Post-meter energy storage system optimization scheduling method integrating battery thermal management and building thermal inertia

Abstract

The invention discloses a post-meter energy storage system optimization scheduling method fusing battery thermal management and building thermal inertia, and the method comprises the following steps: designing a thermal management system, considering the thermal inertia characteristic of a building envelope, and further constructing a battery chamber thermal balance equation; establishing a battery thermal-electric coupling attenuation model: based on an Arrhenius equation and an electrochemical kinetics theory, establishing a battery capacity thermal-electric coupling attenuation model fusing the temperature, the discharge rate and the discharge depth; and constructing a post-table energy storage operation optimization model fusing battery thermal management and building thermal inertia. By dynamically adjusting the power of the air conditioning system and the rotating speed of the convection fan and fully utilizing the passive thermal inertia of the building envelope structure, the thermal management energy consumption is reduced to the greatest extent while the optimal working temperature of the battery is maintained, and the energy-saving benefit brought by the building envelope structure for the battery thermal management system is accurately quantified. The system operation cost is effectively reduced, and the service life of the battery is optimized.

Description

Technical Field

[0001] This invention relates to an optimized scheduling method for a post-meter energy storage system that integrates battery thermal management and building thermal inertia. Background Technology

[0002] Off-meter energy storage is an energy storage device installed inside a building, mainly used to achieve peak shaving and valley filling, reduce peak building load, provide flexible resources for users, thereby improving economic efficiency and energy self-sufficiency.

[0003] Studies have shown that batteries generate heat during charging and discharging, directly interacting with the building's internal thermal environment. Battery performance, lifespan, and economics are all affected by operating temperature, making an efficient Battery Management System (BTM) crucial. On the other hand, the thermal inertia of the building envelope, as a passive form of thermal energy storage, is equivalent to a flexible virtual energy storage element. By pre-regulating building temperature, it can enhance the building's peak-shaving capacity and operational flexibility. However, existing research often overlooks the energy consumption of the BTM itself and fails to fully explore the interaction potential between the BTM and the building's internal thermal environment. This makes it difficult for existing scheduling strategies to accurately assess the additional energy consumption brought by the thermal management system, leading to deviations in system operating economics. Furthermore, due to the lack of active utilization of building thermal inertia, the thermal management system can only passively respond to battery temperature rises, causing not only a surge in air conditioning load during peak hours but also difficulty in maintaining the optimal battery temperature under extreme conditions, thus accelerating battery capacity decay and severely shortening the lifespan of the energy storage system. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a simple and low-cost method for optimizing the scheduling of a post-meter energy storage system that integrates battery thermal management and building thermal inertia.

[0005] The technical solution of this invention to solve the above-mentioned technical problems is: an optimized scheduling method for a post-meter energy storage system that integrates battery thermal management and building thermal inertia, comprising the following steps:

[0006] Step S1: Design a thermal management system. Based on the thermal management system, consider the thermal inertia characteristics of the building envelope and further construct the thermal balance equation for the battery room.

[0007] Step S2: Establish a battery thermal-electric coupling decay model: Based on the Arrhenius equation and electrochemical kinetics theory, establish a battery capacity thermal-electric coupling decay model that integrates temperature, discharge rate and depth of discharge;

[0008] Step S3: Construct an optimized operation model for post-meter energy storage that integrates battery thermal management and building thermal inertia. Utilize the active temperature control capability of the central air conditioning system and the forced convection effect of the fans to maintain the battery room operating temperature within the optimal range. At the same time, explore the virtual energy storage characteristics of the building to implement summer pre-cooling and winter pre-heating, and formulate a post-meter energy storage scheduling strategy that takes into account both battery operating temperature and operational economy.

[0009] In the above-mentioned optimized scheduling method for post-meter energy storage systems that integrates battery thermal management and building thermal inertia, in step S1, the thermal management system includes an air conditioner and a fan, wherein the fan is configured at the front end of the axis of each battery unit, and the fan and the ventilation hole at the rear end of the axis of the battery unit together form a directional forced convection air duct.

