Grid-friendly interactive building energy flexible management system and method

Abstract

The present invention provides a grid-friendly interactive building energy flexible management system, comprising: a data acquisition module (10), a calculation module (20) and a control module (30). The data acquisition module is capable of acquiring the real-time electricity price curve and the weather forecast data for the next day. The calculation module is connected to the data acquisition module by signal and is capable of: generating a first predicted power generation curve, a first predicted power consumption curve and a full-day predicted electricity price curve for the next day based on the weather forecast data for the next day, the historical power consumption curve and the real-time electricity price curve for the next day; comparing the first predicted power generation and the first predicted power consumption on the next day; generating different strategies based on the comparison results and adjusting the full-day predicted electricity price curve in real time; and acquiring real-time operation data for training. The control module is connected to the calculation module by signal to receive each strategy and is capable of controlling the operation of the building energy system. A building energy flexible management method is also provided.

Description

Technical Field

[0001] The present invention relates to the field of building energy, and in particular to a grid-friendly interactive building energy flexible management system and method. Background Art

[0002] Currently, installing photovoltaics on buildings has become an important means of energy conservation and carbon reduction in China and the world. Large buildings are coupled with multiple energy systems to supply energy to the buildings. Building photovoltaics and building energy systems together constitute the building energy microgrid.

[0003] With the development of new power systems dominated by renewable energy, dynamic economics and power supply stability have become new challenges for electricity users. Many countries around the world, such as Australia, have begun implementing dynamic real-time electricity pricing to regulate the dynamic balance of supply and demand in the electricity market. Unlike my country, which implements stable peak-valley electricity prices based on provincially released data, real-time electricity prices are determined based on the instantaneous supply and demand balance and the safe operation of the power system within a given time period (half an hour or less), taking into account the long-term and short-term marginal costs of electricity. This reflects the changing relationship between supply and demand at each moment. Peak-valley electricity pricing, on the other hand, divides the 24-hour day into multiple periods—peak, flat, and valley—based on grid load fluctuations, and sets different electricity price levels for each period. Therefore, the difference between real-time and peak-valley electricity prices is that real-time prices can be any value within a range for each period, rather than a fixed price for each period. Peak-valley prices, on the other hand, are fixed for each period.

[0004] Rigid fluctuations in renewable energy output add uncertainty to the power grid. With the frequent occurrence of extreme weather events and rapid regional development, many areas are facing daytime power rationing during peak demand periods. Ensuring a stable power supply during these periods and improving the flexibility and stability of building energy supply are challenges that many buildings are seeking to address.

[0005] Currently, building energy microgrids and large power grids operate relatively independently. Faced with future real-time fluctuations in grid electricity prices and building electricity consumption, building operation and maintenance personnel cannot make real-time dynamic adjustments based solely on experience to determine the most economical and stable system operation strategy for two-way protection. Summary of the Invention

[0006] The purpose of the present invention is to provide a grid-friendly interactive building energy flexible management system that can dynamically adjust its operation strategy according to real-time electricity prices, real-time weather conditions and real-time grid demand to achieve efficient energy utilization and reduce electricity costs.

[0007] Another object of the present invention is to provide a flexible building energy management method that can dynamically adjust operating strategies based on real-time electricity prices, real-time weather conditions, and real-time grid demand to achieve efficient energy utilization and reduce electricity costs.

[0008] The present invention provides a grid-friendly interactive building energy flexible management system, comprising: a data acquisition module, a calculation module, and a control module. The data acquisition module can obtain the real-time electricity price curve and the weather forecast data for the next day. The calculation module is signal-connected to the data acquisition module and is configured to: generate a first predicted power generation curve for the next day based on the weather forecast data for the next day on the previous day, generate a first predicted power consumption curve for the next day based on the weather forecast data for the next day and the historical power consumption curve of the building stored therein, and generate a full-day predicted electricity charge curve for the next day based on the real-time electricity price curve for the next day and the curve of the first predicted power consumption for the next day; compare whether the first predicted power generation is greater than the first predicted power consumption at the i-th moment (i is an integer greater than 0) of the next day, if the judgment result is yes, execute the first cold and heat storage operation strategy and the first power storage operation strategy, and adjust the full-day predicted electricity charge curve in real time; if the step judgment result is no, execute the second power storage operation strategy and the second cold and heat storage operation strategy, and adjust the full-day predicted electricity charge curve in real time; and obtain real-time operation data including real-time electricity charges at the i-th moment as training data, and continue to compare whether the first predicted power generation is greater than the first predicted power consumption at the i+1-th moment. The control module is connected to the calculation module by signal to receive various strategies made by the calculation module, and is capable of controlling the operation of the building's power storage system and the cold and heat storage system.

[0009] This grid-friendly and interactive building energy flexible management system combines the next day's real-time electricity price curve, weather forecast data and the building's historical electricity consumption curve to predict the next day's power generation and electricity consumption based on the previous day. It can also dynamically adjust the operating strategy in real time during the next day's operation to achieve efficient energy utilization and reduce electricity costs, providing an accurate reference for building energy management.

[0010] In another exemplary embodiment of the grid-friendly interactive building energy flexible management system, the calculation module is configured to be further capable of: on the previous day, generating a curve of the first predicted power generation for the next day based on the weather forecast data for the next day, generating a curve of the first predicted power consumption for the next day based on the weather forecast data for the next day and the historical power consumption curve of the building stored therein, and generating a full-day predicted electricity price curve for the next day based on the real-time electricity price curve for the next day and the curve of the first predicted power consumption for the next day; at the i-th moment of the next day, predicting the second predicted power generation and the second predicted power consumption at the i+1-th moment, comparing the second predicted power generation at the i+1-th moment with the first predicted power generation at that moment, and comparing the second predicted power consumption at the i+1-th moment with the first predicted power consumption at that moment; if the deviations between the second predicted power generation and the first predicted power generation and between the second predicted power consumption and the first predicted power consumption are both within the preset deviation acceptance domain, then comparing whether the first predicted power generation at the i-th moment is greater than the first predicted power consumption. The first predicted electricity consumption at the time is determined as follows: if the judgment result is yes, the first cold and heat storage operation strategy and the first electricity storage operation strategy are executed, and the all-day predicted electricity price curve is adjusted in real time; if the judgment result is no, the second electricity storage operation strategy and the second cold and heat storage operation strategy are executed, and the all-day predicted electricity price curve is adjusted in real time; if at least one of the deviation between the second predicted power generation and the first predicted power generation and the deviation between the second predicted electricity consumption and the first predicted electricity consumption exceeds the preset deviation acceptance domain, an early warning prompt is triggered, the curve of the first predicted power generation and the curve of the first predicted electricity consumption are discarded, and the curve of the first predicted power generation and the curve of the first predicted electricity consumption are regenerated according to the weather forecast data for the remaining time of the day, and the second predicted power generation and the second predicted electricity consumption at the i+1th time are re-predicted, and the real-time operation data including the real-time electricity price at the i-th time is obtained as training data, and at the i+1th time, the second predicted power generation and the second predicted electricity consumption at the i+2th time are continued to be predicted.

