TWI891347B - Energy management system and management method using machine learning to convert control rights between cloud and local based on contract capacity - Google Patents

Abstract

Energy management methodology contains: receiving information such as power usage, and power generation of a specific field transmitted by a local server and receiving the weather information from weather service provider; predicting future power usage and future power generation based on weather forecast and historical power usage and generation data; calculating the difference between future power consumption volume and future power generating volume; and compare the difference to the contract capacity of the specific field. When the difference is greater than or equal to the first threshold of the contract capacity, the local server controls the energy storage device according to the second threshold set by the cloud server. When the difference is less than the first threshold ratio value of the contract capacity, the cloud server controls the energy storage device according to the third threshold set by the cloud server.

Description

Machine learning applied to energy management system with contracted capacity and cloud-to-ground control rights conversion and its management method

The present invention relates to a system and method, and more particularly to an energy storage device, an energy management system, and an energy management method that collaborates with cloud-based predictions of future electricity consumption and generation and real-time ground-based computing.

As Taiwan's energy policy shifts toward becoming a technological island, the development of green energy technology is essential to ensuring both sustainable development and a good living environment for its people. Energy storage systems can not only address the potential instability of green energy supply in the short term and mitigate Taipower's need for continuous expansion of generating units by shifting peak loads, but can also become the ideal solution for individual households to achieve their ultimate goal of self-sufficiency in the long term.

Current energy management systems employ two approaches: one uses a cloud-based computing system to generate control commands for the ground, which then performs energy storage control according to these commands. Another approach uses a computing system directly at the ground to generate control commands for energy storage control. The cloud-based approach may be unable to transmit control commands to the ground due to network outages, resulting in energy storage control failures. Furthermore, the cloud requires high-frequency, real-time data transmission, which incurs high transmission costs. Generating control commands directly at the ground requires a computing system at the ground, incurring additional construction costs.

Therefore, there is still room for improvement in conventional energy management devices and methods.

One embodiment of the present invention provides an energy management method, comprising: receiving power consumption information of a workplace and power generation information of a green energy power generation device transmitted by a local server, and receiving weather information from a weather server; predicting a future power consumption and a future power generation of the workplace based on the weather information, the power consumption information, and the power generation information; calculating a difference between the future power consumption and the future power generation; and comparing the difference with a contract capacity of the workplace power consumption, wherein when the difference is greater than or equal to the contract capacity, the workplace power consumption is calculated. When the difference is less than the first threshold multiplier value of the contracted capacity, the local server generates a control instruction to control an energy storage device at the work site based on a second threshold multiplier value set by a cloud server and the real-time power consumption and real-time power generation of the work site. When the difference is less than the first threshold multiplier value of the contracted capacity, the cloud server generates a control instruction to control the energy storage device based on a third threshold multiplier value set by the cloud server, wherein the second threshold multiplier value is greater than the first threshold multiplier value, and both the first threshold multiplier value and the second threshold multiplier value are less than 1.

In some embodiments, when the difference is greater than or equal to the first threshold multiplier of the contracted capacity, the cloud server transfers control of the energy storage device to the local server. Based on the real-time power consumption of the work site and the real-time power generation of the green energy generation device, the local server executes control instructions to control the energy storage device to standby or discharge.

In some embodiments, the energy management method further includes: when the future electricity consumption is greater than the contracted capacity, the cloud server increases the second threshold multiplier value.

In some embodiments, the increased second threshold multiplier value is greater than 1.

In some embodiments, the energy management method further includes executing a control finger of the local server to control the energy storage device to discharge when a difference between the real-time power consumption and the real-time power generation is greater than the increased second threshold multiplier value of the contracted capacity.

In some embodiments, the energy management method further includes transferring control of the energy storage device from the local server to the cloud server when the difference is less than the first threshold multiplier value of the contracted capacity.

