A carbon emission efficiency scheduling method for an underground station energy storage system and related devices

Abstract

The application discloses a carbon emission efficiency scheduling method of an underground station energy storage system and related devices, and belongs to the technical field of rail transit energy management and energy storage optimization scheduling. The method comprises the following steps: calculating a carbon scheduling index according to the state of charge of the energy storage system, the station predicted load, the energy storage degradation rate, the time-varying carbon emission factor and the carbon emission factor change rate; and determining a preferential discharging interval or a preferential charging interval according to the size of the carbon scheduling index. The application divides the operation mode of the energy storage system based on the size of the carbon scheduling index by constructing the carbon scheduling index, so that the energy storage system discharges when the carbon scheduling index is high and charges when the carbon scheduling index is low. The reverse carbon emission problem that charging is performed in a high carbon period and discharging is performed in a low carbon period due to the fact that the charging and discharging control is only dependent on the peak-valley electricity price difference can be avoided, so that the overall carbon emission intensity of the station is effectively reduced.

Description

Technical Field

[0001] This invention relates to the field of energy management and energy storage optimization scheduling technology for urban rail transit, and in particular to a carbon emission efficiency scheduling method and related device for an underground station energy storage system. Background Technology

[0002] Deep underground railway stations are typically buried at depths exceeding 120-150 meters, with some reaching 200-1000 meters. Due to the lack of natural lighting, dense electromechanical equipment, ventilation and air conditioning loads accounting for more than 60% of the total load, and long operating hours, annual electricity consumption can reach millions of kilowatt-hours, resulting in carbon emission intensity far exceeding that of ordinary above-ground buildings.

[0003] Currently, energy storage systems have been put into use in some subway stations. Their core function is peak shaving and valley filling, that is, charging the energy storage battery during the off-peak hours of the power grid and discharging the battery to supply power to the station during the peak hours of the power grid, thereby effectively reducing the overall peak electricity cost of the station. However, the existing energy storage scheduling strategy has the following defects: (1) It only focuses on economic efficiency and ignores carbon emissions: The charging and discharging scheduling strategy of traditional energy storage systems takes the peak-valley electricity price difference as the only optimization target and does not consider the time-varying nature of the power grid carbon emission factor. This may lead to the reverse carbon emission effect of discharging during high carbon emission periods and charging during low carbon emission periods, which is inconsistent with the goal of low-carbon operation; (2) It lacks a special scheduling index for deep-buried stations: The existing energy storage scheduling methods are mostly general-purpose and do not design a special evaluation index for the characteristics of concentrated load and high carbon emission pressure of deep-buried stations, making it difficult to achieve the optimal carbon emission efficiency. Summary of the Invention

[0004] The purpose of this invention is to overcome the problems of the prior art and provide a carbon emission efficiency scheduling method and related device for underground station energy storage systems.

[0005] The objective of this invention is achieved through the following technical solution: a carbon emission efficiency scheduling method for an underground station energy storage system, the method comprising the following steps: The time-varying carbon emission factor of the power grid is obtained, and the carbon emission change trend in the future time window is predicted based on the historical carbon emission factor to obtain the carbon emission factor change rate. Real-time data collection of the energy storage system's state of charge, predicted station load, and energy storage degradation rate; combined with time-varying carbon emission factors and carbon emission factor change rate to calculate the carbon dispatch index. Based on historical carbon dispatch data, the carbon dispatch index is analyzed to determine the high-carbon and low-carbon operating thresholds. If the current carbon dispatch index is greater than or equal to the high-carbon operating threshold, the energy storage system enters the carbon priority mode; if the current carbon dispatch index is less than or equal to the low-carbon operating threshold, the energy storage system enters the lifespan priority mode. In carbon-priority mode, a discharge power control strategy aimed at minimizing carbon emissions is generated and executed; in lifetime-priority mode, a charging power control strategy aimed at minimizing energy storage degradation is generated and executed.

[0006] In one example, the carbon scheduling index is calculated as follows: CDI = EF norm +w 1 •SOC dev +w 2 •d EF / dt+w 3 •P load +w 4 •R deg in, CDI For carbon dispatch index; EF norm This is the normalized value of the time-varying carbon emission factor; SOC dev This refers to the deviation between the current state of charge and the target state of charge of the energy storage system. d EF / dt The rate of change of carbon emission factor; P load Forecast the load for the station; R deg The energy storage degradation rate; w 1 , w 2 , w 3 , w 4 All are weighting coefficients.