[0011] This flexible building energy management system predicts power generation and consumption at each moment of the following day and compares these predictions with the previous day's predictions for the same moment. This allows for dynamic optimization of energy storage operation strategies, helping to reduce energy waste and adapt to changes in grid demand. By setting a preset deviation acceptance range, it triggers timely warnings when significant deviations between two forecasts occur, ensuring the accuracy and reliability of energy management decisions, the continuous optimization of energy management strategies, and the minimization of electricity costs.

[0012] In another exemplary embodiment of a grid-friendly interactive building energy flexible management system, the calculation module is configured to: obtain real-time operating data including real-time electricity charges at the i-th moment as training data, and at the i+1th moment, continue to predict the second predicted power generation and the second predicted power consumption at the i+2th moment, and be able to: at the i-th moment, compare the real-time power generation at the i-1th moment and the second predicted power generation at that moment, and the real-time power consumption at the i-1th moment and the second predicted power consumption at that moment to see whether the difference is within a preset deviation acceptance domain; if the judgment result is yes, input the real-time power generation and real-time power consumption at the i-1th moment into the training set of the calculation module for training, and continue to predict the second predicted power generation and the second predicted power consumption at the i+2th moment at the i+1th moment; if the judgment result is no, trigger an early warning prompt, input the real-time power generation and real-time power consumption at the i-1th moment into the training set of the calculation module for training, regenerate the first power generation curve and the first power consumption curve according to the weather forecast data for the remaining time of the day, and re-predict the second predicted power generation and the second predicted power consumption at the i+1th moment.

[0013] This building's flexible energy management system compares real-time power generation and consumption data at each moment with the previous forecast to verify the predicted values. It also trains the computing module in real time, continuously improving forecast accuracy and optimizing energy use. The use of a preset deviation acceptance field ensures timely adjustments in the face of forecast errors, reducing energy waste and increased costs caused by inaccurate forecasts. Its self-learning capabilities enable it to adapt to changing environments and usage patterns, ensuring maximum energy efficiency over the long term.

[0014] In another exemplary embodiment of the grid-friendly interactive building energy flexible management system, the first cold and heat storage operation strategy is: at the i-th moment of the next day, when the first predicted power generation is greater than the first predicted power consumption, the cold and heat storage system operates at full load to prepare cold water and / or hot water for cold and / or heat storage, wherein the amount of cold water and / or hot water prepared is calculated by the formula shown in formula (1):

[0015] Q 冷水和 / 或热水 =min(Q capa , Q need ) Formula (1)

[0016] Among them, Q capa is the maximum capacity of the cold and heat storage system; Q need The cooling and / or heating capacity required by the building from the end of the excess power generation to the start of the next excess power generation.

[0017] By implementing the first cold and heat storage operation strategy, the system automatically adjusts the operation of the cold and heat storage system when power generation is predicted to exceed power consumption, ensuring efficient energy utilization and reducing energy waste. This strategy uses mathematical formulas to accurately calculate the required amount of cold and / or hot water, improving the operational accuracy of the cold and heat storage system and thus optimizing energy management efficiency.

[0018] In another exemplary embodiment of the grid-friendly interactive building energy flexible management system, the second predicted power consumption at the i-th moment is calculated according to the formula shown in formula (2):

[0019] E 用电量,i =(Q base +Q inner ×c person ) / η IPLV,SYS Formula (2)

[0020] Among them, E 用电量 , i is the second predicted power consumption at time i; Q base It is the basic energy consumption of buildings, mainly the energy consumption of heating and air conditioning; Q inner C is the energy used for internal functions of the building, including lighting, elevators, domestic hot water, sockets and other functional energy, which is a design reference value; person is the personnel utilization coefficient, obtained according to the historical personnel load table; η IPLV,SYS For real-time system energy efficiency.

[0021] The second calculation formula for predicting electricity consumption takes into account multiple influencing factors, making the prediction more comprehensive and accurate, improving the flexibility and responsiveness of energy distribution, and helping to achieve more detailed energy management, higher energy savings and cost-effectiveness.

[0022] In another exemplary embodiment of a grid-friendly interactive building energy flexible management system, the first power storage operation strategy is specifically as follows: when the energy storage of the cold and heat storage system reaches the calculated value of formula (1), and the photovoltaic system still has excess photovoltaic power, the power storage system begins to charge until the real-time power generation is less than or equal to the real-time power consumption; if the real-time power generation is still greater than the real-time power consumption after the power storage system is fully charged, the remaining power of the building after the photovoltaic power supply is connected to the grid.

[0023] The first energy storage strategy allows for further storage of excess energy from the photovoltaic system after the cold and heat storage system reaches its set energy storage capacity, thereby improving overall energy utilization. This strategy ensures optimal cold and heat storage, avoids over- or under-storage, and safeguards the building's energy supply. Once the energy storage system is fully charged, any excess energy can be fed into the grid, providing additional power resources and increasing the energy system's flexibility and external power supply capabilities.

