature at all times Average ambient temperature throughout the year The temperature of the medium inside the energy storage device. The average heat loss rate throughout the year. for Energy storage is always available. for Always accumulating energy.

[0009] Furthermore, the method for configuring the output design capacity includes: The corrected physical storage volume is obtained by correcting the initial physical storage volume based on the annual average heat loss rate. The expression is as follows: ; That To correct the physical energy storage volume, For the initial physical energy storage volume, This represents the average annual heat loss rate. Using the modified physical energy storage volume, weighted design load, and time-of-use electricity price structure as fixed input sets, and the chiller capacity, heat exchanger area, and designed energy storage device volume as decision variables, a multi-objective function for full-cycle energy storage cost and availability is constructed, expressed as: ; ; ; ; in The multi-objective function is used to calculate the total lifecycle energy storage cost and availability. For total lifecycle cost, For estimated costs, To improve the availability of energy storage systems, , , These are the unit capacity cost of the refrigeration unit, the unit area cost of the heat exchanger, and the unit volume cost of the energy storage device. For fixed investments such as pipeline pump sets, The total capacity of the refrigeration unit. For heat exchanger area, For the final volume of the energy storage device, For the system's lifespan, For year indexing, The discount rate is... Annual operating costs, For the number of clustering scenarios, For clustering scenario indexing, for Prior probabilities in clustering scenarios for Scene Power is constantly drawn from the power grid. for Time-of-use electricity pricing For time step, , , They are respectively Real-time host cooling capacity, physical energy storage cooling capacity, and virtual energy storage cooling capacity; With the goal of minimizing the multi-objective function of full-cycle energy storage cost and availability, collaborative optimization is performed within a set of constraints to output the design capacity configuration. The set of constraints includes cooling capacity constraints, main unit output constraints, heat exchanger constraints, energy storage device volume constraints, and virtual energy storage constraints. The design capacity configuration includes the design energy storage device volume, the capacity of the main unit plus the refrigeration unit, and the design heat exchanger area. The design capacity configuration and reference data of the building to be designed are substituted into the simulation model for hourly operation throughout the year to verify whether the virtual-physical coordinated cooling capacity at each moment meets the cumulative demand of the typical hourly load curve. The actual cooling satisfaction rate is calculated, and the energy storage utilization efficiency is adjusted according to the actual cooling satisfaction rate. The design capacity configuration is recalculated until the actual cooling satisfaction rate meets the requirements, and the adjusted design capacity configuration is output. The energy storage utilization efficiency includes virtual energy storage utilization efficiency and physical energy storage utilization efficiency.

[0010] The beneficial effects of this invention are: This invention provides a capacity design method for an energy storage air conditioning system covering the entire life cycle. Compared with existing technologies, this invention has the following technical advantages: This invention constructs a daily weather-load coupled feature vector and identifies multiple typical operating scenarios based on a Gaussian kernel probabilistic clustering model. It also considers the prior probability of the scenarios to calculate a weighted design load, thereby achieving dynamic scenario-based design covering the entire cycle and improving the system's adaptability throughout the year. This invention quantifies the virtual energy storage capacity of building envelope and indoor air and participates in system peak regulation, realizing the synergistic utilization of virtual energy storage and physical energy storage devices, which can reduce investment and land area of ​​physical energy storage. This invention calculates the heat loss rate by matching the geometric features of the energy storage device with the comprehensive heat transfer coefficient of the insulation layer and matching the physical energy storage volume with the geometric features of the energy storage device. At the same time, it considers the differential heat loss contribution between the available energy storage capacity and the residual energy storage capacity, and accurately corrects the initial physical energy storage volume, thereby improving the accuracy of capacity design. This invention constructs a multi-objective function with the goals of minimizing the total life cycle cost and maximizing the availability of the energy storage system. Using the modified physical energy storage volume, weighted design load, and time-of-use electricity price structure as fixed inputs, it performs collaborative optimization and iterative solution on the capacity of the refrigeration unit, the area of ​​the heat exchanger, and the volume of the energy storage device. This overcomes the problems of local optima and equipment mismatch caused by traditional step-by-step independent design, and can achieve the optimal total life cycle cost, significantly improving the economy and operational reliability of the energy storage air conditioning system. Attached Figure Description

[0011] Figure 1 This is a flowchart illustrating the steps of a capacity design method for an energy storage air conditioning system covering the entire lifecycle, as described in this invention. Detailed Implementation

[0012] The present invention will be further described below through specific embodiments. The illustrative embodiments and descriptions herein are used to explain the present invention, but are not intended to limit the present invention.

