Non-grid-connected energy storage type elevator charging and discharging strategy determining method, device and equipment

Abstract

The invention relates to a non-grid-connected energy storage type elevator charging and discharging strategy determining method, device and equipment. The method comprises the steps that load prediction parameters and energy storage capacity parameters of an elevator system are obtained; load prediction is carried out based on the load prediction parameters, and time sequence prediction data of elevator power consumption and power generation in the future time period are generated; carrying out charging and discharging capacity evaluation based on the energy storage capacity parameters, and generating allowable charging and discharging power of the elevator energy-saving device; the time sequence prediction data, the allowable charging and discharging power and the time-of-use electricity price information are mapped to a unified time axis, and a plurality of charging and discharging income links meeting preset constraint conditions are recognized according to the preset constraint conditions of the allowable charging and discharging power and elevator operation working conditions; and selecting the charging and discharging income link with the maximum net income value as an optimal charging and discharging income link, and generating a charging and discharging control strategy. The method can be adapted to a specific scene, and net income maximization is determined to be realized in a full life cycle.

Description

Technical Field

[0001] This application relates to the field of elevator charging and discharging technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for determining the charging and discharging strategy of a non-grid-connected energy storage elevator. Background Technology

[0002] With the acceleration of urbanization and the widespread adoption of high-rise buildings, elevators have become an indispensable vertical transportation tool in modern buildings. To reduce elevator operating costs and improve energy efficiency, elevator energy-saving technologies, especially energy recovery systems based on energy storage devices, have been widely applied. These systems typically recover and regenerate energy during elevator braking and store it in batteries or supercapacitors. When the elevator consumes energy, the stored energy is released, thereby reducing direct consumption of electricity from the power grid and playing a role in peak shaving and valley filling.

[0003] However, traditional elevator energy storage systems still face significant challenges in maximizing economic benefits. Most existing solutions employ relatively simple control strategies, such as relying solely on fixed peak-valley electricity price periods for patterned charging and discharging, or reactive control based solely on the elevator's real-time power status. These methods fail to adequately consider the highly time-varying and random nature of elevator loads, as well as the inherent patterns closely related to building usage scenarios (e.g., office buildings, residences, shopping malls). Furthermore, the performance boundaries of the energy storage device itself (e.g., charging and discharging capacity, safe operating range, and lifespan degradation costs) are often simplified to fixed parameters, failing to be dynamically and meticulously considered in scheduling decisions. This makes it difficult for existing systems to achieve theoretically optimal economic benefits in complex real-world operating environments, easily leading to problems such as "insufficient charging affecting discharging benefits" or "overcharging accelerating equipment wear," thus limiting the overall benefits and promotion potential of elevator energy storage technology.

[0004] Therefore, there is an urgent need for a method, device, computer equipment, computer-readable storage medium, and computer program product for determining the charging and discharging strategy of off-grid energy storage elevators, which can be adapted to specific scenarios and determine the charging and discharging strategy that maximizes net benefits throughout the entire life cycle. Summary of the Invention

[0005] Therefore, it is necessary to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for determining the charging and discharging strategy of an off-grid energy storage elevator that can be adapted to specific scenarios and determine the charging and discharging strategy that maximizes net benefits throughout the entire life cycle, in order to address the above-mentioned technical problems.

[0006] Firstly, this application provides a method for determining the charging and discharging strategy of an off-grid energy storage elevator, including:

[0007] Acquire multi-source parameters of the elevator system, including load prediction parameters and energy storage capacity parameters;

[0008] Based on the load forecasting parameters, load forecasting is performed to generate time series forecast data of elevator power consumption and power generation in future periods.

[0009] Based on the energy storage capacity parameters, the charging and discharging capacity is evaluated to generate the allowable charging and discharging power of the elevator energy-saving device.

[0010] The time series prediction data, the allowable charging and discharging power, and the time-of-use electricity price information are mapped to a unified time axis, and multiple charging and discharging revenue links that meet the preset constraints are identified based on the preset constraints of the allowable charging and discharging power and the elevator operating conditions.

[0011] The charging and discharging benefit link with the largest net benefit value is selected as the optimal charging and discharging benefit link, and a charging and discharging control strategy is generated based on the optimal charging and discharging benefit link.

[0012] In one embodiment, the step of generating time-series forecast data of elevator power consumption and power generation in future periods based on the load forecast parameters includes:

[0013] Based on the elevator operating parameters in the load forecast parameters, the basic power consumption sequence and the basic power generation sequence are calculated.

[0014] Using the elevator usage scenario parameters in the load prediction parameters, obtain the scenario-time weighting coefficient, building occupancy rate weighting coefficient, and equipment utilization rate weighting coefficient associated with building type and usage period;

[0015] Based on the scenario-time weight coefficient, the building occupancy rate weight coefficient, and the equipment utilization rate weight coefficient, the basic power consumption sequence and the basic power generation sequence are subjected to a first-level weighted correction to obtain the scenario-corrected power consumption sequence and the scenario-corrected power generation sequence.

[0016] Obtain the random fluctuation parameter and environmental and operating condition parameters from the load forecast parameters, and obtain the random fluctuation weight coefficient based on the random fluctuation parameter, and obtain the environmental and operating condition correction coefficient based on the environmental and operating condition parameters;

[0017] Using the random fluctuation weighting coefficient and the environmental condition correction coefficient, a second-level product correction is performed on the scenario-corrected power consumption sequence and the scenario-corrected power generation sequence to output the time series prediction data.

[0018] In one embodiment, the calculation formula for performing a second-level product correction on the scenario-corrected power consumption sequence and the scenario-corrected power generation sequence using the random fluctuation weighting coefficient and the environmental condition correction coefficient includes:

[0019] ;

[0020] in, This represents the predicted power consumption or power generation for the i-th time period. This represents the power consumption or power generation in the i-th time period after the first-level weighted correction, where γ is the random fluctuation weighting coefficient. The random fluctuation factor is δ, which is the correction coefficient for the environmental conditions. It is a comprehensive influencing factor of environmental operating conditions.

[0021] In one embodiment, the step of identifying multiple charging and discharging benefit links that satisfy the preset constraints based on the allowed charging and discharging power and the elevator operating conditions includes:

[0022] On the unified time axis, based on the preset constraints of the allowable charging and discharging power and the elevator operating conditions, all combinations of charging and discharging periods are traversed.

[0023] For each of the aforementioned time period combinations, based on the time series prediction data, the total available charging amount for the charging period and the total required discharging amount for the discharging period are calculated.

[0024] If the total available charging amount is not less than the total required discharging amount, and the electricity price corresponding to the charging period is lower than the electricity price corresponding to the discharging period, the current period combination is determined to be an effective period combination.

[0025] Based on the planned charge and discharge amounts corresponding to multiple effective time period combinations, the charge and discharge revenue links that meet the preset constraints are determined.

[0026] In one embodiment, the step of evaluating the charge / discharge capacity based on the energy storage capacity parameters to generate the allowable charge / discharge power of the elevator energy-saving device includes:

[0027] Obtain real-time status data of the elevator energy-saving device from the energy storage capacity parameters. The real-time status data includes the current state of charge, battery temperature, and fault diagnosis signals.

[0028] Based on the current state of charge and the battery temperature, a preset discharge power mapping relationship is queried to determine the theoretical maximum charge and discharge power under the current conditions; and based on the fault diagnosis signal, a power limiting coefficient is determined.