[0024] In another schematic implementation of a grid-friendly interactive building energy flexible management system, the second power storage operation strategy is specifically: determine whether there is a power restriction period on the next day. The power restriction period is the period when the power grid is outage during peak power consumption. The building energy flexible management system is provided with a guaranteed power consumption mode. Under the guaranteed power consumption mode, the building energy flexible management system calculates the guaranteed power consumption for the next day when generating the first predicted power generation curve and the first predicted power consumption curve for the next day. The guaranteed power consumption is the power consumption required for production and life of the building guaranteed by the power storage system during the power restriction period; if it is determined that there is a power restriction period on the next day, then determine whether the guaranteed power consumption at the i-th moment of the next day is greater than the emergency power consumption of the building, and the building should The emergency power consumption is the power consumption of the building during the power-rationing period. If the guaranteed power consumption at the i-th moment of the next day is greater than the emergency power consumption of the building, the guaranteed power consumption will be used to power the building during the power-rationing period and the period before and after the power-rationing period. During the non-power-rationing period, the guaranteed power consumption, mains power and photovoltaic power are used to power the building. If the guaranteed power consumption at the i-th moment of the next day is not greater than the emergency power consumption of the building, the energy storage system will be used to power the building during the power-rationing period, or a reminder will be sent to the user during the non-power-rationing period to ask whether to use mains power to charge the energy storage system. If there is no power-rationing period on the next day, the guaranteed power consumption limit function of the energy storage system will be turned off, so that all the power of the energy storage system will be used for photovoltaic power generation storage and peak power supply to the building.

[0025] The second energy storage operation strategy predicts power outages and calculates guaranteed power consumption, ensuring normal building operation during peak grid hours and improving the reliability of the building's energy system. This strategy utilizes guaranteed power consumption during and before power outages, reducing reliance on the grid and lowering building operating costs. During normal power outages, the building's flexible energy management system reminds users to choose appropriate charging times, enhancing their control over energy management and increasing energy flexibility.

[0026] In another exemplary implementation of a grid-friendly interactive building energy flexible management system, the second cold and heat storage operation strategy is specifically as follows: when photovoltaic power generation is greater than building power consumption, the storage system is prioritized for charging; if photovoltaic power is still over-generated after the storage system is fully charged, the cold and heat storage system operates at full load until photovoltaic power is no longer over-generated, or the cold and heat storage capacity meets the demand for the next day calculated from the previous day; if the real-time power generation is still greater than the real-time power consumption after the cold and heat storage capacity meets the demand for the next day calculated from the previous day, the remaining power is incorporated into the grid.

[0027] The second cold and heat storage operation strategy prioritizes charging the energy storage system, ensuring efficient energy storage and providing a stable backup for the building's energy supply. Once the energy storage system is fully charged, the system automatically switches to cold and heat storage operation, fully utilizing excess photovoltaic power and improving energy efficiency. This strategy calculates and ensures that the stored cold and heat capacity meets demand before integrating the remaining power into the grid, helping to balance grid load and generating economic benefits for the building.

[0028] In another exemplary embodiment of a grid-friendly flexible building energy management system, the flexible building energy management system further includes a black start protection device, signal-connected to the computing module, comprising a logic control module and a switching module. The logic control module monitors the grid status and, at the beginning of a power-cutoff period, issues a control command to automatically switch from the mains power supply mode to the power storage system mode. At the end of the power-cutoff period, the switching module also issues a control command to automatically switch from the power storage system mode to the mains power supply mode. The switching module receives and executes control commands from the logic control module.

[0029] The black start protection device ensures uninterrupted power supply to buildings during power outages, enhancing the automation level of the building's energy system. It automatically restores the mains power supply after the blackout period, reducing manual intervention and improving the efficiency and convenience of building energy management. It also provides additional protection against grid instability or emergencies, enhancing the resilience and security of the building's energy system.

[0030] Another exemplary embodiment of a grid-friendly building energy flexible management system also includes a user interface module and an information transmission module. The user interface module is signal-connected to the computing module and features a data visualization interface. This interface allows users to start and stop the system, display the current operating mode, and, when an optimized operating strategy is available and needs to be changed, prompts the user to select a new strategy. The information transmission module is signal-connected to the data acquisition module and the computing module and is capable of establishing a local area network for devices within the building and connecting the building to the external network.

[0031] This allows users to easily identify system status and potential optimization strategies, improves operational convenience and system manageability, and helps guide users to make more economical and efficient energy management decisions. It helps reduce energy costs and improve building energy efficiency, and ensures efficient communication between devices within the building and between the building and external networks.

[0032] The present invention also provides a building energy flexible management method, comprising the following steps: starting the above-mentioned grid-friendly interactive building energy flexible management system; obtaining the real-time electricity price curve and weather forecast data for the next day; on the previous day, generating a first predicted power generation curve for the next day based on the weather forecast data for the next day, generating a first predicted power consumption curve for the next day based on the weather forecast data for the next day and the stored historical power consumption curve of the building, and generating a full-day predicted electricity cost curve for the next day based on the real-time electricity price curve for the next day and the first predicted power consumption curve for the next day; and at the i-th moment of the next day, comparing whether the first predicted power generation is greater than the first predicted power consumption at that moment, if the judgment result is yes, executing the first cold and heat storage operation strategy and the first power storage operation strategy, and adjusting the full-day predicted electricity cost curve in real time, if the judgment result is no, executing the second power storage operation strategy and the second cold and heat storage operation strategy, and adjusting the full-day predicted electricity cost curve in real time; obtaining real-time operation data including the real-time electricity cost at the i-th moment as training data, and continuing to compare whether the first predicted power generation is greater than the first predicted power consumption at that moment at the i+1-th moment.

[0033] This flexible building energy management method predicts the next day's power generation and consumption by obtaining the next day's real-time electricity price curve, weather forecast data and the building's historical electricity consumption curve. It also dynamically adjusts the operating strategy in real time during the next day's operation to achieve efficient energy utilization and reduce electricity costs, providing an accurate reference for building energy management. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The following drawings are only used to schematically illustrate and explain the present invention and are not intended to limit the scope of the present invention.

[0035] Figure 1 A schematic structural diagram of an exemplary implementation of a grid-friendly interactive building energy flexible management system.