[0007] In one example, the predicted load of the station includes ventilation and air conditioning system load, lighting load, power load and auxiliary load; in the calculation of the carbon dispatch index, the weighting coefficient of ventilation and air conditioning system load is higher than the weighting coefficients of lighting load, power load and auxiliary load.

[0008] In one example, if the current carbon dispatch index is between the low-carbon operation threshold and the high-carbon operation threshold, the economy priority mode is entered, and an energy storage charging and discharging power control strategy with the goal of minimizing electricity costs is generated and executed.

[0009] In one example, in the carbon priority mode, the discharge power control strategy is executed in conjunction with the waste heat recovery control of the heat pump system: the heat pump system uses waste heat recovery to replace the electric heating equipment for heating, and the energy storage system discharges within the priority discharge range to supply power to the heat pump system and the station's electrical equipment.

[0010] In one example, during the operation of the energy storage system, the energy storage degradation rate is calculated in real time and fed back into the calculation of the carbon dispatch index.

[0011] In one example, when the energy storage system enters carbon priority mode and is not in the off-peak electricity price period, the carbon emission reduction generated by charging is converted into carbon emission cost according to the preset carbon trading market price, and the scheduling strategy that minimizes the weighted sum of carbon emission cost and electricity cost is selected.

[0012] It should be further noted that the technical features corresponding to the above examples can be combined or replaced to form new technical solutions.

[0013] The present invention also includes a computer program product comprising a computer program that, when executed by a processor, implements the steps of the carbon emission efficiency scheduling method for an underground station energy storage system formed by any or a combination of the above examples.

[0014] The present invention also includes a storage medium storing computer instructions that, when executed, perform the steps of the carbon emission efficiency scheduling method for an underground station energy storage system formed by any or more of the above examples.

[0015] The present invention also includes a terminal comprising a memory and a processor, the memory storing computer instructions executable on the processor, wherein the processor, when executing the computer instructions, performs the steps of the carbon emission efficiency scheduling method for the underground station energy storage system formed by any or more of the above examples.

[0016] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention utilizes time-varying carbon emission factors, carbon emission factor change rates, station predicted load, energy storage electrical status, and energy storage degradation rates to construct and calculate a carbon dispatch index that reflects the trend changes of carbon emission factors and load, providing a quantifiable decision-making basis for energy storage carbon emission efficiency dispatch. The energy storage charging and discharging behavior is determined by the operating mode defined by the CDI, rather than being directly triggered by the level of a single carbon emission factor. Specifically, the operating mode of the energy storage system is divided according to the magnitude of the comprehensive index, i.e., the carbon dispatch index. This enables the energy storage system to discharge when the carbon dispatch index is high and charge when the carbon dispatch index is low. This avoids the reverse carbon emission problem caused by relying solely on peak-valley electricity price differences for charging and discharging control, which results in charging during high-carbon periods and discharging during low-carbon periods. It ensures that the energy storage charging and discharging behavior is consistent with the trend of grid carbon emission changes, thereby effectively reducing the overall carbon emission intensity of the station.

[0017] 2. The ventilation and air conditioning system load is the main source of energy consumption in the station. Setting the weight coefficient of the ventilation and air conditioning system load to be higher than that of other loads will make the carbon dispatch index most sensitive to changes in the main energy consumption sources of the station. This will ensure that the energy storage charging and discharging strategy responds first to fluctuations in the ventilation and air conditioning load, making the energy storage charging and discharging strategy more in line with the high energy consumption operation characteristics of deep underground spaces, and improving carbon emission reduction efficiency and dispatch targeting.

[0018] 3. Further divide the economic priority mode according to the carbon dispatch index. Under the economic priority mode, control the energy storage charging and discharging power with the goal of minimizing electricity costs, avoid unnecessary charging and discharging cycles in the economic priority mode, and reduce the station's electricity expenses while ensuring the overall carbon reduction effect.

[0019] 4. By coordinating energy storage discharge power control with heat pump waste heat recovery, two carbon reduction mechanisms—waste heat recovery replacing heating electricity and energy storage discharge replacing grid electricity purchase—are superimposed to form a system-level carbon reduction solution, further ensuring the carbon reduction effect.

