Optimization method for thermal energy storage in regional integrated energy systems that balance resilience and reliability

Abstract

This invention relates to a method for optimizing thermal energy storage in a regional integrated energy system that balances resilience and reliability. The method includes: S1, collecting relevant data of the regional integrated energy system; S2, modeling each device and component within the regional integrated energy system, establishing an energy flow diagram, constraining energy flow balance, and establishing an optimized scheduling model for the regional integrated energy system; S3, calculating the minimum reserve thermal energy storage under source-end interruption conditions using a rolling optimization algorithm; and S4, substituting the minimum reserve thermal energy storage as a constraint into the optimized scheduling model of the regional integrated energy system to obtain the hourly heat storage capacity during actual operation. Compared with existing technologies, this invention optimizes thermal energy storage by balancing resilience and reliability, and is more reasonable and accurate.

Description

Technical Field

[0001] This invention relates to the technical field of regional integrated energy system applications, and in particular to an optimized method for thermal energy storage in regional integrated energy systems that balances resilience and reliability. Background Technology

[0002] In recent years, severe environmental pollution and climate change have increasingly drawn public attention. To address these issues, the government encourages the use of environmentally friendly zero-carbon energy sources, such as solar and wind power. However, due to the intermittent and unstable nature of renewable energy, traditional fossil fuels cannot be completely replaced. Compared to coal, which is currently China's primary energy source, natural gas is cleaner and will be strongly promoted in the coming years.

[0003] However, it is undeniable that power or gas supply interruptions caused by extreme events are often unpredictable and can lead to significant losses for energy users. Regional integrated energy systems can effectively mitigate this problem. By integrating different energy types as substitutes for others, regional integrated energy systems can significantly improve energy efficiency and supply flexibility through the conversion and integration of different energy types. When one system fails, another will be used as a replacement. On the other hand, energy storage systems also play a crucial role in responding to emergency power outages. Furthermore, they can help resolve supply and demand mismatches and fully utilize peak and off-peak energy prices, reducing total energy costs. Therefore, energy storage systems are considered an important component of regional integrated energy systems. In particular, it is necessary to optimize the design parameters of energy storage systems, such as backup capacity, from both technical and economic perspectives. A well-designed system can significantly improve its resilience and reliability, avoiding or reducing energy supply interruptions under extreme events. Currently, electrical storage is costly and has a limited lifespan. Therefore, using ice storage and thermal storage technologies to store energy as heat can effectively reduce losses caused by external energy interruptions under extreme events.

[0004] Existing research on improving the resilience of regional integrated energy systems mainly focuses on increasing the capacity of energy storage systems, while neglecting the impact on system operating costs. A small increase in resilience achieved by optimizing energy storage capacity can lead to a significant increase in system operating costs. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a method for optimizing thermal energy storage in regional integrated energy systems that takes into account both resilience and reliability.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] A method for optimizing thermal energy storage in a regional integrated energy system that balances resilience and reliability, the method comprising:

[0008] S1. Collect relevant data on the regional integrated energy system;

[0009] S2. Model each equipment component in the regional integrated energy system, establish the energy flow diagram of the regional integrated energy system, constrain the energy flow balance, and establish an optimal scheduling model for the regional integrated energy system.

[0010] S3. Calculate the minimum standby thermal energy storage under the condition of source-end interruption using the rolling optimization algorithm;

[0011] S4. Substitute the minimum reserve thermal energy storage as a constraint into the regional integrated energy system optimization scheduling model to obtain the hourly heat storage capacity in actual operation.

[0012] Preferably, the data collected in step S1 includes: internal combustion engine performance parameters, heat pump performance parameters, dual-condition refrigeration unit performance parameters, absorption chiller performance parameters, energy storage device performance parameters, external power grid fault recovery time, external gas grid fault recovery time, typical daily electricity, heat and cooling load data of users, time-of-use electricity price, and gas purchase cost.