[0029] Based on the theoretical maximum charging and discharging power and the power limitation coefficient, and combined with the voltage operating range and charged state operating range of the elevator energy-saving device for boundary verification, the allowable charging and discharging power is calculated and output.

[0030] In one embodiment, after generating the charge / discharge control strategy based on the optimal charge / discharge benefit link, the method further includes:

[0031] The charging and discharging control strategy is executed, and the actual operating data of the elevator and the real-time status data of the elevator energy-saving device are monitored in real time.

[0032] The actual operating data is compared with the time series prediction data;

[0033] If the comparison result exceeds the preset threshold, the charging and discharging control strategy is updated based on the latest actual operating data and the real-time status data of the elevator energy-saving device.

[0034] Secondly, this application also provides a device for determining the charging and discharging strategy of an off-grid energy storage elevator, comprising:

[0035] The acquisition module is used to acquire multi-source parameters of the elevator system, including load prediction parameters and energy storage capacity parameters.

[0036] The prediction module is used to perform load prediction based on the load prediction parameters and generate time series prediction data of elevator power consumption and power generation in future periods.

[0037] The evaluation module is used to evaluate the charging and discharging capabilities based on the energy storage capacity parameters and generate the allowable charging and discharging power of the elevator energy-saving device.

[0038] The identification module is used to map the time series prediction data, the allowable charging and discharging power, and the time-of-use electricity price information to a unified time axis, and to identify multiple charging and discharging revenue links that meet the preset constraints based on the preset constraints of the allowable charging and discharging power and the elevator operating conditions.

[0039] The strategy generation module is used to select the charging and discharging benefit link with the largest net benefit value as the optimal charging and discharging benefit link, and generate a charging and discharging control strategy based on the optimal charging and discharging benefit link.

[0040] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0041] Acquire multi-source parameters of the elevator system, including load prediction parameters and energy storage capacity parameters;

[0042] Based on the load forecasting parameters, load forecasting is performed to generate time series forecast data of elevator power consumption and power generation in future periods.

[0043] Based on the energy storage capacity parameters, the charging and discharging capacity is evaluated to generate the allowable charging and discharging power of the elevator energy-saving device.

[0044] The time series prediction data, the allowable charging and discharging power, and the time-of-use electricity price information are mapped to a unified time axis, and multiple charging and discharging revenue links that meet the preset constraints are identified based on the preset constraints of the allowable charging and discharging power and the elevator operating conditions.

[0045] The charging and discharging benefit link with the largest net benefit value is selected as the optimal charging and discharging benefit link, and a charging and discharging control strategy is generated based on the optimal charging and discharging benefit link.

[0046] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0047] Acquire multi-source parameters of the elevator system, including load prediction parameters and energy storage capacity parameters;

[0048] Based on the load forecasting parameters, load forecasting is performed to generate time series forecast data of elevator power consumption and power generation in future periods.

[0049] Based on the energy storage capacity parameters, the charging and discharging capacity is evaluated to generate the allowable charging and discharging power of the elevator energy-saving device.

[0050] The time series prediction data, the allowable charging and discharging power, and the time-of-use electricity price information are mapped to a unified time axis, and multiple charging and discharging revenue links that meet the preset constraints are identified based on the preset constraints of the allowable charging and discharging power and the elevator operating conditions.

[0051] The charging and discharging benefit link with the largest net benefit value is selected as the optimal charging and discharging benefit link, and a charging and discharging control strategy is generated based on the optimal charging and discharging benefit link.

[0052] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0053] Acquire multi-source parameters of the elevator system, including load prediction parameters and energy storage capacity parameters;

[0054] Based on the load forecasting parameters, load forecasting is performed to generate time series forecast data of elevator power consumption and power generation in future periods.

[0055] Based on the energy storage capacity parameters, the charging and discharging capacity is evaluated to generate the allowable charging and discharging power of the elevator energy-saving device.

[0056] The time series prediction data, the allowable charging and discharging power, and the time-of-use electricity price information are mapped to a unified time axis, and multiple charging and discharging revenue links that meet the preset constraints are identified based on the preset constraints of the allowable charging and discharging power and the elevator operating conditions.

[0057] The charging and discharging benefit link with the largest net benefit value is selected as the optimal charging and discharging benefit link, and a charging and discharging control strategy is generated based on the optimal charging and discharging benefit link.

[0058] The aforementioned method, device, computer equipment, computer-readable storage medium, and computer program product for determining the charging and discharging strategy of off-grid energy storage elevators solve the problems of single data dimension and insufficient prediction accuracy in traditional solutions by acquiring and integrating two core parameters: elevator load forecasting and energy storage capacity. This provides a reliable data foundation for subsequent optimization decisions. By aligning and integrating load and power generation forecasts, dynamic energy storage capacity assessment results, and time-of-use electricity price information on a unified time axis, and introducing preset constraints and operating condition screening, all potential charging and discharging benefit links can be identified from numerous feasible solutions, breaking through the traditional decision-making mode that relies on fixed rules or local optimization. Finally, by calculating and comparing the net benefits of each link to select the optimal link, a refined control strategy that takes into account real-time electricity prices, the safe operating boundary of the energy storage device, and the actual elevator operating needs can be generated. This significantly improves the overall economic operating efficiency and energy utilization efficiency of the elevator energy storage system while ensuring system safety and equipment lifespan. Attached Figure Description

[0059] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0060] Figure 1 This is a diagram illustrating the application environment of a method for determining the charging and discharging strategy of an off-grid energy storage elevator in one embodiment.

[0061] Figure 2 This is a flowchart illustrating a method for determining the charging and discharging strategy of an off-grid energy storage elevator in one embodiment.

[0062] Figure 3 This is a flowchart illustrating the method for determining the charging and discharging strategy of an off-grid energy storage elevator in another embodiment.

[0063] Figure 4 This is a structural block diagram of a device for determining the charging and discharging strategy of an off-grid energy storage elevator in one embodiment.

[0064] Figure 5 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0065] 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. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0066] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0067] In one exemplary embodiment, such as Figure 1 As shown, a method for determining the charging and discharging strategy of an off-grid energy storage elevator is provided. Taking the application of this method to a server as an example, the method includes the following steps S102 to S110. Wherein:

[0068] Step S102: Obtain multi-source parameters of the elevator system, including load prediction parameters and energy storage capacity parameters.

[0069] Specifically, load forecasting parameters are used to characterize the energy consumption characteristics of elevators and external influencing factors. They include at least parameters related to the elevator's own operation (such as historical and real-time speed, load, and direction of travel), usage scenario parameters of the building where the elevator is located (such as whether the building type is office or residential, and the corresponding typical weekday / holiday patterns, occupancy rate, and equipment usage frequency), random fluctuation parameters representing occasional events (such as sudden high loads, planned or fault-related shutdowns), and environmental and operating condition parameters reflecting long-term changes (such as the impact of ambient temperature on motor efficiency and performance degradation of elevator equipment due to aging).

[0070] Energy storage capacity parameters focus on the real-time status and inherent performance of elevator energy-saving devices (such as lithium batteries, supercapacitors, and other energy storage systems). These parameters include at least real-time status data directly monitored by sensors (such as the current state of charge (SOC) of the energy storage unit, battery temperature, fault diagnosis signals, etc.) and inherent performance parameters obtained from equipment specifications or configuration files (such as the allowable safe voltage operating range and the set safe operating range of the state of charge).