[0010] The above-mentioned optimized scheduling method for post-meter energy storage systems that integrates battery thermal management and building thermal inertia, in step S1, the process of constructing the battery room thermal balance equation is as follows:

[0011] Step S11, construct the battery cell thermal balance equation: Assuming the battery generates heat through its core, resulting in a uniform internal temperature; the battery surface temperature is uniform; and thermal radiation during heat dissipation is negligible; then the battery cell thermal balance equation is:

[0012] ;

[0013] In the formula: for Time flows through the first The current of each battery cell; for Time of the first Internal resistance of each battery cell; The thickness of the battery cell; The thermal conductivity of the battery cell; To transfer heat conduction area; For convective heat dissipation area; The convective heat dissipation coefficient; for Time of the first The temperature of each battery cell; for Constant battery compartment temperature; For the first The thermal capacity of each battery cell;

[0014] Step S12, calculate the internal resistance of the battery cell: The internal resistance of the battery cell is an affine function of temperature, calculated using the following formula:

[0015] ;

[0016] In the formula: This represents the battery's internal resistance at standard temperature. is the temperature coefficient of resistance; is the standard temperature of the battery cell;

[0017] Convection heat dissipation coefficient and Fan speed at any time Linear dependence, expressed as follows:

[0018] ;

[0019] In the formula: This is the convection loss coefficient; This refers to the fan speed coefficient; for Fan speed at any given time; This refers to the fan speed under standard operating conditions.

[0020] The relationship between fan speed and power is as follows:

[0021] ;

[0022] In the formula: for The fan operating power at any given time; The rated power of the fan;

[0023] Step S13, construct the thermal balance equations for the battery chamber and its walls: The heat transfer is determined by the air conditioner's cooling / heating power, the heat dissipated from the battery unit into the fan-driven airflow through convection, and the heat transfer through the surrounding walls.

[0024] The thermal balance equation for the battery compartment is:

[0025] ;

[0026] The heat balance equation for the battery compartment walls is:

[0027] ;

[0028] In the formula: This refers to the number of battery modules; This refers to the number of battery cells contained in a battery module. Indicates the wall; The index number is for the battery compartment enclosure wall, with values ​​from 1 to 4, representing the four side walls of the battery compartment respectively; The outdoor ambient temperature at that moment. Thermal resistance of the battery compartment wall; The heat capacity of the air in the battery compartment; The heat capacity of the battery compartment wall; Representing the battery compartment wall Temperature at any given time; g is the index number of the battery compartment wall; represent The battery compartment is equipped with air conditioning for both cooling and heating.

[0029] ;

[0030] In the formula: Represents the energy efficiency coefficient of the battery compartment air conditioner. for The power of the air conditioning in the battery room at all times.

[0031] The above-mentioned optimized scheduling method for post-meter energy storage systems that integrates battery thermal management and building thermal inertia, specifically step S2, is as follows:

[0032] Step S21, Construct the capacity loss model: The capacity loss model is described by a semi-empirical model, and the battery degradation capacity is:

[0033] ;

[0034] In the formula: Battery calendar capacity loss; This refers to the settling time; , These are all empirical parameters of the model; It is the activation energy; It is the gas constant; This refers to the state of charge of the battery when it is at rest. Power-law factor;

[0035] Step S22, establish a cycle life loss model: taking operating temperature and discharge rate as influencing factors, establish the cycle life loss model as follows:

[0036] ;

[0037] In the formula: For energy storage systems Cycle capacity loss during each discharge; For energy storage systems Sub-discharge throughput; This is the highest temperature in the battery module; , , , , These are all empirical parameters of the model; This refers to the battery discharge rate.

[0038] Step S23, calculate total capacity loss: Considering calendar loss and cycle loss, calculate the total battery capacity loss on day n. The calculation formula is:

[0039] ;

[0040] In the formula: N represents the total number of discharges of the energy storage system up to day n.

[0041] The above-mentioned optimized scheduling method for post-meter energy storage systems that integrates battery thermal management and building thermal inertia includes the following constraints in step S2:

[0042] Energy storage system operating constraints:

[0043] ;

[0044] In the formula: for In the scene Real-time energy storage system capacity; for In the scene Energy storage system capacity; represent In the scene Discharge power of the energy storage system at any time; represent In the scene The charging power of the instantaneous energy storage system; Represents the sampling interval; The self-discharge rate of the energy storage system; To improve the operating efficiency of energy storage systems; , These are the upper and lower limits of the energy storage system capacity, respectively. , These are the upper and lower limits of the discharge power of the energy storage system, respectively. , These are the upper and lower limits of the charging power for the energy storage system; , These are the discharge status flag and the charging status flag, respectively. This indicates that the system is in a discharging state. This indicates that the system is in a charging state; This represents the capacity at the start of energy storage dispatch. This represents the capacity at the end of the energy storage dispatch process.

[0045] Air conditioning operation constraints:

[0046] ;

[0047] In the formula: , These are the upper and lower limits of the air conditioner's operating power, respectively.