[0013] Preferably, the energy flow balance is constrained in step S1, as follows:

[0014]

[0015]

[0016]

[0017] F t g =F t CCHP

[0018] Where: P t e and F t g P represents the system's electricity and gas consumption at time t, respectively. t shed , and P represents the shear load of electricity, heat, and cold at time t, respectively. t CCHP , and F t CCHP P represents the power supply, heating supply, and gas consumption of the combined cooling, heating, and power (CCHP) system at time t. t HP , and P represents the total power consumption of the heat pump, the heating capacity of the i-th heat pump, and the cooling capacity at time t, respectively. t IS , and Let represent the total power consumption of the ice storage system, the cooling capacity of the i-th dual-mode unit, and the ice-making capacity at time t, respectively. and Let these represent the electrical load, thermal load, and cooling load of the system at time t, respectively. and Let represent the heat absorbed and heat released by the i-th absorption chiller at time t, respectively. and Let represent the heat charging power and heat dissipation power of the hot water tank at time t, respectively. and Let denot represent the charging cooling power and discharging cooling power of the ice storage tank at time t, respectively. This represents the operating state of the internal combustion engine at time t. and N represents the operating status of the i-th dual-condition chiller, heat pump, and absorption chiller at time t, respectively. HP N DC and N AC These represent the installed capacity of heat pumps, dual-mode units, and absorption chillers, respectively.

[0019] Preferably, the regional integrated energy system optimization scheduling model takes the minimum daily operating cost as its objective function, and is expressed as follows:

[0020]

[0021] Where C represents the system's daily operating cost. and Let P represent the electricity price and natural gas price at time t, respectively. t e and F t g P represents the system's electricity and gas consumption at time t, respectively. t shed , and Let ε represent the shear load of electricity, heat, and cold at time t, respectively. e ε h and ε c These represent the penalty coefficients for electrical, thermal, and cold loads, respectively.

[0022] Preferably, step S3 specifically involves: dividing the selected typical day into 24 time domains, calculating the minimum backup energy storage required for each time domain in the event of an external power or gas supply interruption, and taking the larger value at each moment under the two conditions as the minimum backup thermal energy storage of the regional integrated energy system at the corresponding moment.

[0023] Preferably, it is assumed that the power or gas supply is interrupted hourly, with the following constraints:

[0024] Assuming an external power outage occurs:

[0025] Assuming the external gas supply is interrupted:

[0026] Among them, P t e and F t g Let t represent the system's electricity consumption and gas consumption at time t, respectively. n This refers to the moment when the external power or gas supply is interrupted. and These represent the duration of the interruption of external power and external gas supply, respectively.

[0027] Preferably, the minimum reserve thermal energy storage of the regional integrated energy system is expressed as:

[0028] W t RE,c / h =max{W t min,e,c / h W t min,g,c / h}

[0029] Among them, W t RE,c / h W is the minimum backup thermal storage for the regional integrated energy system at time t. t min,e,c / h W represents the minimum backup thermal energy required in the event of a power outage at time t. t min,g,c / h Let t be the minimum backup thermal energy required in the event of a gas supply interruption at time t.

[0030] Preferably, the method further includes evaluating the optimized system, including:

[0031] S5. Use Markov probability models to model equipment failures and use Monte Carlo simulation to simulate the system operation and scheduling of backup thermal energy storage.

[0032] S6. By comparing the simulation results of regional integrated energy systems that do not take into account and those that do take into account thermal reserves, the resilience, reliability and economy of the optimized regional integrated energy system are evaluated.

[0033] Preferably, the equipment fault model in the equipment fault modeling process is expressed by the following formula:

[0034]

[0035]

[0036]

[0037] Among them, Ω ECE It represents a collection of energy conversion devices. Let λ represent the state transition probability of device j at time t, and let λ represent the failure probability of the corresponding device. Let u represent a uniformly distributed random number within the interval [0,1]. t This represents the set of operating states of energy conversion equipment at time t. This corresponds to the operating state of the internal combustion engine, absorption chiller, heat pump, and dual-condition chiller at time t. This represents the state of device j at time t.

[0038] Preferably, the user satisfaction model in resilience evaluation is as follows:

[0039] ω e =P t shed / L e

[0040]

[0041]

[0042]

[0043]

[0044]

[0045] Where, ω e ω h and ω c These represent user satisfaction with the system's power supply, heating, and cooling loads, respectively. and P represents the user's minimum satisfaction with the system's power supply, heating, and cooling loads, respectively. t shed , and L represents the shear load of electricity, heat, and cold at time t, respectively. e L h and L c These represent the system's electrical load, thermal load, and cooling load, respectively.