[0071] Step S104: Based on the load forecasting parameters, perform load forecasting to generate time series forecast data of elevator power consumption and power generation in the future period.

[0072] Specifically, firstly, by using the elevator operating parameters in the load forecast parameters and combining them with the power characteristic model of the elevator drive system, the basic power consumption sequence and basic power generation sequence for each future period are calculated, which mainly reflect the energy change trend of the elevator under ideal standard operating conditions.

[0073] Next, in order to more accurately match the actual usage patterns of the building where the elevator is located, elevator usage scenario parameters need to be introduced for the first-level correction. This correction process transforms the universal basic prediction into scenario-corrected power consumption sequences and scenario-corrected power generation sequences that reflect the flow patterns and usage intensity of people in specific scenarios.

[0074] Finally, to further improve the robustness and accuracy of predictions, the impact of random events and long-term operating environment must be considered. By introducing random fluctuation parameters and environmental and operating condition parameters, corresponding random fluctuation weighting coefficients and environmental and operating condition correction coefficients are obtained, and a second-level product correction is performed on the scenario-corrected sequence. The random fluctuation weighting coefficient is mainly used to quantify the impact of sudden load changes or temporary equipment anomalies; the environmental and operating condition correction coefficient is used to quantify the systematic shift in energy efficiency caused by changes in ambient temperature, equipment aging, etc.

[0075] Step S106: Evaluate the charging and discharging capabilities based on the energy storage capacity parameters to generate the allowable charging and discharging power of the elevator energy-saving device.

[0076] Specifically, based on the current state of charge (SOC) and battery temperature of the elevator energy-saving device, the pre-set charging and discharging power mapping relationship (usually in the form of a MAP chart or data table) can be queried to assess the maximum charging and discharging power value that the energy storage unit can theoretically withstand under the current SOC and temperature conditions.

[0077] The evaluation process also needs to consider the equipment's health status and rigid safety limits. Therefore, further constraint verification is required by combining other factors in the energy storage capacity parameters: First, based on real-time acquired fault diagnosis signals, if the device has a fault or warning, the theoretical maximum power should be lowered or reduced to zero using a power limitation coefficient; Second, the inherent safe operating boundaries of the energy storage device must be strictly followed, namely its permissible voltage operating range ([V_min, V_max]) and state of charge operating range ([SOC_min, SOC_max]), ensuring that the evaluated power does not cause the voltage or SOC to exceed these safety limits. By comprehensively considering theoretical capacity, health status, and safety boundaries, the system dynamically calculates and outputs a currently feasible permissible charge and discharge power.

[0078] Step S108: Map the time series prediction data, allowable charging and discharging power, and time-of-use electricity price information to a unified time axis, and identify multiple charging and discharging revenue links that meet the preset constraints based on the preset constraints of allowable charging and discharging power and elevator operating conditions.

[0079] Specifically, time-series forecast data, allowable charging and discharging power, and time-of-use electricity price information are mapped to a unified time axis. The purpose is to align energy supply and demand forecasts, system capacity boundaries, and external economic signals for future periods on the same time base. Based on this unified time axis, the charging and discharging revenue chain can be identified.

[0080] The primary condition for a link to be effective is that energy balance can be achieved, meaning the predicted total available charging capacity is not less than the predicted total demand for discharging. Simultaneously, the combination must have a basic arbitrage opportunity, meaning the time-of-use electricity price for charging periods should be lower than the time-of-use electricity price for discharging periods.

[0081] Step S110: Select the charging and discharging benefit link with the largest net benefit value as the optimal charging and discharging benefit link, and generate a charging and discharging control strategy based on the optimal charging and discharging benefit link.

[0082] Specifically, the calculation of net revenue involves a detailed accounting of each charge-discharge revenue link. The calculation is based on the specific charging and discharging periods and planned charge / discharge volumes corresponding to that link. The net revenue value mainly consists of two parts: positive revenue and costs. The positive revenue is the difference between the total electricity revenue during the discharging period and the total electricity cost during the charging period, i.e., arbitrage revenue based on time-of-use pricing. The cost part includes the energy conversion loss costs that inevitably occur when performing the charge / discharge operation of that link, as well as the equipment degradation costs incurred due to battery cycling. The latter is usually calculated based on the degradation cost per cycle and the equivalent number of cycles calculated from the charge / discharge volume of this link. The net revenue calculated using this formula comprehensively reflects the actual economic value of each link after deducting all relevant costs.

[0083] After calculating the net benefit value of all links, the system compares and selects the link with the largest net benefit value, defining it as the "optimal charging and discharging benefit link". Finally, based on the key parameters specified by this optimal link, such as the charging start and end time, the discharging start and end time, and the planned charging and discharging power (derived from the planned charging and discharging amount and the time period length), a charging and discharging control strategy with clear time nodes and power commands is generated and can be directly sent to the local controller of the elevator energy-saving device, thus completing the transformation from global optimization decision to specific execution commands.

[0084] The aforementioned method for determining the charging and discharging strategy of off-grid energy storage elevators addresses the problems of single data dimension and insufficient prediction accuracy in traditional schemes by acquiring and integrating two core parameters: elevator load forecasting and energy storage capacity. This provides a reliable data foundation for subsequent optimization decisions. By aligning and integrating load and power generation forecasts, dynamic energy storage capacity assessment results, and time-of-use electricity price information on a unified time axis, and introducing preset constraints and operating condition screening, all potential charging and discharging benefit links can be identified from numerous feasible schemes, breaking through the traditional decision-making mode that relies on fixed rules or local optimization. Finally, by calculating and comparing the net benefits of each link to select the optimal link, a refined control strategy that takes into account real-time electricity prices, the safe operating boundary of the energy storage device, and the actual elevator operating needs can be generated. This significantly improves the overall economic operating efficiency and energy utilization efficiency of the elevator energy storage system while ensuring system safety and equipment lifespan.

[0085] In one exemplary embodiment, such as Figure 2 As shown, load forecasting is performed based on load forecasting parameters to generate time series forecast data of elevator power consumption and power generation for future periods, including:

[0086] Step S202: Based on the elevator operation parameters in the load forecast parameters, calculate the basic power consumption sequence and the basic power generation sequence.

[0087] Step S204: Using the elevator usage scenario parameters in the load forecast parameters, obtain the scenario-time weight coefficient, building occupancy rate weight coefficient, and equipment utilization rate weight coefficient associated with building type and usage period.

[0088] Step S206: Based on the scenario-time weight coefficient, building occupancy rate weight coefficient, and equipment utilization rate weight coefficient, perform the first-level weighted correction on the basic power consumption sequence and the basic power generation sequence to obtain the scenario-corrected power consumption sequence and the scenario-corrected power generation sequence.

[0089] Step S208: Obtain the random fluctuation parameter and environmental and operating condition parameters in the load forecast parameters, and obtain the random fluctuation weight coefficient based on the random fluctuation parameter, and obtain the environmental and operating condition correction coefficient based on the environmental and operating condition parameters.

[0090] Step S210: Using random fluctuation weighting coefficients and environmental condition correction coefficients, perform a second-level product correction on the scenario-corrected power consumption sequence and the scenario-corrected power generation sequence, and output time series prediction data.