[0048] Battery compartment thermal dynamics model:

[0049] ;

[0050] Battery cell temperature constraints:

[0051]

[0052] Battery compartment temperature constraints:

[0053] ;

[0054] In the formula: and These represent the lower and upper limits of the battery cell temperature, respectively; and These represent the lower and upper temperature limits of the battery compartment, respectively.

[0055] Power balance constraints:

[0056]

[0057] In the formula: This represents the base load of the building at time t in scenario ε; This represents the amount of electricity a building purchases from the grid at time t in scenario ε.

[0058] Tie line power constraints:

[0059] ;

[0060] In the formula: This represents the maximum interaction power between the power distribution network and the office building.

[0061] The above-mentioned optimized scheduling method for post-meter energy storage systems that integrates battery thermal management and building thermal inertia, in step S3, involves electricity purchase cost, thermal management cost, and battery aging cost in the post-meter energy storage operation optimization model, specifically:

[0062]

[0063]

[0064]

[0065]

[0066] In the formula: For total cost, To find the minimum value function, For electricity purchase costs; A set representing the scheduling period; This represents the total number of time periods in the optimized scheduling cycle; for Time-of-use electricity pricing; Costs associated with battery aging; for Scene Power purchased from the power grid at all times; For thermal management costs; The cost per unit capacity of energy storage batteries.

[0067] The beneficial effects of this invention are as follows: By dynamically adjusting the power of the air conditioning system and the speed of the convection fan, and making full use of the passive thermal inertia of the building envelope, this invention maintains the optimal operating temperature of the battery while minimizing thermal management energy consumption. By accurately quantifying the energy-saving benefits brought by the building envelope to the battery thermal management system, this invention effectively reduces system operating costs and optimizes battery life. Attached Figure Description

[0068] Figure 1 This is a flowchart of the present invention.

[0069] Figure 2 This is a schematic diagram of typical daily outdoor building temperatures in summer and winter.

[0070] Figure 3 This includes data on building air conditioning load output, building base load, and time-of-use electricity pricing for typical summer and winter days.

[0071] Figure 4 The graph shows the summer power versus SOC curves for the first scheme.

[0072] Figure 5 This is a summer battery compartment temperature curve using the first scheme.

[0073] Figure 6 The output curve of the summer thermal management system using the first scheme is shown.

[0074] Figure 7 The graph shows the winter power versus SOC curves for the first scheme.

[0075] Figure 8 This is a temperature curve of the battery compartment in winter when using the first scheme.

[0076] Figure 9 The output curve of the winter thermal management system using the first scheme is shown.

[0077] Figure 10 This is a schematic diagram illustrating the operating status of the battery's equivalent virtual energy storage indoors during a typical summer operating day.

[0078] Figure 11 This is a schematic diagram illustrating the operating status of the battery's equivalent virtual energy storage indoors during a typical winter operating day.

[0079] Figure 12 The graph shows the summer power versus SOC curves for the second scheme.

[0080] Figure 13 This is a summer battery compartment temperature curve using the second scheme.

[0081] Figure 14 The graph shows the winter power versus SOC curves for the second scheme.

[0082] Figure 15 This is a temperature curve of the battery compartment in winter when using the second scheme.

[0083] Figure 16 The graph shows the summer power versus SOC curves for the third scheme.

[0084] Figure 17 This is a summer battery compartment temperature curve using the third scheme.

[0085] Figure 18 The output curve of the summer thermal management system using the third scheme is shown.

[0086] Figure 19 The graph shows the winter power versus SOC curves for the third scheme.

[0087] Figure 20 This is a temperature curve of the battery compartment in winter when the third scheme is used.

[0088] Figure 21 The output curve of the winter thermal management system using the third scheme is shown. Detailed Implementation

[0089] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0090] like Figure 1 As shown, an optimized scheduling method for a post-meter energy storage system that integrates battery thermal management and building thermal inertia includes the following steps:

[0091] Step S1: Design a thermal management system. Based on the thermal management system, consider the thermal inertia characteristics of the building envelope and further construct the thermal balance equation for the battery room.

[0092] This embodiment assumes that the energy storage system is deployed in a dedicated battery room and equipped with a dedicated fire suppression system. A thermal management system is designed to maintain the battery cells within their optimal operating temperature range (25℃-45℃). The thermal management system includes air conditioning and fans. The fans are positioned at the front end of the axis of each battery cell, and the fans and the ventilation holes at the rear end of the battery cell axis together form a directional forced convection air duct. The control strategies of the thermal management system include: 1) Active temperature regulation: When the battery temperature rises, the corresponding fan will accelerate to enhance convective cooling. At the same time, the air conditioning will also adjust its output power to maintain a stable room temperature; 2) Passive energy saving: Utilizing the "virtual energy storage" characteristics formed by the building's thermal inertia, measures such as "pre-cooling" before the summer load peak and "pre-heating" before the winter heating peak are taken to reduce the peak energy consumption and operating costs of the air conditioning system.

