Energy management method and device for elevator energy supply system

Abstract

The invention provides an energy management method and device for an elevator energy supply system. The method is executed by a cloud server and a local controller which work cooperatively. A cloud server obtains historical and real-time operation data of an energy supply system, predicts the available output power of a photovoltaic unit and the comprehensive power demand of an elevator load in a first time period in the future, generates an energy scheduling strategy of the first time period through optimization calculation, and issues the energy scheduling strategy to a local controller; the local controller collects the local real-time operation state of the energy supply system, selects the current execution strategy and controls the energy flow path of each unit so as to supply power to the elevator load or recover the regenerated energy of the elevator load. According to the method, the energy-saving economy can be improved, the two free energy sources including photovoltaic power generation and elevator regeneration energy are preferentially utilized to the maximum extent through cloud collaborative scheduling, the energy storage charging and discharging strategy is optimized to make full use of the commercial power peak-valley price difference, and the overall operation electric charge of an elevator system is remarkably reduced.

Description

Technical Field

[0001] This application relates to the technical field of power management and elevator energy saving, and in particular to an energy management method and device for an elevator power supply system that can coordinate and switch photovoltaic, energy storage, mains power and elevator regenerative energy. Background Technology

[0002] With the widespread application of renewable energy and the continuous improvement of building energy efficiency requirements, integrating photovoltaic power generation and efficient recycling of braking regenerative energy in elevator systems to reduce dependence on grid power has become an important direction for energy-saving technologies.

[0003] Existing elevator energy supply systems typically focus on the recovery or replenishment of single types of energy. For example, some systems can recover regenerative energy generated by elevator braking or use photovoltaic power generation for supplementary power supply, but they generally lack overall coordinated optimization and forward-looking scheduling of factors such as photovoltaic output, energy storage status, elevator load, and grid electricity prices. This makes it difficult for the system to maximize the use of free solar and renewable energy, and the overall energy utilization efficiency needs to be improved. It may also affect the economic operating life of energy storage batteries and prevent the system from achieving optimal economic scheduling under time-of-use pricing mechanisms.

[0004] In existing technologies, such as patent document CN115693887A, an elevator power supply method based on solar photovoltaic power generation is disclosed. This method charges an energy storage device through a photovoltaic device and estimates that an appropriate amount of electricity will be supplemented from the grid during off-peak hours. However, this method still has the following limitations: First, its energy management is based on simple estimation and fixed rules, failing to coordinate and optimize the dynamic relationship between photovoltaic power generation, elevator regenerative energy, and energy storage charging and discharging; second, it lacks accurate prediction of photovoltaic output and elevator load demand, thus failing to perform forward-looking and optimized charging and discharging scheduling of energy storage based on future changes in energy supply and demand; finally, its strategy makes it difficult to ensure that while prioritizing the consumption of photovoltaic and regenerative energy, which are both free energy sources, the electricity that must be supplemented from the grid comes as much as possible from off-peak hours when the electricity price is lowest.

[0005] Therefore, there is an urgent need in the existing technology for a system and method that can realize intelligent collaborative management and optimized scheduling of photovoltaic, energy storage, mains power and elevator regenerative energy, so as to fundamentally improve the overall energy efficiency and economy of elevator systems. Summary of the Invention

[0006] To solve or improve the aforementioned problems in the existing technology, the first aspect of this application provides an energy management method for an elevator power supply system, the power supply system including a photovoltaic unit, an energy storage unit, a mains power unit and an elevator load, the method being executed by a cloud server and a local controller working in concert;

[0007] The cloud server performs the following steps:

[0008] Based on the energy supply system operation data, the available output power of the photovoltaic units and the comprehensive power demand of the elevator load are predicted in the first period of the future.

[0009] Preferably, the overall power demand includes the positive power demand from elevator drive power consumption and the negative power demand from braking power generation.

[0010] Based on available output power, comprehensive power demand, current status information of energy storage units, and preset grid power cost information, an energy dispatch strategy for the first time period is generated through optimized calculation. The energy dispatch strategy is used to plan the energy allocation and switching sequence among photovoltaic units, energy storage units, and grid power units.

[0011] The energy scheduling strategy is distributed to the local controller;

[0012] The local controller performs the following steps:

[0013] Based on the communication status and the effectiveness of the strategy, the energy scheduling strategy issued by the cloud server or the locally stored backup strategy is selected as the current execution strategy;

[0014] Based on the current execution strategy and local real-time operating status, control commands are generated for the photovoltaic unit, energy storage unit and mains power unit.

[0015] Control commands are used to control the energy flow path of each unit in order to supply power to the elevator load or recover its regenerated energy.

[0016] Optionally, the steps performed by the cloud server may also include: acquiring historical and / or real-time operating data of the power supply system.

[0017] Optionally, the steps performed by the local controller may also include: collecting real-time operating data of the power supply system and transmitting the real-time operating data to the cloud server.

[0018] Optionally, the aforementioned prediction of the available output power of the photovoltaic units in the first future time period specifically includes:

[0019] Based on the physical parameters of the photovoltaic unit and environmental data, the theoretical clear-sky baseline power for the first time period is calculated.

[0020] Based on historical operating data, the residual of the actual photovoltaic power in the first period relative to the theoretical clear-sky baseline power is predicted by a time-series prediction model.

[0021] The usable output power is obtained by superimposing the theoretical clear-sky baseline power with the predicted residual.

[0022] Optionally, the aforementioned forecast of the overall power demand of the elevator load in the first time period specifically includes:

[0023] Based on historical elevator operation data, time characteristics, and building passenger flow correlation data, the driving power consumption demand curve and braking power generation capacity curve for the first time period are generated simultaneously through a time series prediction model.

[0024] The combined power demand curve is obtained by synthesizing the drive power consumption demand curve and the braking power generation capacity curve.

[0025] Optionally, the constraints on which the aforementioned optimization calculations are based include: the upper and lower limits of the state of charge of the energy storage unit, the maximum charging and discharging power, and the time-of-use electricity price in the grid electricity cost information.

[0026] Optionally, the aforementioned optimization calculations are based on at least one of the following rules:

[0027] Based on a comparison of available output power and overall power demand, the planned charging amount for energy storage units during off-peak hours of grid electricity prices is dynamically adjusted; when the predicted available output power is sufficient, the off-peak charging amount is reduced, and when the predicted available output power is insufficient, the off-peak charging amount is increased.

[0028] When it is predicted that there will be comprehensive power demand during the peak period of the grid electricity price, it is determined whether the expected discharge capacity of the energy storage unit during the peak period can meet the demand; if the expected discharge capacity is insufficient, the energy storage unit is planned to be charged in advance during the flat or valley period of the grid electricity price before the peak period.

[0029] When it is predicted that there will be a period in which the available output power exceeds the immediate absorption capacity of the energy storage unit, the output power of the photovoltaic unit will be limited in the energy dispatch strategy.

[0030] Optionally, an energy scheduling strategy for the first time period can be generated through optimized calculations, specifically including:

[0031] The net power requirement for the first time period is calculated based on the available output power and the overall power demand.

[0032] Time-by-time analysis of net power demand and constraints is performed to generate energy dispatch strategies;

[0033] The energy dispatch strategy includes the planned state-of-charge trajectory of the energy storage unit, the planned charge and discharge power curve, and the switching time points of the power supply paths between the photovoltaic unit, the energy storage unit, and the grid unit during the first time period.

[0034] Optionally, the local controller determines the current execution strategy based on the following rules:

[0035] When the local controller communicates normally with the cloud server, the energy scheduling policy issued by the cloud server is used as the current execution policy.

[0036] When communication is interrupted, if the locally stored backup policy is still valid, the backup policy will be used as the current execution policy.

[0037] When communication is interrupted and there is no effective local backup policy, the static policy generated based on fixed rules will be used as the current execution policy.

[0038] Optionally, the local controller may also include the following before generating control commands:

[0039] Input the current execution strategy and local real-time running status into the strategy arbitration module;

[0040] The strategy arbitration module is configured to: dynamically correct control commands based on real-time collected DC bus voltage values ​​to maintain voltage stability, and / or, when a system fault signal is detected, forcibly generate safety control commands that override the currently executed strategy.

[0041] Optionally, the static strategy is executed at least based on a preset time-of-use electricity price period, including:

[0042] During off-peak hours when the mains electricity price is low, the mains power unit will prioritize supplying power to the elevator load and charging the energy storage unit.

[0043] During peak hours of the mains electricity price, it is prohibited for the mains power unit to supply power to the elevator load; priority should be given to power supply by the photovoltaic unit and the energy storage unit.

[0044] Furthermore, the power supply path between the photovoltaic unit, the energy storage unit, and the mains power unit is dynamically adjusted based on the real-time state of charge of the energy storage unit.

[0045] Optionally, the duration of the first period can be 6 to 72 hours.

