Energy-saving generator set operation control method and system

Abstract

The invention discloses an energy-saving generator set operation control method and system, and relates to the technical field of generator control, and the method comprises the steps: monitoring the operation parameters of a power grid; identifying a power grid operation state according to the power grid operation parameters; if the running state of the power grid is instantaneous load sudden increase, auxiliary energy storage equipment is controlled to release electric energy; adjusting the power generation load of the generator set according to the operation characteristics of the generator set; generating a charging strategy of the auxiliary energy storage equipment according to the operation state of the power grid, the operation characteristics of the generator set, the fuel cost and the emission cost of the generator set; and according to the capacity of the auxiliary energy storage equipment and the charging strategy, adjusting the operation configuration of the generator set. According to the invention, the power generation load of the generator set and the charging strategy of the auxiliary energy storage equipment can be combined, so that the generator set operates in a high-efficiency load interval, the operation control of the generator set is realized, the reliability is improved, and the fuel consumption is reduced.

Description

Technical Field

[0001] This invention relates to the field of generator control technology, and in particular to an energy-saving generator set operation control method and system. Background Technology

[0002] In modern industrial production environments, ensuring a stable and reliable power supply is crucial, especially in independently operating power systems. To minimize operating costs and fuel consumption while ensuring power continuity, existing systems typically deploy multiple generator units operating in parallel. The core concept of this multi-unit parallel configuration is to flexibly adjust the number of operating units and the load distribution among them, so that each operating unit can operate within the optimal range of its fuel efficiency curve, thereby achieving overall fuel savings.

[0003] However, in real-world industrial applications, the startup process of generator sets, from cold start to stable grid connection, typically takes several minutes or even longer. This means that if the system load suddenly increases significantly, exceeding the total capacity of currently operating units, standby units cannot be immediately deployed within milliseconds or seconds to compensate for the sudden power shortfall. This inherent physical response delay causes a significant drop in grid voltage and frequency within a short period, resulting in severe power quality disturbances. To address the power supply reliability risks posed by this startup delay, existing systems keep multiple generator sets in hot standby mode even when the total system load is low. This means these units operate at low load rates, leading to a sharp decline in fuel efficiency, high fuel consumption, and low operational control reliability.

[0004] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0005] The main objective of this invention is to propose an energy-saving generator set operation control method and system, which can combine the generator set's power generation load and the charging strategy of auxiliary energy storage devices to enable the generator set to operate in a high-efficiency load range, thereby achieving generator set operation control, improving reliability, and reducing fuel consumption.

[0006] On one hand, embodiments of the present invention provide an energy-saving generator set operation control method, including the following steps: Monitor power grid operating parameters; Identify the power grid operating status based on the power grid operating parameters; If the power grid is in a state of sudden load increase, the auxiliary energy storage device is controlled to release electrical energy to stabilize the power grid; After the auxiliary energy storage device releases electrical energy, the power generation load of the generator set is adjusted according to the operating characteristics of the generator set; After the power generation load meets the load demand, a charging strategy for the auxiliary energy storage device is generated based on the power grid operating status, generator set operating characteristics, fuel cost, and generator set emission cost. Based on the capacity of the auxiliary energy storage device and the charging strategy, the operating configuration of the generator set is adjusted so that the generator set operates in the high-efficiency load range.

[0007] On the other hand, embodiments of the present invention provide an energy-saving generator set operation control system, including: The data monitoring module is used to monitor power grid operating parameters; A power grid status identification module is used to identify the power grid operating status based on the power grid operating parameters. An energy storage device control module is used to control an auxiliary energy storage device to release electrical energy to stabilize the power grid if the power grid operating state is a sudden increase in load. The power generation load adjustment module is used to adjust the power generation load of the generator set according to the operating characteristics of the generator set after the auxiliary energy storage device releases electrical energy. The charging strategy generation module is used to generate a charging strategy for the auxiliary energy storage device after the power generation load meets the load demand, based on the grid operating status, generator set operating characteristics, fuel cost and generator set emission cost. The operation configuration adjustment module is used to adjust the operation configuration of the generator set according to the capacity of the auxiliary energy storage device and the charging strategy, so that the generator set operates in the high-efficiency load range.

[0008] The embodiments of this application include at least the following beneficial effects: First, the embodiments of this application monitor the grid operating parameters, and then identify the grid operating status based on the grid operating parameters. If the grid operating status is a sudden increase in load, the auxiliary energy storage device is controlled to release electrical energy. Then, the generator load is adjusted according to the generator set operating characteristics. Based on the grid operating status, generator set operating characteristics, fuel costs, and generator set emission costs, a charging strategy for the auxiliary energy storage device is generated. Finally, the generator set operating configuration is adjusted according to the capacity of the auxiliary energy storage device and the charging strategy. This allows the generator set to operate in a high-efficiency load range by combining the generator load and the charging strategy of the auxiliary energy storage device, thereby achieving generator set operation control, improving reliability, and reducing fuel consumption.

[0009] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the description and the drawings. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below.

[0011] Figure 1 This is a flowchart of an energy-saving generator set operation control method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of an energy-saving generator set operation control system according to an embodiment of the present invention. Detailed Implementation

[0012] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments.

[0013] In modern industrial production environments, ensuring a stable and reliable power supply is crucial, especially in independently operating power systems, such as factories and mines in remote areas, or as backup power for large data centers. To minimize operating costs and fuel consumption while ensuring power continuity, these systems typically deploy multiple generator sets operating in parallel. The core concept of this multi-unit parallel configuration is to flexibly adjust the number of operating units and the load distribution among them, allowing each unit to operate within its optimal fuel efficiency curve range (typically 75% to 85% of rated load), thereby achieving overall fuel savings. For example, when the total system load is low, the control system, according to a preset strategy, instructs some generator sets to shut down and disconnect from the grid, allowing the remaining units to bear a higher load rate and improve their operating efficiency. Conversely, when the system load increases and approaches the maximum capacity of the currently operating units, the control system activates standby units, integrating them into the grid after a series of complex synchronization processes to share the load. This strategy based on unit start-up and shutdown and dynamic load distribution is theoretically considered an effective way to achieve energy-saving operation of independent power systems.

[0014] However, in actual industrial applications, this seemingly direct and efficient energy-saving strategy faces a series of complex and interconnected challenges at the implementation level. First, the generator start-up process is not instantaneous, especially for large diesel or gas generator sets. From a cold start to stable grid connection, it requires a time-consuming and precise process. This includes engine preheating, self-checks of the lubrication and cooling systems, engine acceleration to rated speed, generator excitation and voltage establishment, precise frequency and phase synchronization with the grid, and finally, grid connection and load sharing. This series of steps typically takes several minutes or even longer. This means that if the system load suddenly increases significantly, exceeding the total capacity of currently operating units, even with sufficient backup units, it is impossible to immediately deploy them within milliseconds or seconds to compensate for the sudden power shortage. This inherent physical response delay causes a significant drop in grid voltage and frequency within a short period, resulting in severe power quality disturbances. Such a sudden drop in voltage and frequency is fatal to precision industrial equipment connected to the power grid (such as PLC control systems, frequency converters, industrial robots, etc.), which may cause equipment malfunctions, data loss, production line shutdowns, and even trigger a chain reaction, causing wider production interruptions and economic losses.

[0015] To effectively address the power supply reliability risks caused by the aforementioned startup delays, engineering practice often necessitates a compromise in operational strategy. A common approach is to maintain at least one or more generator units in hot standby mode, even when the total system load is low and theoretically more units could be shut down to improve efficiency. This means that while these units are operational, they operate under extremely low loads (e.g., perhaps only 10% to 20% of rated load), primarily to quickly respond to sudden load increases. When the system load suddenly rises, these hot standby units can rapidly increase the load, responding to power demands within a relatively short time (typically seconds to tens of seconds), effectively suppressing drastic fluctuations in grid voltage and frequency and ensuring power quality. However, the cost of this strategy is obvious: internal combustion generators operating at low load rates experience a sharp decline in fuel efficiency, with fuel consumption per unit of electricity generated far exceeding that at their optimal efficiency point (typically around 75% of rated load), resulting in high fuel consumption. This has led to a situation where the control method, which was originally intended to achieve energy conservation through unit start-up and shutdown, has fallen into a dilemma of fuel waste due to maintaining reserve capacity, while ensuring power supply reliability, resulting in low operational control reliability.