[0036] Figure 2 The flowchart is used to illustrate an exemplary implementation of a flexible building energy management method.

[0037] Figure 3 Flowchart for explaining the second power storage operation strategy.

[0038] Figure 4 The flowchart is used to illustrate another exemplary embodiment of the flexible building energy management method.

[0039] Figure 5 The flowchart is used to illustrate another exemplary implementation of the flexible building energy management method.

[0040] Description of labels

[0041] 10Data acquisition module

[0042] 20 computing modules

[0043] 30 control module

[0044] 40 black start support device

[0045] 41 logic control module

[0046] 42 switching modules

[0047] 50 user interface modules

[0048] 60 information transmission modules. DETAILED DESCRIPTION

[0049] In order to have a clearer understanding of the technical features, purposes and effects of the invention, the specific embodiments of the present invention are now described with reference to the accompanying drawings. The same reference numerals in the drawings represent components with the same structure or similar structures but the same functions.

[0050] In this document, “illustrative” means “serving as an example, instance or illustration”, and any diagram or implementation described in this document as “illustrative” should not be interpreted as a more preferred or more advantageous technical solution.

[0051] Figure 1 This is a schematic diagram of a schematic implementation of a building energy flexible management system with grid-friendly interaction. Figure 1 The grid-friendly interactive building energy flexible management system includes: a data acquisition module 10, a calculation module 20 and a control module 30.

[0052] The data acquisition module 10 can obtain the real-time electricity price curve and weather forecast data for the next day. Currently, there are three ways to obtain the real-time electricity price curve:

[0053] Obtain data from power company or energy supplier websites and apps: Most power companies or energy suppliers provide online platforms or mobile apps with data acquisition modules that can retrieve current, dynamic, and real-time electricity price information. This information typically includes electricity prices for different time periods and real-time market prices.

[0054] Obtaining data from power market information platforms: Some countries' power markets provide public, real-time market price information. The data acquisition module can obtain this information from relevant power market information platforms or websites. These platforms typically provide information such as price trends and market supply and demand over different time periods.

[0055] Obtaining data from smart meters and energy management systems: A user's smart meter or energy management system connects to the power company or energy supplier's system to obtain dynamic, real-time electricity price information and display it on the user's energy management platform. The data acquisition module can obtain real-time electricity prices from the smart meter or energy management system.

[0056] In a specific embodiment, the data acquisition module has a network interface, a communication interface, a data interface and an application program interface to obtain the real-time electricity price curve of the next day through any one of the above three methods.

[0057] The network interface is used to connect to the Internet and can be an Ethernet interface, Wi-Fi interface, or cellular network interface to connect the data acquisition module to the Internet. The communication interface is used to connect the smart meter and energy management system and can be a serial port interface such as RS-485, RS-232, or a wireless communication interface such as Zigbee or LoRaWAN. The data interface is used to exchange data with the power company or energy supplier's system and can include using standard data transmission protocols such as HTTP, MQTT, and Modbus to obtain real-time electricity price information. The application program interface is used to obtain dynamic real-time electricity price information through an application or website, and can also be used to call the obtained electricity price data through an API (Application Programming Interface).

[0058] The data acquisition module can also obtain weather forecast data from the Internet through a network interface, including real-time temperature, wind speed, sunshine and other information; it can also obtain data from weather APIs such as OpenWeatherMap and Weather.com through an application program interface.

[0059] The computing module 20 is signal-connected to the data acquisition module 10. The computing module 20 is a server, such as a GPU server, that provides computing resources, runs energy optimization algorithms, generates optimization solutions, and stores historical operating data. This historical operating data includes the building's historical electricity consumption curve.

[0060] Figure 2 This is a flow chart for illustrating an exemplary implementation of a flexible building energy management method. Figure 2 , the computing module 20 is configured to be able to execute Figure 2 Steps S300 to S500 shown in the flowchart.

[0061] S300: On the previous day, a curve of the first predicted power generation for the next day is generated based on the weather forecast data for the next day, a curve of the first predicted power consumption for the next day is generated based on the weather forecast data for the next day and the historical power consumption curve of the building stored therein, and a curve of the full-day predicted electricity charge for the next day is generated based on the real-time electricity price curve for the next day and the curve of the first predicted power consumption for the next day.

[0062] S400: At the i-th moment of the next day, where i is, for example, 1, 2, ..., 24, or can be 1, 2, ..., 48 if the 24 hours of a day are divided into 48 time periods, compare whether the first predicted power generation is greater than the first predicted power consumption at that moment.

[0063] If the judgment result of step S400 is yes, then the process proceeds to step S401: executing the first cold and heat storage operation strategy and the first electricity storage operation strategy, and adjusting the full-day predicted electricity rate curve in real time.

[0064] If the judgment result of step S400 is no, then proceed to step S402: execute the second power storage operation strategy and the second cold and heat storage operation strategy, and adjust the full-day predicted electricity rate curve in real time.

[0065] S500: Acquire real-time operation data including real-time electricity charges as training data, and continue to execute step S400 at the i+1th time.

[0066] It should be noted that the real-time operation data including the actual electricity charges at the i-th moment enters the calculation module. On the operation day, the full-day predicted electricity charge curve can only adjust the partial curve of the remaining time, and the partial curve of the past time is composed of the actual electricity charges.

[0067] Control module 30 is signal-connected to computing module 20 to receive the strategies generated by computing module 20 and to control the operation of the building's power storage system and cooling and heat storage system. Control module 30 can be any controller well known to those skilled in the art and will not be described in detail here.

[0068] This grid-friendly and interactive building energy flexible management system combines the next day's real-time electricity price curve, weather forecast data and the building's historical electricity consumption curve to predict the next day's power generation and electricity consumption based on the previous day. It can also dynamically adjust the operating strategy in real time during the next day's operation to achieve efficient energy utilization and reduce electricity costs, providing an accurate reference for building energy management.