[0046] Optionally, the operational data obtained in the method may include at least: historical output and real-time irradiance data of photovoltaic units, historical and real-time state of charge and health data of energy storage units, historical power consumption and operating mode data of elevator loads, and historical electricity price and real-time power data of grid electricity.

[0047] A second aspect of this application provides an energy management device for an elevator power supply system, the power supply system including a photovoltaic unit, an energy storage unit, a mains power unit, and an elevator load, the device including a cloud server and a local controller working together;

[0048] Cloud servers include:

[0049] The prediction module is used to predict the available output power of the photovoltaic unit and the comprehensive power demand of the elevator load in the first time period in the future, based on the operating data of the power supply system; preferably, the comprehensive power demand includes the positive power demand of drive power consumption and the negative power demand of braking power generation.

[0050] The strategy generation module is used to generate an energy dispatch strategy for the first time period based on available output power, comprehensive power demand, current status information of energy storage units, and preset grid power cost information. The energy dispatch strategy is used to plan the energy allocation and switching sequence between photovoltaic units, energy storage units, and grid power units.

[0051] The strategy distribution module is used to distribute energy scheduling strategies to the local controller;

[0052] The local controller includes:

[0053] The strategy selection module is used to select either the energy scheduling strategy issued by the cloud server or the locally stored backup strategy as the current execution strategy based on the communication status and the validity of the strategy.

[0054] The instruction generation module is used to generate control instructions for the photovoltaic unit, energy storage unit and mains power unit based on the current execution strategy and local real-time operating status.

[0055] The execution control module is used to control the energy flow path of each unit according to control commands, so as to supply power to the elevator load or recover its regenerated energy.

[0056] Optionally, the cloud server may also include a data acquisition module for acquiring historical and / or real-time operating data of the energy supply system.

[0057] Optionally, the local controller may also include a data acquisition module for acquiring real-time operating data of the power supply system and transmitting the real-time operating data to a cloud server.

[0058] Optionally, the prediction module is configured to predict the available output power of the photovoltaic unit in the following way:

[0059] Based on the physical parameters of the photovoltaic cells and environmental data, the theoretical clear-sky baseline power for the first time period in the future is calculated.

[0060] Based on historical operating data, the residual between the actual photovoltaic power and the theoretical clear-sky baseline power in the first period of the future is predicted by a time-series prediction model.

[0061] The theoretical clear-sky baseline power is superimposed with the predicted residual to obtain the predicted result of the available output power.

[0062] Optionally, the prediction module is configured to predict the overall power demand of the elevator load in the following ways:

[0063] Based on historical elevator operation data, time characteristics, and building passenger flow correlation data, the driving power consumption demand curve and braking power generation capacity curve for the first time period in the future are generated simultaneously through a time series prediction model.

[0064] The combined power demand curve is obtained by synthesizing the drive power consumption demand curve and the braking power generation capacity curve.

[0065] Optionally, the constraints on which the strategy generation module performs optimization calculations include: the upper and lower limits of the state of charge of the energy storage unit, the maximum charging and discharging power, and the time-of-use electricity price in the grid cost information.

[0066] Optionally, the policy generation module is configured as follows:

[0067] The net power requirement for the first time period is calculated based on the available output power and the overall power demand.

[0068] Time-by-time analysis of net power demand and constraints is performed to generate energy dispatch strategies;

[0069] The generated energy dispatch strategy includes the planned state-of-charge trajectory of the energy storage unit, the planned charge and discharge power curve, and the switching time points of the power supply paths between the photovoltaic unit, the energy storage unit, and the grid unit during the first time period.

[0070] Optionally, the policy generation module is configured to generate an energy scheduling policy based on at least one of the following rules:

[0071] Based on a comparison of available output power and overall power demand, the planned charging amount for energy storage units during off-peak hours of grid electricity prices is dynamically adjusted; when the predicted available output power is sufficient, the off-peak charging amount is reduced, and when the predicted available output power is insufficient, the off-peak charging amount is increased.

[0072] When it is predicted that there will be comprehensive power demand during the peak period of the grid electricity price, it is determined whether the expected discharge capacity of the energy storage unit during the peak period can meet the demand; if the expected discharge capacity is insufficient, the energy storage unit is planned to be charged in advance during the flat or valley period of the grid electricity price before the peak period.

[0073] When it is predicted that there will be a period in which the available output power exceeds the immediate absorption capacity of the energy storage unit, the output power of the photovoltaic unit will be limited in the energy dispatch strategy.

[0074] Optionally, the strategy selection module is configured to determine the current execution strategy based on the following rules:

[0075] When the local controller communicates normally with the cloud server, the energy scheduling policy issued by the cloud server is used as the current execution policy.

[0076] When communication is interrupted, if the locally stored backup policy is still valid, the backup policy will be used as the current execution policy.

[0077] When communication is interrupted and there is no effective local backup policy, the static policy generated based on fixed rules will be used as the current execution policy.

[0078] Optionally, a strategy arbitration module may also be included;

[0079] The strategy arbitration module is configured to: receive the current execution strategy and the local real-time operating status, dynamically correct the control commands based on the real-time collected DC bus voltage value to maintain voltage stability, and / or, when a system fault signal is detected, forcibly generate a safety control command that overrides the current execution strategy.

[0080] The energy management method and apparatus provided in this application have the following beneficial effects:

[0081] It can improve energy efficiency and economy. Through cloud-based collaborative scheduling, it prioritizes and maximizes the use of two free energy sources: photovoltaic power generation and elevator regenerative energy. It also optimizes energy storage charging and discharging strategies to make full use of the peak-valley price difference of the grid electricity, significantly reducing the overall operating electricity cost of the elevator system.

[0082] It can optimize energy utilization efficiency and improve the prediction accuracy of photovoltaic output and elevator load demand by adopting a prediction method based on integrated physical models. This makes the energy dispatch strategy more forward-looking, reduces energy waste or supply-demand mismatch, and improves the overall energy utilization efficiency.

[0083] In addition, the local controller's strategy arbitration module can correct instructions based on real-time status and force a switch to safe mode in case of a fault, ensuring the continuity of elevator power supply and the stability of bus voltage, thus achieving a balance between energy saving and safe operation. Attached Figure Description

[0084] To more clearly illustrate the technical solutions in the embodiments of this application or the background art, the accompanying drawings used in the embodiments of this application or the background art will be described below.

[0085] Figure 1 This is a flowchart illustrating the steps executed by a cloud server in an energy management method provided in this application embodiment;

[0086] Figure 2 This is a flowchart illustrating the steps executed by the local controller in an energy management method provided in this application embodiment;

[0087] Figure 3 This is an exemplary architecture diagram of an energy management system provided in the embodiments of this application;

[0088] Figure 4 This is a flowchart illustrating a specific embodiment of an energy management method provided in this application. Detailed Implementation

[0089] In this specification, it will also be understood that when a module / unit / server / controller is referred to as being "connected" to other modules / units / servers / controllers, such as being "connected" to other modules / units / servers / controllers, the module / unit / server / controller may be directly connected to or directly coupled to the module / unit / server / controller, or there may be an intermediary third module / unit / server / controller; in addition, in the embodiments of this application, "connection" may specifically be a data transmission connection.

[0090] The present application will now be described more fully below with reference to the accompanying drawings. However, the present application may be implemented in many different ways and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided herein to make the present application more detailed and complete, and to fully convey the scope of the present application to those skilled in the art.

[0091] To address or improve upon the problems existing in the prior art, this application provides a photovoltaic-energy storage-mains power coordinated energy management method for elevator power supply systems. The core concept lies in employing a collaborative operation mode that combines high-performance cloud server optimization with lightweight rule-based control of a local controller. This achieves optimal utilization of photovoltaic power generation, energy storage systems, and mains power while maintaining elevator operational safety and power supply continuity. The method supports online operation in the cloud and also supports offline autonomous operation when the cloud is unavailable.

[0092] Specifically, this energy management method is applied to energy supply systems that include photovoltaic units, energy storage units, mains power units, and elevator loads.

[0093] The energy management method provided in this application embodiment is executed by a cloud server and a local controller working together.

[0094] Among them, such as Figure 1 As shown, the cloud server performs the following steps:

[0095] S11: Obtain historical and real-time operating data of the power supply system;

[0096] S12: Based on operational data, predict the available output power of the photovoltaic units and the comprehensive power demand of the elevator load in the first time period in the future. Typically, the comprehensive power demand includes the positive power demand of drive power consumption and the negative power demand of braking power generation.

[0097] S13: Based on available output power, comprehensive power demand, current status information of energy storage units, and preset grid power cost information, the energy dispatch strategy for the first time period is generated through optimized calculation. The energy dispatch strategy is used to plan the energy allocation and switching sequence among photovoltaic units, energy storage units, and grid power units.

[0098] S14: Send the energy scheduling strategy to the local controller.