[0013] The present invention provides a capacity design method for an energy storage air conditioning system covering the entire life cycle, comprising the following steps: like Figure 1As shown, this embodiment includes the following steps: The reference data of the building to be designed is obtained to construct a daily weather-load coupled feature vector. The input is a probabilistic clustering model based on Gaussian kernel to identify the operation scenario and obtain the operation scenario features and typical hourly load curves. The virtual energy storage capacity of the building is calculated based on the building information, and the weighted design load is calculated by combining the characteristics of the operating scenario and typical hourly load curves. The initial physical energy storage volume is calculated based on the weighted design load and the material parameters of the energy storage device. The geometric characteristics of the energy storage device are matched based on the initial physical energy storage volume. The annual average heat loss rate is calculated by combining the hourly meteorological environmental data throughout the year. The modified physical energy storage volume is obtained by correcting the initial physical energy storage volume based on the annual average heat loss rate. A fixed input set and decision variable set are determined, and a multi-objective function for the full-cycle energy storage cost and availability is constructed. The design capacity configuration is then output through collaborative optimization. The reference data includes hourly meteorological environmental data throughout the year, air conditioning load data, building information, and time-of-use electricity pricing structure; the building information includes thermal property parameters of the building envelope and geometric parameters of the indoor space. The operational scenario features include the prior probabilities, mean vectors, and covariance matrices of various scenarios; The building information includes interior space geometric parameters and building envelope thermal properties. The geometric features of the matching energy storage device include the surface area to volume ratio and the comprehensive heat transfer coefficient of the insulation layer; The fixed input set includes the modified physical energy storage capacity, weighted design load, and time-of-use pricing structure. The set of decision variables includes the capacity of the refrigeration unit, the area of ​​the heat exchanger, and the volume of the designed energy storage device.

[0014] In this embodiment, the method for obtaining operational scenario characteristics and typical hourly load curves includes: The system acquires hourly meteorological environmental data, air conditioning load data, building information, and time-of-use electricity price structure for the building under design throughout the year. It then calculates the daily average outdoor temperature, daily temperature fluctuation standard deviation, daily average humidity, total solar radiation, daily cumulative cooling and heating load, and the ratio of daily peak load to average load to form a daily meteorological-load coupling feature vector. A dynamic scene clustering algorithm based on Gaussian kernels is used to classify the operating days throughout the year, and a joint probability density function of weather and load is constructed, the expression of which is: ; in Let the weather-load joint probability density function be... This represents the daily weather-load coupling characteristic vector. For the number of clustering scenarios, For clustering scenario indexing, for Prior probabilities in clustering scenarios express Feature vectors in clustering scenarios around Clustering scenario mean vector The discrete distribution pattern, for Covariance matrix in clustering scenarios; The daily meteorological-load coupled feature vector is input into the meteorological-load joint probability density function to identify the annual operation scenario, obtain the operation scenario features, and extract the typical hourly load curves corresponding to the scenario. In actual assessments, operating scenarios include extremely hot high load, hot medium load, transitional season, cold high load, and standard operating conditions, with prior probabilities. It is determined by the percentage of days in the year.