[0069] In an exemplary embodiment, the first cold and heat storage operation strategy is as follows: at time i of the next day, when the first predicted power generation is greater than the first predicted power consumption, the cold and heat storage system operates at full load, preparing cold water and / or hot water for cold and / or heat storage. The cold and heat storage system is well known to those skilled in the art and will not be described in detail here. The amount of cold water and / or hot water prepared is calculated using the formula shown in formula (1):

[0070] Q 冷水和 / 或热水 =min(Q capa , Q need ) Formula (1)

[0071] Among them, Q capa is the maximum capacity of the cold and heat storage system; Q need The cooling and / or heating required by the building from the end of the excess power generation to the start of the next excess power generation is calculated using the following two formulas.

[0072]

[0073] In the above calculation Q need冷水 and Q need热水 In the formula, the specific heat of chilled water and the specific heat of hot water are constants, ΔT is the temperature difference during the cooling or heating process, and the cooling load and heating load are the total cooling and heating loads that the building needs to consume in the next day. Its prediction method is similar to the method for predicting the first predicted electricity consumption. The prediction is based on the next day's weather forecast data, historical cooling and heating load data, and building operation data, including building operation data such as occupancy density, equipment usage, and lighting conditions.

[0074] The first power storage operation strategy is as follows: When the energy storage of the cold and heat storage system reaches the value calculated by equation (1), and the photovoltaic system still has excess photovoltaic power, the power storage system begins charging until the real-time power generation is less than or equal to the real-time power consumption. If the real-time power generation is still greater than the real-time power consumption after the power storage system is fully charged, the remaining power from the photovoltaic power supply to the building is connected to the grid. The power storage system is well known to those skilled in the art and will not be described in detail here.

[0075] It should be noted that when the first predicted power generation is greater than the first predicted power consumption and the excess power generation is large, although the first cold and heat storage operation strategy has a higher priority than the first electricity storage operation strategy, the building energy flexible management system determines that the cold and heat storage system cannot fully absorb the photovoltaic power. The first cold and heat storage operation strategy and the first electricity storage operation strategy will be implemented simultaneously, that is, cold and heat storage and electricity storage will be carried out at the same time.

[0076] After the first cold and heat storage operation strategy and the first electricity storage operation strategy are executed, the building energy flexible management system will adjust the daily predicted electricity price curve in real time to make it closer to the actual situation. The electricity meters in the building will update the actual real-time electricity prices in real time. The calculation module will also obtain the real-time operation data of the building energy flexible management system and use the real-time operation data as training data to obtain more accurate predictions.

[0077] By implementing the first cold and heat storage operation strategy, the system automatically adjusts the operation of the cold and heat storage system when power generation is predicted to exceed power consumption, ensuring efficient energy utilization and reducing energy waste. This strategy uses mathematical formulas to accurately calculate the required amount of cold and / or hot water, improving the operational accuracy of the cold and heat storage system and thus optimizing energy management efficiency.

[0078] The first energy storage strategy allows for further storage of excess energy from the photovoltaic system after the cold and heat storage system reaches its set energy storage capacity, thereby improving overall energy utilization. This strategy ensures optimal cold and heat storage, avoids over- or under-storage, and safeguards the building's energy supply. Once the energy storage system is fully charged, any excess energy can be fed into the grid, providing additional power resources and increasing the energy system's flexibility and external power supply capabilities.

[0079] Figure 3 Flowchart for explaining the second power storage operation strategy. Figure 3 In an exemplary embodiment, the second power storage operation strategy specifically includes the following steps.

[0080] S1: Determine whether there is a power-rationing period on the next day. The power-rationing period is the period when the power grid is out of power during peak hours. The building energy flexible management system is provided with a guaranteed power consumption mode. In the guaranteed power consumption mode, the building energy flexible management system calculates the guaranteed power consumption for the next day when generating the first predicted power generation curve and the first predicted power consumption curve for the next day. The guaranteed power consumption is the power consumption required for production and life of the building by the energy storage system during the power-rationing period.

[0081] If the result of step S1 is yes, then go to step S2: determine whether the guaranteed power consumption at the i-th moment of the next day is greater than the building emergency power consumption, where the building emergency power consumption is the power consumption of the building during the power-limited period.

[0082] If the judgment result of step S2 is yes, then enter S21: during the power-restricted period and the period before and after the power-restricted period, the building is powered by the guaranteed power consumption, and during the non-power-restricted period, the building is powered by the guaranteed power consumption, mains power and photovoltaic power.

[0083] If the judgment result of step S2 is no, then enter S22: during the power-restricted period, use the power storage system to power the building, or during the non-power-restricted period, send a reminder to the user whether to use the mains power to charge the power storage system.

[0084] If the result of step S1 is no, then go to step S3: turn off the guaranteed power consumption limit function of the power storage system, so that all the power of the power storage system is used for photovoltaic power generation storage and peak power price power supply of the building.

[0085] The second energy storage operation strategy predicts power outages and calculates guaranteed power consumption, ensuring normal building operation during peak grid hours and improving the reliability of the building's energy system. This strategy utilizes guaranteed power consumption during and before power outages, reducing reliance on the grid and lowering building operating costs. During normal power outages, the building's flexible energy management system reminds users to choose appropriate charging times, enhancing their control over energy management and increasing energy flexibility.

[0086] In an exemplary embodiment, the second cold and heat storage operation strategy is specifically as follows:

[0087] When the photovoltaic power generation is greater than the building's electricity consumption, the storage system will be activated first to charge;

[0088] If the photovoltaic power generation still exceeds the capacity after the storage system is fully charged, the cold and heat storage system will operate at full capacity until the photovoltaic power generation stops exceeding the capacity or the cold and heat storage capacity meets the next day's demand calculated on the previous day.

[0089] If the stored cold and heat energy meets the demand for the next day calculated on the previous day, and the real-time power generation is still greater than the real-time power consumption, the remaining power will be incorporated into the power grid.

[0090] The second cold and heat storage operation strategy prioritizes charging the energy storage system, ensuring efficient energy storage and providing a stable backup for the building's energy supply. Once the energy storage system is fully charged, the system automatically switches to cold and heat storage operation, fully utilizing excess photovoltaic power and improving energy efficiency. This strategy calculates and ensures that the stored cold and heat capacity meets demand before integrating the remaining power into the grid, helping to balance grid load and generating economic benefits for the building.