[0099] like Figure 2 As shown, the local controller performs the following steps:

[0100] S21: Collect the local real-time operating status of the power supply system;

[0101] S22: Based on the communication status and the effectiveness of the strategy, select the energy scheduling strategy issued by the cloud server or the locally stored backup strategy as the current execution strategy;

[0102] S23: Based on the current execution strategy and local real-time operating status, generate control commands for the photovoltaic unit, energy storage unit and mains power unit;

[0103] S24: Control the energy flow path of each unit according to the control command to supply power to the elevator load or recover its regenerated energy.

[0104] In an optional embodiment, the cloud server may not perform step S11: obtaining historical and real-time operating data of the power supply system. In this case, the cloud server has pre-stored historical operating data of the power supply system.

[0105] In an optional embodiment, the local controller may omit step S21: collecting the local real-time operating status of the power supply system. In this case, the method utilizes historical operating data of the power supply system pre-stored on a cloud server or the local controller.

[0106] Explained, the aforementioned cloud server refers to a computing platform deployed remotely (relative to elevator load) with strong data processing and model training capabilities. In this application, it plays the role of global optimization and long-term strategy formulation. For example, the cloud server is configured to: acquire and manage historical and real-time operational data from the local system through a data interface; run a prediction algorithm model to generate predictions of the available photovoltaic output power and the overall power demand of the elevator in future periods; based on the prediction results, energy storage status, grid electricity costs, and other information, solve the problem through an optimization algorithm to generate the optimal energy scheduling strategy; and finally, distribute the strategy to the local controller through a secure communication link. Its core lies in utilizing cloud computing power to achieve data-driven forward prediction and global optimization.

[0107] The aforementioned local controller refers to a control device deployed at the elevator load site, responsible for real-time data acquisition, command execution, and safety closed-loop. In this application, it plays the role of strategy execution and real-time safety assurance. Specifically, the local controller is configured to: acquire real-time operating status of photovoltaic, energy storage, mains power, and elevator local conditions; intelligently select and lock the energy strategy (cloud strategy, local cache strategy, or static rule strategy) to be executed based on communication with the cloud server; generate specific equipment control commands based on the selected strategy and real-time status; and, through built-in strategy arbitration logic, perform final correction or safety overlay based on bus voltage stability or system fault signals before command execution to ensure power supply continuity. Its core lies in achieving reliable strategy execution, real-time system stability, and safety fallback in fault scenarios.

[0108] Specifically, the cloud server and local controller can work together via data communication networks (such as 4G / 5G or fiber optics) to form a collaborative "cloud-local" decision-making and execution system. The cloud server is responsible for forward-looking optimization calculations, while the local controller is responsible for real-time control and protection. Through this division of labor and collaboration, they jointly achieve comprehensive optimization of the elevator energy system in terms of efficiency, economy, and safety.

[0109] In a preferred embodiment, predicting the available output power of the photovoltaic unit in the first future time period in step S12 specifically includes:

[0110] Based on the physical parameters of the photovoltaic cells and environmental data, the theoretical clear-sky baseline power for the first time period in the future is calculated.

[0111] Based on historical operating data, the residual between the actual photovoltaic power and the theoretical clear-sky baseline power in the first period of the future is predicted by a time-series prediction model.

[0112] The theoretical clear-sky baseline power is superimposed with the predicted residual to obtain the predicted result of the available output power.

[0113] In another terminology, clear-sky baseline power can also be referred to as clear-sky baseline power.

[0114] In a typical embodiment, photovoltaic power generation prediction is performed using the following method.

[0115] By calculating the clear-sky baseline power As a physical prior; using a Long Short-Term Memory (LSTM) network for historical residuals:

[0116]

[0117] Perform sequence-to-sequence prediction (in the previous formula, R(t) represents the historical residuals). For clear-sky baseline power, To obtain the measured power, the residual prediction for the next time period T (6-72 hours) is output, and then added to the physical baseline to obtain the final photovoltaic available power curve. Uncertainty (confidence intervals) can be realized using Monte Carlo dropout or quantile regression.

[0118] The specific technical process is as follows:

[0119] First, based on fixed geometric conditions such as the latitude, longitude, altitude, module tilt angle, and azimuth angle of the photovoltaic power station, the solar zenith angle and azimuth angle for the target time series are calculated, and the clear-sky baseline irradiance for the corresponding time is generated using a clear-sky irradiance model. Then, using physical parameters such as module nominal power, temperature coefficient, and photoelectric conversion efficiency, the clear-sky irradiance is converted into a theoretically achievable clear-sky baseline power, forming a clear-sky baseline power curve. This curve represents the theoretical maximum power output of a photovoltaic system under ideal cloudless conditions, providing a physical prior constraint for subsequent deep model predictions.

[0120] After obtaining the physical baseline, a time-series prediction model based on long-term collected historical measured photovoltaic power, ambient temperature, irradiance, inverter operating parameters, and historical weather records is constructed. This model uses a fixed-length historical input window as its feature sequence, and limits the feature vector at each time step to measured photovoltaic power, measured irradiance, module temperature, clear-sky baseline power, cloud cover, shortwave irradiance, ambient temperature, and time-coded information. The model learns the time dependencies of these multi-dimensional feature sequences and outputs a residual prediction curve of equal length to the future prediction interval. The residual represents the offset of the measured power relative to the physical baseline power.

[0121] Subsequently, by superimposing the residual prediction results output by the deep learning model with the physical clear-sky baseline power, the photovoltaic power (available electricity) prediction curve for the future target time period is obtained. This curve can provide more accurate prediction results than a simple physical model under the influence of meteorological fluctuations, cloud changes, and ambient temperature.

[0122] Furthermore, to enhance the reliability of the predictions, this application enables a random deactivation mechanism for the LSTM model during the inference stage and performs multiple forward propagations to statistically analyze the prediction mean and variance, thereby constructing a confidence interval for the photovoltaic prediction results and providing a risk reference for system energy dispatch.

[0123] In a preferred embodiment, the prediction of the comprehensive power demand of the elevator load in the first time period in step S12 specifically includes:

[0124] Based on historical elevator operation data, time characteristics, and building passenger flow correlation data, a time series prediction model is used to simultaneously generate the driving power consumption demand prediction curve and the braking power generation capacity prediction curve for the first time period in the future.

[0125] The combined power demand forecast curve is obtained by synthesizing the driving power consumption demand forecast curve and the braking power generation capacity forecast curve.

[0126] In a typical embodiment, a time series forecasting model is employed. Inputs include historical elevator operation data, real-time operating status, building passenger flow statistics, time characteristic data, environmental impact factors, and elevator drive system operating parameters. The output is a forecast curve of the elevator's overall power demand over a future time interval. The curve includes both the drive power (electricity consumption) requirement and the recyclable renewable energy (i.e., the energy generated by braking).

[0127] More specifically, firstly, based on the operational data collected by the elevator controller, frequency converter, and energy metering module, a raw dataset is constructed, including door zone operating time, up and down directions, acceleration and deceleration sequence, load percentage, drive power, regenerative power, elevator travel length, and operating mode. Then, combined with the building's access counting system or historical passenger flow statistics model, a passenger flow time series closely related to elevator usage is obtained, and synchronized with the elevator's own operational data to form a multi-dimensional input feature matrix.

[0128] Subsequently, using the mechanical and electrical characteristic models of the variable frequency drive system, physical consistency processing is performed on historical operating data. This includes slicing the operating sections, normalizing the load-power relationship, and integrating the regenerative energy data to construct standardized basic characteristics of elevator operating conditions. Simultaneously, this application converts time information into periodic time codes, including daily cycle codes, weekly cycle codes, holiday markings, and building characteristic codes, to reflect the periodicity and statistical regularity of elevator use.

[0129] After obtaining the multidimensional standardized features, this application utilizes a Long Short-Term Memory (LSTM) neural network to construct a time-series prediction model for elevator load and regenerative energy. The model takes a historical feature sequence within a fixed time window as input, including actual driving power, regenerative power, load percentage, door area activity, number of trips, passenger flow statistics, ambient temperature, and time coding. By learning the temporal dependency structure between features, the model outputs a predicted sequence of comprehensive elevator power demand with a length equal to the future prediction interval. This sequence includes both positive power demand (elevator drive consumption) and negative power demand (elevator regenerative feedback capability).

[0130] By incorporating elevator dynamics constraints and historical operating condition statistics into the prediction process, this prediction model can maintain high prediction accuracy during peak, off-peak, and abnormal usage scenarios. In the model inference stage, this application performs multiple random deactivation forward calculations on the LSTM inference to obtain the mean and confidence interval of the predicted power, thereby providing a risk assessment capability for future elevator peak load and regenerative energy capacity.

[0131] Regarding the energy scheduling strategy for the first time period generated through optimization calculation in step S13 above, in a typical embodiment, an optimization algorithm (linear programming, model predictive control MPC or dynamic programming DP) is executed using a cloud server to obtain the SOC change, energy storage charging and discharging power change, and energy path switching strategy for the future time T.