[0020] 5. Real-time calculation of energy storage degradation rate and feedback to carbon dispatch index calculation form a closed-loop dispatch mechanism based on equipment status. The lifespan degradation factor of energy storage equipment is dynamically included in the dispatch strategy, which can extend the lifespan of energy storage equipment while ensuring carbon reduction effect.

[0021] 6. When there is a conflict between carbon emission efficiency scheduling and electricity cost economic scheduling, choose the scheduling strategy that minimizes the weighted sum of carbon emission costs and electricity costs. That is, when a conflict occurs, choose to abandon charging or delay charging, so as to balance the carbon emission reduction benefits and electricity costs. Attached Figure Description

[0022] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. The accompanying drawings are provided to provide a further understanding of the present application and constitute a part of the present application. The same reference numerals are used in these drawings to denote the same or similar parts. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation of the present application.

[0023] Figure 1 This is a flowchart of a method provided in an embodiment of the present invention; Figure 2 A decision logic diagram for a conflict between carbon emission efficiency scheduling and economic scheduling, provided as an embodiment of the present invention; Figure 3 This is a schematic diagram of carbon scheduling index calculation and scheduling interval division provided in an embodiment of the present invention. Detailed Implementation

[0024] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] Furthermore, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0026] This invention provides a carbon emission efficiency scheduling method for a deep underground station energy storage system based on time-varying carbon emission factors. By introducing a carbon scheduling index, the method achieves energy storage charging and discharging scheduling with carbon emission factors as the dominant signal, thereby reducing the overall carbon emission intensity of the station.

[0027] In one embodiment, such as Figure 1 As shown, a carbon emission efficiency scheduling method for an underground station energy storage system includes the following steps: S1: Obtain the time-varying carbon emission factor of the power grid, and predict the carbon emission change trend in the future time window based on the historical carbon emission factor to obtain the carbon emission factor change rate.

[0028] In step S1, the time-varying carbon emission factor reflects the carbon dioxide emissions corresponding to the power supply provided by the power grid at different times; the carbon emission factor change rate represents the trend of carbon emission factor changes over a future period, i.e., the dynamic direction of carbon emission factor change. In this step, the carbon emission factor at the current time and at each time in the past period is obtained in real time through the power grid data platform, and the carbon emission factor at each time within the future time window is predicted using prediction methods such as exponential smoothing. The carbon emission factor change rate is calculated based on the difference between the carbon emission factor at the current time and the predicted value of the carbon emission factor at the next time.

[0029] S2: Real-time acquisition of the state of charge of the energy storage system, the predicted load of the station, and the energy storage degradation rate, combined with the time-varying carbon emission factor and the carbon emission factor change rate to calculate the Carbon Dispatch Index (CDI).

[0030] In step S2, the state of charge (SBC) of the energy storage system is the ratio of the current remaining capacity of the energy storage battery to its rated capacity, reflecting the available energy storage capacity level of the system. This can be estimated by real-time acquisition of the battery pack's voltage, current, and temperature through the battery management system. The station's predicted load is a forecast of the station's total power consumption over a future period, including various power demands such as ventilation, air conditioning, lighting, and power. This is obtained by inferring and predicting historical load data over time. The energy storage degradation rate is the rate at which the energy storage system's capacity decays or its performance degrades during charge-discharge cycles. This can be calculated using parameters such as the cumulative number of charge-discharge cycles, operating temperature, and operating time. The carbon dispatch index (CDI) represents the degree of operational state deviation of the energy storage system under dynamic carbon-energy-load changes. Its value reflects whether the energy storage system should prioritize discharging or charging; that is, the charging and discharging strategy of the energy storage system is selected based on the CDI value.

[0031] S3: Analyze the carbon dispatch index based on historical carbon dispatch data to determine the high-carbon operation threshold and low-carbon operation threshold of the carbon dispatch index; if the current carbon dispatch index is greater than or equal to the high-carbon operation threshold, the energy storage system enters the carbon priority mode; if the current carbon dispatch index is less than or equal to the low-carbon operation threshold, the energy storage system enters the lifespan priority mode.