In some embodiments, the cloud server generates a control instruction to control the energy storage device to perform a discharge, further comprising: determining whether the amount of discharge of the energy storage device is greater than a stored amount of the energy storage device; and when the amount of discharge of the energy storage device is greater than the stored amount of the energy storage device, executing a control instruction of the cloud server to control the energy storage device to charge with a mains power supply until the stored amount of the energy storage device is greater than the discharged amount.

In some embodiments, the cloud server generates a control instruction to control the energy storage device to perform charging, further comprising: determining whether the amount charged by the energy storage device is greater than a chargeable amount of the energy storage device; and when the amount charged by the energy storage device is greater than the chargeable amount of the energy storage device, executing a control instruction from the cloud server to control the energy storage device to discharge until the chargeable amount of the energy storage device is greater than the charged amount.

In some embodiments, the energy management method further includes obtaining control of the energy storage device by the local server when a communication connection between the cloud server and the local server is interrupted.

Another embodiment of the present invention provides an energy management system comprising: a cloud server; a local server located at a workplace and communicatively connected to the cloud server; the workplace including a green energy generation device and an energy storage device; the cloud server further comprising: a storage unit storing at least one instruction; and a processing unit electrically coupled to the storage unit and configured to access the at least one instruction from the storage unit to execute the energy management method described above.

This energy management system and method integrates the forecasting capabilities of a cloud server with the real-time control capabilities of a local server to manage energy storage device power allocation. The cloud server predicts future power generation and consumption at the site based on workplace power consumption and generation data, as well as third-party weather information, to control the charging and discharging of the energy storage device. If future power consumption exceeds contracted capacity, the local server takes over real-time charge and discharge control of the energy storage device. This solution not only leverages the cloud server's capabilities for large-scale computing, forecasting, and data storage, but also offers the local server's ability to instantly manage the charging and discharging of the energy storage device when power consumption exceeds contracted capacity.

100: Energy Management System

110: Energy storage device

120: Cloud Server

121: Processing Components

122: Memory

123: Cloud Database

130: Ground Server

140: Weather Server

150: Mains power

160: Green Energy Generating Device

200: Internet

300: Energy Management Process

301-308: Steps

401-403: Steps

501-503: Steps

The drawings herein are incorporated into and constitute a part of this specification. These drawings illustrate embodiments consistent with the present invention and, together with the specification, are used to illustrate the technical solutions of the embodiments of the present invention.

Figure 1 shows a block diagram of an energy management system according to some embodiments of the present invention.

Figure 2 shows a block diagram of a cloud server according to some embodiments of the present invention.

Figure 3 shows a schematic diagram of the energy management process according to some embodiments of this case.

Figure 4 schematically illustrates the control flow of the energy storage device during discharge according to some embodiments of the present invention.

Figure 5 shows a schematic diagram of the control flow of the energy storage device during charging according to some embodiments of the present invention.

The following disclosure provides numerous different embodiments or examples for implementing various features of the present invention. Components and configurations from specific examples are used in the following discussion to simplify the disclosure. Any examples discussed are for illustrative purposes only and are not intended to limit the scope or meaning of the present invention or its examples in any way. Furthermore, while references to numbers and/or letters may be repeated throughout the various examples, this repetition is for simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed below.

Unless otherwise noted, terms used throughout this specification and claims generally have their ordinary meanings as used in the art, within the context of this disclosure, and in the specific context. Certain terms used to describe the present disclosure are discussed below and elsewhere in this specification to provide additional guidance to those skilled in the art regarding the description of the present disclosure.

As used herein, "coupling" or "connection" may refer to two or more elements being in direct physical or electrical contact with each other, or indirect physical or electrical contact with each other. "Coupling" or "connection" may also refer to the mutual operation or movement of two or more elements.

In this document, the terms "first," "second," and "third," etc., are used to describe various elements, components, regions, layers, and/or blocks, and it is understood that these elements, components, regions, layers, and/or blocks should not be limited by these terms. These terms are limited to identifying a single element, component, region, layer, and/or block. Therefore, a first element, component, region, layer, and/or block in the following text could also be referred to as a second element, component, region, layer, and/or block without departing from the spirit of the present invention. As used herein, the term "and/or" includes any combination of one or more of the listed items. The term "and/or" mentioned in this document refers to any combination of any one, all, or at least one of the listed elements.