[0016] In an independent power system composed of multiple heterogeneous generator sets with different operating characteristics and constraints, it is necessary to design an operation control method that can effectively overcome the contradictions caused by unit start-up delays and low efficiency under low load when facing highly random and unpredictable load fluctuations, ensuring power quality and power supply reliability, while comprehensively considering the operating characteristics, maintenance requirements, real-time fuel costs, and environmental emission constraints of each unit, thereby minimizing the overall operating cost of the system and maximizing environmental benefits throughout its entire life cycle.

[0017] The embodiments of this application will be explained in detail below with reference to the accompanying drawings: Figure 1 This is an optional flowchart of an energy-saving generator set operation control method provided in an embodiment of this application. Figure 1 The method may include, but is not limited to, steps S101 to S106.

[0018] Step S101: Monitor power grid operating parameters; Step S102: Identify the power grid operating status based on the power grid operating parameters; Step S103: If the power grid is in a state of instantaneous load surge, control the auxiliary energy storage device to release electrical energy to stabilize the power grid; Step S104: After the auxiliary energy storage device releases electrical energy, adjust the power generation load of the generator set according to the operating characteristics of the generator set; Step S105: After the power generation load meets the load demand, generate a charging strategy for the auxiliary energy storage device based on the grid operating status, generator set operating characteristics, fuel cost and generator set emission cost. Step S106: Adjust the operating configuration of the generator set according to the capacity and charging strategy of the auxiliary energy storage device so that the generator set operates in the high-efficiency load range.

[0019] Steps S101 to S106 as shown in the embodiments of this application can combine the power generation load of the generator set and the charging strategy of the auxiliary energy storage device to enable the generator set to operate in the high-efficiency load range, thereby realizing generator set operation control, improving reliability, and reducing fuel consumption.

[0020] In some embodiments, steps S101-S106 can begin by monitoring power grid operating parameters. This is achieved by deploying sensors and data acquisition units at key nodes of the power grid. For example, devices such as smart meters, voltage transformers, and current transformers can be used to collect data on power grid voltage, current, frequency, active power, and reactive power in real time. This data is then transmitted to a central control unit for processing.

[0021] Then, based on the grid operating parameters, the grid operating status is identified. This can be determined using thresholds or pattern recognition algorithms. For example, when the grid frequency or voltage drops sharply within a very short time (e.g., tens to hundreds of milliseconds) and the active power demand increases dramatically, the current grid operating status can be determined as a sudden load surge. It can be understood that the grid operating status is a comprehensive judgment based on grid operating parameters, indicating the current operating mode or abnormal situation of the grid, such as normal operation, sudden load surge, sudden load drop, or fault.

[0022] If the power grid experiences a sudden surge in load, auxiliary energy storage devices are controlled to release electrical energy to stabilize the grid. For example, the control unit can immediately send a discharge command to the auxiliary energy storage device, enabling it to rapidly inject high-power electrical energy into the grid within milliseconds. This effectively compensates for the power gap caused by generator response delays and suppresses drastic fluctuations in grid voltage and frequency. Auxiliary energy storage devices are understood to be devices capable of storing electrical energy and rapidly releasing it when needed, such as battery energy storage systems and supercapacitors. Their main function is to provide instantaneous power support in place of generators when their response is delayed.

[0023] After the auxiliary energy storage device releases electrical energy, the generator set's power load is adjusted according to its operating characteristics. While the auxiliary energy storage device provides instantaneous support, the control system gradually increases the generator set's output power based on its ramp rate and current load, allowing it to gradually take on the increased load. This process is smooth and controlled, avoiding any impact on the generator set. It's understandable that generator set operating characteristics refer to the generator set's performance under different loads, including fuel consumption rate curves, start-up time, ramp rate, maximum output power, and minimum stable operating power.

[0024] After the power generation load meets the demand, a charging strategy for the auxiliary energy storage device is generated based on the grid operating status, generator operating characteristics, fuel costs, and generator emission costs. Once the grid stabilizes and the generators have assumed most of the load, the auxiliary energy storage device may be in a lower state of charge. At this point, the control system comprehensively considers the real-time grid conditions (such as the risk of another load surge), the generator's fuel efficiency and emission levels under different loads, and current fuel prices and emission costs to determine when and how much power to charge the auxiliary energy storage device. Fuel costs refer to the expenses incurred by the generator during power generation. Generator emission costs refer to the environmental costs or penalties incurred due to pollutant emissions generated during power generation. The charging strategy is a charging plan developed for the auxiliary energy storage device, including charging power, charging time, and target state of charge, aiming to optimize the generator's operating efficiency and the lifespan of the energy storage device.

[0025] Finally, based on the capacity and charging strategy of the auxiliary energy storage device, the operating configuration of the generator set is adjusted to ensure that the generator set operates within its high-efficiency load range. If the charging strategy indicates that the auxiliary energy storage device needs to be charged at a certain power, the control system calculates the total load required for the generator set to provide that additional charging power while meeting the grid load demand. The system adjusts the operating point of the generator set so that, under the new total load, it operates within the range where its fuel efficiency is highest or its unit power generation cost is lowest. This may involve adjusting the load distribution of individual units, or optimizing the start-up, shutdown, and load distribution of multiple units operating in parallel.

[0026] Through the above technical solution, this embodiment effectively addresses the challenges posed by sudden surges in grid load by introducing auxiliary energy storage devices and combining them with intelligent control strategies. When grid operating parameters indicate a sudden increase in load, the auxiliary energy storage device can rapidly release electrical energy, stabilizing grid voltage and frequency within milliseconds, thus avoiding power quality issues caused by the lag in response of traditional generator sets. While the auxiliary energy storage device provides instantaneous support, the generator set's power generation load is smoothly adjusted, gradually assuming the increased load. Once the power generation load meets the demand, the system intelligently generates a charging strategy for the auxiliary energy storage device, comprehensively considering grid conditions, generator set operating characteristics, fuel costs, and emission costs. This strategy not only considers the energy storage device's own needs but also incorporates the generator set's operational economy. Ultimately, based on the energy storage device's capacity and charging strategy, the generator set's operating configuration is adjusted to ensure it operates within the most efficient load range while meeting both grid and energy storage charging requirements. This embodiment achieves rapid grid stabilization, smooth generator set transition, and maximized operating efficiency. In this way, this embodiment effectively resolves the contradiction between power supply reliability and operational economy, enabling generator sets to significantly reduce fuel consumption and emission costs while ensuring grid stability.

[0027] In some embodiments, step S106, adjusting the operating configuration of the generator set according to the capacity and charging strategy of the auxiliary energy storage device, may include, but is not limited to, the following steps: Step S201: Based on the capacity and discharge command of the auxiliary energy storage device, perform a micro-discharge test on the auxiliary energy storage device to obtain the device discharge test results. The device discharge test results include the device response time, instantaneous power output curve, and voltage recovery status after the discharge ends. Step S202: Compare the equipment discharge test results with the nominal performance parameters of the auxiliary energy storage equipment to obtain the comparison results; Step S203: Based on the comparison results, determine the instantaneous power output capability and effective discharge duration of the auxiliary energy storage device; Step S204: Adjust the operating configuration of the generator set according to the charging strategy, instantaneous power output capability, and effective discharge duration.