[0032] In step S3, historical carbon dispatch data is a collection of carbon dispatch index values ​​calculated at each dispatch time in the past, i.e., historical carbon dispatch index records accumulated during system operation. High-carbon operation thresholds and low-carbon operation thresholds are determined by statistical analysis (such as quantile analysis) of historical carbon dispatch data. In this embodiment, the high-carbon operation threshold is the 85th quantile of the CDI, and the low-carbon operation threshold is the 15th quantile of the CDI. When the carbon dispatch index is higher than the high-carbon operation threshold, it indicates that the current grid carbon emission factor is high or the load demand is large, and the carbon emission cost of grid power supply is higher than that of energy storage power supply. The energy storage system is then controlled to switch to a discharge state, i.e., the energy storage system switches to carbon priority mode, at which point the energy storage system operates with the lowest carbon emission power. When the carbon dispatch index is lower than the low-carbon operation threshold, it indicates that the current grid carbon emission factor is low, and the carbon emission cost of grid power supply is lower than that of energy storage power supply. The energy storage system is then controlled to switch to a charging state, i.e., the energy storage system switches to lifetime priority mode, at which point the energy storage system operates with the minimum degradation power.

[0033] S4: In carbon priority mode, a discharge power control strategy aimed at minimizing carbon emissions is generated and executed, enabling the energy storage system to replace grid power supply and reduce high-carbon power consumption; in lifetime priority mode, a charging power control strategy aimed at minimizing energy storage degradation is generated and executed, storing electrical energy in the energy storage system during low-carbon periods for use during subsequent high-carbon periods.

[0034] In step S4, under the carbon-priority mode, the energy storage system determines its discharge power with the goal of maximizing the substitution of high-carbon grid power. That is, while meeting the station's load requirements, it prioritizes using the stored energy to replace grid power purchases, thereby reducing the total amount of high-carbon electricity consumed by the station from the grid. Under the lifespan-priority mode, the system determines its charging power with the goal of minimizing energy storage capacity decay. That is, while meeting the energy storage system's need to store low-carbon energy, it performs constant current charging at a set current lower than the rated charging current, and stops charging when the set upper limit of state of charge is reached. This reduces irreversible damage to the energy storage medium caused by high-current charging or overcharging / discharging, extending the lifespan of the energy storage equipment while acquiring low-carbon energy.

[0035] In one embodiment, the carbon dispatch index is calculated as follows: CDI = EF norm +w 1 •SOC dev +w 2 •d EF / dt+w 3 •P load +w 4 •R deg ; EF norm =EF / EF max ; SOC dev =SOC-SOC target ; in, CDI The carbon dispatch index comprehensively reflects the urgency of whether the energy storage system should prioritize discharging or charging under the current carbon emission factor, remaining energy storage capacity, and future load demand. EF norm This is the normalized value of the time-varying carbon emission factor; EF max This is the upper limit threshold for carbon emission factors; SOC devThe current state of charge of the energy storage system SOC With the target state of charge SOC target Deviation between; d EF / dt The rate of change of carbon emission factor; P load Forecast load for stations relative to baseline load P base Normalized load representation; R deg The energy storage capacity degradation rate or cycle degradation rate of energy storage equipment; w 1 , w 2 , w 3 , w 4 All are weighting coefficients.

[0036] In this invention, the carbon dispatch index (CDI) incorporates forecast information from future time windows during calculation to reflect the trend changes in carbon emission factors and load, rather than being based solely on the current state. Furthermore, the CDI serves as a state-space control variable for the energy storage system, driving multi-mode dispatch decisions rather than a single evaluation indicator.

[0037] In one embodiment, the CDI calculation comprehensively considers the ratio of the current carbon emission factor to the high threshold of the carbon emission factor, the difference between the energy storage SOC and the target SOC, and the predicted peak load of the station in the future time domain. The predicted station load includes ventilation and air conditioning system load, lighting load, power load, and auxiliary load, with the ventilation and air conditioning system load accounting for the majority of the total station load. In the calculation of the carbon dispatch index, the weighting coefficient of the ventilation and air conditioning system load is higher than that of the lighting load, power load, and auxiliary load. This reflects the long-term operational characteristic of the ventilation and air conditioning system in deeply buried underground stations occupying the majority of energy consumption, thereby improving the responsiveness of the carbon dispatch strategy to high-energy-consuming loads.