[0132] More specifically, this step employs an energy optimization and scheduling strategy generation method based on energy storage state constraints, battery aging characteristics, and the coupling of photovoltaic forecasting and elevator load forecasting. This method is used to determine the energy allocation scheme among photovoltaic power generation, grid power, and the energy storage system within a future target time period. This method can maximize the utilization of photovoltaic energy and elevator regenerative energy while ensuring elevator power supply safety, and reduce the use of high-priced electricity during peak hours by rationally scheduling off-peak charging amounts and timing, thereby improving overall energy efficiency.

[0133] In this step, the input parameters specifically include the current state of charge (SOC(0)) of the energy storage, the maximum allowable charge and discharge power of the battery management system (BMS), the available battery capacity and aging characteristic parameters, and the photovoltaic available output power prediction curve. Elevator comprehensive power demand forecast curve And the peak, flat, and valley electricity price ranges. The output parameters are the energy dispatch strategy set for the future target time period (i.e., the first time period in the embodiment), including energy storage charging strategy, energy storage discharging strategy, photovoltaic priority use strategy, grid power switching strategy, and energy path switching instructions.

[0134] In a typical embodiment, the specific process of optimizing the calculation to generate the energy scheduling strategy for the first time period includes:

[0135] First, based on the current State of Charge (SOC) of the energy storage system, the battery aging model, and the dynamic power limitations of the Battery Management System (BMS), the available energy boundary of the energy storage system is constructed, including the upper limit of dischargeable energy, the upper limit of rechargeable energy, and the maximum instantaneous charge / discharge capacity. Based on this, combined with... and Calculate the net power demand at each future time point. This net power represents the reference energy storage charging and discharging power required by the system without considering the mains input.

[0136] Subsequently, through the analysis of various future moments... By performing time-period analysis against the upper / lower limits of energy storage constraints, we can determine whether the following scenarios are likely to occur in future time periods:

[0137] (1) Photovoltaic power generation is insufficient and energy storage capacity is insufficient to support load demand;

[0138] (2) Photovoltaic power generation is high, but the remaining energy storage capacity is insufficient to accommodate photovoltaic power.

[0139] (3) There will be high load demand during peak electricity price periods in the future, but the energy storage capacity is insufficient to undertake the power supply task during peak periods;

[0140] (4) The future off-peak electricity price charging window is insufficient to charge energy storage to the strategic demand.

[0141] To address the above situation, an energy optimization strategy is constructed based on the principle of prioritizing energy conservation and electricity price constraints, and the future scheduling behavior is dynamically adjusted accordingly:

[0142] When it is predicted that the future photovoltaic power can cover most of the load demand, the amount of charging during off-peak electricity prices is reduced to improve the utilization rate of photovoltaic power. Conversely, when it is predicted that the future photovoltaic power generation will be insufficient, the amount of energy storage charging is increased in advance during off-peak hours so that the energy storage energy has sufficient capacity to support the operation of the load during flat or even peak hours, thereby reducing the use of grid power during peak hours.

[0143] When a large load demand is predicted during peak electricity price periods, the state of charge of energy storage should be predicted in advance. Does it meet the discharge capacity requirements of the peak period? If not, start charging earlier in the flat or valley period so that the energy storage can maintain an appropriate high SOC level during the peak period to achieve the energy saving goal.

[0144] For periods when photovoltaic power is about to generate a large amount of surplus electricity, the remaining capacity of the energy storage and the maximum charging power allowed by the BMS are used to determine whether the energy storage can effectively absorb the photovoltaic energy. If the energy storage capacity is insufficient, controlled power limiting measures are implemented to limit the photovoltaic output in order to prevent bus overvoltage or trigger protection actions.

[0145] Finally, based on the above prediction, judgment, and optimization process, a set of energy scheduling strategies for the future target time period is generated, including:

[0146] (1) The target amount of energy storage charging during off-peak hours should be increased first under the predicted conditions of insufficient photovoltaic power;

[0147] (2) Photovoltaic priority power supply strategy and photovoltaic overflow management strategy;

[0148] (3) Strategies for planning the timing of mains power connection to avoid charging at high prices during peak hours;

[0149] (4) Future energy path switching instructions, including combined paths of energy storage → bus, mains power → bus, photovoltaic → energy storage, and photovoltaic → load.

[0150] Using the above method, this application can pre-set the energy storage scheduling quantity and charging and discharging strategy under future load changes and photovoltaic fluctuations, so as to achieve the energy-saving optimization goals of maximizing photovoltaic resources, minimizing elevator operation energy consumption, and maximizing the avoidance of high grid electricity prices.

[0151] In view of the foregoing, in the preferred embodiment, the constraints on which the optimization calculation in step S13 is based include: the upper and lower limits of the state of charge (SOC) of the energy storage unit, the maximum charging and discharging power, and the time-of-use electricity price in the grid electricity cost information.

[0152] In view of the foregoing, in the preferred embodiment, the energy dispatch strategy generated in step S13 specifically includes the planned state-of-charge trajectory of the energy storage unit, the planned charge and discharge power curve, and the switching time points of the power supply paths between the photovoltaic unit, the energy storage unit, and the mains power unit in the first time period in the future.

[0153] In view of the foregoing, in a preferred embodiment, the optimization calculation in step S13 is based on at least one of the following rules:

[0154] Based on a comparison of available output power and overall power demand, the planned charging amount for energy storage units during off-peak hours of grid electricity prices is dynamically adjusted; when the predicted available output power is sufficient, the off-peak charging amount is reduced, and when the predicted available output power is insufficient, the off-peak charging amount is increased.

[0155] When it is predicted that there will be comprehensive power demand during the peak period of the grid electricity price, it is determined whether the expected discharge capacity of the energy storage unit during the peak period can meet the demand; if the expected discharge capacity is insufficient, the energy storage unit is planned to be charged in advance during the flat or valley period of the grid electricity price before the peak period.

[0156] When it is predicted that there will be a period in which the available output power exceeds the immediate absorption capacity of the energy storage unit, the output power of the photovoltaic unit will be limited in the energy dispatch strategy.

[0157] In step S14 above: the energy scheduling policy is sent to the local controller. Typically, the energy scheduling policy is sent to the local controller securely via MQTT, HTTPS, or Modbus-TCP.

[0158] In a typical embodiment, when the cloud server triggers the release of the energy scheduling policy, the cloud server first performs a signature or digest calculation on the generated energy scheduling policy data to form a policy instruction package with non-repudiation.

[0159] The aforementioned policy instruction packets are transmitted via a TLS encrypted channel. The TLS key is established using a device-unique certificate or a pre-configured key system, thereby preventing man-in-the-middle attacks and decryption risks. To improve the robustness of policy distribution, the system employs a message integrity detection mechanism. When the local controller receives the policy packet, it verifies the digital signature, message digest, or message sequence number to confirm that the policy data has not been tampered with, has not been duplicated, and that the transmission order is correct.

[0160] In a preferred embodiment, the method further includes cloud server detection and dynamic correction. Specifically, when the weather changes suddenly or the load is abnormal, the cloud server will re-predict and generate the energy scheduling strategy and update and push it to the local controller.

[0161] For the local controller in the embodiment, typically, after system startup, it first enters the initialization process and continuously collects local real-time operating parameters, including photovoltaic output, elevator load power, energy storage state of charge (SOC), mains power status, regenerative energy status, and environmental measurement data. After the data collection is completed, the local controller determines whether the cloud server is online based on the communication status and uses this to determine the subsequent strategy path.

[0162] When the cloud server is online, the local controller reports the collected data to the cloud server in real time. After receiving the data, the cloud server generates a corresponding energy scheduling strategy based on its large-scale computing power and prediction model, and returns the energy scheduling strategy for the time period T (6 to 72 hours) after T1 (5 to 30 minutes).

[0163] Every fixed interval T2 (1 to 10 minutes), the local controller reports a unique code (usually a timestamp) of its locally stored energy scheduling policy. The cloud server compares this unique code with the unique code of the energy scheduling policy determined by the cloud server to determine the policy's timeliness and whether the effective time window matches, thus preventing communication issues from causing the local controller's energy scheduling policy to be outdated. When the verification matches, the local controller directly adopts the latest energy scheduling policy and enters the execution process of that policy. If the verification does not match, the local controller receives a new energy scheduling policy from the cloud server, updates itself based on the new policy, and executes it immediately.

[0164] When the cloud server is offline or experiencing communication issues, the local controller will enter offline mode. In offline mode, the local controller first determines whether it retains the forecasting strategies issued when the cloud was online. If no forecasting strategies are saved, the system automatically enters static strategy mode, implementing energy management through time-of-use pricing, fixed SOC threshold rules, and static logic prioritizing photovoltaic / grid / energy storage.