[0046] Compared with the prior art, the present invention has the following advantages:

[0047] On the one hand, this invention considers using optimization algorithms to accurately calculate the required increase in energy storage capacity, optimize the scheduling model, and reduce unnecessary energy storage losses. On the other hand, it also considers the effect of energy storage capacity on improving system resilience and reliability. Considering only the improvement of system resilience is one-sided. The increase in energy storage capacity will simultaneously optimize the system's resilience and reliability. It is necessary to fully reflect the improvement effect of energy storage optimization on the system's resilience, reliability, and other safe operation indicators. Attached Figure Description

[0048] Figure 1 This is a flowchart illustrating an optimization method for thermal energy storage in a regional integrated energy system that balances resilience and reliability, according to the present invention.

[0049] Figure 2 This invention provides load diagrams for typical summer and winter days in a regional integrated energy system thermal energy storage optimization method that balances resilience and reliability.

[0050] Figure 3 This is a diagram of the energy flow structure of a regional integrated energy system in the thermal energy storage optimization method for a regional integrated energy system that takes into account both resilience and reliability, as described in this invention.

[0051] Figure 4 This is a comparison diagram of minimum standby thermal energy storage under different source-end interruptions in the thermal energy storage optimization method of a regional integrated energy system that takes into account both resilience and reliability, according to the present invention.

[0052] Figure 5 A comparison chart of thermal energy storage capacity before and after optimization using the thermal energy storage optimization method for regional integrated energy systems that balances resilience and reliability, as proposed in this invention.

[0053] Figure 6 The image shows the Monte Carlo method convergence curves of daily operating costs under different scenarios for the thermal energy storage optimization method of a regional integrated energy system that takes into account both resilience and reliability, according to the present invention. Detailed Implementation

[0054] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. Note that the following description of the embodiments is merely illustrative and is not intended to limit its applicability or use, nor is the present invention limited to the following embodiments.

[0055] Example

[0056] like Figure 1 As shown in the figure, this embodiment provides a method for optimizing thermal energy storage in a regional integrated energy system that balances resilience and reliability. The method includes:

[0057] S1. Collect relevant data on the regional integrated energy system, including: internal combustion engine performance parameters, heat pump performance parameters, dual-condition chiller performance parameters, absorption chiller performance parameters, energy storage device performance parameters, external power grid fault recovery time, external gas grid fault recovery time, typical daily electricity, heat, and cooling load data of users, time-of-use electricity prices, and gas purchase costs. The equipment performance parameters in this embodiment are shown in Table 1.

[0058] Table 1 Equipment Performance Parameters

[0059]

[0060]

[0061] According to existing literature, the average repair time for external power grid faults is 2 hours, and the average repair time for external natural gas grid faults is 4 hours.

[0062] This case study selects load data from an energy station in the Lingang New Area of ​​Shanghai. Typical daily load data for summer and winter are as follows: Figure 2 As shown.

[0063] According to Shanghai's electricity and natural gas price regulations, the electricity and natural gas prices for the case study are shown in Table 2 below.

[0064] Table 2. Electricity and Natural Gas Prices in Shanghai

[0065]

[0066] S2. Model each equipment component in the regional integrated energy system, establish the energy flow diagram of the regional integrated energy system, constrain the energy flow balance, and establish an optimal scheduling model for the regional integrated energy system.

[0067] Based on the collected data, output models were established for energy conversion and storage devices within the regional integrated energy system, and energy flow diagrams for electricity, natural gas, heat, and cooling within the regional integrated energy system were created, such as... Figure 3 As shown.

[0068] according to Figure 3 The energy flow diagram constrains the energy flow balance of the integrated energy system in this region:

[0069]

[0070]

[0071]

[0072] F t g =F t CCHP

[0073] Where: P t e and F t g P represents the system's electricity and gas consumption at time t, respectively. t shed , and P represents the shear load of electricity, heat, and cold at time t, respectively. t CCHP , and F t CCHP P represents the power supply, heating supply, and gas consumption of the combined cooling, heating, and power (CCHP) system at time t. t HP , and P represents the total power consumption of the heat pump, the heating capacity of the i-th heat pump, and the cooling capacity at time t, respectively. t IS , and Let represent the total power consumption of the ice storage system, the cooling capacity of the i-th dual-mode unit, and the ice-making capacity at time t, respectively. and Let these represent the electrical load, thermal load, and cooling load of the system at time t, respectively. and Let represent the heat absorbed and heat released by the i-th absorption chiller at time t, respectively. and Let represent the heat charging power and heat dissipation power of the hot water tank at time t, respectively. and Let denot represent the charging cooling power and discharging cooling power of the ice storage tank at time t, respectively. This represents the operating state of the internal combustion engine at time t. and N represents the operating status of the i-th dual-condition chiller, heat pump, and absorption chiller at time t, respectively. HP N DC and N AC These represent the installed capacity of heat pumps, dual-mode units, and absorption chillers, respectively.