[0028] In some embodiments, a micro-discharge test can be performed on the auxiliary energy storage device based on its capacity and discharge command to obtain the device's discharge test results. In the micro-discharge test, a specific discharge command is issued to induce the auxiliary energy storage device to release energy briefly and in small amounts, without significantly affecting grid operation or the state of charge of the auxiliary energy storage device. The purpose is to obtain dynamic performance data of the device under current operating conditions, including its response speed to the discharge command (device response time), the maximum power output change it can provide in a short time (instantaneous power output curve), and the time and manner required for its terminal voltage to recover to a stable state after the discharge (voltage recovery after discharge). These parameters are key indicators for evaluating the actual health status and performance of the auxiliary energy storage device.

[0029] The discharge test results of the equipment are then compared with the nominal performance parameters of the auxiliary energy storage equipment to obtain the comparison results. The actual measured equipment response time, instantaneous power output curve, and voltage recovery can be compared with the ideal performance parameters specified at the time of manufacture or design. The purpose is to identify whether there are deviations or degradations in the equipment performance, quantify the degree of such deviations, and thus provide a data basis for subsequent performance evaluation.

[0030] Based on the comparison results, the instantaneous power output capability and effective discharge duration of the auxiliary energy storage device are determined. Based on the performance deviations revealed by the comparative analysis, the maximum instantaneous power that the auxiliary energy storage device can stably output under the current condition, and the duration it can continuously discharge at that power, can be reassessed and corrected. For example, if the test results show that the device response time has increased or the instantaneous power output curve has decreased, its instantaneous power output capability should be reduced accordingly; if the voltage recovery is slow, it may mean that its effective discharge duration has shortened. The purpose is to obtain the most realistic and reliable performance parameters of the auxiliary energy storage device to guide subsequent operational configuration adjustments.

[0031] Finally, based on the charging strategy, instantaneous power output capacity, and effective discharge duration, the operating configuration of the generator set is adjusted. After obtaining the corrected actual performance parameters of the auxiliary energy storage device, and in conjunction with the pre-generated charging strategy, the generator set's operating parameters, such as generation load, start-up and shutdown plans, and reserve capacity, can be finely adjusted. The aim is to ensure more efficient and precise coordination between the generator set and the auxiliary energy storage device, meeting the grid's load demands while fully utilizing the actual performance of the auxiliary energy storage device, and avoiding unnecessary losses.

[0032] To illustrate this technical solution more clearly, a specific example is used below. Assume an auxiliary energy storage device in a power grid system has been operating for several years, with a nominal instantaneous power output capacity of 5MW and an effective discharge duration of 30 minutes. However, due to long-term charge-discharge cycles and environmental factors, the actual performance of this device may have degraded. The system first conducts a small discharge test on the auxiliary energy storage device based on its capacity and discharge command. For example, the system can issue a discharge command lasting 5 seconds with a power output of 0.5MW. During this process, the data monitoring module records the device's response time (e.g., the time from command issuance to power output reaching 0.5MW), the instantaneous power output curve (e.g., the trend of power output change within 5 seconds), and the voltage recovery to a stable state after the discharge. Assume the test results show that the device's response time is slightly prolonged, the instantaneous power output curve shows a slight downward trend after reaching its peak, and the voltage recovery speed after the discharge is slower. Comparing these device discharge test results with the initial nominal performance parameters of the auxiliary energy storage device, the comparison results indicate a certain degree of performance degradation.

[0033] Based on this comparison, the system will redetermine the instantaneous power output capacity and effective discharge duration of the auxiliary energy storage device. For example, its instantaneous power output capacity may be corrected to 4.5MW, and the effective discharge duration to 25 minutes. Subsequently, when generating the charging strategy and adjusting the operating configuration of the generator set, the system will no longer use the original nominal parameters of 5MW and 30 minutes, but will adopt the corrected 4.5MW and 25 minutes. This means that when the grid experiences a sudden surge in load, the system will rely more conservatively on the auxiliary energy storage device and may adjust the generator set's power generation load earlier or faster to compensate for the impact of the auxiliary energy storage device's performance degradation. At the same time, the charging strategy will also take into account the device's corrected capacity, avoiding overcharging or charging in unsuitable intervals, thereby ensuring more precise coordination between the generator set and the auxiliary energy storage device. This not only guarantees the stable operation of the grid but also effectively protects the auxiliary energy storage device and extends its service life.

[0034] Through the above technical solution, this embodiment evaluates the actual performance of auxiliary energy storage devices in real time and dynamically, and adjusts their instantaneous power output capacity and effective discharge duration accordingly, making the operation configuration adjustment of the generator set more precise and reliable. This not only significantly improves the stability and response speed of the power grid, ensuring that the auxiliary energy storage devices can effectively support the power grid with their true capacity during sudden load surges, but also optimizes the power generation load distribution of the generator set, allowing it to operate in the high-efficiency load range for a longer period, thereby reducing fuel and emission costs. Furthermore, by avoiding overuse or improper operation of auxiliary energy storage devices with degraded performance, it also helps extend their service life, reduce maintenance costs, and overall improve the economy and reliability of the energy-saving generator set operation control system.

[0035] In some embodiments, in step S204, adjusting the operating configuration of the generator set according to the charging strategy, instantaneous power output capability, and effective discharge duration may include, but is not limited to, the following steps: Micro-disturbance tests were conducted on the auxiliary energy storage equipment, and the micro-disturbance test results were obtained; Based on the micro-perturbation test results, calculate the power output attenuation coefficient and the discharge duration attenuation coefficient; The instantaneous power output capability is corrected based on the power output attenuation coefficient; The effective discharge duration is corrected based on the discharge duration decay coefficient; The operating configuration of the generator set is adjusted based on the charging strategy, the corrected instantaneous power output capability, and the effective discharge duration.

[0036] In some embodiments, a micro-perturbation test can be performed on the auxiliary energy storage device to obtain the test results. A very small charge / discharge pulse or power fluctuation can be applied to the auxiliary energy storage device during normal operation or standby to probe its current dynamic response characteristics. The amplitude of this micro-perturbation test is typically much smaller than that of conventional charge / discharge operations, aiming to obtain real-time performance data of the device under current operating conditions without affecting grid stability. Its purpose is to capture subtle performance drifts caused by changes in the internal state of the auxiliary energy storage device (such as temperature, state of charge, and aging). The micro-perturbation test results refer to the transient changes in parameters such as voltage, current, and power exhibited by the auxiliary energy storage device in response to the micro-perturbation test. For example, this may include the power response curve after the micro-perturbation input, the voltage drop or rise magnitude, and the time required to recover to a stable state.

[0037] Then, based on the micro-perturbation test results, the power output attenuation coefficient and the discharge duration attenuation coefficient are calculated. The power output attenuation coefficient quantifies the percentage decrease in the instantaneous power output capability of the auxiliary energy storage device relative to its theoretical or previously assessed value, based on the micro-perturbation test results. For example, it can be calculated by analyzing the deviation between the actual and expected output power of the device during the micro-perturbation test. Its purpose is to accurately reflect the instantaneous power that the device can actually provide under the current state. The discharge duration attenuation coefficient quantifies the percentage reduction in the effective discharge duration of the auxiliary energy storage device relative to its theoretical or previously assessed value, based on the micro-perturbation test results. For example, it can be calculated by analyzing the deviation between the time the device maintains its discharge capability at a specific power level and the expected duration during the micro-perturbation test. Its purpose is to accurately reflect the actual discharge duration that the device can sustain under the current state.

[0038] Then, the instantaneous power output capability is corrected based on the power output attenuation coefficient. The effective discharge duration is also corrected based on the discharge duration attenuation coefficient. The instantaneous power output capability obtained through micro-discharge testing and comparison with nominal parameters can be multiplied by the power output attenuation coefficient, and the effective discharge duration can be multiplied by the discharge duration attenuation coefficient to obtain a correction value that better reflects the actual performance of the auxiliary energy storage device. For example, if the power output attenuation coefficient is 0.95, the corrected instantaneous power output capability is 95% of the original capability.

[0039] Finally, the generator set's operating configuration is adjusted based on the charging strategy, the corrected instantaneous power output capacity, and the effective discharge duration. When generating the charging strategy and adjusting the generator set's power generation load and operating configuration, these real-time corrected auxiliary energy storage device performance parameters can be taken into account to ensure that the generator set's operating configuration more accurately matches the actual capacity of the auxiliary energy storage devices, thereby achieving more efficient and stable grid operation control.