[0091] Specifically, firstly, using elevator operating parameters from the load forecast parameters (such as power generation during heavy-load downward / light-load upward travel across different floor spans, power generation during heavy-load upward / light-load downward travel across different floor spans, and power of elevator fixed auxiliary equipment) as input, and combining them with the physical characteristic model of the elevator traction machine and drive system, the basic power consumption sequence and basic power generation sequence for each future time interval are calculated. This basic sequence reflects the theoretical energy flow of the elevator under standard operating conditions.

[0092] The formula for calculating power consumption is as follows:

[0093] , where i = 1, 2, ..., n.

[0094] Let be the power consumption during the i-th time period; Δt is the duration of each time period. The average power of auxiliary equipment (such as elevator control cabinets); This represents the power consumption of the elevator during the i-th time period.

[0095] The formula for calculating power generation is:

[0096] , where i = 1, 2, ..., n.

[0097] Let be the power generation during the i-th time period; Δt be the duration of each time period. This represents the amount of electricity generated by the elevator during the i-th time period.

[0098] Subsequently, to ensure the forecast aligns with the actual operating environment, a first-level weighted correction is required. This step utilizes elevator usage scenario parameters from the load forecast parameters, specifically including: mapping the corresponding scenario-time weight coefficient based on the building type (e.g., office building, hospital, residential) and the current date and time (weekday / weekend / holiday); obtaining the actual occupancy rate weight coefficient for the building; and statistically defined or predefined equipment utilization rate weight coefficients. Using these three coefficients, the aforementioned basic power consumption sequence and basic power generation sequence are weighted to obtain the scenario-corrected power consumption sequence and scenario-corrected power generation sequence. This correction aims to adapt the general basic forecast to the specific usage patterns and intensities of a particular building.

[0099] Among them, the scene-time weight coefficient Adjustments were made based on historical scenario data. Here, 's' represents the scenario type (e.g., office building, commercial apartment, residential building, commercial complex, industrial park, hospital, school, etc.), and 't' represents the time period.

[0100] Occupancy rate weighting coefficient Adjustments will be made based on the actual occupancy rate of the building.

[0101] Utilization rate weighting coefficient Adjust according to the frequency of equipment or space usage.

[0102] The following formula can be used to predict elevator power generation / consumption by scenario and time period:

[0103]

[0104] in, This represents the power generation / consumption of the elevator equipment during the i-th time period.

[0105] Finally, to further enhance the robustness of the forecast and address uncertainties, a second-level product correction is performed. This stage introduces stochastic fluctuation parameters and environmental and operating condition parameters from the load forecast parameters. The stochastic fluctuation parameters are used to generate stochastic fluctuation weighting coefficients to quantify the impact of sudden load events or temporary equipment anomalies, such as sudden load analysis and fault / maintenance handling. The environmental and operating condition parameters are used to generate environmental and operating condition correction coefficients to quantify the energy efficiency degradation caused by systematic factors such as changes in ambient temperature and long-term equipment aging. These two coefficients are then used to perform a product correction on the scenario-corrected sequence.

[0106] Random fluctuation weighting coefficient Used to quantify the impact of abnormal operating conditions such as sudden loads, elevator maintenance or malfunctions; environmental condition correction factor. It is used to quantitatively consider the impact of operating conditions such as ambient temperature and elevator aging.

[0107] The predicted values ​​are calibrated a second time using the following correction formula:

[0108]

[0109] in, The coefficient representing the influence of random fluctuations. This is the comprehensive impact coefficient of environmental operating conditions.

[0110] when At that time, the corrected predicted values ​​of elevator power consumption / generation at different time periods The boundary condition will be zeroed out. This boundary condition can be used to verify the prediction results under abnormal operating conditions, such as when the elevator needs to be disconnected from the mains power for maintenance.

[0111] In this embodiment, the refined forecasting process of hierarchical weighting and product correction significantly improves the accuracy and adaptability of load forecasting. It not only overcomes the shortcomings of traditional methods in not considering complex operating environments and random factors, but also enables the forecasting results to closely match the actual usage patterns and real-time operating conditions of specific buildings, providing a reliable and accurate data input basis for subsequent charging and discharging decisions based on optimal economic benefits.

[0112] In an exemplary embodiment, the calculation formula for the second-level product correction of the scenario-corrected power consumption sequence and the scenario-corrected power generation sequence, using a random fluctuation weighting coefficient and an environmental condition correction coefficient, includes:

[0113] ;

[0114] in, This represents the predicted power consumption or power generation for the i-th time period. This represents the electricity consumption or power generation in the i-th time period after the first level of weighted correction, where γ is the random fluctuation weighting coefficient. This represents the random fluctuation influence factor. δ is the environmental condition correction coefficient. It is a comprehensive influencing factor of environmental operating conditions.

[0115] Specifically, The term is used to quantify the impact of sudden, sporadic fluctuations. The random fluctuation weighting coefficient γ controls the intensity of this impact, and the random fluctuation impact factor... This quantifies the expected magnitude of a specific event (such as a sudden surge in passenger flow or temporary maintenance). This item can be positive or negative and is used to simulate the instantaneous surge or drop in the predicted value.

[0116] The term is used to quantify the impact of long-term, systematic deviations. The environmental condition correction coefficient δ characterizes the degree of impact of slowly changing factors such as ambient temperature and equipment aging on overall energy efficiency. The comprehensive environmental condition impact factor... This reflects the specific magnitude of these factors under current conditions, and this item is mainly used to correct the trend of the predicted value.

[0117] By multiplying this comprehensive adjustment factor by the scene baseline prediction, the final output is... It not only includes elevator operation patterns and building usage patterns, but also dynamically incorporates random disturbances and long-term performance changes, making the prediction results both scenario-specific and adaptable to uncertainty.

[0118] In this embodiment, random factors and slowly changing operating conditions that are difficult to model precisely are transformed into quantifiable and adjustable parameters and fused together, thereby achieving a refined mathematical representation of the complex energy change behavior of elevators. This significantly improves the robustness and accuracy of the prediction model in actual operation and reduces the risk of strategy failure due to prediction bias.

[0119] In one exemplary embodiment, such as Figure 3 As shown, based on the preset constraints of allowable charging and discharging power and the elevator operating conditions, multiple charging and discharging revenue chains that meet the preset constraints are identified, including:

[0120] Step S302: On a unified time axis, based on the preset constraints of the allowable charging and discharging power and the elevator operating conditions, traverse all combinations of charging and discharging periods.

[0121] Step S304: For each time period combination, calculate the total available charging amount during the charging period and the total required discharging amount during the discharging period based on time series prediction data.

[0122] Step S306: If the total available charging amount is not less than the total required discharging amount, and the electricity price corresponding to the charging period is lower than the electricity price corresponding to the discharging period, then determine the current time period combination as an effective time period combination.

[0123] Step S308: Based on the planned charge and discharge amounts corresponding to multiple effective time period combinations, determine the charge and discharge revenue links that meet the preset constraints.

[0124] Specifically, on a unified time axis, under the premise of satisfying the constraints of the allowed charging and discharging power (such as maximum power limit and minimum duration) and the basic operating logic of the elevator (for example, discharging cannot be scheduled during the time when the elevator itself consumes power), all logically possible combinations of "charging period - discharging period" are enumerated to form an initial candidate set.