Conventional energy storage control methods, where a cloud-based computing system generates control commands for local energy storage control, may fail to provide these commands due to network outages. Furthermore, the cloud requires high-frequency, real-time data transmission, which incurs prohibitive transmission costs. Furthermore, if the local computing system generates control commands directly, a separate local computing system would be expensive to build. Therefore, this energy management system integrates the cloud and local systems and, based on third-party data, determines a control strategy for the energy storage device.

FIG1 is a block diagram of an energy management system according to some embodiments of the present invention. Energy management system 100 primarily comprises an energy storage device 110, a cloud server 120, and a local server 130 capable of communicating with cloud server 120, for example, via the internet 200. In some embodiments, cloud server 120 is further coupled to a weather server 140, which provides weather information to cloud server 120. In some embodiments, weather information includes satellite imagery, cloud cover changes, cloud reflectivity, wind direction, and wind speed, and other weather information that can impact green energy generation, such as solar power or wind power. In some embodiments, cloud server 120 is further coupled to a green energy generator 160, such as a solar power generator or a wind turbine, located at a worksite. Green energy generator 160 provides power generation information to cloud server 120. Local server 130 receives cloud control commands generated by cloud server 120 and controls the charging and discharging of energy storage device 110 based on either cloud control commands or local control commands generated by local server 130. In some embodiments, energy storage device 110 can be charged either via utility power 150 or via green energy generator 160.

Figure 2 shows a block diagram of a cloud server according to some embodiments of the present invention. Please refer to Figures 1 and 2 together. In some embodiments, cloud server 120 primarily includes a processing element 121, a memory 122, and a cloud database 123. In some embodiments, processing element 121 is coupled to cloud database 123, which stores weather information provided by weather server 140, workplace electrical equipment data, charge and discharge information of energy storage device 110, the reserve capacity of energy storage device 110, a contracted capacity, and power generation information provided by green energy generator 160. In some embodiments, the contracted capacity is a fixed monthly power consumption. Regardless of whether electricity usage reaches the contracted capacity, a basic charge will be applied based on the contracted capacity. Conversely, if electricity usage exceeds the contracted capacity, a penalty will be imposed in addition to the basic charge. In some embodiments, the processing element 121 can retrieve weather information, workplace electrical equipment data, charge and discharge information of the energy storage device 110, and reserve power, contracted capacity, and power generation information of the green energy generator 160 stored in the cloud database 123 to perform a forecast. This forecast predicts the future power generation of the green energy generator 160 and the future power consumption of the workplace electrical equipment to generate a cloud control instruction. In some embodiments, the memory 122 stores at least a plurality of computer-readable instructions. The processing element 121 is electrically coupled to the memory 122 to access computer-readable instructions from the memory 122 and execute a prediction model application to predict the future power generation of the green energy power generation device 160 and the future power consumption of electrical equipment in the workplace. Based on the predicted future power generation and power consumption, the processing element 121 generates cloud-based control instructions and transmits them to the local server 130. In some embodiments, the processing element 121 periodically performs future power generation and power consumption predictions and transmits updated cloud-based control instructions to the local server 130 in real time. In some embodiments, the predictions are performed every 10 or 15 minutes, and the updated cloud-based control instructions are periodically transmitted to the local server 130. As such, because this prediction model incorporates weather information, it can provide more accurate forecasts of future power generation and consumption. Furthermore, cloud-based control instructions can be adjusted based on seasonal changes in electricity prices or time periods.