[0040] To illustrate this technical solution more clearly, a specific example is used below. Assume that after a micro-discharge test, the instantaneous power output capability of an auxiliary energy storage device is determined to be 10MW, with an effective discharge duration of 30 minutes. During subsequent operation, the system periodically performs micro-perturbation tests on the auxiliary energy storage device. For example, in one test, by applying a 0.1MW micro-discharge pulse, a slight deviation between the actual response curve and the ideal response curve was detected. Based on these micro-perturbation test results, the system calculates a power output attenuation coefficient of 0.98 and a discharge duration attenuation coefficient of 0.97. Therefore, the instantaneous power output capability of the auxiliary energy storage device is corrected, resulting in a corrected capability of 10MW * 0.98 = 9.8MW. Simultaneously, the effective discharge duration is corrected, resulting in a corrected duration of 30 minutes * 0.97 = 29.1 minutes.

[0041] When generating new charging strategies or adjusting generator set operating configurations, the system will no longer use the original 10MW and 30 minutes, but instead adopt revised 9.8MW and 29.1 minutes as the actual capacity parameters of the auxiliary energy storage device. For example, when the grid predicts that a future short-term load surge will require the auxiliary energy storage device to provide 9.5MW of instantaneous power, the system will determine whether the device can meet the demand based on the revised 9.8MW capacity, and adjust the generator set's power generation load accordingly to ensure that the generator set operates within its high-efficiency load range, while reserving sufficient charging space for the auxiliary energy storage device. This dynamic correction mechanism ensures that the generator set operating configuration decision is based on the most realistic performance state of the auxiliary energy storage device, thereby improving control accuracy and overall system stability.

[0042] Through the above technical solution, this embodiment can dynamically track the actual performance changes of auxiliary energy storage devices by using real-time micro-disturbance testing and attenuation coefficient correction. This allows the generator set's operational configuration adjustments to more accurately match the current capabilities of the auxiliary energy storage devices. This not only improves the accuracy and adaptability of the generator set's operational configuration and effectively avoids the risk of grid instability caused by equipment performance degradation, but also helps to more fully and rationally utilize the actual potential of the auxiliary energy storage devices, thereby further enhancing the energy-saving effect and operational reliability of the entire generator set's operation control system.

[0043] In some embodiments, in step S105, a charging strategy for the auxiliary energy storage device is generated based on the grid operating status, generator set operating characteristics, fuel costs, and generator set emission costs. This may include, but is not limited to, the following steps: Monitor the operating data of auxiliary energy storage devices, including internal temperature, voltage, current, and number of charge / discharge cycles; Based on operational data, assess the health status and remaining lifespan of auxiliary energy storage devices; Determine the maximum charging power and optimal charging range based on health status and remaining lifespan; Predict future short-term load impacts based on power grid operating status and historical load impact data; Calculate the minimum state of charge required for the auxiliary energy storage device based on future short-term load impacts. A charging strategy is generated based on the maximum charging power, optimal charging range, generator set operating characteristics, fuel cost, generator set emission cost, and minimum state of charge.

[0044] In some embodiments, the operational data of the auxiliary energy storage device can be monitored first. Key parameters generated by the auxiliary energy storage device during actual operation can be collected in real time or periodically. This operational data includes internal temperature, voltage, current, and charge / discharge cycle count. Internal temperature is an important indicator reflecting the device's thermal management status; excessively high temperatures may accelerate device aging. Voltage and current directly reflect the device's real-time charge / discharge status and power output. The charge / discharge cycle count is a core parameter for assessing the degree of device lifespan degradation. The acquisition of this data aims to provide a foundation for subsequent device condition assessment.

[0045] Then, based on the operational data, the health status and remaining useful life of the auxiliary energy storage equipment are assessed. State of Health (SOH) is typically quantified by analyzing parameters such as capacity decay and internal resistance changes, while Remaining Useful Life (RUL) is predicted based on SOH trends, historical operating loads, and a pre-defined lifespan model. The purpose of this assessment is to comprehensively understand the current performance level of the auxiliary energy storage equipment and its availability over a future period.

[0046] Then, based on the health status and remaining lifespan, the maximum charging power and optimal charging range are determined. The health status of auxiliary energy storage devices directly affects their ability to charge safely and efficiently. For example, for devices in poor health or nearing the end of their lifespan, in order to avoid further damage or prolong their service life, it is usually necessary to limit their maximum charging power and restrict their charging operations to a specific state of charge range that minimizes damage to the device, i.e., the optimal charging range.

[0047] Based on grid operating status and historical load impact data, future short-term load impacts can be predicted. Statistical analysis and machine learning models can be used to learn the patterns, frequency, and magnitude of historical load impacts, and combined with current grid operating parameters to predict potential load surges or drops in the near future. Simultaneously, based on future short-term load impacts, the minimum state of charge (SOC) required for auxiliary energy storage devices is calculated to ensure sufficient responsiveness in emergency situations. The minimum SOC refers to the minimum energy reserves that auxiliary energy storage devices must maintain to ensure stable grid operation when a predicted future short-term load impact occurs.

[0048] Finally, a charging strategy is generated based on the maximum charging power, optimal charging range, generator set operating characteristics, fuel cost, generator set emission cost, and minimum state of charge. This charging strategy is a comprehensive decision-making scheme that not only considers the economic operating objectives of the generator set (such as fuel cost and emission cost), but also fully incorporates the health status and life expectancy of the auxiliary energy storage equipment itself, as well as preparations for dealing with future grid uncertainties, thus forming a charging plan that is both economical and efficient, as well as safe and reliable.

[0049] To illustrate this technical solution more clearly, a specific example is used below. Assume the auxiliary energy storage device is a lithium-ion battery energy storage system. After the power generation load meets the load demand, the system first monitors the internal temperature, voltage, current, and charge / discharge cycle count of the battery energy storage system. For example, if the battery's charge / discharge cycle count is found to be high and the internal temperature is slightly above the normal range, the system will assess the battery energy storage system's health status as below average, with a remaining lifespan of approximately 60% of the nominal lifespan. Based on this assessment, the system will determine that the maximum charging power of the battery energy storage system should be limited to 80% of the rated power, and the optimal charging range is 30% to 70% of the state of charge (SOC). Simultaneously, combining historical grid load surge data, the system predicts a moderate-intensity load surge that may occur within the next two hours, and therefore calculates that the minimum SOC required by the battery energy storage system to cope with this surge should be 50%. Finally, when generating the charging strategy, the system will comprehensively consider the generator set's operating characteristics, current fuel and emission costs, and the aforementioned corrected maximum charging power, optimal charging range, and minimum SOC. For example, even if the current generator set is capable of charging at a higher power, the system may choose to charge at a lower power to avoid battery overheating and extend its lifespan, while ensuring that the battery state of charge reaches more than 50% before the predicted load impact arrives, thereby optimizing the long-term operating efficiency of auxiliary energy storage equipment while ensuring grid stability.

[0050] Through the above technical solution, this embodiment can significantly extend the service life of auxiliary energy storage equipment and reduce its total life cycle cost. Furthermore, because the charging strategy fully considers potential future load surges, the auxiliary energy storage equipment can always maintain the state of charge required to cope with emergencies, thereby greatly improving the stability and reliability of the power grid. This embodiment achieves efficient operation of the generator set while also optimizing the management of the auxiliary energy storage equipment, realizing a multiple balance between economic benefits, equipment lifespan, and power grid security.