[0165] If a forecasting strategy has been saved locally, the system further determines whether the strategy remains effective. This determination includes assessing whether the current actual State of Charge (SOC) falls within the SOC range predicted by the cloud server strategy, whether the key parameters upon which the strategy relies (such as time windows, photovoltaic forecast demand, and minimum reserve capacity requirements) are still reasonable, and whether the current operating state deviates from the tolerance threshold of the forecasting model. When the forecasting strategy still meets the execution conditions, the local controller will continue to execute the previously saved strategy; if the forecasting strategy fails or exceeds the executable range, it will automatically switch to static strategy operation mode.

[0166] In view of the foregoing, in a typical embodiment, step S22 specifically includes:

[0167] When the local controller communicates normally with the cloud server, the energy scheduling policy issued by the cloud server is used as the current execution policy.

[0168] When communication is interrupted, if the locally stored backup policy is still valid, the backup policy will be used as the current execution policy.

[0169] When communication is interrupted and there is no effective local backup policy, the static policy generated based on fixed rules will be used as the current execution policy.

[0170] In a preferred embodiment, the static strategy is executed at least based on a preset time-of-use electricity price period, including:

[0171] During off-peak hours when the mains electricity price is low, the mains power unit will prioritize supplying power to the elevator load and charging the energy storage unit.

[0172] During peak hours of the mains electricity price, it is prohibited for the mains power unit to supply power to the elevator load; priority should be given to power supply by the photovoltaic unit and the energy storage unit.

[0173] Furthermore, the power supply path between the photovoltaic unit, the energy storage unit, and the mains power unit is dynamically adjusted based on the real-time state of charge of the energy storage unit.

[0174] In a preferred embodiment, step S23, before generating control commands, further includes:

[0175] Input the current execution strategy and local real-time running status into the strategy arbitration module;

[0176] The strategy arbitration module is configured to: dynamically correct control commands based on real-time collected DC bus voltage values ​​to maintain voltage stability, and / or, when a system fault signal is detected, forcibly generate safety control commands that override the currently executed strategy.

[0177] Regardless of whether the local controller uses an energy scheduling strategy issued by the cloud server, a static strategy, or an offline backup strategy, all these strategies will eventually enter a unified strategy arbitration module.

[0178] The strategy arbitration module, serving as the underlying strategy execution framework, ensures that the energy path selection of different strategies meets global consistency, security, and real-time requirements during actual execution. Based on real-time bus voltage, elevator load changes, regenerative energy levels, photovoltaic output fluctuations, and dynamic changes in energy storage SOC, the strategy arbitration module adjusts strategy commands in a timely manner, thereby ensuring stable system operation.

[0179] The aforementioned strategy arbitration module, serving as the final execution and coordination layer for energy management at the local controller, is used to determine and correct the output commands of the upper-level strategy, ensuring that the switching and control of energy paths always meet the safety and stability requirements of elevator operation. This module mainly includes two core functions: bus voltage stabilization control and safety priority control.

[0180] The bus voltage stabilization control dynamically adjusts the execution commands of the energy path control and voltage regulation management unit based on real-time acquisition of DC bus voltage, elevator instantaneous load power, energy storage output current, and photovoltaic power output fluctuation information, using a high-speed closed-loop control algorithm to ensure that the DC bus voltage remains within the set voltage range. This includes dynamically correcting the power distribution among photovoltaic, energy storage, and mains power sources to avoid sudden changes in bus voltage caused by strategy switching; and prioritizing bus voltage stability overriding or correcting strategy outputs when there is a conflict between strategy logic commands and bus voltage stability.

[0181] The safety priority control logic overrides all other optional strategies in abnormal or emergency situations, ensuring that the elevator receives a stable and uninterrupted power supply under all circumstances. Safety priority control is automatically triggered when the system detects a mains power outage or mains voltage exceeding the safe range, or when the elevator control system issues a power outage protection signal, fault signal, or safety operation alarm; when the energy storage system malfunctions, experiences overvoltage / undervoltage, overcurrent, or abnormal temperature affecting power supply continuity; or when the photovoltaic system fails or causes severe fluctuations in bus voltage. In safety priority mode, the strategy arbitration module immediately performs the following actions: interrupts all executable strategies, forcibly switches the energy path to independent energy storage power supply mode (during a power outage and when energy storage is normal) or independent mains power supply mode (when mains power is normal but energy storage is abnormal), ensures continuous operation of the elevator drive, issues safety and stability control commands to the energy path control and voltage regulation management unit, ensures the bus voltage remains stable and without fluctuations, and maintains this mode until the system detects that the mains power has returned to normal and stabilized for more than a preset time window.

[0182] In the event of a power outage, the strategy arbitration module will not immediately resume the energy-saving strategy after the mains power is restored. Instead, it will first make a comprehensive judgment on the stability of the bus through indicators such as voltage, current, and power fluctuations, and then reconnect to the conventional strategy control logic to avoid elevator power supply pulsation and oscillation caused by repeated switching during periods of unstable mains power.

[0183] In a typical embodiment, the aforementioned static strategy is jointly formulated by the local controller based on preset time-based electricity price information, real-time battery state of charge (SOC), photovoltaic power output, elevator regenerative energy status, and load demand, and is used to achieve basic energy scheduling.

[0184] Obtaining a static strategy involves the following steps:

[0185] S31: Determine peak and valley periods

[0186] Peak and valley time information for the local controller can be manually set or distributed from the cloud when the system is online. If valid peak and valley time periods cannot be obtained locally, the system automatically reverts to the typical time periods for the location. Taking my country as an example, the valley period is from 0:00 to 8:00, the flat period is from 11:00 to 16:00, and the peak periods are from 8:00 to 11:00 and from 16:00 to 24:00. By identifying these time periods, the local controller can perform energy management actions based on fixed logic without requiring large-scale computation.

[0187] S32: Implement different energy-saving strategies based on different peak and off-peak periods.

[0188] During off-peak hours, when grid electricity costs are lowest and photovoltaic power is either nonexistent or insufficient, the system prioritizes grid power for elevator operation while simultaneously charging the energy storage batteries to reach a preset higher state of charge range. For example, 60% to 90%. During this stage, the regenerative energy recovered from elevator operation and the electricity generated by the photovoltaic system are preferentially sent to the energy storage system for storage. When the battery is fully charged, the system will automatically disconnect the photovoltaic grid connection to avoid excessive voltage, and simultaneously switch the elevator load to be powered by the energy storage system, allowing the stored electricity to be released appropriately. When the energy storage SOC drops to a set lower limit... (e.g., 90%), the system resumes photovoltaic injection and grid power supply mode, keeping the battery at the expected high power level.

[0189] During periods of low load, the system simultaneously utilizes both photovoltaic (PV) and energy storage power. PV power prioritizes the elevator's needs; if there is surplus power, it continues to charge the energy storage battery. If PV output is insufficient, the energy storage compensates for the difference. During this time, the regenerative energy generated by the elevator operation continues to be recycled into the energy storage battery. Once the battery is fully charged, the system will temporarily cut off PV input and allow the elevator to continue using energy storage power until the State of Charge (SOC) drops to a preset lower limit. (e.g., 90%), at this point, photovoltaic power is switched back on. In scenarios where photovoltaic power is consistently insufficient, the energy storage battery is effectively in a discharge maintenance phase. If the SOC drops to the safe lower limit... (e.g., 60%), to avoid being forced to use high-priced electricity to replenish power during peak hours, the system will initiate mains charging to restore the SOC to the target range. ( (e.g., 80%). Once the battery reaches the set value, the system will automatically terminate mains charging and resume the combined photovoltaic and energy storage power supply mode.

[0190] During peak periods, the system primarily uses photovoltaic (PV) and energy storage as its power supply paths, avoiding the use of expensive grid electricity. The energy storage battery is considered the main source of power for the load during this phase, continuing to absorb PV energy when PV is abundant and discharging when PV is insufficient. When the battery is fully charged, the system cuts off PV input, allowing the elevator load to be entirely handled by the energy storage, while the regenerative energy generated by the elevator continues to be stored in the storage. When the battery's state of charge (SOC) drops to a set lower limit... When the photovoltaic system reaches 90% capacity, it will be connected to the solar grid again.

[0191] Regardless of the time period, if insufficient photovoltaic power continues and the State of Charge (SOC) drops to a deeper lower limit... (e.g., 30%), the system will stop supplying power to the load from the energy storage, allowing the elevator to be powered solely by the mains electricity, thus reserving some stored energy for emergency use. To handle the regenerative energy generated by elevator braking, light-load upward movement, or heavy-load downward movement, the system will decide whether to activate the braking resistor based on the real-time output of the photovoltaic system: if the photovoltaic output power... Higher than the equivalent power of regenerative energy (The ratio of regenerated energy to time over a given period) indicates that the system activates the braking resistor to consume regenerated energy, while the photovoltaic system independently charges the energy storage. If the photovoltaic output is insufficient, the photovoltaic input is cut off, allowing the energy storage to replenish its energy through the recovery of regenerated energy. When the energy storage SOC recovers to the low-energy set upper limit... ( Once the power consumption reaches 50% or higher, the system will resume the combined photovoltaic and energy storage power supply mode and shut down the braking resistor to recover regenerated energy with maximum efficiency.