[0074] A regional integrated energy system optimization scheduling model is established with the goal of minimizing daily operating costs. The objective function mainly includes the electricity and natural gas costs for a typical day, as well as the penalty for load shedding. The specific objective function is as follows:

[0075]

[0076] Where: C represents the system's daily operating cost. and Let ε represent the electricity price and natural gas price at time t, respectively. e ε h and ε c These represent the penalty coefficients for electrical, thermal, and cold loads, respectively.

[0077] S3. Calculate the minimum standby thermal energy storage under source-end interruption conditions using a rolling optimization algorithm. Specifically:

[0078] The selected typical day is divided into 24 time domains. The minimum backup energy storage required for an external power or gas supply interruption in each time domain is calculated one by one. The larger value at each moment under the two conditions is taken as the minimum backup thermal energy storage of the regional integrated energy system at the corresponding moment.

[0079] Assuming an hourly interruption of power or gas supply, the constraints are as follows:

[0080] Assuming an external power outage occurs:

[0081] Assuming the external gas supply is interrupted:

[0082] Among them, P t e and F t g Let t represent the system's electricity consumption and gas consumption at time t, respectively. n This refers to the moment when the external power or gas supply is interrupted. and These represent the duration of the interruption of external power and external gas supply, respectively.

[0083] The minimum reserve thermal storage for a regional integrated energy system is expressed as:

[0084] W t RE,c / h =max{W t min,e,c / h W t min,g,c / h}

[0085] Among them, W t RE,c / h W is the minimum backup thermal storage for the regional integrated energy system at time t. t min,e,c / h W represents the minimum backup thermal energy required in the event of a power outage at time t. t min,g,c / h Let t be the minimum backup thermal energy required in the event of a gas supply interruption at time t.

[0086] Minimum standby thermal energy storage that takes into account both power outages and natural gas outages, such as Figure 4As shown in the figure, during a typical summer day, electrically driven equipment bears a large cooling load, which can be fully supported by electricity. Therefore, in the event of a natural gas outage, the backup thermal energy storage is equivalent to the daily energy storage, and no additional backup storage is needed. However, in the event of a power outage, gas-driven equipment cannot simultaneously meet the needs of cooling and electricity loads, thus requiring additional cooling energy reserves to compensate for the deficiency and balance the system's various energy demands. Backup energy storage is higher than daily energy storage and occurs mostly during the daytime when energy demand is higher. During a typical winter day, both electrically driven and gas-driven equipment bear the heating load demand. In the event of a power or natural gas outage, equipment relying on only one energy source cannot fully meet the demand. By selecting the maximum value of backup thermal energy under different conditions, the system's energy demand in extreme situations can be met.

[0087] S4. Substitute the minimum reserve thermal energy storage as a constraint into the regional integrated energy system optimization scheduling model to obtain the hourly heat storage capacity in actual operation.

[0088] A comparison of hourly thermal storage, minimum reserve thermal storage, and hourly thermal storage before and after optimization of the regional integrated energy system is as follows: Figure 5 As shown in the figure, the heat storage capacity is compared between typical summer and winter days. On a typical summer day, the storage capacity from 18:00 to 20:00 is zero before optimization. If the external power supply is interrupted during this time, the system's load demand cannot be met. The stored cold energy significantly improves the system's reliability and flexibility. Further optimization of the minimum standby cold storage capacity can improve the system's economics. The rolling optimization algorithm obtains the minimum standby cold storage capacity by simulating the power outage time successively. There is no correlation between standby storage capacities at adjacent times, and the impact of electricity prices is not considered. The figure shows that the minimum standby cold storage capacity needs to be increased during the 20:00-21:00 period, but this is during peak electricity prices, making charging during this period significantly uneconomical. Therefore, by optimizing energy storage, the overall energy storage level is improved, allowing energy storage for 20:00 to be moved forward to 21:00, reducing electricity purchases during peak hours and lowering the system's daily operating costs.