[0051] In some embodiments, in step S105, a charging strategy for the auxiliary energy storage device is generated based on the grid operating status, generator set operating characteristics, fuel costs, and generator set emission costs. This may include, but is not limited to, the following steps: Step S301: Determine the target priority weights based on the generator set emission cost, fuel cost, generator set operating characteristics, auxiliary energy storage equipment state of charge, and auxiliary energy storage equipment health status. The target priority weights include emission cost priority weights, fuel cost priority weights, and equipment lifespan priority weights. Step S302: Calculate the comprehensive charging cost based on fuel cost, generator set emission cost, auxiliary energy storage equipment charge / discharge efficiency curve, and auxiliary energy storage equipment health status; Step S303: Determine the target charging power and charging duration based on the target priority weight, grid operating status, and overall charging cost; Step S304: Generate a charging strategy based on the target charging power and charging duration.

[0052] In some embodiments, considering only macroscopic factors may be insufficient to optimize the charging strategy, particularly in balancing long-term economic benefits, environmental protection, and the lifespan of the auxiliary energy storage device itself. For example, if the charging strategy fails to adequately consider the real-time state of charge and health of the auxiliary energy storage device, or lacks a cost assessment mechanism that can comprehensively weigh multiple objectives, it may lead to inefficient charging processes, premature device aging, or failure to maximize the overall system's operational benefits.

[0053] To this end, priority weights for objectives can be determined first, based on generator emission costs, fuel costs, generator operating characteristics, the state of charge (SBC) of auxiliary energy storage devices, and the health status of auxiliary energy storage devices. These priority weights include emission cost priority weights, fuel cost priority weights, and equipment lifespan priority weights. Different optimization objectives (such as reducing emissions, saving fuel, and extending equipment lifespan) can be assigned corresponding weights based on multiple dimensions, including generator emission costs, fuel costs, generator operating characteristics, SBC of auxiliary energy storage devices, and the health status of auxiliary energy storage devices. Specifically, the emission cost priority weight measures the degree of importance placed on reducing generator emissions during the charging strategy formulation process; the fuel cost priority weight measures the degree of importance placed on reducing fuel consumption; and the equipment lifespan priority weight measures the degree of importance placed on protecting auxiliary energy storage devices and extending their service life. These weights can be dynamically adjusted based on actual operational needs, policy guidance, or economic benefit analysis. For example, in areas with strict environmental requirements, the emission cost priority weight can be set to a higher value; during periods of significant fuel price fluctuations, the fuel cost priority weight can be increased; and when the health status of auxiliary energy storage devices is poor, the equipment lifespan priority weight can be increased to avoid overcharging and discharging.

[0054] Then, based on fuel costs, generator emission costs, the charge / discharge efficiency curve of the auxiliary energy storage device, and the health status of the auxiliary energy storage device, the overall charging cost is calculated. The state of charge (SBC) of the auxiliary energy storage device refers to the percentage of electrical energy currently stored, reflecting its available charge / discharge capacity. The health status of the auxiliary energy storage device is an assessment of its overall performance and lifespan, which can be evaluated using indicators such as internal impedance, capacity decay rate, and cycle count. The charge / discharge efficiency curve of the auxiliary energy storage device shows the variation in energy conversion efficiency under different charge / discharge powers, reflecting energy loss during the charging and discharging process.

[0055] Then, based on the target priority weights, grid operating status, and overall charging cost, the target charging power and charging duration are determined. Factors such as fuel costs, generator emission costs, and the charging / discharging efficiency curves and health status of auxiliary energy storage equipment can be comprehensively considered to quantify the total economic or environmental cost of the charging process. For example, the overall charging cost can be calculated as the sum of fuel consumption costs, emission penalty costs, and costs deducted due to charging / discharging efficiency losses and equipment lifespan degradation.

[0056] Finally, a charging strategy is generated based on the target charging power and charging duration. Taking into account target priority weights, grid operating conditions, and overall charging costs, the optimal charging power and duration that best meet the current optimization objectives are calculated. For example, when the grid is stable and the overall charging cost is low, a higher charging power can be determined to quickly replenish the auxiliary energy storage device's power; while when the grid fluctuates or the device's health is poor, a lower charging power and a longer charging duration may be determined to ensure smooth charging and protect the device.

[0057] To illustrate this technical solution more clearly, a specific example is used below. Assume that at a certain moment, the power grid is operating stably, and the generator load has met the load demand. At this time, a charging strategy needs to be generated for the auxiliary energy storage device. The system first determines the priority weights of the targets based on the current generator emission costs, fuel costs, generator operating characteristics, the auxiliary energy storage device's state of charge (e.g., currently at 30%), and the auxiliary energy storage device's health status (e.g., assessed as good). For example, if current environmental policies are becoming stricter, the emission cost priority weight might be set to 0.4; if fuel prices are moderate, the fuel cost priority weight might be set to 0.3; and due to the good health status of the device, the device lifespan priority weight might be set to 0.3.

[0058] Next, the system calculates the overall charging cost based on fuel costs, generator emission costs, the charging and discharging efficiency curves of the auxiliary energy storage device, and the health status of the auxiliary energy storage device. For example, by considering current fuel prices, generator emission coefficients, efficiency losses of the auxiliary energy storage device at different charging powers, and potential impacts on device lifespan, a quantified overall charging cost value is calculated. Finally, the system determines the target charging power and charging duration based on these target priority weights, grid operating status, and the calculated overall charging cost. For example, if the overall cost is low and the grid is stable, the system may determine to charge at a moderately high power (e.g., 500kW) for 2 hours to quickly increase the state of charge of the auxiliary energy storage device to 80%, while balancing cost and device lifespan. If the auxiliary energy storage device is in poor health, even with a low overall cost, the system may choose a lower charging power (e.g., 300kW) and a longer charging duration to slow down device aging. In this way, this embodiment can generate a dynamically optimized charging strategy that meets actual needs based on multi-dimensional real-time information.

[0059] Through the above technical solution, this embodiment incorporates considerations of the auxiliary energy storage device's state of charge, health status, target priority weights, and overall charging costs, enabling the generated charging strategy to more comprehensively balance economic benefits, environmental benefits, and equipment lifespan. Specifically, this embodiment effectively avoids sacrificing other important objectives by excessively pursuing one goal. For example, while reducing fuel costs, it also considers reducing emissions and extending the lifespan of auxiliary energy storage devices. Therefore, it not only ensures that generator sets operate within their high-efficiency load range, further improving energy-saving effects, but also significantly reduces the long-term operating and maintenance costs of generator sets, improves the utilization efficiency and reliability of auxiliary energy storage devices, and thus provides more reliable and economical support for the stable operation of the power grid.

[0060] In some embodiments, after determining the target priority weight in step S301 based on the generator set emission cost, fuel cost, generator set operating characteristics, auxiliary energy storage device state of charge, and auxiliary energy storage device health status, the method may further include, but is not limited to, the following steps: Step S401: Obtain priority conflict resolution strategy, power grid stability level, and load impact urgency level; Step S402: If the target indicator reaches the preset critical threshold, multi-dimensional indicator conflict detection is performed to obtain the indicator conflict detection result. The target indicators include the grid stability, the urgency of the load impact, the state of charge of the auxiliary energy storage device, or the health status of the auxiliary energy storage device. Step S403: If the indicator conflict detection result indicates that a conflict exists, then determine the direction and magnitude of priority weight adjustment according to the priority conflict resolution strategy. Step S404: Adjust the target priority weight according to the direction and magnitude of the priority weight adjustment.

[0061] In some embodiments, priority conflict resolution strategies, grid stability, and load surge urgency can be obtained first. Priority conflict resolution strategies refer to a predefined set of rules, algorithms, or decision logic used to guide the adjustment of priority weights for each objective when conflicts arise among multiple target indicators. This strategy can be built based on expert experience, historical data analysis, or machine learning models, aiming to ensure the system can make optimal or suboptimal decisions under complex operating conditions. Grid stability measures the degree to which the grid's operating state deviates from its normal stable state, and can be quantified, for example, through parameters such as frequency deviation, voltage fluctuation, and power oscillation. Load surge urgency refers to the potential threat posed by sudden changes in grid load to system stability, and can be assessed, for example, through indicators such as load change rate and surge duration.