[0038] In one embodiment, the calculation weight parameters or judgment threshold of the carbon dispatch index are dynamically adjusted according to the grid carbon emission intensity level (such as the fluctuation range of carbon emission factors in different seasons or time periods) and the station operation period (such as the load characteristics corresponding to peak / off-peak / low-peak passenger flow periods) to adapt to the changes in grid carbon emission characteristics and station energy consumption patterns, so that the energy storage dispatch strategy remains optimal in different scenarios.

[0039] In one embodiment, if the current carbon dispatch index is between the low-carbon operation threshold and the high-carbon operation threshold, the economy-first mode is entered, an energy storage charge-discharge power control strategy targeting the minimum electricity cost is generated and executed, and the energy storage system operates at the minimum cost power at this time.

[0040] Specifically, if the current carbon dispatch index is between the low-carbon operation threshold and the high-carbon operation threshold (15th percentile of CDI < CDI < 85th percentile of CDI), it indicates that the carbon emission factor is at a medium level, and the difference between the carbon emission cost of grid power supply and the carbon emission cost of energy storage power supply is not obvious. At this time, the energy storage system is controlled to switch to the economy-first mode, the charge-discharge direction is determined according to peak-valley electricity prices, and a charge-discharge power control instruction targeting the minimum electricity cost is generated, that is: the energy storage system is controlled to charge during low electricity price periods and discharge during high electricity price periods, so that the electricity purchase expense is minimized.

[0041] By combining this embodiment with the above embodiments, it can be seen that when CDI ≥ high-carbon operation threshold (85th percentile of CDI), the carbon-first mode is entered, and a discharge power control strategy targeting the minimum carbon emission is generated and executed; when CDI ≤ low-carbon operation threshold (15th percentile of CDI), the life-first mode is entered, and a charging power control strategy targeting the minimum energy storage degradation is generated and executed; when low-carbon operation threshold < CDI < high-carbon operation threshold, the economy-first mode is entered, and at this time, an energy storage charge-discharge power control strategy targeting the minimum electricity cost is generated and executed.

[0042] In one embodiment, under the carbon-first mode, the discharge power control strategy is executed in coordination with the waste heat recovery control of the heat pump system: the heat pump system uses heat pump waste heat recovery to replace electric heating equipment for heating, and the energy storage system discharges in the priority discharge interval to supply power for the heat pump system and the power-using equipment of the station. Through the coordination of replacing electric heating power consumption with heat pump waste heat recovery and replacing grid power purchase with energy storage discharge, the total amount of power purchased by the station from the power grid is reduced, so as to reduce the operation cost.

[0043] In one embodiment, during the operation of the energy storage system, the energy storage degradation rate is calculated in real time and fed back to the calculation of the carbon dispatch index, forming a closed-loop dispatch mechanism based on the equipment state, so that the carbon dispatch index can dynamically take the energy storage aging factor into account, avoiding over-charging and over-discharging in pursuit of carbon reduction effect, so as to achieve a dynamic balance between the carbon emission reduction target and the equipment life loss.

[0044] In one embodiment, such as Figure 2As shown, when the energy storage system enters carbon-priority mode and is not in the off-peak electricity price period (when there is a conflict between carbon emission efficiency scheduling and electricity cost economic scheduling), the carbon emission reduction generated by charging is converted into carbon emission cost according to the preset carbon trading market price. The scheduling strategy that minimizes the weighted sum of carbon emission cost and electricity cost is selected: the carbon emission cost generated by charging and the charging electricity cost are linearly added together as the comprehensive cost. J The energy storage system will use the electricity cost savings during future discharge periods as the comprehensive benefit. If the comprehensive cost is less than the comprehensive benefit, the system will be controlled to charge at a preset charging power, i.e., the current carbon efficiency scheduling strategy will be implemented. If the comprehensive cost is greater than or equal to the comprehensive benefit, the system will be controlled to abandon charging or delay charging, and the charging power can also be reduced.

[0045] Combining the above embodiments yields a preferred embodiment of the present invention, wherein the carbon scheduling index calculation and scheduling interval division strategy are as follows: Figure 3 As shown, the scheduling strategy of this preferred embodiment during different carbon emission periods will now be explained.