[0125] Subsequently, a substantive energy and economic feasibility verification was performed on each candidate combination. Based on time series forecast data mapped to the corresponding time period of the combination, the total regenerative energy that the elevator can generate during the proposed charging period (total available charging capacity) and the total energy that the elevator needs to obtain from the energy storage device during the proposed discharging period (total demand for discharging) were calculated. The fundamental physical premise for the link to function is that energy can be adequately stored and released; therefore, the total available charging capacity not being less than the total demand for discharging becomes the primary screening condition. Simultaneously, optimizing electricity prices is the core objective; therefore, the electricity price during the charging period must be lower than the electricity price during the discharging period, constituting a key economic screening condition. Candidate combinations that simultaneously meet both conditions are determined as valid time period combinations.

[0126] Finally, for each effective time period combination, a specific operational quantity needs to be determined. Based on the total required discharge for that combination, and in conjunction with constraints such as the allowable charge and discharge power, the actual executable planned charge and discharge quantity under that combination is calculated. Combining the effective time period combination (which defines the charge and discharge time window) and the planned charge and discharge quantity (which defines the operational intensity) together defines a complete charge and discharge revenue chain with clear spatiotemporal parameters and operational quantity values.

[0127] In this embodiment, by systematically enumerating and filtering under multiple conditions, the problem of strategy omission that may be caused by traditional methods relying on fixed rules or local search can be overcome. This provides a complete and high-quality set of candidate strategies for subsequent global optimization based on a refined net profit model, thereby ensuring that the final optimized strategy is not only feasible, but also the economically optimal solution among all possible solutions.

[0128] In one exemplary embodiment, the charge / discharge capacity is assessed based on energy storage capacity parameters to generate the allowable charge / discharge power of the elevator energy-saving device, including:

[0129] Acquire real-time status data of the elevator energy-saving device from the energy storage capacity parameters. The real-time status data includes the current state of charge, battery temperature, and fault diagnosis signals.

[0130] Based on the current state of charge and battery temperature, the preset discharge power mapping relationship is queried to determine the theoretical maximum charge and discharge power under the current conditions; and the power limiting factor is determined based on the fault diagnosis signal.

[0131] Based on the theoretical maximum charging and discharging power and power limitation coefficient, and combined with the voltage operating range and charged state operating range of the elevator energy-saving device for boundary verification, the allowable charging and discharging power is calculated and output.

[0132] Specifically, the real-time status data of the energy storage device is first acquired, including its instantaneous state of charge (SOC), battery temperature, and fault signals. Based on the two key variables, SOC and temperature, the theoretical maximum charge / discharge capacity of the energy storage unit under the current internal conditions is obtained by querying a preset charge / discharge power mapping relationship. Simultaneously, the fault diagnosis signal is converted into a power limiting coefficient, used to derating or zero out the theoretical capacity in the event of a device malfunction.

[0133] Subsequently, to ensure absolute safety, the above results must be verified within the inherent hardware safety boundaries of the energy storage device. These boundaries include the permissible voltage operating range and the state of charge (SOC) operating range. The calculation process must ensure that, under the permissible charge and discharge power, the terminal voltage and SOC of the energy storage device will not exceed these safe ranges. Finally, through comprehensive calculation and constraint verification of the theoretical maximum power, fault limit factor, and safety boundaries, a safe, reliable, and executable permissible charge and discharge power under the current conditions is dynamically generated and output.

[0134] In this embodiment, by integrating real-time status monitoring and safety boundaries, it is ensured that the generated allowable power value is always within the safe operating window of the device, thereby effectively preventing risks such as overcharging, over-discharging, and overheating that could damage the device's lifespan and safety. This provides a crucial and reliable upper limit guarantee for any subsequent charging and discharging strategies, improving the safety and reliability of the entire system from the source.

[0135] In an exemplary embodiment, after generating the charge / discharge control strategy based on the optimal charge / discharge benefit chain, the method further includes:

[0136] Implement charging and discharging control strategies and monitor the actual operating data of the elevator and the real-time status data of the elevator energy-saving device in real time;

[0137] Compare actual operational data with time series forecast data;

[0138] If the comparison result exceeds the preset threshold, the charging and discharging control strategy is updated based on the latest actual operating data and the real-time status data of the elevator energy-saving device.

[0139] Specifically, the generated optimal charging and discharging control strategy is sent to the actuator (such as the local controller of the elevator energy-saving device) and executed. During execution, continuous monitoring is performed to collect two types of key data: first, the actual operating data of the elevator (such as actual speed, load, energy consumption, etc.) to reflect the real load situation; second, the real-time status data of the elevator energy-saving device (such as the latest state of charge, battery temperature, etc.) to reflect the current capacity of the energy storage unit.

[0140] The system then compares the monitored elevator operating data with the previously used time-series forecast data in real time to assess the accuracy of the initial forecast. When the comparison result (e.g., the deviation between actual energy consumption and predicted energy consumption) exceeds a certain preset threshold, it indicates that the actual situation has significantly deviated from expectations, and continuing to implement the original strategy may not achieve the optimal economic goal, or may even cause safety problems.

[0141] At this point, the system triggers a strategy update process. Based on the latest collected actual operating data (as the new load forecast input) and the real-time status data of the elevator energy-saving device (as the new capacity assessment input), it re-executes the entire decision-making process from load forecasting and capacity assessment to revenue link identification and screening, thereby generating an updated charging and discharging control strategy adapted to the latest operating conditions, and replacing the original strategy to continue execution.

[0142] In this embodiment, by comparing predicted and actual operating data in real time, the system can promptly detect strategy mismatch caused by random events, sudden changes in operating conditions, or model errors, and automatically trigger re-optimization. This effectively overcomes the rigidity of traditional open-loop strategies, ensuring that the charging and discharging control strategy can continuously track the optimal operating point of the system, thereby maximizing economic benefits and ensuring the safety and reliability of system operation in a dynamically changing environment over the long term.

[0143] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0144] Based on the same inventive concept, this application also provides a device for determining the charging and discharging strategy of an off-grid energy storage elevator to implement the above-described method for determining the charging and discharging strategy of an off-grid energy storage elevator. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the device for determining the charging and discharging strategy of an off-grid energy storage elevator provided below can be found in the limitations of the method for determining the charging and discharging strategy of an off-grid energy storage elevator described above, and will not be repeated here.

[0145] In one exemplary embodiment, such as Figure 4 As shown, a device for determining the charging and discharging strategy of an off-grid energy storage elevator is provided, comprising:

[0146] The acquisition module 402 is used to acquire multi-source parameters of the elevator system, including load prediction parameters and energy storage capacity parameters.

[0147] Prediction module 404 is used to perform load prediction based on load prediction parameters and generate time series prediction data of elevator power consumption and power generation in future periods.

[0148] Evaluation module 406 is used to evaluate the charging and discharging capacity based on energy storage capacity parameters and generate the allowable charging and discharging power of the elevator energy-saving device;

[0149] The identification module 408 is used to map time series prediction data, allowable charging and discharging power and time-of-use electricity price information to a unified time axis, and identify multiple charging and discharging revenue links that meet the preset constraints based on the preset constraints of allowable charging and discharging power and elevator operating conditions.