[0093] The process of constructing the battery compartment thermal balance equation is as follows:

[0094] Step S11, construct the battery cell thermal balance equation: The heat generated during battery operation consists of irreversible Joule heating dominated by ohmic internal resistance and reversible heat sources caused by entropy change effect. Since reversible heat only has a significant impact at low SOC and low discharge rate, it is ignored in this invention. It is also assumed that the battery generates heat through its core, resulting in a uniform internal temperature; the battery surface temperature is uniform; and the amount of heat radiation during conduction heat dissipation is small and negligible. Therefore, the battery cell thermal balance equation is:

[0095] ;

[0096] In the formula: for Time flows through the first The current of each battery cell; for Time of the first Internal resistance of each battery cell; The thickness of the battery cell; The thermal conductivity of the battery cell; To transfer heat conduction area; For convective heat dissipation area; The convective heat dissipation coefficient; for Time of the first The temperature of each battery cell; for Constant battery compartment temperature; For the first The thermal capacity of each battery cell;

[0097] Step S12, calculate the internal resistance of the battery cell: The internal resistance of the battery cell is an affine function of temperature, calculated using the following formula:

[0098] ;

[0099] In the formula: This represents the battery's internal resistance at standard temperature. The temperature coefficient of resistance; Standard temperature for battery cells;

[0100] Convection heat dissipation coefficient Fan speed at time t Linear dependence, expressed as follows:

[0101] ;

[0102] In the formula: This is the convection loss coefficient; This refers to the fan speed coefficient; for Fan speed at any given time; This refers to the fan speed under standard operating conditions.

[0103] The relationship between fan speed and power is as follows:

[0104] ;

[0105] In the formula: for The fan operating power at any given time; The rated power of the fan;

[0106] Step S13, construct the thermal balance equations for the battery chamber and its walls: The heat transfer is determined by the air conditioner's cooling / heating power, the heat dissipated from the battery unit into the fan-driven airflow through convection, and the heat transfer through the surrounding walls.

[0107] The thermal balance equation for the battery compartment is:

[0108] ;

[0109] The heat balance equation for the battery compartment walls is:

[0110] ;

[0111] In the formula: This refers to the number of battery modules; This refers to the number of battery cells contained in a battery module. Indicates the wall; The index number is for the battery compartment enclosure wall, with values ​​from 1 to 4, representing the four side walls of the battery compartment respectively; The outdoor ambient temperature at that moment. Thermal resistance of the battery compartment wall; The heat capacity of the air in the battery compartment; The heat capacity of the battery compartment wall; Representing the battery compartment wall Temperature at any given time; g is the index number of the battery compartment wall, ranging from 1 to 4, representing the four side walls of the battery compartment respectively; represent The battery compartment is equipped with air conditioning for both cooling and heating.

[0112] ;

[0113] In the formula: Represents the energy efficiency coefficient of the battery compartment air conditioner. for The power of the air conditioning in the battery room at all times.

[0114] Step S2: Establish a battery thermal-electric coupling decay model: Based on the Arrhenius equation and electrochemical kinetics theory, establish a battery capacity thermal-electric coupling decay model that integrates temperature, discharge rate and discharge depth.

[0115] The specific process of step S2 is as follows:

[0116] Step S21, Construct the capacity loss model: The capacity loss model is described by a semi-empirical model, and the battery degradation capacity is:

[0117] ;

[0118] In the formula: Battery calendar capacity loss; This refers to the settling time; , These are all empirical parameters of the model; It is the activation energy; It is the gas constant; This refers to the state of charge of the battery when it is at rest. The exponential term is a power-law factor; The degradation mechanism is reflected by reactions such as SEI film thickening, while the remaining terms describe the temperature-accelerated aging behavior based on Arrhenius's law.

[0119] Step S22, establish a cycle life loss model: taking operating temperature and discharge rate as influencing factors, establish the cycle life loss model as follows:

[0120] ;

[0121] In the formula: For energy storage systems Cycle capacity loss during each discharge; For energy storage systems Sub-discharge throughput; This is the highest temperature in the battery module; , , , , These are all empirical parameters of the model; This refers to the battery discharge rate.