In some embodiments, the prediction model further includes a power generation prediction model for predicting future power generation and a power consumption prediction model for predicting future power consumption. The power generation prediction model extracts power generation data of the green power generation device 160 stored in the cloud database 123 and weather information provided by the weather server 140 as training data. After performing feature engineering, the model is built using the eXtreme Gradient Boosting (XGboost) algorithm to predict the future power generation of the green power generation device 160. The power consumption prediction model extracts workplace electrical equipment data stored in cloud database 123. After combining weekday and holiday data with feature engineering, it uses eXtreme Gradient Boosting (XGboost) to build a power demand prediction model to predict future power consumption of electrical equipment. It is important to note that the algorithm used to build the prediction model is merely an example and does not limit its application to this case. Other artificial intelligence algorithms can also be used in this case.

In some embodiments, after the processing component 121 executes a prediction model application to predict the future power generation of the green energy generator 160 and the future power consumption of the electrical equipment at the workplace, it generates corresponding cloud-based control instructions based on an energy management process to control the charging and discharging of the energy storage device 110. Figure 3 illustrates a schematic diagram of the energy management process according to some embodiments of the present invention. Referring to Figures 1 through 3, the energy management process 300 begins, in step 301, by calculating the difference between future power consumption and future power generation. In some embodiments, the processing component 121 calculates the difference between the predicted future power generation of the green energy generator 160 and the predicted future power consumption of the electrical equipment at the workplace.

In some embodiments, the processing element 121 of the cloud server 120 predicts power consumption and power generation based on the workplace power consumption and power generation of the green energy generator 160, as uploaded by the local server 130. However, to avoid high transmission costs, the cloud server 120 performs predictions and sends cloud control commands based on these predictions over a longer period, such as every 10 or 15 minutes. Therefore, cloud control commands cannot be sent to the cloud to adjust the energy storage device 110 in real time based on the current workplace conditions. To prevent unexpected situations during this long time period from causing electricity consumption to exceed the contracted capacity, this solution reduces the risk of exceeding the contracted capacity by transferring control of the energy storage device 110 of the cloud server 120 to the local server 130 when it is predicted that future electricity consumption will exceed the contracted capacity after deducting future power generation. Therefore, in step 302, the cloud server 120 determines whether the electricity difference is greater than or equal to a first threshold multiplier of the contracted capacity. In some embodiments, the first threshold multiplier of the contracted capacity is 0.85 times the contracted capacity. In other words, when the electricity difference is greater than 0.85 times the contracted capacity, there is a possibility that electricity consumption will exceed the contracted capacity due to unexpected situations in the future. Therefore, processing component 121 first compares the calculated energy difference with 0.85 times the contracted capacity to determine whether to transfer control of energy storage device 110. However, it should be noted that the first threshold multiplier value of 0.85 times the contracted capacity is merely an example and does not limit the application of this solution. The first threshold multiplier value can vary based on different work sites. In some embodiments, a comprehensive assessment of whether future energy consumption is likely to exceed the contracted capacity can be conducted based on the work site's past energy consumption data, as well as predicted future energy consumption and power generation.

If the energy difference is greater than the first threshold multiplier of the contracted capacity, it indicates that there is a possibility that future energy consumption may exceed the contracted capacity due to unexpected circumstances. Therefore, in step 303, the local server obtains control of the energy storage device and the second threshold multiplier. In one embodiment, the cloud server transfers control of the energy storage device to the local server and transmits the second threshold multiplier to the local server. The local server then regulates the energy storage device based on this second threshold multiplier. In some embodiments, the processing element 121 of the cloud server 120 predicts that future electricity consumption, after deducting future power generation, will exceed the contracted capacity by a first threshold multiplier value. In this embodiment, the threshold multiplier value is 0.85 times the contracted capacity, indicating that the expected future power consumption of the workplace electrical equipment is far greater than the future power generated by the green energy generation device 160, making it highly likely that power consumption will exceed the contracted capacity. To prevent power consumption from exceeding the contracted capacity, the processing element 121 of the cloud server 120 transfers control of the energy storage device 110 to the local server 130 and sets a second threshold multiplier value, which is transmitted to the local server 130. The local server 130 controls the energy storage device 110 based on this second threshold multiplier value. In some embodiments, the local server 130 controls the energy storage device 110 to discharge or enter standby mode based on this second threshold multiplier value. Because the local server 130 can monitor the workplace's real-time power consumption and the green energy generator 160's real-time power generation every minute, the local server 130 can instantly respond to current power consumption and power generation conditions by controlling the energy storage device 110 to discharge or enter standby mode, thereby preventing power consumption from exceeding the contracted capacity. The local server 130 controls the energy storage device 110 based on the captured current power consumption and power generation to prevent power consumption from exceeding the contracted capacity. In some embodiments, the first threshold multiplier for transferring control of the energy storage device 110 is 0.85 times the contracted capacity, and the second threshold multiplier is 0.95 times the contracted capacity. However, it should be noted that these first and second threshold multipliers are merely examples and may vary depending on the operating environment or power consumption.