[0192] Through the above static strategies, the local controller can maintain basic optimization of the energy flow path even when offline, making the most of photovoltaic and low-cost electricity, reducing the use of grid electricity during peak hours, and avoiding forced power replenishment during high-price periods due to insufficient energy storage charging. This ensures that the system still has stability, security, and a certain degree of energy saving even without cloud-based forecasting capabilities.

[0193] In a preferred embodiment, the operating data obtained in step S11 includes at least: historical output and real-time irradiance data of the photovoltaic unit, historical and real-time state of charge and health data of the energy storage unit, historical power consumption and operating mode data of the elevator load, and historical electricity price and real-time power data of the grid.

[0194] In a typical implementation, the cloud server continuously receives real-time data from the local controller and / or real-time information obtained from relevant agency websites or APIs. This includes photovoltaic data, energy storage data, mains power data, elevator operation data, system status data, and other data required by the non-AI model. The photovoltaic data includes illuminance, photovoltaic power, weather forecast, and geographical latitude and longitude. The energy storage data includes state of charge (SOC), state of health (SOH), allowable charge and discharge power, and energy storage operation status. The mains power data includes peak and off-peak hours and electricity prices, and electricity consumption. The elevator operation data includes elevator operation status, regenerative power, and load. The system status data includes path switch status, bus voltage, and equipment anomaly information.

[0195] This application also provides an embodiment of an energy management device for an elevator power supply system, such as... Figure 3 As shown, the energy management device in this embodiment is applied to an energy supply system that includes a photovoltaic unit, an energy storage unit, a mains power unit, and an elevator load. This system includes a cloud server and a local controller that work together.

[0196] Cloud servers include:

[0197] The data acquisition module is used to acquire historical and real-time operating data of the energy supply system;

[0198] The prediction module is used to predict the available output power of the photovoltaic unit and the comprehensive power demand of the elevator load in the first time period in the future based on the operating data. The comprehensive power demand includes the positive power demand of drive power consumption and the negative power demand of braking power generation.

[0199] The strategy generation module is used to generate an energy dispatch strategy for the first time period based on available output power, comprehensive power demand, current status information of energy storage units, and preset grid power cost information. The energy dispatch strategy is used to plan the energy allocation and switching sequence between photovoltaic units, energy storage units, and grid power units.

[0200] The strategy distribution module is used to distribute energy scheduling strategies to the local controller;

[0201] The local controller includes:

[0202] The data acquisition module is used to collect the local real-time operating status of the power supply system;

[0203] The strategy selection module is used to select either the energy scheduling strategy issued by the cloud server or the locally stored backup strategy as the current execution strategy based on the communication status and the validity of the strategy.

[0204] The instruction generation module is used to generate control instructions for the photovoltaic unit, energy storage unit and mains power unit based on the current execution strategy and local real-time operating status.

[0205] The execution control module is used to control the energy flow path of each unit according to control commands, so as to supply power to the elevator load or recover its regenerated energy.

[0206] In a preferred embodiment, the prediction module is configured to predict the available output power of the photovoltaic unit in the following manner:

[0207] Based on the physical parameters of the photovoltaic cells and environmental data, the theoretical clear-sky baseline power for the first time period in the future is calculated.

[0208] Based on historical operating data, the residual between the actual photovoltaic power and the theoretical clear-sky baseline power in the first period of the future is predicted by a time-series prediction model.

[0209] The theoretical clear-sky baseline power is superimposed with the predicted residual to obtain the predicted result of the available output power.

[0210] In a preferred embodiment, the prediction module is configured to predict the overall power demand of the elevator load in the following manner:

[0211] Based on historical elevator operation data, time characteristics, and building passenger flow correlation data, the driving power consumption demand curve and braking power generation capacity curve for the first time period in the future are generated simultaneously through a time series prediction model.

[0212] The combined power demand curve is obtained by synthesizing the drive power consumption demand curve and the braking power generation capacity curve.

[0213] In a preferred embodiment, the constraints on which the strategy generation module performs optimization calculations include: the upper and lower limits of the state of charge of the energy storage unit, the maximum charging and discharging power, and the time-of-use electricity price in the grid electricity cost information.

[0214] In a preferred embodiment, the strategy generation module is configured to generate an energy scheduling strategy based on at least one of the following rules:

[0215] Based on a comparison of available output power and overall power demand, the planned charging amount for energy storage units during off-peak hours of grid electricity prices is dynamically adjusted; when the predicted available output power is sufficient, the off-peak charging amount is reduced, and when the predicted available output power is insufficient, the off-peak charging amount is increased.

[0216] When it is predicted that there will be comprehensive power demand during the peak period of the grid electricity price, it is determined whether the expected discharge capacity of the energy storage unit during the peak period can meet the demand; if the expected discharge capacity is insufficient, the energy storage unit is planned to be charged in advance during the flat or valley period of the grid electricity price before the peak period.

[0217] When it is predicted that there will be a period in which the available output power exceeds the immediate absorption capacity of the energy storage unit, the output power of the photovoltaic unit will be limited in the energy dispatch strategy.

[0218] In a preferred embodiment, the policy generation module is configured as follows:

[0219] The net power requirement for the first time period is calculated based on the available output power and the overall power demand.

[0220] Time-by-time analysis of net power demand and constraints is performed to generate energy dispatch strategies;

[0221] The generated energy dispatch strategy includes the planned state-of-charge trajectory of the energy storage unit, the planned charge and discharge power curve, and the switching time points of the power supply paths between the photovoltaic unit, the energy storage unit, and the grid unit during the first time period.

[0222] In a preferred embodiment, the strategy selection module is configured as follows:

[0223] When the local controller communicates normally with the cloud server, the energy scheduling policy issued by the cloud server is used as the current execution policy.

[0224] When communication is interrupted, if the locally stored backup policy is still valid, the backup policy will be used as the current execution policy.

[0225] When communication is interrupted and there is no effective local backup policy, the static policy generated based on fixed rules will be used as the current execution policy.

[0226] In a preferred embodiment, a strategy arbitration module is also included;

[0227] The strategy arbitration module is configured to: receive the current execution strategy and the local real-time operating status, dynamically correct the control commands based on the real-time collected DC bus voltage value to maintain voltage stability, and / or, when a system fault signal is detected, forcibly generate a safety control command that overrides the current execution strategy.

[0228] In order to provide a more complete disclosure of the technical solution of this application, the following is an illustrative description of the various parts of the power supply system in which the method or system in the embodiments are applied.

[0229] The photovoltaic (PV) units in the power supply system are used to collect solar energy and supply power to the energy storage system and elevators via DC lines. They mainly include PV arrays, MPPT / DC-DC conversion units, and a PV monitoring and protection system. The PV array consists of several PV modules connected in series and parallel, used to directly convert solar energy into DC power. The MPPT / DC-DC conversion unit includes a buck-boost DC-DC converter and an MPPT (Maximum Power Point Tracking) algorithm processor, used to track the PV's maximum power point in real time, improving power generation efficiency; maintaining real-time power matching between the PV side and the bus side; and outputting a stable DC voltage for downstream use, ensuring that the PV output does not directly impact the bus. The PV monitoring and protection system includes solar irradiance sensors, PV-side meters, and other data acquisition and control units, used to monitor PV output, voltage, current, and environmental parameters, providing input for power generation prediction and energy dispatch. It includes anti-reverse diodes or MOSFETs and PV-side fuses to prevent reverse current from flowing from the energy storage or bus to the PV. It also includes PV module temperature sensors to correct MPPT algorithm performance and prevent module overheating that could cause power drops. Includes PV-side surge protection and isolation units to protect the MPPT from lightning strikes and sudden voltage changes. Includes a PV access dispatch interface, allowing EMS control of power-limited generation and coordinated operation of energy storage.

[0230] The energy storage unit is used to absorb braking energy, provide transient power support for the elevator, and supply power during low light or high load conditions. It mainly consists of battery modules, a high-voltage circuit control unit, and a battery management system (BMS). The battery modules typically use lithium iron phosphate battery packs, which feature high cycle life, high safety, and compatibility with DC bus voltage levels, primarily storing electrical energy and providing continuous power. The high-voltage circuit control unit includes components such as high-voltage contactors, protective relays, and fuses, used for the safe closing and opening of the battery's charging and discharging circuits, providing overvoltage, overcurrent, and short-circuit protection. The battery management system (BMS) includes functions such as SOC and SOH calculation, total voltage sampling, individual cell voltage and temperature sampling, current sampling, insulation detection, fault protection, active / passive balancing, maximum permissible charge / discharge power, and external control management. It also includes meters to accurately display the charging and discharging power and capacity of the stored energy.