[0122] Step S23, calculate total capacity loss: Considering calendar loss and cycle loss, calculate the total battery capacity loss on day n. The calculation formula is:

[0123] ;

[0124] In the formula: As of the date Total number of discharges in the Tiancun energy storage system.

[0125] Includes the following constraints:

[0126] Energy storage system operating constraints:

[0127] ;

[0128] In the formula: for In the scene Real-time energy storage system capacity; for In the scene Energy storage system capacity; represent In the scene Discharge power of the energy storage system at any time; represent In the scene The charging power of the instantaneous energy storage system; Represents the sampling interval; The self-discharge rate of the energy storage system; To improve the operating efficiency of energy storage systems; , These are the upper and lower limits of the energy storage system capacity, respectively. , These are the upper and lower limits of the discharge power of the energy storage system, respectively. , These are the upper and lower limits of the charging power for the energy storage system; , These are the discharge status flag and the charging status flag, respectively. This indicates that the system is in a discharging state. This indicates that the system is in a charging state; This represents the capacity at the start of energy storage dispatch. This represents the capacity at the end of the energy storage dispatch process.

[0129] Air conditioning operation constraints:

[0130] ;

[0131] In the formula: , These are the upper and lower limits of the air conditioner's operating power, respectively.

[0132] Battery compartment thermal dynamics model:

[0133] ;

[0134] Battery cell temperature constraints:

[0135]

[0136] Battery compartment temperature constraints:

[0137] ;

[0138] In the formula: and These represent the lower and upper limits of the battery cell temperature, respectively; and These represent the lower and upper temperature limits of the battery compartment, respectively.

[0139] Power balance constraints:

[0140]

[0141] In the formula: This represents the base load of the building at time t in scenario ε; This represents the amount of electricity a building purchases from the grid at time t in scenario ε.

[0142] Tie line power constraints:

[0143] ;

[0144] In the formula: This represents the maximum interaction power between the power distribution network and the office building.

[0145] Step S3: Construct an optimized operation model for post-meter energy storage that integrates battery thermal management and building thermal inertia. Utilize the active temperature control capability of the central air conditioning system and the forced convection effect of the fans to maintain the battery room operating temperature within the optimal range. At the same time, explore the virtual energy storage characteristics of the building to implement summer pre-cooling and winter pre-heating, and formulate a post-meter energy storage scheduling strategy that takes into account both battery operating temperature and operational economy.

[0146] The post-meter energy storage operation optimization model involves electricity purchase cost, thermal management cost, and battery aging cost, specifically:

[0147]

[0148]

[0149]

[0150]

[0151] In the formula: For total cost, To find the minimum value function, For electricity purchase costs; The table represents the set of scheduling periods; T represents the total number of scheduling periods in the optimization cycle; for Time-of-use electricity pricing; Costs associated with battery aging; for Scene Power purchased from the power grid at all times; For thermal management costs; The cost per unit capacity of energy storage batteries.

[0152] Simulation examples:

[0153] This simulation uses a 2000-square-meter office building in Wuhan as a case study. The energy storage battery employs a four-level topology: 18 18650 cylindrical cells connected in parallel to form a battery unit; 20 battery units connected in series to form a battery module; 6 battery modules connected in series to form a battery string; and 10 battery strings connected in parallel to form a complete energy storage system. The energy storage battery capacity is 100 kWh.

[0154] A dedicated 15-square-meter space located in a well-ventilated corner on the first floor was selected as the energy storage battery room. The battery room is equipped with two air conditioning units and a wind-cooled heat dissipation system to maintain a stable indoor temperature. The building's indoor thermal comfort temperature range was set at 20℃ to 26℃. The RC thermal network model parameters for the battery room are shown in Table 1, the thermal model parameters for the energy storage battery are shown in Table 2, and the lifespan model parameters for the energy storage battery are shown in Table 3.

[0155]

[0156]

[0157]

[0158] To verify the effectiveness of this simulation model, the following three different cases were set up for simulation analysis:

[0159] The first solution: a scheduling strategy that integrates battery thermal management and building thermal inertia, i.e., the solution of this invention;

[0160] The second option: without a battery thermal management system;

[0161] The third option: disregard building thermal inertia.

[0162] Figure 2 Provides typical daily building outdoor temperatures for summer and winter. Figure 3 It provides data on building air conditioning load output, building base load, and time-of-use electricity prices for typical days in summer and winter.