[0046] Example 1 (Prioritized Discharge During High Carbon Emission Periods) A deeply buried underground railway station, approximately 120 meters deep, operates from 9:00 AM to 11:00 AM on summer weekdays. The system obtains real-time data on the grid's carbon emission factors: 0.65 kgCO2 / kWh (high carbon) from 9:00 AM to 10:00 AM, and 0.62 kgCO2 / kWh from 10:00 AM to 11:00 AM. The energy storage's current State of Charge (SOC) is 90%, and the load is predicted to increase within the next hour.

[0047] (a) Calculation of Carbon Dispatch Index (CDI) In CDI calculation, the ratio of carbon emission factor to high threshold and the difference between SOC and target discharge termination SOC are comprehensively considered. The CDI calculation expression is: CDI = EF norm +w 1 •SOC dev +w 2 •d EF / dt+w 3 •P load +w 4 •R deg ;in, EF norm This is the normalized value of the time-varying carbon emission factor; SOC dev The current state of charge of the energy storage systemSOC With the target state of charge SOC target Deviation between; d EF / dt The rate of change of carbon emission factor; P load Forecast load for stations relative to baseline load P base Normalized load representation; R deg The energy storage capacity degradation rate or cycle degradation rate of energy storage equipment; w 1 This is the weighting coefficient for the state of charge deviation; w 2 The weighting coefficient for the rate of change of carbon emission factors; w 3 Weighting coefficients for predicted load on the station; w 4 This is the weighting coefficient for the energy storage degradation rate.

[0048] In this embodiment, the CDI calculation and normalization process is as follows: EF =0.65 kgCO2 / kWh EF max =0.60 kgCO2 / kWh; EF norm =0.65 / 0.60=1.083; SOC =0.90, SOC target =0.30; SOC dev =0.90-0.30=0.60; d EF / dt =(0.62-0.65) / 1h=-0.03; P load =50kW P loadnorm =50 / 100=0.5; R deg =0.001; Weighting coefficients: w1=0.50, w2=0.30, w3=0.10, w4=0.05; Substituting the values ​​into the calculation: CDI = 0.50 × 1.083 + 0.30 × 0.60 + 0.10 × (-0.03) + 0.10 × 0.5 + 0.05 × 0.001 ≈ 0.76855; This value is higher than the 85th percentile high carbon operation threshold determined by the system based on historical CDI sequence statistics (this threshold is preset to 0.70 in this embodiment), indicating that the current grid carbon emission factor is high, and the energy storage system enters carbon priority mode, generating and executing a discharge power control strategy with the goal of minimizing carbon emissions.

[0049] (ii) Priority discharge strategy A discharge plan was developed: from 9:00 to 11:00, the system will continuously discharge at a power of 80kW, with an estimated discharge of 160kWh, replacing high-carbon mains power.

[0050] (III) Collaborative Carbon Reduction At the same time, the heat pump system increases the proportion of waste heat recovery, replacing part of the electric heating.

[0051] (iv) Technical effects During the two-hour period, the amount of electricity purchased from the grid decreased by 160 kWh, which corresponds to a reduction of approximately 104 kgCO2 in carbon emissions. The overall carbon emission reduction efficiency is about 35% higher than that of the traditional peak shaving and valley filling model.

[0052] Example 2 (Prioritizing charging during low carbon emission periods) During a certain nighttime period from 00:00 to 05:00, wind power generation was high, resulting in a carbon emission factor as low as 0.15 kgCO2 / kWh. The current SOC of energy storage is 20%, and the future load is predicted to be stable.

[0053] (a) Calculation of Carbon Dispatch Index (CDI) Using the same calculation method as in Example 1, EF = 0.15 is much lower than... EF max =0.60, SOC =0.20 is lower than the target charging termination SOC (0.90), and the calculated CDI is -0.85, which is lower than the low-carbon operation threshold (-0.5). The energy storage system enters the lifespan priority mode and generates and executes a charging power control strategy with the goal of minimizing energy storage degradation.

[0054] (ii) Priority charging strategy The system formulates a charging plan: charge continuously at 60kW for 5 hours, charging 300kWh, and charging the energy storage SOC from 20% to 90%, storing low-carbon electricity.

[0055] (III) Technical Effects During the peak hours of the following day, energy storage discharge replaces high-carbon grid electricity. Based on the carbon emission factor difference of 0.65-0.15=0.50 kgCO2 / kWh, the transfer of 300kWh of electricity achieves a carbon reduction of 150 kgCO2.