[0231] The mains power unit is used to connect to the mains power system (grid) and serves as a backup energy source for the system and method in this application. It provides basic power compensation and off-peak charging for energy storage. It mainly includes a mains power meter, an AC main contactor, and an AC-DC conversion unit. The meter measures the energy consumption from the mains power supply. The main contactor performs soft-access control on the mains power input to achieve reliable switching. The AC-DC conversion unit is responsible for converting the mains power into a DC bus voltage output and charging the energy storage when needed.

[0232] The elevator load is the energy consumption end of the method and system provided in this application, mainly including a VVVF frequency converter and the elevator load. The VVVF frequency converter is responsible for converting DC bus power into controllable three-phase AC for elevator motor drive, and converting mechanical potential energy into regenerative electrical energy during braking, light-load upward movement, and heavy-load downward movement, feeding it back to energy storage and mains power through a DC link. The elevator load includes the car, counterweight, pulley system, motor, etc., and its operating status directly affects the energy flow direction. Multiple elevator units can be connected in parallel.

[0233] To implement the method in this application embodiment, the energy supply system may further include an energy path control and voltage regulation management unit: this unit is responsible for executing the optimal path among photovoltaic, energy storage, and mains power from the energy management device, and maintaining a constant and stable DC bus voltage. It can dynamically select the energy source based on the EMS and real-time load demand, supporting automatic switching between photovoltaic, energy storage, and mains power. It can determine the energy transmission path according to instructions issued by the EMS: the priority energy supply path from photovoltaic to the bus, the peak-shaving and supplementary energy path from energy storage to the bus, the supplementary function path from mains rectification to the bus, the energy recovery path from elevator regeneration to energy storage, and the low-cost charging path from mains power to energy storage. It supports Zero-Dip seamless switching, employing voltage synchronization, feedforward control, and high-speed electronic switching technology to ensure no significant drop in DC bus voltage during energy switching, achieving seamless elevator operation switching. It is equipped with a braking resistor and a voltage-driven enable switch as the system's final protection measure, used to handle regenerative energy when energy storage is full, the grid does not allow backfeeding, or in emergency situations.

[0234] In a typical embodiment, the data acquisition module in the local controller collects photovoltaic system data such as light intensity, power generation, photovoltaic module voltage and current, MPPT operating point, etc. in real time via data links; energy storage system data such as battery SOC, SOH, charging and discharging power, single cell voltage, single cell temperature, ambient temperature, and operating status; mains system data such as grid frequency, grid quality, and grid power consumption; elevator system data such as elevator operating status, load changes, and regenerative energy feedback power; and energy path control unit status such as the status of each energy path switch, DC bus voltage, and voltage regulator module operating status.

[0235] In a typical embodiment, the execution control module in the local controller actually executes the control part after arbitration of the cloud server prediction strategy, local security protection and local actual operating conditions, including power control of the photovoltaic system, loop control of the energy storage system, energy path control and switching control of the voltage regulation management unit.

[0236] In this embodiment, the local controller also includes a policy terminal processing unit, which not only receives 24-hour prediction policies pushed from the cloud, but also includes static policies for fallback execution in special circumstances and security policies for timely response.

[0237] In this embodiment, the local controller also includes a remote communication module that supports MQTT / 4G / fiber optic communication and can directly interact with the cloud server.

[0238] In some optional embodiments, the cloud server may also include or be divided into a data management and analysis module, an AI model training module, a strategy optimization and push module, and a remote monitoring and maintenance module. The cloud server trains an energy dispatch model based on a large amount of historical data, enabling next-day photovoltaic forecasting, optimized energy storage charging and discharging, and optimized regenerative braking energy recovery strategies. This data is then pushed to the EMS in real time via a communication module, providing global optimization, long-term strategy training, and information monitoring and display.

[0239] The following example illustrates the methods and systems provided in this application in more detail.

[0240] Example

[0241] This example provides a distributed energy integrated utilization system for home elevators, including multiple distributed energy sources such as photovoltaic systems, wind power systems, and biogas power generation systems. The photovoltaic system is connected to a new energy high-voltage control and charging module via a photovoltaic control interface module, the wind power system via a wind power control interface module, and the biogas power generation system via a biogas control interface module. This module is used to complete the rectification, protection, matching, and voltage processing of various energy sources.

[0242] The new energy high-voltage control charging module outputs processed DC power to the energy management system. The energy management system simultaneously receives input from the mains power grid and the diesel engine, and can dynamically generate energy dispatch strategies based on factors such as distributed energy capacity forecasts, battery status, and elevator operation modes. This enables real-time monitoring of various energy flows and battery status, as well as charging and discharging control of the energy storage system. The energy management system incorporates a multi-dimensional energy prediction and dispatch algorithm to automatically generate the optimal energy dispatch strategy by combining the following key factors:

[0243] Distributed energy capacity forecasting: The energy management system estimates the amount of distributed energy available in the future time period based on data such as photovoltaic irradiance forecast, wind speed forecast, biogas fuel supply, and elevator braking energy recovery model.

[0244] Battery energy storage status (SOC, SOH): The system monitors the battery module and its BMS output information such as state of charge (SOC), state of health (SOH), and charge / discharge rate limits in real time to ensure that the battery operates within a safe range.

[0245] Elevator operation mode recognition: The energy management system acquires the current operating status of the elevator system, including standby, light load operation, full load operation, peak operation, regenerative braking, and other modes. The system can predict future energy consumption fluctuations based on changes in elevator load.

[0246] Energy Availability and Cost Model: The system stores the unit energy cost, priority, and constraints of photovoltaic, wind power, biogas, grid power, and diesel engines, and can automatically update the scheduling strategy based on changes in external energy costs.

[0247] The energy management system generates a real-time, dynamic, multi-source hybrid energy supply strategy by comprehensively calculating the above inputs. For example:

[0248] When the photovoltaic, wind power or biogas energy production capacity is sufficient, the elevator system is driven first, while the battery is charged in a constant voltage or constant current manner.

[0249] When the distributed energy source cannot meet the instantaneous power demand of the elevator during peak mode operation, the power gap will be automatically compensated by the battery.

[0250] When the battery SOC is lower than the preset threshold, the system can automatically switch to AC power or diesel engine as the main power source and replenish the battery.

[0251] If the grid electricity price is at its peak, the system will prioritize low-cost renewable energy and energy storage to reduce grid electricity usage;

[0252] In the event of abnormal mains power or power outage, the system automatically switches to a combined battery and diesel engine power supply to ensure the safe operation of the elevator.

[0253] When the elevator decelerates or brakes, the traction motor switches to generator mode, producing recyclable electrical energy. This energy is then processed by the inverter rectifier module to charge the battery.

[0254] The energy management system manages the charging and discharging direction of the battery modules through a switch control unit, and directs energy to the inverter and energy recovery system. The inverter system converts the DC power from the battery into AC power for the elevator drive system, and feeds regenerated energy back to the battery pack during elevator braking.

[0255] The battery modules are managed and protected through the BMS system, ensuring the safety and reliability of the entire energy storage process. The elevator system's operating status is fed back to the energy management system in real time, enabling the system to optimize the distributed energy supply efficiency based on load prediction, direction of travel, and dynamic energy consumption.

[0256] Through the above process, this application realizes the integrated utilization of multiple energy sources, the recovery and reuse of elevator regenerative braking energy, the dynamic management of energy storage systems, and the optimized control of elevator power supply.

[0257] The above description is only a partial embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the embodiments of this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An energy management method for an elevator power supply system, the power supply system comprising a photovoltaic unit, an energy storage unit, a mains power unit, and an elevator load, characterized in that, The method is executed by a collaborative cloud server and a local controller; The cloud server performs the following steps: Based on the operating data of the energy supply system, the available output power of the photovoltaic unit and the comprehensive power demand of the elevator load are predicted in the first time period in the future. Based on the available output power, comprehensive power demand, current status information of the energy storage unit, and preset grid power cost information, an energy dispatch strategy for the first time period is generated through optimized calculation. The energy dispatch strategy is used to plan the energy allocation and switching sequence among the photovoltaic unit, energy storage unit, and grid power unit. The energy scheduling strategy is sent to the local controller; The local controller performs the following steps: Based on the communication status and the effectiveness of the strategy, the energy scheduling strategy issued by the cloud server or the locally stored backup strategy is selected as the current execution strategy. Based on the current execution strategy and the local real-time operating status, control instructions are generated for the photovoltaic unit, energy storage unit and mains power unit. The energy flow path of each unit is controlled according to the control command in order to supply power to the elevator load or recover its regenerated energy.

2. The method according to claim 1, characterized in that, The steps performed by the cloud server also include: Obtain historical and / or real-time operating data of the energy supply system.

3. The method according to claim 2, characterized in that, The steps performed by the local controller also include: Collect real-time operating data of the power supply system and transmit the real-time operating data to the cloud server.