[0163] Figures 4-9 The first scheme's summer and winter energy storage output curves and thermal management system operation status are shown: The typical daytime energy storage system in summer and winter adopts a "low-charge, high-discharge" strategy. Charging is concentrated during off-peak electricity price periods in the early morning, and during normal electricity price periods in the afternoon and evening, while discharging occurs during peak electricity price periods of 10:00-14:00 and 18:00-21:00. The battery temperature curve is explained below. Taking summer as an example, after continuous charging from 4:00-6:00, the battery temperature reaches a peak at 7:00; after continuous discharging from 10:00-14:00, a significant temperature rise is also observed, with the battery temperature reaching its maximum at 17:00. To maintain a constant indoor temperature, the fan increases its speed to promote heat dissipation, while the air conditioning cooling power is correspondingly increased. This synergistic mechanism not only ensures battery safety but also effectively delays battery calendar aging and cycle aging.

[0164] During typical winter days, the battery thermal management system exhibits similar operating trends, with battery temperatures rising between 10:00-14:00 (continuous discharge) and 14:00-16:00 (massive charging). During the nighttime hours of 00:00-07:00 and 22:00-24:00, due to the low outside temperature (approximately 3-5℃), the air conditioner is set to heating mode to maintain the battery room temperature above 20℃. During the evening peak discharge period of 18:00-21:00, a large amount of heat is generated inside the battery. As a result, the air conditioner switches to cooling mode from 17:00-19:00, with the fan speed increasing to dissipate excess heat in the room.

[0165] Figure 10 , Figure 11 The operation status of battery-equivalent virtual energy storage (VESS) during typical summer and winter operating days is shown respectively. The virtual energy storage system mainly participates in operation optimization through two strategies: one is to increase the air conditioning power to store energy for the building's virtual energy storage before the electricity price rises, thereby reducing the electricity cost during peak electricity price periods; the other is to directly reduce the air conditioning load during peak load periods at the expense of users' energy comfort.

[0166] On summer operating days, the virtual energy storage system begins pre-cooling at 6:00 AM during the low electricity price period. By increasing the air conditioning power, the indoor temperature of the battery room is reduced from 26°C to 23°C, with an instantaneous charging power of 1.1kW. The virtual energy storage capacity reaches its peak of 2.7kWh. When the room temperature rises back to 26°C at 4:00 PM, the energy stored in the virtual energy storage system is fully released.

[0167] On winter operating days, after the peak electricity price period begins at 10:00 AM, the virtual energy storage system employs a similar preheating strategy. From 10:00 AM to 4:00 PM, it stores the Joule heat generated by the batteries and the ambient heat gain, maintaining the battery compartment temperature above 20°C without requiring power from the thermal management system. Furthermore, during the evening peak discharge period from 6:00 PM to 9:00 PM, it releases heat to combat the cold load. Through this synergistic strategy of "preheating + waste heat utilization," the air conditioning heating power during peak hours is reduced by approximately 50%, significantly reducing thermal management costs while ensuring battery temperature.

[0168] To illustrate the effectiveness of the thermal management system proposed in this case, Figures 12-15 The output characteristic curves of the energy storage system in summer and winter are shown without considering the thermal management system. It is easy to see that, compared to the shallow charge-discharge operation mode of the first scheme, the energy storage system exhibits more aggressive high-rate charge-discharge characteristics on each typical day. The second scheme shows a significant increase in battery temperature, with the battery temperature climbing from a maximum of 35 degrees Celsius to 45 degrees Celsius on summer and winter operating days; the average temperature increase is 4.89 degrees Celsius in summer and 2.78 degrees Celsius in winter. Excessive temperature accelerates the thickening of the SEI film, further leading to battery degradation.

[0169] Figures 16-21The diagram illustrates the output characteristics of the energy storage system in summer and winter, without considering the building's thermal inertia. Without considering the virtual energy storage effect, the air conditioning unit only activates when the battery room temperature is about to reach its upper limit. At this time, the energy storage battery follows a "low charge, high discharge" principle, charging during the early morning when electricity prices are low, and discharging concentratedly during peak electricity price periods of 10:00-14:00 and 18:00-20:00. However, due to the lack of pre-stored heat in the building envelope, when the battery enters the high-power discharge phase at 10:00, the Joule heat inside the cell and the rising outdoor temperature at noon create a "thermal superposition" effect, resulting in a much higher rate of temperature rise than in the first scenario. At this time, the air conditioning system must simultaneously activate its powerful cooling mode during the peak electricity price period, and the fans must also increase their speed to suppress the temperature rise. This significantly increases the system's operating costs. On a typical winter day, from 00:00 to 07:00, the battery room air conditioning must start heating to maintain the battery room temperature and prevent a decrease in battery activity. Even during the discharge phase at 10:00 AM, although the heat generated during battery discharge raises the room temperature to some extent, the air conditioning system still needs to be turned on intermittently to combat the cooling load due to the lack of virtual energy storage. Throughout the entire operating cycle, battery thermal management costs increase significantly (9% increase in summer and 3% increase in winter).