[0056] Example 3 (Handling the conflict between carbon emission efficiency and economic efficiency) At a certain time period, the carbon emission factor was 0.68 kgCO2 / kWh (above the high-carbon operation threshold of 0.60), but the electricity price was at a low point (0.30 yuan / kWh). In subsequent periods, the carbon emission factor was 0.25 kgCO2 / kWh (below the low-carbon operation threshold of 0.30), but the electricity price was at a high point (1.20 yuan / kWh). Traditional peak shaving and valley filling strategies would choose to charge at this time and discharge later to profit from the price difference, but this would lead to reverse carbon emissions from high-carbon charging and low-carbon discharging.

[0057] In this invention, the system calculates carbon emission costs based on a preset carbon price (200 yuan / ton, i.e., 0.20 yuan / kgCO2). The carbon emission cost of high-carbon charging is 0.136 yuan / kWh; the carbon revenue corresponding to low-carbon discharging is 0.05 yuan / kWh. The charging electricity price is 0.30 yuan / kWh, the electricity price for discharging as a substitute for electricity purchase is 1.20 yuan / kWh, and the electricity price difference is 0.90 yuan / kWh. The system comprehensively considers electricity revenue and carbon costs, and selects the energy storage charging and discharging power control strategy that minimizes the weighted sum of electricity costs and carbon emission costs under the corresponding operating mode, i.e., abandoning charging or reducing charging volume, and prioritizing discharging during high-carbon periods.

[0058] In summary, this invention constructs and calculates a carbon dispatch index, and classifies the operation mode of the energy storage system based on the magnitude of the carbon dispatch index. This enables the energy storage system to discharge to replace high-carbon grid power during high-carbon periods and charge and store low-carbon electricity during low-carbon periods, thereby eliminating the reverse carbon emission problem of high-carbon charging and low-carbon discharging in traditional peak shaving and valley filling strategies. Furthermore, by setting high-carbon and low-carbon operation thresholds, it achieves automatic switching between carbon-priority, lifespan-priority, and economy-priority modes. When carbon emission efficiency dispatch and electricity cost-effective dispatch conflict, carbon trading prices are introduced for comprehensive cost balancing, ensuring the carbon reduction effect of the station while balancing electricity cost economy and equipment lifespan.

[0059] The present invention also provides a computer program product, comprising a computer program that, when executed by a processor, implements the steps of the carbon emission efficiency scheduling method for an underground station energy storage system formed by any or a combination of the above examples. The processor may be a single-core or multi-core central processing unit or a specific integrated circuit, or one or more integrated circuits configured to implement the present invention.

[0060] The present invention also provides a storage medium having the same inventive concept as the carbon emission efficiency scheduling method for an underground station energy storage system formed by any or more of the above examples, wherein computer instructions are stored thereon, and the computer instructions, when executed, perform the steps of the carbon emission efficiency scheduling method for an underground station energy storage system formed by any or more of the above examples.

[0061] Based on this understanding, the technical solution of this embodiment, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0062] The present invention also provides a terminal having the same inventive concept as any or a combination of examples corresponding to the carbon emission efficiency scheduling method for an underground station energy storage system described above, including a memory and a processor. The memory stores computer instructions executable on the processor, and the processor executes the steps of the carbon emission efficiency scheduling method for an underground station energy storage system described above when executing the computer instructions. The processor may be a single-core or multi-core central processing unit or a specific integrated circuit, or one or more integrated circuits configured to implement the present invention.

[0063] In one example, the terminal, i.e., the electronic device, is represented in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: at least one processing unit (processor) mentioned above, at least one storage unit mentioned above, and a bus connecting different system components (including storage units and processing units).

[0064] The storage unit stores program code that can be executed by the processing unit, causing the processing unit to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit can execute the carbon emission efficiency scheduling method for an underground station energy storage system described above.

[0065] The storage unit may include readable media in the form of volatile storage units, such as random access memory (RAM) and / or cache storage units, and may further include read-only memory (ROM).

[0066] The storage unit may also include a program / utility having a set (at least one) of program modules, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0067] A bus can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus that uses any of the various bus structures.

[0068] Through the above description, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to this exemplary embodiment can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the method of the exemplary embodiment of this application.