[0091] After the second cold and heat storage operation strategy and the second electricity storage operation strategy are executed, the building energy flexible management system will adjust the daily predicted electricity price curve in real time to make it closer to the actual situation. The electricity meters in the building will update the actual real-time electricity price in real time. The calculation module will also obtain the real-time operation data of the building energy flexible management system and use the real-time operation data as training data to obtain more accurate predictions.

[0092] Figure 4FIG. 1 is a flow chart illustrating another exemplary embodiment of a flexible building energy management method. Figure 4 In the exemplary embodiment, the computing module 20 is configured to execute Figure 4 Steps S300 to S500 are shown.

[0093] S300: On the previous day, a curve of the first predicted power generation for the next day is generated based on the weather forecast data for the next day, a curve of the first predicted power consumption for the next day is generated based on the weather forecast data for the next day and the historical power consumption curve of the building stored therein, and a curve of the full-day predicted electricity charge for the next day is generated based on the real-time electricity price curve for the next day and the curve of the first predicted power consumption for the next day.

[0094] S310: At the i-th moment of the next day, a second predicted power generation amount and a second predicted power consumption amount at the i+1-th moment are predicted.

[0095] S320: Compare the second predicted power generation at the i+1th moment with the first predicted power generation at the same moment, and compare the second predicted power consumption at the i+1th moment with the first predicted power consumption at the same moment, and determine whether the errors of the above two sets of data are within the preset deviation acceptance range.

[0096] If the judgment result of step S320 is yes, the process proceeds to step S400 : comparing whether the first predicted power generation at the i-th moment is greater than the first predicted power consumption at the moment.

[0097] If the judgment result of step S400 is yes, then the process proceeds to step S401: executing the first cold and heat storage operation strategy and the first electricity storage operation strategy, and adjusting the full-day predicted electricity rate curve in real time.

[0098] If the judgment result of step S400 is no, then proceed to step S402: execute the second power storage operation strategy and the second cold and heat storage operation strategy, and adjust the full-day predicted electricity rate curve in real time.

[0099] If the judgment result of step S320 is no, then go to step S410: trigger an early warning prompt, abandon the first predicted power generation curve and the first predicted power consumption curve, and regenerate the first predicted power generation curve and the first predicted power consumption curve based on the weather forecast data for the remaining time of the day, and re-predict the second predicted power generation and the second predicted power consumption at the i+1th moment.

[0100] S500: Acquire real-time operation data including real-time electricity charges as training data, and continue to execute step S310 at the i+1th time.

[0101] This flexible building energy management system predicts power generation and consumption at each moment of the following day and compares these predictions with the previous day's predictions for the same moment. This allows for dynamic optimization of energy storage operation strategies, helping to reduce energy waste and adapt to changes in grid demand. By setting a preset deviation acceptance range, it triggers timely warnings when significant deviations between two forecasts occur, ensuring the accuracy and reliability of energy management decisions, the continuous optimization of energy management strategies, and the minimization of electricity costs.

[0102] In this exemplary embodiment, the second predicted power consumption at the i-th moment is calculated according to the formula shown in formula (2):

[0103] E 用电量,i =(Q base +Q inner ×c person ) / η IPLV ,SYS Formula (2)

[0104] Among them, E 用电量 , i is the second predicted power consumption at time i;

[0105] Q base It is the basic energy consumption of buildings, mainly the energy consumption for heating and air conditioning of buildings;

[0106] Q inner This is the energy used for internal building functions, including lighting, elevators, domestic hot water, sockets and other functional energy. It is a design reference value.

[0107] C person is the personnel utilization coefficient, which is obtained based on the historical personnel load table. See Table 1 for details. Table 1 is an example of the historical personnel load table.

[0108] η IPLV,SYS For real-time system energy efficiency.

[0109] Q base , Q inne and C person The three data are all historical operation data stored in the calculation module at the i-th moment. The three data are from the same day in history. The historical date and the current date must have similar or identical weather data, including real-time temperature, wind speed, sunshine and other information. At the same time, the selected historical date should be the same week as the current date, such as Wednesday, to ensure that C person The data of are similar or the same. base , Q inner and C person After confirmation, the calculation module can immediately match and obtain η IPLV,SYS The numerical value of .

[0110] The second calculation formula for predicting electricity consumption takes into account multiple influencing factors, making the prediction more comprehensive and accurate, improving the flexibility and responsiveness of energy distribution, and helping to achieve more detailed energy management, higher energy savings and cost-effectiveness.

[0111] Table 1 Historical personnel load table

[0112]

[0113]

[0114] Figure 5 FIG. 1 is a flow chart illustrating another exemplary embodiment of a flexible building energy management method. Figure 5 , and Figure 4 The difference between the two is that the computing module 20 is configured to be able to execute Figure 5 Steps S510 to S522 are shown.

[0115] S510: At the i-th moment, real-time operation data is obtained, the real-time operation data including the real-time power generation and real-time power consumption at the i-1-th moment, and the difference between the real-time power generation at the i-1-th moment and the second predicted power generation at the moment and the real-time power consumption at the i-1-th moment and the second predicted power consumption at the moment are compared to see whether they are within the preset deviation acceptance range.

[0116] If the judgment result of step S510 is yes, then go to step S521: input the real-time power generation and real-time power consumption at the i-1th moment into the training set of the calculation module for training, and at the i+1th moment, continue to predict the second predicted power generation and second predicted power consumption at the i+2th moment, that is, execute step S310.

[0117] If the judgment result of step S510 is no, then go to step S522: trigger the early warning prompt, input the real-time power generation and real-time power consumption at the i-1th moment into the training set of the calculation module for training, regenerate the first power generation curve and the first power consumption curve according to the weather forecast data for the rest of the day, and re-predict the second predicted power generation and the second predicted power consumption at the i-th moment, that is, re-execute step S310.