[0015] In this embodiment, the method for calculating the weighted design load includes: Calculate the indoor space volume based on the building's interior space geometric parameters. Based on the thermal properties of the building envelope, the equivalent heat capacity and allowable temperature fluctuation range of the inner walls of various building envelopes are determined, and the adjustable width of the indoor temperature comfort zone is determined. The virtual energy storage capacity of a building is calculated using the following expression: ; in For the virtual energy storage capacity of buildings, Indoor air density, The specific heat capacity of air, For the number of building envelope types, for The equivalent heat capacity of the inner wall surface of the enclosure structure. for Permissible temperature fluctuation range of the inner wall surface of the enclosure structure; Based on the building's virtual energy storage capacity, the typical hourly load curve is pre-reduced to obtain the reduced typical load curve, which is then combined with the prior probabilities of each operating scenario. and base load refrigeration unit capacity The weighted design load covering the entire life cycle is calculated using the following expression: ; ; in For weighted design load, For the number of clustering scenarios, For clustering scenario indexing, For peak protection factor, This is the peak-shifting and valley-filling demand coefficient. This is a typical hourly load curve. This is a typical load curve after reduction. For time indexing, For virtual energy storage utilization efficiency, To allow for load transfer duration, This represents the virtual energy storage availability state coefficient. In practical assessments, virtual energy storage capacity characterizes the thermal reserve capacity of the building envelope and indoor air. The permissible temperature fluctuation range of the building envelope's inner wall surface is determined by the dual constraints of material thermal safety (including thermal stress limits and indoor air dew point limits) and human thermal comfort. The equivalent heat capacity of the inner wall surface of the building envelope, i.e., the effective heat capacity of the inner surface layer of the building envelope (exterior walls, roof, floors, etc.) participating in short-term heat exchange, characterizes the buffering capacity of the building envelope to indoor temperature fluctuations. Its expression is: ; in for Internal surface area of ​​the enclosure structure For effective heat storage layer assembly, For effective heat storage layer indexing, , , for Envelope Layer material density, specific heat capacity, and thickness For the effective heat storage coefficient (usually taken as 0.6-0.8), considering the insufficient effective heat storage depth due to temperature wave attenuation during unsteady heat transfer; in engineering, the value is taken from a table according to the type of building envelope: for a 240mm solid brick wall, the value is... kJ / (m 2 ·K), 200mm reinforced concrete wall kJ / (m 2 ·K), 100mm lightweight partition wall kJ / (m 2 ·K); Virtual energy storage available state coefficient The proportion of remaining usable capacity of a building's virtual energy storage system to its total capacity reflects the current state of charge of the thermal cells, and is expressed as: ; The virtual energy storage available state coefficient ranges from (0,1). During the closed-loop verification phase, if the virtual energy storage available state coefficient... If frequent zeroing causes the availability rate to fall below the target, feedback will be sent to S2 to adjust the virtual energy storage utilization efficiency. Or adjustable width for indoor temperature comfort zone The value of (relaxing the comfort zone or reducing the utilization efficiency assumption).

[0016] In this embodiment, the method for calculating the annual average heat loss rate includes: The initial solid energy storage volume is calculated based on the weighted design load and energy storage device material parameters. The overall heat transfer coefficient and shape factor of the insulation layer are then matched to this initial solid energy storage volume, and the surface area to volume ratio is calculated. The expression is as follows: ; ; ; in For the initial physical energy storage volume, For weighted design load, To improve the efficiency of physical energy storage utilization, The density of the cold storage medium, The specific heat capacity of the cold storage medium. For usable temperature difference, The return water temperature, The lowest usable temperature, For surface area, The shape factor, The ratio of surface area to volume; The total stored energy is decomposed into usable energy and residual energy. A time-varying heat loss coefficient function is constructed using hourly meteorological and environmental data throughout the year. The annual average heat loss rate is calculated based on the time-varying heat loss coefficient function, and the expression is as follows: ; ; in The time-varying heat loss coefficient function, The overall heat transfer coefficient of the insulation layer, For time step, This is the ambient temperature correction factor. for ambient temperature at all times Average ambient temperature throughout the year The temperature of the medium inside the energy storage device. The average heat loss rate throughout the year. for Energy storage is always available. for Always accumulating energy; In practical evaluation, when the energy storage device is a cylindrical energy storage tank, the shape factor is... When the energy storage device is a rectangular energy storage tank, the shape factor is... , , , The dimensions are length, width, and height, respectively. The overall heat transfer coefficient of the insulation layer is calculated based on the thermal conductivity and thickness of the insulation material. The total stored energy can be decomposed as follows: ; Among them, energy storage is available. (Using water-based cooling) indicates a temperature higher than the minimum usable temperature. The effective cooling capacity can be released to the air conditioning system through a heat exchanger. As residual energy, The temperature of the medium inside the energy storage device represents the dead zone energy between the temperature of the medium inside the energy storage device and the lowest usable temperature (which cannot be effectively extracted by the heat exchanger due to insufficient heat transfer temperature difference, but is still stored in the medium in the form of sensible heat). The annual average heat loss rate is calculated hourly throughout the year.