[0069] The above detailed embodiments are a description of the present invention. It should not be considered that the specific embodiments of the present invention are limited to these descriptions. For those skilled in the art, several simple deductions and substitutions can be made without departing from the concept of the present invention, and all of these should be considered to fall within the protection scope of the present invention.

Claims

1. A carbon emission efficiency scheduling method for an underground station energy storage system, characterized in that, Includes the following steps: The time-varying carbon emission factor of the power grid is obtained, and the carbon emission change trend in the future time window is predicted based on the historical carbon emission factor to obtain the carbon emission factor change rate. Real-time data collection of the energy storage system's state of charge, predicted station load, and energy storage degradation rate; combined with time-varying carbon emission factors and carbon emission factor change rate to calculate the carbon dispatch index. Based on historical carbon dispatch data, the carbon dispatch index is analyzed to determine the high-carbon and low-carbon operating thresholds. If the current carbon dispatch index is greater than or equal to the high-carbon operating threshold, the energy storage system enters the carbon priority mode; if the current carbon dispatch index is less than or equal to the low-carbon operating threshold, the energy storage system enters the lifespan priority mode. In carbon-priority mode, a discharge power control strategy aimed at minimizing carbon emissions is generated and executed; in lifetime-priority mode, a charging power control strategy aimed at minimizing energy storage degradation is generated and executed.

2. The carbon emission efficiency scheduling method for underground station energy storage systems according to claim 1, characterized in that, The carbon dispatch index is calculated as follows: CDI = EF norm +w 1 •SOC dev +w 2 •d EF / dt+w 3 •P load +w 4 •R deg in, CDI For carbon dispatch index; EF norm This is the normalized value of the time-varying carbon emission factor; SOC dev This refers to the deviation between the current state of charge and the target state of charge of the energy storage system. d EF / dt The rate of change of carbon emission factor; P load Forecast the load for the station; R deg The energy storage degradation rate; w 1 , w 2 , w 3 , w 4 All are weighting coefficients.

3. The carbon emission efficiency scheduling method for underground station energy storage systems according to claim 1, characterized in that, The predicted load of the station includes the load of the ventilation and air conditioning system, the lighting load, the power load, and the auxiliary load. In the calculation of the carbon dispatch index, the weight coefficient of the ventilation and air conditioning system load is higher than that of the lighting load, the power load, and the auxiliary load.

4. The carbon emission efficiency scheduling method for underground station energy storage systems according to claim 1, characterized in that, If the current carbon dispatch index is between the low-carbon operation threshold and the high-carbon operation threshold, it enters the economic priority mode, and generates and executes an energy storage charging and discharging power control strategy with the goal of minimizing electricity costs.

5. The carbon emission efficiency scheduling method for underground station energy storage systems according to claim 1, characterized in that, In the carbon priority mode, the discharge power control strategy and the waste heat recovery control of the heat pump system are executed in coordination: the heat pump system uses waste heat recovery to replace the electric heating equipment for heating, and the energy storage system discharges in the priority discharge range to supply power to the heat pump system and the station's electrical equipment.

6. The carbon emission efficiency scheduling method for an underground station energy storage system according to claim 1, characterized in that, During the operation of the energy storage system, the energy storage degradation rate is calculated in real time and fed back into the carbon dispatch index calculation.

7. The carbon emission efficiency scheduling method for underground station energy storage systems according to claim 1, characterized in that, When the energy storage system enters carbon priority mode and is not in the off-peak electricity price period of peak-valley electricity pricing, the carbon emission reduction generated by charging is converted into carbon emission cost according to the preset carbon trading market price, and the dispatch strategy that minimizes the weighted sum of carbon emission cost and electricity cost is selected.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the carbon emission efficiency scheduling method for the underground station energy storage system according to any one of claims 1-7.

9. A storage medium storing computer instructions thereon, characterized in that, When the computer instructions are executed, they perform the steps of the carbon emission efficiency scheduling method for the underground station energy storage system according to any one of claims 1-7.

10. A terminal comprising a memory and a processor, wherein the memory stores computer instructions executable on the processor, characterized in that, When the processor executes the computer instructions, it performs the steps of the carbon emission efficiency scheduling method for the underground station energy storage system according to any one of claims 1-7.

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