[0170] Tables 4 and 5 present a cost comparison of the three different schemes under two typical daily scenarios. The first scheme effectively suppresses battery temperature rise through the battery thermal management system, significantly mitigating battery capacity degradation caused by high temperatures compared to the second scheme. This results in a 68.9% and 26.8% reduction in battery aging costs in summer and winter, respectively, greatly extending the lifespan of the energy storage equipment. Furthermore, compared to the third scheme, the first scheme's advantage lies in its use of a virtual energy storage pre-cooling / pre-heating mechanism to shift some of the peak load to off-peak periods. This not only reduces direct electricity purchase costs but also significantly reduces thermal management energy consumption costs by decreasing air conditioning cooling / heating demand. Among all schemes, the first scheme maintains the lowest daily total operating cost in both summer and winter (RMB 1174.1 and RMB 1095.6, respectively), fully validating the effectiveness of the model.

[0171]

[0172]

[0173] In summary, considering the post-meter energy storage operation strategy that integrates battery thermal management and building thermal inertia, and by constructing an active management approach combining air conditioning and fans while fully utilizing passive energy-saving methods within the building envelope, the shift from "passive management" to "active collaboration" in battery temperature control has been achieved. Simulation results confirm that this strategy successfully reduces battery lifespan loss, lowers system energy consumption, and effectively reduces peak load. This not only provides a highly valuable innovative path for energy conservation and consumption reduction on the low-voltage user side but also lays a solid theoretical and technical foundation for the deep integration and efficient interaction of future distributed energy systems and green buildings.

Claims

1. A method for optimizing the scheduling of a post-meter energy storage system that integrates battery thermal management and building thermal inertia, characterized in that, Includes the following steps: Step S1: Design a thermal management system. Based on the thermal management system, consider the thermal inertia characteristics of the building envelope and further construct the thermal balance equation for the battery room. Step S2: Establish a battery thermal-electric coupling decay model: Based on the Arrhenius equation and electrochemical kinetics theory, establish a battery capacity thermal-electric coupling decay model that integrates temperature, discharge rate and depth of discharge; Step S3: Construct an optimized operation model for post-meter energy storage that integrates battery thermal management and building thermal inertia. Utilize the active temperature control capability of the central air conditioning system and the forced convection effect of the fans to maintain the battery room operating temperature within the optimal range. At the same time, explore the virtual energy storage characteristics of the building to implement summer pre-cooling and winter pre-heating, and formulate a post-meter energy storage scheduling strategy that takes into account both battery operating temperature and operational economy.

2. The optimized scheduling method for a post-meter energy storage system integrating battery thermal management and building thermal inertia according to claim 1, characterized in that, In step S1, the thermal management system includes an air conditioner and a fan. The fan is positioned at the front end of the axis of each battery cell, and the fan and the ventilation holes at the rear end of the axis of the battery cell together form a directional forced convection air duct.