[0118] This building's flexible energy management system compares real-time power generation and consumption data at each moment with the previous forecast to verify the predicted values. It also trains the computing module in real time, continuously improving forecast accuracy and optimizing energy use. The use of a preset deviation acceptance field ensures timely adjustments in the face of forecast errors, reducing energy waste and increased costs caused by inaccurate forecasts. Its self-learning capabilities enable it to adapt to changing environments and usage patterns, ensuring maximum energy efficiency over the long term.

[0119] In an exemplary embodiment, see Figure 1 The building energy flexible management system further includes a black start guarantee device 40 connected to the calculation module 20 by signal. The black start guarantee device 40 includes a logic control module 41 and a switching module 42.

[0120] The logic control module 41 is capable of monitoring the state of the power grid and, at the beginning of a power-cut period, issuing a control command to automatically switch the power supply mode from the mains to the battery system. Furthermore, at the end of the power-cut period, it is capable of automatically switching the power supply mode from the battery system to the mains. The switching module 42 is capable of receiving and executing the control commands of the logic control module 41.

[0121] In other exemplary embodiments, the black start guarantee device 40 may further include a backup power supply, which can provide the initial energy required to start the power storage system when the power grid is out of power.

[0122] The black start protection device ensures uninterrupted power supply to buildings during power outages, enhancing the automation level of the building's energy system. It automatically restores the mains power supply after the blackout period, reducing manual intervention and improving the efficiency and convenience of building energy management. It also provides additional protection against grid instability or emergencies, enhancing the resilience and security of the building's energy system.

[0123] In an exemplary embodiment, see Figure 1 The building energy flexible management system further includes a user interface module 50 and an information transmission module 60 .

[0124] The user interface module 50 is signal-connected to the computing module 20 and features a data visualization interface. This interface facilitates routine system startup and shutdown operations, displays the current operating mode, and, when an optimized operating strategy is available and the current strategy needs to be changed, prompts the user to select a new strategy. The information transmission module 60 is signal-connected to the data acquisition module 10 and computing module 20 and is capable of establishing a local area network for devices within the building and connecting the building to external networks. The information transmission module 60 can be a network switch, router, or other device well-known in the communications field.

[0125] This allows users to easily identify system status and potential optimization strategies, improves operational convenience and system manageability, and helps guide users to make more economical and efficient energy management decisions. It helps reduce energy costs and improve building energy efficiency, and ensures efficient communication between devices within the building and between the building and external networks.

[0126] The present invention also provides a building energy flexible management method. Figure 2 This is a flow chart for illustrating an exemplary implementation of a flexible building energy management method. Figure 2 , the building energy flexible management method includes steps S100 to S500.

[0127] S100: Start the above-mentioned grid-friendly interactive building energy flexible management system.

[0128] S200: Obtain the real-time electricity price curve and weather forecast data for the next day.

[0129] S300: On the previous day, a curve of the first predicted power generation for the next day is generated based on the weather forecast data for the next day, a curve of the first predicted power consumption for the next day is generated based on the weather forecast data for the next day and the historical power consumption curve of the building, and a curve of the full-day predicted electricity charge for the next day is generated based on the real-time electricity price curve for the next day and the curve of the first predicted power consumption for the next day.

[0130] S400: At the i-th moment of the next day, compare whether the first predicted power generation is greater than the first predicted power consumption at that moment.

[0131] If the judgment result of step S400 is yes, then the process proceeds to step S401: executing the first cold and heat storage operation strategy and the first electricity storage operation strategy, and adjusting the full-day predicted electricity rate curve in real time.

[0132] If the judgment result of step S400 is no, then proceed to step S402: execute the second power storage operation strategy and the second cold and heat storage operation strategy, and adjust the full-day predicted electricity rate curve in real time.

[0133] S500: Acquire real-time operation data including real-time electricity charges at the i-th moment, and execute step S400 at the i+1-th moment.

[0134] This grid-friendly and interactive building energy flexible management system combines the next day's real-time electricity price curve, weather forecast data and the building's historical electricity consumption curve to predict the next day's power generation and electricity consumption based on the previous day. It can also dynamically adjust the operating strategy in real time during the next day's operation to achieve efficient energy utilization and reduce electricity costs, providing an accurate reference for building energy management.

[0135] It should be understood that although this specification is described according to various embodiments, not every embodiment contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

[0136] The series of detailed descriptions listed above are only specific descriptions of feasible embodiments of the present invention. They are not intended to limit the scope of protection of the present invention. Any equivalent implementation scheme or changes that do not deviate from the technical spirit of the present invention, such as the combination, division or repetition of features, should be included in the scope of protection of the present invention.

Claims

1. A grid-friendly and interactive building energy flexible management system, characterized by: include: a data acquisition module (10) capable of acquiring the next day's real-time electricity price curve and the next day's weather forecast data; A computing module (20) is signal-connected to the data acquisition module (10) and is configured to: On the previous day, a first predicted power generation curve for the next day is generated based on the weather forecast data for the next day, a first predicted power consumption curve for the next day is generated based on the weather forecast data for the next day and the historical power consumption curve of the building stored therein, and a full-day predicted electricity charge curve for the next day is generated based on the real-time electricity price curve for the next day and the first predicted power consumption curve for the next day. At the i-th moment of the next day, i is an integer greater than 0, Predict the second predicted power generation at time i+1 and the second predicted power consumption at that time, Compare the second predicted power generation at the i+1th moment with the first predicted power generation at the same moment, and compare the second predicted power consumption at the i+1th moment with the first predicted power consumption at the same moment, If the deviations between the second predicted power generation and the first predicted power generation, as well as between the second predicted power consumption and the first predicted power consumption, are both within the preset deviation acceptance range, then the first predicted power generation is compared to see whether it is greater than the first predicted power consumption at that moment. If the judgment result is yes, the first cold and heat storage operation strategy and the first electricity storage operation strategy are executed, and the full-day predicted electricity rate curve is adjusted in real time. If the result of the step is no, the second power storage operation strategy and the second cold and heat storage operation strategy are executed, and the full-day predicted electricity rate curve is adjusted in real time. If at least one of the deviation between the second predicted power generation and the first predicted power generation and the deviation between the second predicted power consumption and the first predicted power consumption exceeds the preset deviation acceptance range, an early warning prompt is triggered, the curve of the first predicted power generation and the curve of the first predicted power consumption are discarded, and the curve of the first predicted power generation and the curve of the first predicted power consumption are regenerated based on the weather forecast data for the remaining time of the day, and the second predicted power generation and the second predicted power consumption at the i+1th moment are re-predicted, and Acquire real-time operating data including real-time electricity charges, real-time power generation, and real-time power consumption at the i-th moment as training data, and continue to predict the second predicted power generation and the second predicted power consumption at the i+2th moment at the i+1th moment; as well as A control module (30) is connected to the calculation module (20) by signal so as to receive various strategies made by the calculation module (20) and is capable of controlling the operation of the building's power storage system and the cold and heat storage system.