[0062] If a target indicator reaches a preset critical threshold, multi-dimensional indicator conflict detection is performed to obtain the indicator conflict detection result. This process can determine whether multiple target indicators simultaneously reach or exceed their respective preset critical thresholds and assess whether there are contradictions or competition among these indicators. For example, a conflict may exist when the grid stability is low (requiring rapid response from auxiliary energy storage devices) and the state of charge of the auxiliary energy storage devices is also low (avoiding excessive discharge). The target indicators refer to key performance parameters that need to be focused on during generator operation control, including grid stability, load impact urgency, auxiliary energy storage device state of charge, or auxiliary energy storage device health status. The preset critical threshold is a boundary value set for each target indicator, which can be set using historical experimental data or expert experience. When any target indicator reaches or exceeds this threshold, it indicates that the system may face potential risks or be in a non-ideal operating state. The indicator conflict detection result is the output of this detection process, which can be a Boolean value (conflict indication / no conflict indication) or a detailed conflict report. The state of charge (SBC) of an auxiliary energy storage device refers to the percentage of electrical energy currently stored in the device, while the health status of the auxiliary energy storage device reflects the degree of performance degradation and remaining lifespan of the device.

[0063] If the indicator conflict detection result indicates a conflict, the direction and magnitude of priority weight adjustment are determined according to the priority conflict resolution strategy. A conflict indication means the system has identified a situation requiring priority weight adjustment. The direction and magnitude of priority weight adjustment refer to how to specifically increase or decrease the value of a particular priority weight after a conflict is detected, guiding the system towards a better outcome. For example, in a highly unstable power grid, the priority weight for power grid stability can be increased, while the priority weight for emission costs can be appropriately decreased. Finally, the target priority weight is adjusted according to the direction and magnitude of the priority weight adjustment.

[0064] Through the above technical solution, this embodiment effectively avoids the decision-making imbalance caused by fixed priority weights when multiple objective indicators simultaneously reach critical states. By introducing an intelligent conflict detection and resolution mechanism, this embodiment enables the generator set operation control system to adjust charging strategies more flexibly and intelligently when facing complex grid operating conditions and auxiliary energy storage equipment status. This not only ensures stable grid operation and effectively responds to emergencies such as sudden load surges, but also maximizes the balance between multiple optimization objectives such as fuel cost, emission cost, and auxiliary energy storage equipment lifespan while ensuring system safety. This dynamic adjustment capability significantly improves the robustness and adaptability of the generator set operation control strategy, thereby achieving a better balance between energy conservation, emission reduction, and grid stability.

[0065] In some embodiments, in step S403, determining the direction and magnitude of priority weight adjustment according to the priority conflict resolution strategy may include, but is not limited to, the following steps: Obtain the composite state decision table, which contains a predefined priority weight adjustment strategy for composite critical states; The composite state decision table is matched with the quantified state to obtain the matching result. The quantified state includes the grid stability, the urgency of the load impact, the state of charge of the auxiliary energy storage device, and the health status of the auxiliary energy storage device. If the matching result indicates that a matching decision strategy exists, then the direction and magnitude of priority weight adjustment are determined according to the decision strategy. If the matching result is that there is no matching decision strategy, then calculate the degree of deviation between the quantified state and each composite state in the composite state decision table according to the priority conflict resolution strategy. A weighted average of multiple deviation levels is used to obtain the comprehensive deviation value; Based on the overall deviation value, determine the direction and magnitude of priority weight adjustment.

[0066] In some embodiments, due to the complex and variable operating conditions of the power grid, a single priority conflict resolution strategy may be insufficient to accurately address complex conflict scenarios where multiple target indicators (such as grid stability, load impact urgency, auxiliary energy storage device state of charge, and auxiliary energy storage device health status) are simultaneously at a critical state. In such cases, relying solely on general strategies for adjustments may result in insufficient precision in the direction and magnitude of adjustments, failing to effectively balance the needs of all parties and thus affecting the overall efficiency of generator unit operation and the stability of the power grid.

[0067] To this end, a composite state decision table can be obtained first. This table contains predefined priority weight adjustment strategies for composite critical states. A composite critical state refers to a complex situation where multiple quantified states, such as grid stability, load impact urgency, auxiliary energy storage device state of charge, and auxiliary energy storage device health, simultaneously reach or approach their critical thresholds. The predefined priority weight adjustment strategy refers to the optimal weight adjustment scheme obtained through expert experience or simulation optimization for these specific composite critical states, including the direction of adjustment (e.g., increasing or decreasing the weight of a certain priority) and the magnitude of the adjustment.

[0068] The composite state decision table is then matched with the quantified state to obtain the matching result, which aims to determine whether the current actual operating state matches any predefined composite critical state in the decision table. The quantified state refers to a real-time quantitative description of the current power grid operation, including real-time values ​​of key indicators such as grid stability, load impact urgency, auxiliary energy storage device state of charge, and auxiliary energy storage device health status.

[0069] If the matching result indicates the existence of a matching decision strategy, it means that the current complex critical state has been identified by the decision table and has a corresponding optimization strategy. At this point, the direction and magnitude of priority weight adjustments can be determined based on the decision strategy. This approach ensures that the optimal adjustment scheme is executed quickly and accurately in known complex scenarios.

[0070] If the matching result indicates that no matching decision strategy exists, it means that the current complex critical state is not explicitly defined in the decision table, or deviates from the existing definition. In this case, the degree of deviation between the quantified state and each complex state in the complex state decision table can be calculated based on the priority conflict resolution strategy. The degree of deviation can be quantified using Euclidean distance, Manhattan distance, or other multidimensional distance metrics to reflect the similarity or difference between the current state and each known complex state.

[0071] The weighted average of multiple deviations is then calculated to obtain a comprehensive deviation value. The weights of the weighted average can be set according to the importance or sensitivity of each quantified state in the current system operation. For example, in the case of a highly unstable power grid, the deviation weight of the power grid stability can be higher. This comprehensive deviation value can comprehensively reflect the overall deviation between the current unknown composite state and the known optimization strategy.

[0072] Finally, based on the comprehensive deviation value, the direction and magnitude of priority weight adjustment are determined. This allows the system to generate a reasonable adjustment scheme that adapts to the current state by interpolation or extrapolation, based on the similarity with the known optimal strategy, when there is no direct matching strategy.

[0073] Through the above technical solution, this embodiment can significantly improve the accuracy and adaptability of priority weight adjustment. When facing complex scenarios where multiple target indicators are simultaneously at a critical state, the system no longer relies on a single general strategy, but can intelligently match based on a composite state decision table, or dynamically adjust through deviation calculation when no direct match is available. This makes the adjustment of generator unit operation configuration more precise, and can more effectively balance multiple objectives such as grid stability, the urgency of load impacts, and the state of charge and health of auxiliary energy storage devices. Therefore, while ensuring grid safety and stability, it maximizes the optimization of generator unit operating efficiency and economy, and reduces fuel and emission costs.

[0074] In some embodiments, after obtaining the priority conflict resolution strategy in step S401, the method may further include, but is not limited to, the following steps: Step S501: Monitor the performance data of the auxiliary energy storage equipment; Step S502: Perform time series analysis on the performance index data to identify performance indicators with series changes; Step S503: Perform time-series correlation analysis on the sequence change performance index and target priority weight to obtain the time-series correlation analysis results; Step S504: Adjust the priority conflict resolution strategy based on the results of the time series correlation analysis.

[0075] In some embodiments, the performance indicators of auxiliary energy storage devices, such as their charge / discharge efficiency and capacity decay, may dynamically change over time and with varying usage frequency. If the priority conflict resolution strategy fails to reflect these dynamic changes in a timely and accurate manner, it may result in the inability to fully consider the actual operating status and long-term health of the auxiliary energy storage devices when resolving priority conflicts, thereby affecting the optimization effect of the overall control strategy and the long-term stability of the system.

[0076] Therefore, performance data of auxiliary energy storage devices can be monitored first. Key parameters of the auxiliary energy storage devices during operation can be continuously collected and recorded, such as battery voltage, current, temperature, internal resistance, charge / discharge cycle count, energy throughput, and State of Health (SOH). This data comprehensively reflects the current operating status and performance degradation trend of the auxiliary energy storage devices.