3. The optimized scheduling method for a post-meter energy storage system integrating battery thermal management and building thermal inertia according to claim 2, characterized in that, In step S1, the process of constructing the battery compartment thermal balance equation is as follows: Step S11, construct the battery cell thermal balance equation: Assuming the battery generates heat through its core, resulting in a uniform internal temperature; the battery surface temperature is uniform; and thermal radiation during heat dissipation is negligible; then the battery cell thermal balance equation is: ； In the formula: for Time flows through the first The current of each battery cell; for Time of the first Internal resistance of each battery cell; The thickness of the battery cell; The thermal conductivity of the battery cell; To transfer heat conduction area; For convective heat dissipation area; The convective heat dissipation coefficient; for Time of the first The temperature of each battery cell; for Constant battery compartment temperature; For the first The thermal capacity of each battery cell; Step S12, calculate the internal resistance of the battery cell: The internal resistance of the battery cell is an affine function of temperature, calculated using the following formula: ； In the formula: This represents the battery's internal resistance at standard temperature. The temperature coefficient of resistance; Standard temperature for battery cells; Convection heat dissipation coefficient and Fan speed at any time Linear dependence, expressed as follows: ； In the formula: This is the convection loss coefficient; This refers to the fan speed coefficient; for Fan speed at any given time; This refers to the fan speed under standard operating conditions. The relationship between fan speed and power is as follows: ； In the formula: for The fan operating power at any given time; The rated power of the fan; Step S13, construct the thermal balance equations for the battery chamber and its walls: The heat transfer is determined by the air conditioner's cooling / heating power, the heat dissipated from the battery unit into the fan-driven airflow through convection, and the heat transfer through the surrounding walls. The thermal balance equation for the battery compartment is: ； The heat balance equation for the battery compartment walls is: ； In the formula: This refers to the number of battery modules; This refers to the number of battery cells contained in a battery module. Indicates the wall; The index number is for the battery compartment enclosure wall, with values ​​from 1 to 4, representing the four side walls of the battery compartment respectively; The outdoor ambient temperature at that moment. Thermal resistance of the battery compartment wall; The heat capacity of the air in the battery compartment; The heat capacity of the battery compartment wall; Representing the battery compartment wall Temperature at any given time; g is the index number of the battery compartment wall; represent The battery compartment is equipped with air conditioning for both cooling and heating. ； In the formula: Represents the energy efficiency coefficient of the battery compartment air conditioner. for The power of the air conditioning in the battery room at all times.

4. The optimized scheduling method for a post-meter energy storage system integrating battery thermal management and building thermal inertia according to claim 3, characterized in that, The specific process of step S2 is as follows: Step S21, Construct the capacity loss model: The capacity loss model is described by a semi-empirical model, and the battery degradation capacity is: ； In the formula: Battery calendar capacity loss; This refers to the settling time; , These are all empirical parameters of the model; Activation energy; It is the gas constant; This refers to the state of charge of the battery when it is at rest. Power-law factor; Step S22, establish a cycle life loss model: taking operating temperature and discharge rate as influencing factors, establish the cycle life loss model as follows: ； In the formula: For energy storage systems Cycle capacity loss during each discharge; For energy storage systems Sub-discharge throughput; This is the highest temperature in the battery module; , , , , These are all empirical parameters of the model; This refers to the battery discharge rate. Step S23, calculate total capacity loss: considering calendar loss and cycle loss, the first Total capacity loss of the battery The calculation formula is: ； In the formula: N represents the number of digits up to the 1st digit. Total number of discharges in the Tiancun energy storage system.

5. The optimized scheduling method for a post-meter energy storage system integrating battery thermal management and building thermal inertia according to claim 4, characterized in that, Step S2 includes the following constraints: Energy storage system operating constraints: ； In the formula: for In the scene Real-time energy storage system capacity; for In the scene Energy storage system capacity; represent In the scene Discharge power of the energy storage system at any time; represent In the scene The charging power of the instantaneous energy storage system; Represents the sampling interval; The self-discharge rate of the energy storage system; To improve the operating efficiency of energy storage systems; , These are the upper and lower limits of the energy storage system capacity, respectively. , These are the upper and lower limits of the discharge power of the energy storage system, respectively. , These are the upper and lower limits of the charging power for the energy storage system; , These are the discharge status flag and the charging status flag, respectively. This indicates that the system is in a discharging state. This indicates that the system is in a charging state; This represents the capacity at the start of energy storage dispatch. This represents the capacity at the end of the energy storage dispatch process. Air conditioning operation constraints: ； In the formula: , These are the upper and lower limits of the air conditioner's operating power, respectively. Battery compartment thermal dynamics model: ； Battery cell temperature constraints: ; Battery compartment temperature constraints: ； In the formula: and These represent the lower and upper limits of the battery cell temperature, respectively; and These represent the lower and upper temperature limits of the battery compartment, respectively. Power balance constraints: ; In the formula: This represents the base load of the building at time t in scenario ε; This represents the amount of electricity a building purchases from the grid at time t in scenario ε. Tie line power constraints: ； In the formula: This represents the maximum interaction power between the power distribution network and the office building.

6. The optimized scheduling method for a post-meter energy storage system integrating battery thermal management and building thermal inertia according to claim 5, characterized in that, In step S3, the post-meter energy storage operation optimization model involves electricity purchase cost, thermal management cost, and battery aging cost, specifically: ； ； ； ； In the formula: For total cost, To find the minimum value function, For electricity purchase costs; A set representing the scheduling period; This represents the total number of time periods in the optimized scheduling cycle; for Time-of-use electricity pricing; Costs associated with battery aging; for Scene Power purchased from the power grid at all times; For thermal management costs; The cost per unit capacity of energy storage batteries.

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