In step 304, the local server generates a control command to control the energy storage device based on the second threshold multiplier value. In some embodiments, after the local server 130 obtains control of the energy storage device 110, it generates a control command to control the energy storage device to discharge or enter standby mode based on a second threshold multiplier value set by the cloud server 120. In some embodiments, after the processing element 121 transfers control of the energy storage device 110 from the cloud server 120 to the local server 130, the local server 130 monitors the real-time power consumption of the work site and the real-time power generation of the green energy generation device in real time, for example, every minute. In some embodiments, when the energy difference is greater than the second threshold multiplier of the contracted capacity, 0.95 times the contracted capacity, the ground server 130 generates a corresponding ground control instruction to control the energy storage device 110 to preemptively discharge the energy, ensuring that the future energy difference does not exceed the contracted capacity. In other embodiments, when the energy difference is less than the second threshold multiplier of the contracted capacity, 0.95 times the contracted capacity, the ground server 130 generates a corresponding ground control instruction to control the energy storage device 110 to enter standby mode.

In some embodiments, after the local server 130 gains control of the energy storage device 110, step 301 is re-executed. At a specific interval, such as every 10 or 15 minutes, the processing element 121 of the cloud server 120 re-calculates the power consumption of the workplace and the power generated by the green energy generator 160 based on the power consumption of the workplace uploaded by the local server 130, and then re-executes step 3. 02, when it is predicted that future electricity consumption, even after deducting future power generation, will still exceed the first threshold multiplier value of the contracted capacity and may significantly exceed the contracted capacity, in step 303, in addition to continuing to control the energy storage device 110 through the local server 130, the processing element 121 of the cloud server 120 also synchronously adjusts the second threshold multiplier value at which the local server 130 instructs the energy storage device 110 to discharge. This is because the penalty for exceeding the contracted capacity increases with the greater the excess. Therefore, to prevent a significant excess of contracted capacity due to subsequent large increases in electricity consumption or weak power generation, the second threshold multiplier value at which the local server 130 generates a local control instruction instructing the energy storage device 110 to discharge is increased accordingly. In some embodiments, when the processing element 121 of the cloud server 120 predicts that future power consumption will significantly exceed the contracted capacity even after deducting future power generation, resulting in a triple penalty and potentially depleting the battery, the second threshold multiplier value can be raised from 0.95 times the contracted capacity to 1.1 times the contracted capacity. In other words, only when the difference between the predicted future power consumption and future power generation exceeds 1.1 times the contracted capacity will the local server 130 generate a local control command to control the energy storage device 110 to discharge. This preserves power while not exceeding the triple penalty and prevents premature battery depletion.