[0077] Then, time series analysis is performed on the performance index data to identify performance indicators with sequential changes. Statistical or machine learning methods can be used to process and analyze continuously collected performance index data, aiming to reveal patterns, trends, periodicity, and outliers in the data over time, thereby identifying performance indicators with significant time dependence or predictable patterns of change. For example, methods such as moving averages, exponential smoothing, and ARIMA models can be used to predict future trends in performance indicators or detect early signs of performance degradation. Performance indicators with sequential changes refer to those whose values ​​or behavioral patterns show clear patterns of change over time, such as a gradual increase in internal resistance or a slow decrease in capacity.

[0078] Next, a time-series correlation analysis is performed on the performance indicators of the changing sequence and the target priority weights to obtain the results. This analysis can assess whether there is a statistical correlation between these dynamically changing performance indicators and the target priority weights (such as emission cost priority weight, fuel cost priority weight, and equipment lifespan priority weight) determined when generating the charging strategy, as well as the strength and direction of this correlation. For example, by calculating the Pearson correlation coefficient and Granger causality test, a positive correlation can be found between the decline in the health status of auxiliary energy storage equipment and the increase in equipment lifespan priority weight, or a certain correlation with the need to adjust the fuel cost priority weight. The time-series correlation analysis results will quantify this correlation, providing data support for subsequent strategy adjustments.

[0079] Finally, the priority conflict resolution strategy is adjusted based on the time-series correlation analysis results. The original priority conflict resolution strategy can be dynamically modified or optimized based on the quantitative time-series correlation analysis results. For example, if the analysis results indicate that the health status of auxiliary energy storage devices is continuously deteriorating and is strongly correlated with the device lifespan priority weight, the priority conflict resolution strategy can be adjusted to prioritize protecting device lifespan in the event of future conflicts. This could be achieved by limiting charging power or adjusting charging ranges to extend device lifespan, even if this might slightly increase fuel costs or emissions in the short term. This adjustment can involve modifying the decision rules, weighting coefficients, or threshold parameters in the strategy to ensure that the strategy adapts to actual changes in the performance of auxiliary energy storage devices.

[0080] Through the above technical solution, this embodiment can accurately reflect the actual operating status and performance degradation trend of auxiliary energy storage equipment, thereby making decisions that are more in line with current realities and long-term optimization goals when resolving conflicts among multiple indicators. This not only helps extend the service life of auxiliary energy storage equipment and reduce maintenance costs, but also, while ensuring grid stability, more effectively balances fuel costs and emission costs, achieving truly energy-efficient and high-performance operation of generator sets.

[0081] In some embodiments, in step S504, adjusting the priority conflict resolution strategy based on the time-series correlation analysis results may include, but is not limited to, the following steps: Obtain the degree of impact of priority weight adjustments on target metrics under the current system operating mode; Determine the initial causal relationship based on the degree of impact; Coupled analysis of initial causal relationships and target indicators is conducted to identify negative coupling effects; Based on the negative coupling effect and the pre-set coupling effect handling strategies, determine the direction and intensity of causal relationship adjustment; By adjusting the direction and intensity of the causal relationship, the initial causal relationship is adjusted to obtain the target causal relationship; Based on the results of time-series correlation analysis and the causal relationship of the objectives, the priority conflict resolution strategy is adjusted.

[0082] In some embodiments, relying solely on time-series correlation analysis results for strategy adjustments may fail to fully reveal the complex causal relationships and potential negative coupling effects between different priority weight adjustments and system target indicators. This adjustment method may lead to blind strategy adjustments, and may even adversely affect other key indicators while optimizing one indicator, thereby impacting the long-term stability and economy of the entire generator set operation control system.

[0083] To this end, we can first determine the impact of priority weight adjustments on target indicators under the current system operating mode. We can quantify the specific impact of adjustments to different priority weights (e.g., emission cost priority weight, fuel cost priority weight, and equipment lifespan priority weight) on target indicators such as grid stability, load impact urgency, auxiliary energy storage device state of charge, and auxiliary energy storage device health status through historical data analysis, expert experience, or simulation. For example, we can establish a mathematical model that takes the changes in priority weights as input and outputs the expected changes in target indicators.

[0084] Then, based on the degree of impact, the initial causal relationships are determined. A preliminary causal relationship diagram or causal matrix can be constructed based on the degree of impact to clarify which priority weight adjustments will directly or indirectly affect which target indicators, as well as the direction and approximate intensity of the impact. For example, increasing the priority weight of equipment lifespan may lead to a decrease in charging power, thereby affecting the stability of the power grid.

[0085] Further coupling analysis is conducted on the initial causal relationships and target indicators to identify negative coupling effects. This allows for in-depth analysis of whether interactions exist between different causal chains, particularly whether an adjustment in priority weights aimed at optimizing a particular target indicator might unintentionally negatively impact other target indicators. For example, excessively pursuing reduced emission costs might lead to generator sets operating in uneconomical ranges, thereby increasing fuel costs or accelerating equipment wear; this mutually constraining relationship constitutes a negative coupling effect.

[0086] Based on negative coupling effects and pre-defined coupling effect handling strategies, the direction and intensity of causal relationship adjustment are determined. Once negative coupling effects are identified, pre-defined handling strategies (e.g., prioritizing grid stability when negative coupling effects reach a certain threshold, or fine-tuning relevant weights to mitigate conflicts) can be used to determine how to adjust causal relationships to reduce or eliminate these negative impacts.

[0087] By adjusting the direction and intensity of causal relationships, the initial causal relationship is modified to obtain the target causal relationship. The determined adjustment direction and intensity can be applied to the initial causal relationship to form an optimized and corrected target causal relationship that better reflects actual operational conditions. This target causal relationship can more accurately reflect the complex dynamic relationship between priority weight adjustments and target indicators.

[0088] Finally, based on the results of time-series correlation analysis and the target causal relationship, the priority conflict resolution strategy is adjusted. The dynamic trend information provided by time-series correlation analysis can be combined with the revised target causal relationship for comprehensive decision-making, resulting in a more precise and intelligent adjustment of the priority conflict resolution strategy. This adjustment not only considers the correlation in historical data but also delves deeper into the underlying mechanisms.

[0089] Through the above technical solution, this embodiment effectively avoids suboptimal solutions or negative impacts that may result from strategy adjustments by deeply exploring the causal relationship between priority weight adjustments and target indicators, and proactively identifying and handling negative coupling effects. As a result, the generator set operation control system can achieve more refined multi-objective optimization, better balancing multiple conflicting objectives such as fuel costs, emission costs, and the lifespan of auxiliary energy storage equipment while ensuring grid stability, thereby improving the overall system's economy, reliability, and sustainability.

[0090] The beneficial effects of implementing the embodiments of the present invention include: First, the embodiments of this application monitor the grid operating parameters, and then identify the grid operating status based on the grid operating parameters. If the grid operating status is a sudden increase in load, the auxiliary energy storage device is controlled to release electrical energy. Then, the generator set's power generation load is adjusted according to the generator set's operating characteristics. Based on the grid operating status, generator set operating characteristics, fuel costs, and generator set emission costs, a charging strategy for the auxiliary energy storage device is generated. Finally, the generator set's operating configuration is adjusted according to the capacity of the auxiliary energy storage device and the charging strategy. This allows the generator set to operate in a high-efficiency load range by combining the generator set's power generation load and the auxiliary energy storage device's charging strategy, thereby achieving generator set operation control, improving reliability, and reducing fuel consumption.