4. The method according to claim 1, characterized in that, The prediction of the available output power of the photovoltaic unit in the first future time period specifically includes: Based on the physical parameters and environmental data of the photovoltaic unit, the theoretical clear-sky baseline power for the first time period is calculated; Based on historical operating data, the residual of the actual photovoltaic power in the first period relative to the theoretical clear-sky baseline power is predicted by a time-series prediction model. The theoretical clear-sky baseline power is superimposed with the predicted residual to obtain the usable output power.

5. The method according to claim 1, characterized in that, The predicted comprehensive power demand of the elevator load in the first future time period specifically includes: Based on historical elevator operation data, time characteristics, and building passenger flow correlation data, the driving power consumption demand curve and braking power generation capacity curve for the first time period are generated simultaneously through a time series prediction model. The combined power demand curve is obtained by combining the drive power consumption demand curve with the braking power generation capacity curve.

6. The method according to any one of claims 1 to 5, characterized in that, The constraints on which the optimization calculation is based include: the upper and lower limits of the state of charge of the energy storage unit, the maximum charging and discharging power, and the time-of-use electricity price in the grid electricity cost information.

7. The method according to claim 6, characterized in that, The energy scheduling strategy for the first time period is generated through optimized calculation, specifically including: The net power requirement for the first time period is calculated based on the available output power and the overall power requirement. The net power demand and the constraints are analyzed on a time-by-time basis to generate the energy scheduling strategy; The energy dispatch strategy includes the planned state-of-charge trajectory of the energy storage unit, the planned charge and discharge power curve, and the switching time points of the power supply paths between the photovoltaic unit, the energy storage unit, and the mains power unit during the first time period.

8. The method according to claim 6, characterized in that, The optimization calculation is based on at least one of the following rules: Based on the comparison between the available output power and the comprehensive power demand, the planned charging amount for the energy storage unit during off-peak hours of the mains electricity price is dynamically adjusted; when the predicted available output power is sufficient, the off-peak charging amount is reduced, and when the predicted available output power is insufficient, the off-peak charging amount is increased. When it is predicted that there will be comprehensive power demand during the peak period of the grid electricity price, it is determined whether the expected discharge capacity of the energy storage unit during the peak period can meet the demand; if the expected discharge capacity is insufficient, the energy storage unit is planned to be charged in advance during the flat or valley period of the grid electricity price before the peak period. When it is predicted that there will be a period in which the available output power exceeds the immediate absorption capacity of the energy storage unit, the energy dispatch strategy will plan to limit the output power of the photovoltaic unit.

9. The method according to claim 1, characterized in that, The local controller determines the current execution strategy based on the following rules: When the local controller communicates normally with the cloud server, the energy scheduling strategy issued by the cloud server shall be used as the current execution strategy. When communication is interrupted, if the locally stored backup policy is still valid, the backup policy will be used as the current execution policy. When communication is interrupted and there is no effective local backup policy, the static policy generated based on fixed rules will be used as the current execution policy.

10. The method according to claim 1 or 9, characterized in that, Before generating the control command, the local controller performs the following steps: The current execution strategy and the local real-time running status are input into the strategy arbitration module; The strategy arbitration module is configured to: dynamically correct control commands based on real-time collected DC bus voltage values ​​to maintain voltage stability, and / or, when a system fault signal is detected, forcibly generate a safety control command that overrides the currently executed strategy.

11. The method according to claim 9, characterized in that, The static strategy is executed at least based on preset time-of-use electricity pricing periods, including: During off-peak hours when the mains electricity price is low, the mains power unit will prioritize supplying power to the elevator load and charging the energy storage unit. During peak hours of the mains electricity price, it is prohibited for the mains power unit to supply power to the elevator load; priority should be given to power supply by the photovoltaic unit and the energy storage unit. Furthermore, the power supply path between the photovoltaic unit, the energy storage unit, and the mains power unit is dynamically adjusted based on the real-time state of charge of the energy storage unit.

12. The method according to any one of claims 1 to 3, characterized in that, The operational data includes at least: historical output and real-time irradiance data of photovoltaic units, historical and real-time state of charge and health data of energy storage units, historical power consumption and operating mode data of elevator loads, and historical electricity price and real-time power data of grid electricity.

13. An energy management device for an elevator power supply system, the power supply system comprising a photovoltaic unit, an energy storage unit, a mains power unit, and an elevator load, characterized in that, The device includes a cloud server and a local controller that work together. The cloud server includes: The prediction module is used to predict the available output power of the photovoltaic unit and the comprehensive power demand of the elevator load in the first future time period based on the operating data of the energy supply system. The strategy generation module is used to generate an energy dispatch strategy for the first time period based on the available output power, comprehensive power demand, current status information of the energy storage unit, and preset grid power cost information. The energy dispatch strategy is used to plan the energy allocation and switching sequence among the photovoltaic unit, energy storage unit, and grid power unit. The strategy distribution module is used to distribute the energy scheduling strategy to the local controller; The local controller includes: The strategy selection module is used to select either the energy scheduling strategy issued by the cloud server or the locally stored backup strategy as the current execution strategy based on the communication status and the validity of the strategy. The instruction generation module is used to generate control instructions for the photovoltaic unit, energy storage unit and mains power unit based on the current execution strategy and local real-time operating status. The execution control module is used to control the energy flow path of each unit according to the control instructions, so as to supply power to the elevator load or recover its regenerated energy.

14. The apparatus according to claim 13, characterized in that, The cloud server also includes: The data acquisition module is used to acquire historical and / or real-time operating data of the energy supply system.

15. The apparatus according to claim 14, characterized in that, The local controller also includes: The data acquisition module is used to collect real-time operating data of the energy supply system and transmit the real-time operating data to the cloud server.

16. The apparatus according to claim 13, characterized in that, The prediction module is configured to predict the available output power of the photovoltaic unit in the following manner: Based on the physical parameters and environmental data of the photovoltaic unit, the theoretical clear-sky baseline power for the first time period in the future is calculated; Based on historical operating data, the residual of the actual photovoltaic power in the first future period relative to the theoretical clear-sky baseline power is predicted by a time-series prediction model. The theoretical clear-sky baseline power is superimposed with the predicted residual to obtain the predicted result of the available output power.

17. The apparatus according to claim 13, characterized in that, The prediction module is configured to predict the overall power demand of the elevator load in the following manner: Based on historical elevator operation data, time characteristics, and building passenger flow correlation data, the driving power consumption demand curve and braking power generation capacity curve for the first time period in the future are generated simultaneously through a time series prediction model. The combined power demand curve is obtained by combining the drive power consumption demand curve with the braking power generation capacity curve.

18. The apparatus according to any one of claims 13 to 17, characterized in that, The constraints on which the strategy generation module performs optimization calculations include: the upper and lower limits of the state of charge of the energy storage unit, the maximum charging and discharging power, and the time-of-use electricity price in the grid cost information.

19. The apparatus according to claim 18, characterized in that, The strategy generation module is configured as follows: The net power requirement for the first time period is calculated based on the available output power and the overall power requirement. The net power demand and the constraints are analyzed on a time-by-time basis to generate the energy scheduling strategy; The generated energy dispatch strategy includes the planned state-of-charge trajectory of the energy storage unit, the planned charge and discharge power curve, and the switching time points of the power supply paths between the photovoltaic unit, the energy storage unit, and the grid unit during the first time period.

20. The apparatus according to claim 18, characterized in that, The strategy generation module is configured to generate an energy scheduling strategy based on at least one of the following rules: Based on the comparison between the available output power and the comprehensive power demand, the planned charging amount for the energy storage unit during off-peak hours of the mains electricity price is dynamically adjusted; when the predicted available output power is sufficient, the off-peak charging amount is reduced, and when the predicted available output power is insufficient, the off-peak charging amount is increased. When it is predicted that there will be comprehensive power demand during the peak period of the grid electricity price, it is determined whether the expected discharge capacity of the energy storage unit during the peak period can meet the demand; if the expected discharge capacity is insufficient, the energy storage unit is planned to be charged in advance during the flat or valley period of the grid electricity price before the peak period. When it is predicted that there will be a period in which the available output power exceeds the immediate absorption capacity of the energy storage unit, the energy dispatch strategy will plan to limit the output power of the photovoltaic unit.

21. The apparatus according to claim 13, characterized in that, The strategy selection module is configured to determine the current execution strategy based on the following rules: When the local controller communicates normally with the cloud server, the energy scheduling strategy issued by the cloud server shall be used as the current execution strategy. When communication is interrupted, if the locally stored backup policy is still valid, the backup policy will be used as the current execution policy. When communication is interrupted and there is no effective local backup policy, the static policy generated based on fixed rules will be used as the current execution policy.

22. The apparatus according to claim 13 or 21, characterized in that, It also includes a strategy arbitration module; The strategy arbitration module is configured to: receive the current execution strategy and the local real-time operating status, dynamically correct the control command based on the real-time collected DC bus voltage value to maintain voltage stability, and / or, when a system fault signal is detected, forcibly generate a safety control command that overrides the current execution strategy.

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