In other embodiments, after the processing element 121 of the cloud server 120 performs a new round of forecasts based on the workplace power consumption and the power generation of the green energy generator 160 uploaded by the local server 130, it determines in step 302 that, while the difference between the future power consumption and the future power generation is still greater than the first threshold multiplier of the contracted capacity, the future power generation of the green energy generator 160 will increase. In step 303, while the local server 130 continues to control the energy storage device 110, the processing element 121 of the cloud server 120 simultaneously lowers the second threshold multiplier at which the local server 130 instructs the energy storage device 110 to discharge. In some embodiments, the processing element 121 of the cloud server 120 lowers the second threshold multiplier from 0.95 times the contracted capacity to 0.9 times the contracted capacity, allowing the energy storage device 110 to discharge first and reducing the proportion of utility power used. In this case, after the local server 130 assumes control of the energy storage device 110, the cloud server 120 will further adjust the second threshold multiplier based on the forecast results after each new round of power consumption forecasting, if it still determines that the local server 130 should assume control of the energy storage device 110.

In other embodiments, after the processing element 121 of the cloud server 120 performs a new round of forecasting based on the workplace power consumption and the power generation of the green energy power generation device 160 uploaded by the local server 130, if it is determined in step 302 that the power difference is not greater than or equal to the first threshold multiplier value of the contracted capacity, step 305 is executed, and the cloud server obtains control of the energy storage device. In some embodiments, the energy storage device 110 is controlled to charge, discharge, or standby. And in step 306, it is determined whether the power difference is less than or equal to a third threshold multiplier value of the contracted capacity. In some embodiments, the third threshold multiplier value of the contracted capacity is 0.1 times the contracted capacity. The processing element 121 compares the calculated power difference with 0.1 times the contracted capacity. However, it is worth noting that the third threshold multiplier value of 0.1 times the contract capacity is for illustration only and does not limit the application of this case. The third threshold multiplier value can be varied according to different work environments.

If the energy difference is less than or equal to the third threshold multiplier of the contracted capacity, in step 307, the cloud server generates a control instruction to control the energy storage device to charge. In some embodiments, the energy difference is less than the third threshold multiplier of the contracted capacity, i.e., 0.1 times the contracted capacity, indicating that the expected future energy consumption of the workplace electrical equipment is not significantly different from the future energy generated by the green energy generator 160. In other words, the future energy generated by the green energy generator 160 can almost fully meet the needs of the workplace electrical equipment, and the risk of exceeding the contracted capacity is minimal. Therefore, when the power difference is less than the third threshold multiplier of the contracted capacity, i.e., 0.1 times the contracted capacity, processing component 121 generates a corresponding cloud control instruction to control green energy generator 160 to preemptively charge energy storage device 110. Next, step 301 is executed. At a specific interval, such as every 10 or 15 minutes, processing component 121 of cloud server 120 re-calculates power consumption based on the workplace power consumption uploaded by local server 130 and the power generated by green energy generator 160.

If the energy difference is greater than the third threshold multiplier value of the contracted capacity, that is, if the energy difference is between the first and third threshold multipliers of the contracted capacity, in step 308, the cloud server generates a control instruction to control the energy storage device to charge, discharge, or enter standby mode. In one embodiment, cloud server 120 executes the cloud server's control instruction to control energy storage device 110 to charge, discharge, or enter standby mode based on the future power consumption and power generation of the workplace. In some embodiments, if the future power consumption of the workplace increases, the energy storage device may be controlled to discharge. In some embodiments, if the workplace's future electricity consumption is stable and the electricity price is currently low, the energy storage device 110 can be controlled to charge first, allowing the utility to supply the workplace. Alternatively, if the electricity price is currently high, the energy storage device 110 can be controlled to discharge to supply the workplace, thereby saving electricity costs. Next, step 301 is executed. The processing element 121 of the cloud server 120 re-calculates the electricity consumption of the workplace based on the electricity consumption uploaded by the local server 130 and the power generated by the green energy generator 160 at a specific time interval, such as every 10 or 15 minutes.

In other embodiments, when communication between the cloud server 120 and the local server 130 is interrupted, that is, when the local server 130 does not receive the cloud control command regularly transmitted by the cloud server 120 after a specific period of time, the local server 130 determines that communication with the cloud server 120 is interrupted. At this time, control of the energy storage device 110 is automatically transferred to the local server 130.