[0150] The strategy generation module 410 is used to select the charging and discharging benefit link with the largest net benefit value as the optimal charging and discharging benefit link, and generate a charging and discharging control strategy based on the optimal charging and discharging benefit link.

[0151] In an exemplary embodiment, the prediction module 404 is specifically used to calculate and obtain a basic power consumption sequence and a basic power generation sequence based on elevator operating parameters in the load prediction parameters; using elevator usage scenario parameters in the load prediction parameters, obtain scenario-time weighting coefficients, building occupancy rate weighting coefficients, and equipment utilization rate weighting coefficients associated with building type and usage period; perform a first-level weighted correction on the basic power consumption sequence and the basic power generation sequence based on the scenario-time weighting coefficients, building occupancy rate weighting coefficients, and equipment utilization rate weighting coefficients to obtain scenario-corrected power consumption sequence and scenario-corrected power generation sequence; obtain random fluctuation parameters and environmental and operating condition parameters in the load prediction parameters, and obtain random fluctuation weighting coefficients based on the random fluctuation parameters and environmental and operating condition correction coefficients based on the environmental and operating condition parameters; use the random fluctuation weighting coefficients and environmental and operating condition correction coefficients to perform a second-level product correction on the scenario-corrected power consumption sequence and the scenario-corrected power generation sequence, and output time series prediction data.

[0152] In an exemplary embodiment, the calculation formula for the second-level product correction of the scenario-corrected power consumption sequence and the scenario-corrected power generation sequence, using a random fluctuation weighting coefficient and an environmental condition correction coefficient, includes:

[0153] ;

[0154] in, This represents the predicted power consumption or power generation for the i-th time period. This represents the electricity consumption or power generation in the i-th time period after the first level of weighted correction, where γ is the random fluctuation weighting coefficient. The random fluctuation factor is δ, which is the environmental condition correction coefficient. It is a comprehensive influencing factor of environmental operating conditions.

[0155] In an exemplary embodiment, the identification module 408 is specifically configured to, on a unified time axis, traverse all time period combinations consisting of charging and discharging periods based on preset constraints of allowable charging and discharging power and elevator operating conditions; for each time period combination, calculate the available charging amount and the required discharging amount for the charging period based on time series prediction data; determine the current time period combination as an effective time period combination if the available charging amount is not less than the required discharging amount and the electricity price corresponding to the charging period is lower than the electricity price corresponding to the discharging period; and determine the charging and discharging revenue chain that meets the preset constraints based on the planned charging and discharging amounts corresponding to multiple effective time period combinations.

[0156] In an exemplary embodiment, the evaluation module 406 is specifically used to acquire real-time status data of the elevator energy-saving device in the energy storage capacity parameters. The real-time status data includes the current state of charge, battery temperature, and fault diagnosis signals. Based on the current state of charge and battery temperature, the module queries a preset discharge power mapping relationship to determine the theoretical maximum charge and discharge power under the current conditions. Based on the fault diagnosis signals, the module determines the power limitation coefficient. Based on the theoretical maximum charge and discharge power and the power limitation coefficient, and combined with the voltage operating range and state of charge operating range of the elevator energy-saving device, the module performs boundary verification, calculates and outputs the allowable charge and discharge power.

[0157] In an exemplary embodiment, the update module is configured to execute a charge-discharge control strategy and monitor the actual operating data of the elevator and the real-time status data of the elevator energy-saving device in real time; compare the actual operating data with time series prediction data; and update the charge-discharge control strategy based on the latest actual operating data and the real-time status data of the elevator energy-saving device if the comparison result exceeds a preset threshold.

[0158] The modules in the aforementioned off-grid energy storage elevator charging and discharging strategy determination device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0159] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 5As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores multi-source parameter data of the elevator system. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a method for determining the charging and discharging strategy of a non-grid-connected energy storage elevator.

[0160] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0161] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0162] Obtain multi-source parameters of the elevator system, including load forecasting parameters and energy storage capacity parameters;

[0163] Load forecasting is performed based on load forecasting parameters to generate time series forecast data of elevator power consumption and power generation in future periods.

[0164] Based on the energy storage capacity parameters, the charging and discharging capacity is evaluated to generate the allowable charging and discharging power of the elevator energy-saving device.

[0165] The time series forecast data, allowable charging and discharging power, and time-of-use electricity price information are mapped to a unified time axis, and multiple charging and discharging revenue links that meet the preset constraints are identified based on the preset constraints of allowable charging and discharging power and elevator operating conditions.

[0166] The charging and discharging benefit link with the largest net benefit value is selected as the optimal charging and discharging benefit link, and a charging and discharging control strategy is generated based on the optimal charging and discharging benefit link.

[0167] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0168] Based on the elevator operation parameters in the load forecast parameters, the basic power consumption sequence and the basic power generation sequence are calculated.

[0169] By utilizing elevator usage scenario parameters in the load forecast parameters, we can obtain scenario-time weighting coefficients, building occupancy rate weighting coefficients, and equipment utilization rate weighting coefficients that are associated with building type and usage period.

[0170] Based on the scenario-time weight coefficient, building occupancy rate weight coefficient, and equipment utilization rate weight coefficient, the basic power consumption sequence and the basic power generation sequence are subjected to the first level of weighted correction to obtain the scenario-corrected power consumption sequence and the scenario-corrected power generation sequence.

[0171] Obtain the random fluctuation parameter and environmental and operating condition parameters from the load forecast parameters, and obtain the random fluctuation weight coefficient based on the random fluctuation parameter, and obtain the environmental and operating condition correction coefficient based on the environmental and operating condition parameters;

[0172] Using random fluctuation weighting coefficients and environmental condition correction coefficients, a second-level product correction is performed on the scenario-corrected power consumption sequence and the scenario-corrected power generation sequence to output time series prediction data.

[0173] In one embodiment, the calculation formula for the second-level product correction of the scenario-corrected power consumption sequence and the scenario-corrected power generation sequence, using a random fluctuation weighting coefficient and an environmental condition correction coefficient, includes:

[0174] ;

[0175] in, This represents the predicted power consumption or power generation for the i-th time period. This represents the electricity consumption or power generation in the i-th time period after the first level of weighted correction, where γ is the random fluctuation weighting coefficient. The random fluctuation factor is δ, which is the environmental condition correction coefficient. It is a comprehensive influencing factor of environmental operating conditions.

[0176] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0177] On a unified time axis, based on the preset constraints of the allowable charging and discharging power and the elevator operating conditions, all combinations of charging and discharging periods are traversed.

[0178] For each time period combination, based on time series forecast data, the total available charging amount during the charging period and the total required discharging amount during the discharging period are calculated.

[0179] If the total available charging volume is not less than the total demand for discharging, and the electricity price corresponding to the charging period is lower than the electricity price corresponding to the discharging period, the current time period combination is determined to be an effective time period combination.

[0180] Based on the planned charge and discharge volumes corresponding to multiple effective time period combinations, the charge and discharge revenue links that meet the preset constraints are determined.

[0181] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0182] Acquire real-time status data of the elevator energy-saving device from the energy storage capacity parameters. The real-time status data includes the current state of charge, battery temperature, and fault diagnosis signals.

[0183] Based on the current state of charge and battery temperature, the preset discharge power mapping relationship is queried to determine the theoretical maximum charge and discharge power under the current conditions; and the power limiting factor is determined based on the fault diagnosis signal.