[0017] In this embodiment, the method for configuring the output design capacity includes: The corrected physical storage volume is obtained by correcting the initial physical storage volume based on the annual average heat loss rate. The expression is as follows: ; That To correct the physical energy storage volume, For the initial physical energy storage volume, This represents the average annual heat loss rate. Using the modified physical energy storage volume, weighted design load, and time-of-use electricity price structure as fixed input sets, and the chiller capacity, heat exchanger area, and designed energy storage device volume as decision variables, a multi-objective function for full-cycle energy storage cost and availability is constructed, expressed as: ; ; ; ; in The multi-objective function is used to calculate the total lifecycle energy storage cost and availability. For total lifecycle cost, For estimated costs, To improve the availability of energy storage systems, , , These are the unit capacity cost of the refrigeration unit, the unit area cost of the heat exchanger, and the unit volume cost of the energy storage device. For fixed investments such as pipeline pump sets, The total capacity of the refrigeration unit. For heat exchanger area, For the final volume of the energy storage device, For the system's lifespan, For year indexing, The discount rate is... Annual operating costs, For the number of clustering scenarios, For clustering scenario indexing, for Prior probabilities in clustering scenarios for Scene Power is constantly drawn from the power grid. for Time-of-use electricity pricing For time step, , , They are respectively Real-time host cooling capacity, physical energy storage cooling capacity, and virtual energy storage cooling capacity; With the goal of minimizing the multi-objective function of full-cycle energy storage cost and availability, collaborative optimization is performed within a set of constraints to output the design capacity configuration. The set of constraints includes cooling capacity constraints, main unit output constraints, heat exchanger constraints, energy storage device volume constraints, and virtual energy storage constraints. The design capacity configuration includes the design energy storage device volume, the capacity of the main unit plus the refrigeration unit, and the design heat exchanger area. The design capacity configuration and reference data of the building to be designed are substituted into the simulation model for hourly operation throughout the year to verify whether the virtual-physical coordinated cooling capacity at each moment meets the cumulative demand of the typical hourly load curve. The actual cooling satisfaction rate is calculated, and the energy storage utilization efficiency is adjusted according to the actual cooling satisfaction rate. The design capacity configuration is recalculated until the actual cooling satisfaction rate meets the requirements, and the adjusted design capacity configuration is output. The energy storage utilization efficiency includes virtual energy storage utilization efficiency and physical energy storage utilization efficiency. In actual evaluation, during collaborative optimization, the initial condition is that the modified physical energy storage volume is equal to the designed energy storage device volume, and the refrigeration unit capacity and heat exchanger area are taken as the historical design average of similar buildings; the total capacity of the refrigeration unit includes the capacity of the refrigeration unit added to the refrigeration unit and the base load refrigeration unit capacity. The set of constraints includes cooling capacity constraints ( ,in To allow for cooling deficit), main unit output constraints ( ), heat exchanger constraints ( ,in (Logarithmic mean temperature difference), energy storage device volume constraint ( ,in The safety margin coefficient is set at 0.05-0.1, and the virtual energy storage constraint is set (the daily virtual energy storage call shall not exceed the capacity limit). Substitute the design capacity configuration and reference data of the building to be designed into the simulation model for hourly operation throughout the year. Based on the building's virtual energy storage capacity and the final volume of the energy storage device, verify whether the virtual-physical coordinated cooling capacity at each moment meets the cumulative demand of the typical hourly load curve (whether the sum of the physical energy storage cooling capacity and the virtual energy storage cooling capacity is greater than the typical hourly load curve). Calculate the actual availability rate. If it is less than the preset availability rate threshold, feed back to step S2 to adjust the virtual energy storage utilization efficiency or step S3 to adjust the physical energy storage utilization efficiency. Repeat the subsequent steps until the availability rate requirement is met, forming a data closed loop, and output the adjusted design capacity configuration. Taking an 8-story office building in East China as an example, with a building area of ​​10,000 square meters... 2 The air-conditioned area is 8000m² 2 The floor height is 3.0m, and a water storage cooling system (storage temperature difference 7℃) is adopted. After S1 step identification, the annual operation cluster is as follows: (1) Extremely hot high load scenario, prior probability 0.6, typical daily peak load 900kW, daily cumulative cooling load 5500kWh; (2) Hot medium load scenario, prior probability 0.4, typical daily peak load 600kW, daily cumulative cooling load 3600kWh; According to building information (effective volume 2400m²) 3 Indoor air density: 1.2 kg / m³ 3 The calculated virtual energy storage capacity is 539.5 kWh, assuming an air specific heat capacity of 1.005 kJ / (kg·pK), an adjustable indoor temperature comfort zone width of 2.5℃, and an enclosure structure including exterior walls and roof. With peak load protection factor and peak shifting demand factor both set to 1.0, virtual energy storage utilization efficiency set to 0.85, allowable load transfer duration set to 3 hours, state factor set to 0.9, and base load chiller capacity set to 400 kW, a typical hourly load curve is pre-reduced and the weighted design load covering the entire cycle is calculated to be 1268.4 kWh. The physical energy storage utilization efficiency is 0.88, and the density of the cold storage medium is 1000 kg / m³. 3 The specific heat capacity of the cold storage medium is 1.161 kWh / (m³). 3 With a usable temperature difference of 7℃ (K), the initial physical energy storage volume is calculated to be 137.0 m³. 3 Geometric matching was performed: the energy storage device is a cylindrical closed energy storage tank, with a calculated shape factor of 4.836 and a surface area of ​​124.6 m². 2 The surface area to volume ratio is 0.908 m². -1 The overall heat transfer coefficient of the insulation layer is 0.24 W / (m²). 2 ·K); Using a time step of 1 hour, an ambient temperature correction factor of 0.18, an annual average ambient temperature of 18℃, and an internal medium temperature of 5℃, the annual average heat loss rate is calculated to be 1.08% based on the weighted ambient temperature distribution for both scenarios, and the physical energy storage volume is corrected to 138.7 m³. 3 ; Input fixed set: Corrected physical energy storage volume 138.7m³ 3 The weighted design load is 1268.4 kWh, and the time-of-use electricity pricing structure (peak / flat / valley prices are RMB 1.0 / kWh, RMB 0.6 / kWh, and RMB 0.3 / kWh, respectively). Initial values ​​of decision variables (historical averages of similar buildings): total chiller capacity 950 kW (base load 400 kW + peak load 550 kW), heat exchanger area 80 m². 2 The designed energy storage device has a volume of 138.7 m³. 3 Based on the multi-objective function of full-cycle energy storage cost and availability, collaborative optimization is performed within the constraint set. When the multi-objective function is minimized, the preliminary design capacity configuration is output: the designed energy storage device volume is 146m³. 3 The refrigeration unit has a capacity of 480kW and a designed heat exchanger area of ​​75m². 2 ; Substituting the preliminary design capacity configuration into the year-round hourly simulation, under extremely hot and high-load scenarios, the actual cooling satisfaction rate was 93% during certain periods (peak load), failing to meet the 95% cooling satisfaction rate threshold. Under moderately hot and medium-load scenarios, the actual cooling satisfaction rate was 100% for all periods. The virtual energy storage utilization efficiency was reduced to 0.70 (reducing thermal inertia dependence and increasing physical energy storage redundancy). Steps S2-S4 were then re-executed to obtain the optimized design capacity configuration: a design energy storage device volume of 159m³. 3 The refrigeration unit has a capacity of 520kW and a designed heat exchanger area of ​​79m². 2 The actual cooling supply satisfaction rate for the extremely hot high-load scenario and the hot medium-load scenario was 96.9% and 100% respectively, which met the design requirements and output the corresponding design capacity configuration.