FIG4 is a schematic diagram of the control flow of the energy storage device when discharging according to some embodiments of the present invention. When the cloud server 120 predicts that the energy storage device 110 will need to discharge in the future, in order to avoid the battery power of the energy storage device 110 being insufficient for discharge, the present invention further executes step 401 before the energy storage device 110 is expected to discharge, comparing whether the discharge amount of the energy storage device 110 is greater than the current stored power of the energy storage device 110. When the energy storage device 110 is expected to discharge, the energy storage device 110 is discharged. When the discharged amount is greater than the current stored amount of energy storage device 110, it indicates that the energy storage device does not have enough remaining power to discharge. To prevent the energy storage device 110 from being depleted, in step 402, the energy storage device 110 is controlled to be charged with AC power, without exceeding a second threshold, until the stored amount of energy storage device 110 is greater than the expected discharge amount of energy storage device 110. Conversely, when the expected discharge amount of energy storage device 110 is less than the current stored amount of energy storage device 110, it indicates that the current battery level of energy storage device 110 is sufficient for discharge. In step 403, the energy storage device 110 is controlled to discharge, charge, or enter standby mode. In some embodiments, because the battery capacity of energy storage device 110 is sufficient for future discharge, cloud server 120 can control energy storage device 110 to charge during low-price periods and discharge during high-price periods based on time-based electricity prices, thereby earning the price difference.

Figure 5 schematically illustrates the control flow for charging an energy storage device according to some embodiments of the present invention. When cloud server 120 anticipates a future charging demand for energy storage device 110, to prevent the energy storage device 110's chargeable capacity from being less than the intended charge, the present invention further performs step 501 before charging energy storage device 110 to compare whether the expected charge capacity of energy storage device 110 is greater than the energy storage device 110's chargeable capacity. When the estimated charge capacity of energy storage device 110 is greater than the chargeable capacity of energy storage device 110, it indicates that energy storage device 110 does not have enough space for charging. Therefore, in step 502, energy storage device 110 is controlled to discharge first. When the chargeable capacity of energy storage device 110 is greater than the estimated charge capacity of energy storage device 110, energy storage device 110 is controlled to charge again. Conversely, when the estimated charge capacity of energy storage device 110 is less than the chargeable capacity of energy storage device 110, energy storage device 110 is controlled to charge, discharge, or enter standby mode in step 503. In some embodiments, since the battery capacity of the energy storage device 110 is sufficient for future charging, the cloud server 120 can first control the energy storage device 110 to charge during low-price periods and discharge during high-price periods based on time-based electricity prices, thereby earning the price difference.

In some embodiments, the processing element 121 can also generate a corresponding cloud control instruction based on the difference in electricity prices during different time periods to control the energy storage device 110 to charge during low-price periods and discharge during high-price periods, thereby earning the difference in electricity prices.

In summary, this energy management system integrates the forecasting capabilities of a cloud server with the real-time control capabilities of a local server to manage the energy storage device's power allocation. Based on power consumption and generation data uploaded from the workplace, along with third-party weather information, the high-performance cloud server performs big data analysis to predict future power generation and consumption at the workplace. Based on this data, cloud-based control commands for regulating the energy storage device are generated. These commands are regularly updated and transmitted to the local server to control the charging and discharging of the energy storage device. When the difference between future power generation and consumption does not pose a risk of exceeding the contracted capacity, the cloud server will continue to control the energy storage device's charging and discharging. However, if the difference between future power generation and consumption poses a risk of exceeding the contracted capacity, the local server will take over control of the energy storage device's charging and discharging. This solution not only leverages the high computing power of the cloud server to provide long-term power generation and consumption trend forecasts, but also allows for immediate control of the energy storage device by the local server if power consumption threatens to exceed the contracted capacity or if a power outage occurs, preventing power consumption from exceeding the contracted capacity.