[0091] like Figure 2 As shown, this embodiment of the invention also provides an energy-saving generator set operation control system, including: Data monitoring module 601 is used to monitor power grid operating parameters; The power grid status identification module 602 is used to identify the power grid operating status based on power grid operating parameters; The energy storage device control module 603 is used to control the auxiliary energy storage device to release electrical energy to stabilize the power grid if the power grid operating state is a sudden increase in load. The power generation load adjustment module 604 is used to adjust the power generation load of the generator set according to the operating characteristics of the generator set after the auxiliary energy storage device releases electrical energy. The charging strategy generation module 605 is used to generate a charging strategy for the auxiliary energy storage device based on the grid operating status, generator set operating characteristics, fuel cost and generator set emission cost after the power generation load meets the load demand. The operation configuration adjustment module 606 is used to adjust the operation configuration of the generator set according to the capacity and charging strategy of the auxiliary energy storage device, so that the generator set operates in the high-efficiency load range.

[0092] The content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0093] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

Claims

1. An energy-saving generator set operation control method, characterized in that, Includes the following steps: Monitor power grid operating parameters; Identify the power grid operating status based on the power grid operating parameters; If the power grid is in a state of sudden load increase, the auxiliary energy storage device is controlled to release electrical energy to stabilize the power grid; After the auxiliary energy storage device releases electrical energy, the power generation load of the generator set is adjusted according to the operating characteristics of the generator set; After the power generation load meets the load demand, a charging strategy for the auxiliary energy storage device is generated based on the power grid operating status, generator set operating characteristics, fuel cost, and generator set emission cost. Based on the capacity of the auxiliary energy storage device and the charging strategy, the operating configuration of the generator set is adjusted so that the generator set operates in the high-efficiency load range.

2. The method according to claim 1, characterized in that, The step of adjusting the operating configuration of the generator set according to the capacity of the auxiliary energy storage device and the charging strategy includes: Based on the capacity and discharge command of the auxiliary energy storage device, a micro-discharge test is performed on the auxiliary energy storage device to obtain the device discharge test results. The device discharge test results include the device response time, instantaneous power output curve, and voltage recovery after the discharge ends. The discharge test results of the device are compared with the nominal performance parameters of the auxiliary energy storage device to obtain the comparison results; Based on the comparison results, the instantaneous power output capability and effective discharge duration of the auxiliary energy storage device are determined; The operating configuration of the generator set is adjusted according to the charging strategy, the instantaneous power output capability, and the effective discharge duration.

3. The method according to claim 2, characterized in that, The step of adjusting the operating configuration of the generator set according to the charging strategy, the instantaneous power output capability, and the effective discharge duration includes: The auxiliary energy storage device was subjected to a micro-perturbation test, and the micro-perturbation test results were obtained. Based on the micro-perturbation test results, calculate the power output attenuation coefficient and the discharge duration attenuation coefficient; The instantaneous power output capability is corrected based on the power output attenuation coefficient. The effective discharge duration is corrected based on the discharge duration decay coefficient. The operating configuration of the generator set is adjusted based on the charging strategy, the corrected instantaneous power output capability, and the effective discharge duration.

4. The method according to claim 1, characterized in that, The step of generating a charging strategy for the auxiliary energy storage device based on the grid operating status, generator set operating characteristics, fuel costs, and generator set emission costs includes: Monitor the operating data of the auxiliary energy storage device, including internal temperature, voltage, current, and number of charge / discharge cycles; Based on the operational data, assess the health status and remaining lifespan of the auxiliary energy storage device; Based on the health status and remaining lifespan, determine the maximum charging power and the optimal charging range; Based on the power grid operating status and historical load impact data, predict future short-term load impacts; Calculate the minimum state of charge required for the auxiliary energy storage device based on the future short-term load impact; The charging strategy is generated based on the maximum charging power, the optimal charging range, the generator set operating characteristics, the fuel cost, the generator set emission cost, and the minimum state of charge.

5. The method according to claim 1, characterized in that, The step of generating a charging strategy for the auxiliary energy storage device based on the grid operating status, generator set operating characteristics, fuel costs, and generator set emission costs includes: Based on the generator set emission cost, the fuel cost, the generator set operating characteristics, the auxiliary energy storage device state of charge, and the auxiliary energy storage device health status, target priority weights are determined, including emission cost priority weights, fuel cost priority weights, and equipment lifespan priority weights. Calculate the comprehensive charging cost based on the fuel cost, the generator set emission cost, the charging and discharging efficiency curve of the auxiliary energy storage device, and the health status of the auxiliary energy storage device. The target charging power and charging duration are determined based on the target priority weight, the power grid operating status, and the overall charging cost. The charging strategy is generated based on the target charging power and the charging duration.

6. The method according to claim 5, characterized in that, After determining the target priority weights based on the generator set emission costs, fuel costs, generator set operating characteristics, auxiliary energy storage device state of charge, and auxiliary energy storage device health status, the method further includes: Obtain priority conflict resolution strategies, grid stability, and load surge urgency. If the target indicator reaches the preset critical threshold, multi-dimensional indicator conflict detection is performed to obtain the indicator conflict detection result. The target indicators include the grid stability, the load impact urgency, the state of charge of the auxiliary energy storage device, or the health status of the auxiliary energy storage device. If the indicator conflict detection result indicates the existence of a conflict, then the direction and magnitude of priority weight adjustment are determined according to the priority conflict resolution strategy. The target priority weight is adjusted according to the direction and magnitude of the priority weight adjustment.

7. The method according to claim 6, characterized in that, The step of determining the direction and magnitude of priority weight adjustment according to the priority conflict resolution strategy includes: Obtain a composite state decision table, which contains a predefined priority weight adjustment strategy for composite critical states; The composite state decision table is matched with the quantified state to obtain the matching result. The quantified state includes the grid stability level, the load impact urgency level, the auxiliary energy storage device's state of charge, and the auxiliary energy storage device's health status. If the matching result indicates that a matching decision strategy exists, then the direction and magnitude of the priority weight adjustment are determined according to the decision strategy. If the matching result is that there is no matching decision strategy, then according to the priority conflict resolution strategy, the degree of deviation between the quantified state and each composite state in the composite state decision table is calculated. A weighted average of the multiple deviation levels is used to obtain a comprehensive deviation value; Based on the comprehensive deviation value, the direction and magnitude of the priority weight adjustment are determined.

8. The method according to claim 6, characterized in that, After obtaining the priority conflict resolution strategy, the method further includes: Monitor the performance data of the auxiliary energy storage device; Perform time series analysis on the performance index data to identify performance indicators with sequence changes; A time-series correlation analysis was performed on the sequence change performance index and the target priority weight to obtain the time-series correlation analysis results. Based on the results of the time-series correlation analysis, the priority conflict resolution strategy is adjusted.

9. The method according to claim 8, characterized in that, The step of adjusting the priority conflict resolution strategy based on the time-series correlation analysis results includes: Obtain the degree of impact of priority weight adjustment on the target indicator under the current system operating mode; Based on the degree of influence, determine the initial causal relationship; A coupling analysis is performed on the initial causal relationship and the target indicator to identify negative coupling effects; Based on the negative coupling effect and the preset coupling effect handling strategy, determine the direction and intensity of causal relationship adjustment; The direction and intensity of the causal relationship are adjusted according to the causal relationship to obtain the target causal relationship. Based on the time-series correlation analysis results and the target causal relationship, the priority conflict resolution strategy is adjusted.

10. An energy-saving generator set operation control system, characterized in that, include: The data monitoring module is used to monitor power grid operating parameters; A power grid status identification module is used to identify the power grid operating status based on the power grid operating parameters. An energy storage device control module is used to control an auxiliary energy storage device to release electrical energy to stabilize the power grid if the power grid operating state is a sudden increase in load. The power generation load adjustment module is used to adjust the power generation load of the generator set according to the operating characteristics of the generator set after the auxiliary energy storage device releases electrical energy. The charging strategy generation module is used to generate a charging strategy for the auxiliary energy storage device after the power generation load meets the load demand, based on the grid operating status, generator set operating characteristics, fuel cost and generator set emission cost. The operation configuration adjustment module is used to adjust the operation configuration of the generator set according to the capacity of the auxiliary energy storage device and the charging strategy, so that the generator set operates in the high-efficiency load range.

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