[0018] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A capacity design method for an energy storage air conditioning system covering the entire life cycle, characterized in that, Includes the following steps: S1. Obtain reference data of the building to be designed to construct a daily weather-load coupling feature vector, input it into a probabilistic clustering model based on Gaussian kernel to identify the operation scenario, and obtain the operation scenario features and typical hourly load curves; S2. Calculate the building's virtual energy storage capacity based on building information, and calculate the weighted design load by combining the characteristics of the operating scenario and typical hourly load curves; S3. Calculate the initial physical energy storage volume based on the weighted design load and energy storage device material parameters, match the geometric characteristics of the energy storage device based on the initial physical energy storage volume, and calculate the annual average heat loss rate in combination with the hourly meteorological environmental data throughout the year. S4. Based on the annual average heat loss rate, the initial physical energy storage volume is corrected to obtain the corrected physical energy storage volume. The fixed input set and decision variable set are determined, and a multi-objective function of full-cycle energy storage cost and availability is constructed. The design capacity configuration is then output through collaborative optimization. The reference data includes hourly meteorological environmental data throughout the year, air conditioning load data, building information, and time-of-use electricity pricing structure; the building information includes thermal property parameters of the building envelope and geometric parameters of the indoor space. The operational scenario features include the prior probabilities, mean vectors, and covariance matrices of various scenarios; The building information includes interior space geometric parameters and building envelope thermal properties. The geometric features of the matching energy storage device include the surface area to volume ratio and the comprehensive heat transfer coefficient of the insulation layer; The fixed input set includes the modified physical energy storage capacity, weighted design load, and time-of-use pricing structure. The set of decision variables includes the capacity of the refrigeration unit, the area of ​​the heat exchanger, and the volume of the designed energy storage device.