Additionally, the above examples include example steps in sequence, but these steps do not necessarily need to be performed in the order shown. Performing these steps in a different order is contemplated by this disclosure. Steps may be added, substituted, changed in order, and/or omitted as appropriate within the spirit and scope of the embodiments of this disclosure.

Although this application has been disclosed above in terms of implementation, it is not intended to limit this application. Anyone skilled in the art may make various modifications and improvements without departing from the spirit and scope of this application. Therefore, the scope of protection of this application shall be determined by the scope of the patent application attached hereto.

100: Energy Management System

110: Energy storage device

120: Cloud Server

130: Ground Server

140: Weather Server

150: Mains power

160: Green Energy Generating Device

200: Internet

Claims (10)

An energy management method includes: using a processing element to receive power consumption information of a workplace transmitted by a local server, power generation information of a green energy power generation device, and weather information provided by a weather server; using the processing element to predict a future power consumption and a future power generation of the workplace based on the weather information, the power consumption information, and the power generation information; using the processing element to calculate a difference between the future power consumption and the future power generation; and using the processing element to compare the difference with a contract capacity of the workplace power consumption, wherein when the difference is greater than or equal to When the contract capacity reaches a first threshold multiplier value, the local server generates a control instruction to control an energy storage device at the work site based on a second threshold multiplier value set by a cloud server and the real-time power consumption and real-time power generation of the work site; when the difference is less than the first threshold multiplier value of the contract capacity, the cloud server generates a control instruction to control the energy storage device based on a third threshold multiplier value set by the cloud server, wherein the second threshold multiplier value is greater than the first threshold multiplier value, and both the first threshold multiplier value and the second threshold multiplier value are less than 1. The energy management method as described in claim 1 further includes: when the difference is greater than or equal to the first threshold multiplier value of the contracted capacity, the cloud server transfers control of the energy storage device to the local server, and executes the control instruction generated by the local server to control the energy storage device to standby or discharge according to the real-time power consumption of the work site and the real-time power generation of the green energy power generation device. The energy management method as described in claim 2 further includes: when the future electricity consumption is greater than the contracted capacity, the cloud server increases the second threshold multiplier value. The energy management method as described in claim 3, wherein the increased second threshold multiplier value is greater than 1. The energy management method as described in claim 3 further includes executing the control instruction generated by the local server to control the discharge of the energy storage device when a difference between the real-time power consumption and the real-time power generation is greater than the second threshold multiplier value after the contract capacity is increased. The energy management method as described in claim 1 further includes the local server transferring control of the energy storage device to the cloud server when the difference is less than the first threshold multiplier value of the contracted capacity. The energy management method as described in claim 1, wherein the cloud server generates a control instruction to control the energy storage device to perform a discharge, further comprising: using the processing element to determine whether the amount of discharge of the energy storage device is greater than a stored power of the energy storage device; and when the amount of discharge of the energy storage device is greater than the stored power of the energy storage device, executing the control instruction generated by the cloud server to control the energy storage device to charge with a mains power until the stored power of the energy storage device is greater than the discharged amount. The energy management method as described in claim 1, wherein the cloud server generates a control instruction to control the energy storage device to perform a charging, further includes: using the processing element to determine whether the amount of charge of the energy storage device is greater than a chargeable amount of the energy storage device; and when the amount of charge of the energy storage device is greater than the chargeable amount of the energy storage device, executing the control instruction generated by the cloud server to control the energy storage device to discharge until the chargeable amount of the energy storage device is greater than the charged amount. The energy management method as described in claim 1 further includes obtaining control of the energy storage device by the local server when a communication connection between the cloud server and the local server is interrupted. An energy management system includes: a cloud server; a local server located at a workplace and communicatively connected to the cloud server; the workplace includes a green energy power generation device and an energy storage device, wherein the cloud server further includes: a storage unit storing at least one instruction; and a processing element electrically coupled to the storage unit and configured to access the at least one instruction from the storage unit to execute the energy management method described in claim 1-9.