[0184] Based on the theoretical maximum charging and discharging power and power limitation coefficient, and combined with the voltage operating range and charged state operating range of the elevator energy-saving device for boundary verification, the allowable charging and discharging power is calculated and output.

[0185] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0186] Implement charging and discharging control strategies and monitor the actual operating data of the elevator and the real-time status data of the elevator energy-saving device in real time;

[0187] Compare actual operational data with time series forecast data;

[0188] If the comparison result exceeds the preset threshold, the charging and discharging control strategy is updated based on the latest actual operating data and the real-time status data of the elevator energy-saving device.

[0189] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0190] Obtain multi-source parameters of the elevator system, including load forecasting parameters and energy storage capacity parameters;

[0191] Load forecasting is performed based on load forecasting parameters to generate time series forecast data of elevator power consumption and power generation in future periods.

[0192] Based on the energy storage capacity parameters, the charging and discharging capacity is evaluated to generate the allowable charging and discharging power of the elevator energy-saving device.

[0193] The time series forecast data, allowable charging and discharging power, and time-of-use electricity price information are mapped to a unified time axis, and multiple charging and discharging revenue links that meet the preset constraints are identified based on the preset constraints of allowable charging and discharging power and elevator operating conditions.

[0194] The charging and discharging benefit link with the largest net benefit value is selected as the optimal charging and discharging benefit link, and a charging and discharging control strategy is generated based on the optimal charging and discharging benefit link.

[0195] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0196] Based on the elevator operation parameters in the load forecast parameters, the basic power consumption sequence and the basic power generation sequence are calculated.

[0197] By utilizing elevator usage scenario parameters in the load forecast parameters, we can obtain scenario-time weighting coefficients, building occupancy rate weighting coefficients, and equipment utilization rate weighting coefficients that are associated with building type and usage period.

[0198] Based on the scenario-time weight coefficient, building occupancy rate weight coefficient, and equipment utilization rate weight coefficient, the basic power consumption sequence and the basic power generation sequence are subjected to the first level of weighted correction to obtain the scenario-corrected power consumption sequence and the scenario-corrected power generation sequence.

[0199] Obtain the random fluctuation parameter and environmental and operating condition parameters from the load forecast parameters, and obtain the random fluctuation weight coefficient based on the random fluctuation parameter, and obtain the environmental and operating condition correction coefficient based on the environmental and operating condition parameters;

[0200] Using random fluctuation weighting coefficients and environmental condition correction coefficients, a second-level product correction is performed on the scenario-corrected power consumption sequence and the scenario-corrected power generation sequence to output time series prediction data.

[0201] In one embodiment, the calculation formula for the second-level product correction of the scenario-corrected power consumption sequence and the scenario-corrected power generation sequence, using a random fluctuation weighting coefficient and an environmental condition correction coefficient, includes:

[0202] ;

[0203] in, This represents the predicted power consumption or power generation for the i-th time period. This represents the electricity consumption or power generation in the i-th time period after the first level of weighted correction, where γ is the random fluctuation weighting coefficient. The random fluctuation factor is δ, which is the environmental condition correction coefficient. It is a comprehensive influencing factor of environmental operating conditions.

[0204] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0205] On a unified time axis, based on the preset constraints of the allowable charging and discharging power and the elevator operating conditions, all combinations of charging and discharging periods are traversed.

[0206] For each time period combination, based on time series forecast data, the total available charging amount during the charging period and the total required discharging amount during the discharging period are calculated.

[0207] If the total available charging volume is not less than the total demand for discharging, and the electricity price corresponding to the charging period is lower than the electricity price corresponding to the discharging period, the current time period combination is determined to be an effective time period combination.

[0208] Based on the planned charge and discharge volumes corresponding to multiple effective time period combinations, the charge and discharge revenue links that meet the preset constraints are determined.

[0209] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0210] Acquire real-time status data of the elevator energy-saving device from the energy storage capacity parameters. The real-time status data includes the current state of charge, battery temperature, and fault diagnosis signals.

[0211] Based on the current state of charge and battery temperature, the preset discharge power mapping relationship is queried to determine the theoretical maximum charge and discharge power under the current conditions; and the power limiting factor is determined based on the fault diagnosis signal.

[0212] Based on the theoretical maximum charging and discharging power and power limitation coefficient, and combined with the voltage operating range and charged state operating range of the elevator energy-saving device for boundary verification, the allowable charging and discharging power is calculated and output.

[0213] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0214] Implement charging and discharging control strategies and monitor the actual operating data of the elevator and the real-time status data of the elevator energy-saving device in real time;

[0215] Compare actual operational data with time series forecast data;

[0216] If the comparison result exceeds the preset threshold, the charging and discharging control strategy is updated based on the latest actual operating data and the real-time status data of the elevator energy-saving device.

[0217] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0218] Obtain multi-source parameters of the elevator system, including load forecasting parameters and energy storage capacity parameters;

[0219] Load forecasting is performed based on load forecasting parameters to generate time series forecast data of elevator power consumption and power generation in future periods.

[0220] Based on the energy storage capacity parameters, the charging and discharging capacity is evaluated to generate the allowable charging and discharging power of the elevator energy-saving device.

[0221] The time series forecast data, allowable charging and discharging power, and time-of-use electricity price information are mapped to a unified time axis, and multiple charging and discharging revenue links that meet the preset constraints are identified based on the preset constraints of allowable charging and discharging power and elevator operating conditions.

[0222] The charging and discharging benefit link with the largest net benefit value is selected as the optimal charging and discharging benefit link, and a charging and discharging control strategy is generated based on the optimal charging and discharging benefit link.

[0223] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0224] Based on the elevator operation parameters in the load forecast parameters, the basic power consumption sequence and the basic power generation sequence are calculated.

[0225] By utilizing elevator usage scenario parameters in the load forecast parameters, we can obtain scenario-time weighting coefficients, building occupancy rate weighting coefficients, and equipment utilization rate weighting coefficients that are associated with building type and usage period.

[0226] Based on the scenario-time weight coefficient, building occupancy rate weight coefficient, and equipment utilization rate weight coefficient, the basic power consumption sequence and the basic power generation sequence are subjected to the first level of weighted correction to obtain the scenario-corrected power consumption sequence and the scenario-corrected power generation sequence.

[0227] Obtain the random fluctuation parameter and environmental and operating condition parameters from the load forecast parameters, and obtain the random fluctuation weight coefficient based on the random fluctuation parameter, and obtain the environmental and operating condition correction coefficient based on the environmental and operating condition parameters;

[0228] Using random fluctuation weighting coefficients and environmental condition correction coefficients, a second-level product correction is performed on the scenario-corrected power consumption sequence and the scenario-corrected power generation sequence to output time series prediction data.

[0229] In one embodiment, the calculation formula for the second-level product correction of the scenario-corrected power consumption sequence and the scenario-corrected power generation sequence, using a random fluctuation weighting coefficient and an environmental condition correction coefficient, includes:

[0230] ;

[0231] in, This represents the predicted power consumption or power generation for the i-th time period. This represents the electricity consumption or power generation in the i-th time period after the first level of weighted correction, where γ is the random fluctuation weighting coefficient. The random fluctuation factor is δ, which is the environmental condition correction coefficient. It is a comprehensive influencing factor of environmental operating conditions.