[0089] S5. Use Markov probability models to model equipment failures and use Monte Carlo simulation to simulate the system operation and scheduling of backup thermal energy storage.

[0090] In the process of equipment fault modeling, the fault model of the equipment is expressed by the following formula:

[0091]

[0092]

[0093]

[0094] Among them, ΩECE It represents a collection of energy conversion devices. Let λ represent the state transition probability of device j at time t, and let λ represent the failure probability of the corresponding device. Let u represent a uniformly distributed random number within the interval [0,1]. t This represents the set of operating states of energy conversion equipment at time t. This corresponds to the operating state of the internal combustion engine, absorption chiller, heat pump, and dual-condition chiller at time t. Let j represent the state of device j at time t.

[0095] The Monte Carlo method was used to simulate the operation of a regional integrated energy system, taking into account typical daily operating costs under equipment failure conditions. To compare the effectiveness of optimized energy storage, four scenarios were set up, and the Monte Carlo method was iterated 10,000 times until convergence was achieved. Figure 6 As shown in Table 3, the simulated operating costs for a typical summer day are shown in Table 4, and the operating costs for a typical winter day are shown in Table 5.

[0096] Table 3 Comparison of typical daily operating costs in summer

[0097]

[0098] Table 4 Comparison of typical daily operations in summer

[0099]

[0100] S6. By comparing the simulation results of regional integrated energy systems that do not take into account and those that do take into account thermal reserves, the resilience, reliability and economy of the optimized regional integrated energy system are evaluated.

[0101] The user satisfaction model in resilience assessment is as follows:

[0102] ω e =P t shed / L e

[0103]

[0104]

[0105]

[0106]

[0107]

[0108] Where, ω e ω h and ω cThese represent user satisfaction with the system's power supply, heating, and cooling loads, respectively. and P represents the user's minimum satisfaction with the system's power supply, heating, and cooling loads, respectively. t shed , and L represents the shear load of electricity, heat, and cold at time t, respectively. e L h and L c These represent the system's electrical load, thermal load, and cooling load, respectively.

[0109] Tables 5 and 6 list the system users' satisfaction with power outages at different times. When natural gas is interrupted, the electrical load is borne by the external power grid, and the heating and cooling loads are borne by energy storage devices and electric drive devices. Optimized energy storage does not affect the energy supply to users; therefore, user energy satisfaction under gas outage conditions is not discussed.

[0110] Table 5 Summer Load Satisfaction (%)

[0111]

[0112] Table 6 Winter Load Satisfaction (%)

[0113]

[0114] Therefore, as shown above, when external power is interrupted due to extreme events, a regional integrated energy system with thermal energy storage can reduce load shedding, improve user satisfaction, and make the system more resilient. Thermal energy storage also improves system reliability; therefore, when calculating the daily operating costs of a thermal energy storage system, considering both resilience and reliability can reduce the increase in operating costs caused by thermal energy storage. The model and method proposed in this invention can provide a useful tool for analyzing the resilience of regional integrated energy systems under extreme events, and will promote the popularization of regional integrated energy systems and their application in some new directions.

[0115] The above embodiments are merely illustrative and do not constitute a limitation on the scope of the present invention. These embodiments can also be implemented in various other ways, and various omissions, substitutions, and modifications can be made without departing from the technical spirit of the present invention.