2. The capacity design method for an energy storage air conditioning system covering the entire life cycle according to claim 1, characterized in that, The method for obtaining operational scenario characteristics and typical hourly load curves includes: The system acquires hourly meteorological environmental data, air conditioning load data, building information, and time-of-use electricity price structure for the building under design throughout the year. It then calculates the daily average outdoor temperature, daily temperature fluctuation standard deviation, daily average humidity, total solar radiation, daily cumulative cooling and heating load, and the ratio of daily peak load to average load to form a daily meteorological-load coupling feature vector. A dynamic scene clustering algorithm based on Gaussian kernels is used to classify the operating days throughout the year, and a joint probability density function of weather and load is constructed, the expression of which is: ； in Let the weather-load joint probability density function be... This represents the daily weather-load coupling characteristic vector. For the number of clustering scenarios, For clustering scenario indexing, for Prior probabilities in clustering scenarios express Feature vectors in clustering scenarios around Clustering scenario mean vector The discrete distribution pattern, for Covariance matrix in clustering scenarios; The daily meteorological-load coupled feature vector is input into the meteorological-load joint probability density function to identify the annual operation scenario, obtain the operation scenario features, and extract the typical hourly load curves corresponding to the scenario.

3. The capacity design method for an energy storage air conditioning system covering the entire life cycle according to claim 1, characterized in that, The method for calculating the weighted design load includes: Calculate the indoor space volume based on the building's interior space geometric parameters. Based on the thermal properties of the building envelope, the equivalent heat capacity and allowable temperature fluctuation range of the inner walls of various building envelopes are determined, and the adjustable width of the indoor temperature comfort zone is determined. The virtual energy storage capacity of a building is calculated using the following expression: ； in For the virtual energy storage capacity of buildings, Indoor air density, The specific heat capacity of air, For the number of building envelope types, for The equivalent heat capacity of the inner wall surface of the enclosure structure. for Permissible temperature fluctuation range of the inner wall surface of the enclosure structure; Based on the building's virtual energy storage capacity, the typical hourly load curve is pre-reduced to obtain the reduced typical load curve, which is then combined with the prior probabilities of each operating scenario. and base load refrigeration unit capacity The weighted design load covering the entire life cycle is calculated using the following expression: ； ； in For weighted design load, For the number of clustering scenarios, For clustering scenario indexing, For peak protection factor, This is the peak-shifting and valley-filling demand coefficient. This is a typical hourly load curve. This is a typical load curve after reduction. For time indexing, For virtual energy storage utilization efficiency, To allow for load transfer duration, This represents the virtual energy storage availability state coefficient.