[0232] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0233] On a unified time axis, based on the preset constraints of the allowable charging and discharging power and the elevator operating conditions, all combinations of charging and discharging periods are traversed.

[0234] For each time period combination, based on time series forecast data, the total available charging amount during the charging period and the total required discharging amount during the discharging period are calculated.

[0235] If the total available charging volume is not less than the total demand for discharging, and the electricity price corresponding to the charging period is lower than the electricity price corresponding to the discharging period, the current time period combination is determined to be an effective time period combination.

[0236] Based on the planned charge and discharge volumes corresponding to multiple effective time period combinations, the charge and discharge revenue links that meet the preset constraints are determined.

[0237] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0238] Acquire real-time status data of the elevator energy-saving device from the energy storage capacity parameters. The real-time status data includes the current state of charge, battery temperature, and fault diagnosis signals.

[0239] Based on the current state of charge and battery temperature, the preset discharge power mapping relationship is queried to determine the theoretical maximum charge and discharge power under the current conditions; and the power limiting factor is determined based on the fault diagnosis signal.

[0240] Based on the theoretical maximum charging and discharging power and power limitation coefficient, and combined with the voltage operating range and charged state operating range of the elevator energy-saving device for boundary verification, the allowable charging and discharging power is calculated and output.

[0241] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0242] Implement charging and discharging control strategies and monitor the actual operating data of the elevator and the real-time status data of the elevator energy-saving device in real time;

[0243] Compare actual operational data with time series forecast data;

[0244] If the comparison result exceeds the preset threshold, the charging and discharging control strategy is updated based on the latest actual operating data and the real-time status data of the elevator energy-saving device.

[0245] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0246] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0247] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0248] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for determining the charging and discharging strategy of a non-grid-connected energy storage elevator, characterized in that, The method includes: Acquire multi-source parameters of the elevator system, including load prediction parameters and energy storage capacity parameters; Based on the load forecasting parameters, load forecasting is performed to generate time series forecast data of elevator power consumption and power generation in future periods. Based on the energy storage capacity parameters, the charging and discharging capacity is evaluated to generate the allowable charging and discharging power of the elevator energy-saving device. The time series prediction data, the allowable charging and discharging power, and the time-of-use electricity price information are mapped to a unified time axis, and multiple charging and discharging revenue links that meet the preset constraints are identified based on the preset constraints of the allowable charging and discharging power and the elevator operating conditions. The charging and discharging benefit link with the largest net benefit value is selected as the optimal charging and discharging benefit link, and a charging and discharging control strategy is generated based on the optimal charging and discharging benefit link.

2. The method according to claim 1, characterized in that, The process of generating time-series forecast data of elevator power consumption and power generation for future periods based on the load forecast parameters includes: Based on the elevator operating parameters in the load forecast parameters, the basic power consumption sequence and the basic power generation sequence are calculated. Using the elevator usage scenario parameters in the load prediction parameters, obtain the scenario-time weighting coefficient, building occupancy rate weighting coefficient, and equipment utilization rate weighting coefficient associated with building type and usage period; Based on the scenario-time weight coefficient, the building occupancy rate weight coefficient, and the equipment utilization rate weight coefficient, the basic power consumption sequence and the basic power generation sequence are subjected to a first-level weighted correction to obtain the scenario-corrected power consumption sequence and the scenario-corrected power generation sequence. Obtain the random fluctuation parameter and environmental and operating condition parameters from the load forecast parameters, and obtain the random fluctuation weight coefficient based on the random fluctuation parameter, and obtain the environmental and operating condition correction coefficient based on the environmental and operating condition parameters; Using the random fluctuation weighting coefficient and the environmental condition correction coefficient, a second-level product correction is performed on the scenario-corrected power consumption sequence and the scenario-corrected power generation sequence to output the time series prediction data.

3. The method according to claim 2, characterized in that, The calculation formula for the second-level product correction of the scenario-corrected power consumption sequence and the scenario-corrected power generation sequence using the random fluctuation weighting coefficient and the environmental condition correction coefficient includes: ； in, This represents the predicted power consumption or power generation for the i-th time period. This represents the power consumption or power generation in the i-th time period after the first-level weighted correction, where γ is the random fluctuation weighting coefficient. The random fluctuation factor is δ, which is the correction coefficient for the environmental conditions. It is a comprehensive influencing factor of environmental operating conditions.

4. The method according to claim 1, characterized in that, The step of identifying multiple charging and discharging benefit links that satisfy the preset constraints based on the allowed charging and discharging power and the elevator operating conditions includes: On the unified time axis, based on the preset constraints of the allowable charging and discharging power and the elevator operating conditions, all combinations of charging and discharging periods are traversed. For each of the aforementioned time period combinations, based on the time series prediction data, the total available charging amount for the charging period and the total required discharging amount for the discharging period are calculated. If the total available charging amount is not less than the total required discharging amount, and the electricity price corresponding to the charging period is lower than the electricity price corresponding to the discharging period, the current period combination is determined to be an effective period combination. Based on the planned charge and discharge amounts corresponding to multiple effective time period combinations, the charge and discharge revenue links that meet the preset constraints are determined.

5. The method according to claim 1, characterized in that, The step of evaluating the charging and discharging capabilities based on the energy storage capacity parameters to generate the allowable charging and discharging power of the elevator energy-saving device includes: Obtain real-time status data of the elevator energy-saving device from the energy storage capacity parameters. The real-time status data includes the current state of charge, battery temperature, and fault diagnosis signals. Based on the current state of charge and the battery temperature, a preset discharge power mapping relationship is queried to determine the theoretical maximum charge and discharge power under the current conditions; and based on the fault diagnosis signal, a power limiting coefficient is determined. Based on the theoretical maximum charging and discharging power and the power limitation coefficient, and combined with the voltage operating range and charged state operating range of the elevator energy-saving device for boundary verification, the allowable charging and discharging power is calculated and output.

6. The method according to claim 5, characterized in that, After generating the charge / discharge control strategy based on the optimal charge / discharge benefit path, the method further includes: The charging and discharging control strategy is executed, and the actual operating data of the elevator and the real-time status data of the elevator energy-saving device are monitored in real time. The actual operating data is compared with the time series prediction data; If the comparison result exceeds the preset threshold, the charging and discharging control strategy is updated based on the latest actual operating data and the real-time status data of the elevator energy-saving device.

7. A device for determining the charging and discharging strategy of a non-grid-connected energy storage elevator, characterized in that, The device includes: The acquisition module is used to acquire multi-source parameters of the elevator system, including load prediction parameters and energy storage capacity parameters. The prediction module is used to perform load prediction based on the load prediction parameters and generate time series prediction data of elevator power consumption and power generation in future periods. The evaluation module is used to evaluate the charging and discharging capabilities based on the energy storage capacity parameters and generate the allowable charging and discharging power of the elevator energy-saving device. The identification module is used to map the time series prediction data, the allowable charging and discharging power, and the time-of-use electricity price information to a unified time axis, and to identify multiple charging and discharging revenue links that meet the preset constraints based on the preset constraints of the allowable charging and discharging power and the elevator operating conditions. The strategy generation module is used to select the charging and discharging benefit link with the largest net benefit value as the optimal charging and discharging benefit link, and generate a charging and discharging control strategy based on the optimal charging and discharging benefit link.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

Images