Claims

1. A method for optimizing thermal energy storage in a regional integrated energy system that balances resilience and reliability, characterized in that, The method includes: S1. Collect relevant data on the regional integrated energy system; S2. Model each equipment component in the regional integrated energy system, establish the energy flow diagram of the regional integrated energy system, constrain the energy flow balance, and establish an optimal scheduling model for the regional integrated energy system. S3. Calculate the minimum standby thermal energy storage under the condition of source interruption using the rolling optimization algorithm. Specifically, step S3 is as follows: Divide the selected typical day into 24 time domains, calculate the minimum standby thermal energy storage required for external power or gas source interruption in each time domain, and take the larger value at each moment under the two conditions as the minimum standby thermal energy storage of the regional integrated energy system at the corresponding moment. Assuming an hourly interruption of power or gas supply, the constraints are as follows: Assuming an external power outage occurs: Assuming the external gas supply is interrupted: in, and They represent t Real-time system power and gas consumption. This refers to the moment when the external power or gas supply is interrupted. and These represent the duration of the interruption of external power and external gas supply, respectively. The minimum reserve thermal storage for a regional integrated energy system is expressed as: in, for t Minimum standby thermal energy storage for a time-zone integrated energy system for t Minimum backup thermal energy storage required in the event of a constant power outage. for t Minimum backup thermal energy storage required in the event of a gas supply interruption; S4. Substitute the minimum standby thermal energy storage as a constraint into the regional integrated energy system optimization scheduling model to obtain the hourly heat storage of actual operation; S5. Use Markov probability models to model equipment failures and use Monte Carlo simulation to simulate the system operation and scheduling of backup thermal energy storage. S6. By comparing the simulation results of the regional integrated energy system without and with thermal reserve, the resilience, reliability, and economy of the optimized regional integrated energy system are evaluated. The user satisfaction model in the resilience evaluation is as follows: in, , and These represent user satisfaction with the system's power supply, heating, and cooling loads, respectively. , and These represent the minimum user satisfaction with the system's power supply, heating, and cooling loads, respectively. , and They represent t The shedding load of electricity, heat and cold at all times. , and These represent the system's electrical load, thermal load, and cooling load, respectively.

2. The method for optimizing thermal energy storage in a regional integrated energy system that balances resilience and reliability, as described in claim 1, is characterized in that... The data collected in step S1 includes: internal combustion engine performance parameters, heat pump performance parameters, dual-condition refrigeration unit performance parameters, absorption chiller performance parameters, energy storage device performance parameters, external power grid fault recovery time, external gas grid fault recovery time, typical daily electricity, heat and cooling load data of users, time-of-use electricity price, and gas purchase cost.

3. The method for optimizing thermal energy storage in a regional integrated energy system that balances resilience and reliability, as described in claim 1, is characterized in that... The energy flow balance constraint in step S1 is expressed as follows: in: and They represent t Real-time system power and gas consumption. , and They represent t The shedding load of electricity, heat and cold at all times. , and These respectively represent the combined cooling, heating and power (CCHP) system in t The amount of electricity supplied, the amount of heat supplied, and the amount of gas consumed at any given time. , and They represent in t Total power consumption of the heat pump at any time, the first i The heating and cooling capacity of a heat pump , and They represent t Total power consumption of the ice storage system at all times, i The cooling capacity and ice-making capacity of the dual-mode unit , and They represent t The electrical load, thermal load, and cooling load of the system are measured at all times. and They represent the first i Taiwan absorption chiller t The constant absorption and release of heat. and These represent the hot water tank in t The charging and discharging power at any given time and These represent the ice storage tanks at... t The charging and discharging power at any given time, Indicates that the internal combustion engine is t The running status at any given moment, , and They represent the first i Dual-condition chiller units, heat pumps and absorption chillers in t The running status at any given moment, , and These represent the installed capacity of heat pumps, dual-mode units, and absorption chillers, respectively.

4. The method for optimizing thermal energy storage in a regional integrated energy system that balances resilience and reliability, as described in claim 1, is characterized in that... The regional integrated energy system optimization scheduling model, with the objective function of minimizing daily operating costs, is expressed as: in, This indicates the system's daily operating cost. and They represent t Electricity and natural gas prices at specific times. and They represent t Real-time system power and gas consumption. , and They represent t The shedding load of electricity, heat and cold at all times. , and These represent the penalty coefficients for electrical, thermal, and cold loads, respectively.

5. The method for optimizing thermal energy storage in a regional integrated energy system that balances resilience and reliability, as described in claim 1, is characterized in that... In the process of equipment fault modeling, the fault model of the equipment is expressed by the following formula: in, It represents a collection of energy conversion devices. express j This type of equipment t The state transition probability at time t. This indicates the probability of failure for the corresponding device. This represents a uniformly distributed random number within the interval [0,1]. express t A collection of real-time operating statuses of energy conversion equipment. , , , The corresponding terms represent internal combustion engines, absorption chillers, heat pumps, and dual-condition chillers. t The running status at any given moment, express j This type of equipment t The state at any given moment.

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