4. The capacity design method for an energy storage air conditioning system covering the entire life cycle according to claim 1, characterized in that, The method for calculating the annual average heat loss rate includes: The initial solid energy storage volume is calculated based on the weighted design load and energy storage device material parameters. The overall heat transfer coefficient and shape factor of the insulation layer are then matched to this initial solid energy storage volume, and the surface area to volume ratio is calculated. The expression is as follows: ； ； ； in For the initial physical energy storage volume, For weighted design load, To improve the efficiency of physical energy storage utilization, The density of the cold storage medium, The specific heat capacity of the cold storage medium. For usable temperature difference, The return water temperature, The lowest usable temperature, For surface area, The shape factor, The ratio of surface area to volume; The total stored energy is decomposed into usable energy and residual energy. A time-varying heat loss coefficient function is constructed using hourly meteorological and environmental data throughout the year. The annual average heat loss rate is calculated based on the time-varying heat loss coefficient function, and the expression is as follows: ； ； in The time-varying heat loss coefficient function, The overall heat transfer coefficient of the insulation layer, For time step, This is the ambient temperature correction factor. for ambient temperature at all times Average ambient temperature throughout the year The temperature of the medium inside the energy storage device. The average heat loss rate throughout the year. for Energy storage is always available. for Always accumulating energy.

5. The capacity design method for an energy storage air conditioning system covering the entire life cycle according to claim 1, characterized in that, The method for configuring the output design capacity includes: The corrected physical storage volume is obtained by correcting the initial physical storage volume based on the annual average heat loss rate. The expression is as follows: ； That To correct the physical energy storage volume, For the initial physical energy storage volume, This represents the average annual heat loss rate. Using the modified physical energy storage volume, weighted design load, and time-of-use electricity price structure as fixed input sets, and the chiller capacity, heat exchanger area, and designed energy storage device volume as decision variables, a multi-objective function for full-cycle energy storage cost and availability is constructed, expressed as: ； ； ； ； in The multi-objective function is used to calculate the total lifecycle energy storage cost and availability. For total lifecycle cost, For estimated costs, For the availability of energy storage systems, , , These are the unit capacity cost of the refrigeration unit, the unit area cost of the heat exchanger, and the unit volume cost of the energy storage device, respectively. For fixed investments such as pipeline pump sets, The total capacity of the refrigeration unit. For heat exchanger area, The final volume of the energy storage device, For the system's lifespan, For year indexing, The discount rate is... Annual operating costs, For the number of clustering scenarios, For clustering scenario indexing, for Prior probabilities in clustering scenarios for Scene Power is constantly drawn from the power grid. for Time-of-use electricity pricing For time step, , , They are respectively Real-time host cooling capacity, physical energy storage cooling capacity, and virtual energy storage cooling capacity; With the goal of minimizing the multi-objective function of full-cycle energy storage cost and availability, collaborative optimization is performed within a set of constraints to output the design capacity configuration. The set of constraints includes cooling capacity constraints, main unit output constraints, heat exchanger constraints, energy storage device volume constraints, and virtual energy storage constraints. The design capacity configuration includes the design energy storage device volume, the capacity of the main unit plus the refrigeration unit, and the design heat exchanger area. The design capacity configuration and reference data of the building to be designed are substituted into the simulation model for hourly operation throughout the year to verify whether the virtual-physical coordinated cooling capacity at each moment meets the cumulative demand of the typical hourly load curve. The actual cooling satisfaction rate is calculated, and the energy storage utilization efficiency is adjusted according to the actual cooling satisfaction rate. The design capacity configuration is recalculated until the actual cooling satisfaction rate meets the requirements, and the adjusted design capacity configuration is output. The energy storage utilization efficiency includes virtual energy storage utilization efficiency and physical energy storage utilization efficiency.

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