2. The building energy flexible management system according to claim 1, characterized in that: The calculation module (20) is configured to: obtain real-time operation data including real-time electricity charges, real-time power generation and real-time power consumption at the i-th moment as training data, and continue to predict the second predicted power generation and the second predicted power consumption at the i+2 moment at the i+1-th moment, and can: At the i-th moment, compare the difference between the real-time power generation at the i-1th moment and the second predicted power generation at the same moment, and the real-time power consumption at the i-1th moment and the second predicted power consumption at the same moment to see if they are within the preset deviation acceptance range. If the judgment result is yes, the real-time power generation and real-time power consumption at the i-1th moment are input into the training set of the calculation module (20) for training, and at the i+1th moment, the second predicted power generation and the second predicted power consumption at the i+2th moment are continuously predicted. If the judgment result is no, an early warning prompt is triggered, the real-time power generation and real-time power consumption at the i-1th moment are input into the training set of the calculation module (20) for training, the first power generation curve and the first power consumption curve are regenerated according to the weather forecast data for the remaining time of the day, and the second predicted power generation and the second predicted power consumption at the i+1th moment are re-predicted.

3. The building energy flexible management system according to claim 1, characterized in that: The first cold and heat storage operation strategy is: At the i-th moment of the next day, when the first predicted power generation is greater than the first predicted power consumption, the cold and heat storage system operates at full load, preparing cold water and / or hot water for cold and / or heat storage, wherein the amount of cold water and / or hot water prepared is calculated by the formula shown in formula (1): Formula (1) in, The maximum capacity of the cold and heat storage system; The cooling and / or heating required by the building from the end of the over-generation period to the start of the next over-generation period.

4. The building energy flexible management system according to claim 3, characterized in that: The first power storage operation strategy is specifically as follows: When the energy storage of the cold and heat storage system reaches the calculated value of formula (1), and the photovoltaic system still has excess photovoltaic power, the power storage system starts charging until the real-time power generation is less than or equal to the real-time power consumption; If the real-time power generation is still greater than the real-time power consumption after the power storage system is fully charged, the remaining power after the building is supplied with power through photovoltaic power will be connected to the power grid.

5. The building energy flexible management system according to claim 1, characterized in that: The second predicted power consumption at the i-th moment is calculated according to the formula shown in formula (2): Formula (2) in, is the second predicted power consumption at the i-th moment; Basic building energy consumption, including building heating and air-conditioning energy consumption; This is the energy used for internal building functions, including lighting, elevators, domestic hot water, sockets and other functional energy. It is a design reference value. is the personnel utilization coefficient, obtained according to the historical personnel load table; For real-time system energy efficiency.

6. The building energy flexible management system according to claim 1, characterized in that: The second power storage operation strategy is specifically as follows: determining whether there is a power-rationing period on the next day, the power-rationing period being a period of power outage during peak power consumption periods, the building energy flexible management system being provided with a guaranteed power consumption mode, wherein the building energy flexible management system calculates a guaranteed power consumption for the next day when generating the first predicted power generation curve and the first predicted power consumption curve for the next day, the guaranteed power consumption being the power consumption required by the power storage system to guarantee production and daily life in the building during the power-rationing period; If it is determined that there is a power-limited period on the next day, then determine whether the guaranteed power consumption at the i-th moment of the next day is greater than the building emergency power consumption, where the building emergency power consumption is the power consumption of the building during the power-limited period; If the guaranteed power consumption at the i-th moment of the next day is greater than the building emergency power consumption, the guaranteed power consumption is used to supply power to the building during the power-restricted period and the period before and after the power-restricted period; During the non-power-restriction period, the guaranteed power consumption, mains power and photovoltaic power are used to power the building. If the guaranteed power consumption at time i on the next day is not greater than the building's emergency power, the power storage system is used to power the building during the power-restricted period, or a reminder is issued to the user asking whether to charge the power storage system with mains power during the non-power-restricted period. If there is no power restriction period on the next day, the guaranteed power consumption limit function of the power storage system will be turned off, so that all the power of the power storage system can be used for photovoltaic power generation storage and peak power price power supply to the building.

7. The flexible building energy management system according to claim 1, characterized in that: The second cold and heat storage operation strategy is as follows: When the photovoltaic power generation is greater than the building's electricity consumption, the storage system will be activated first to charge; If the photovoltaic power generation still exceeds the capacity after the storage system is fully charged, the cold and heat storage system will operate at full capacity until the photovoltaic power generation stops exceeding the capacity or the cold and heat storage capacity meets the next day's demand calculated on the previous day. If the stored cold and heat energy meets the demand for the next day calculated on the previous day, and the real-time power generation is still greater than the real-time power consumption, the remaining power will be incorporated into the power grid.

8. The flexible building energy management system according to claim 6, characterized in that: The building energy flexible management system further comprises a black start guarantee device (40) connected to the calculation module (20) by signal, and the black start guarantee device (40) comprises: a logic control module (41) capable of monitoring the state of the power grid, issuing a control command to automatically switch the mains power supply mode to the power storage system power supply mode when the power-restriction period begins, and issuing a control command to automatically switch the power storage system power supply mode to the mains power supply system power supply mode when the power-restriction period ends, and A switching module (42) is capable of receiving and executing control commands from the logic control module (41).

9. The flexible building energy management system according to claim 1, wherein: Also includes: A user interface module (50) is connected to the computing module (20) by signal, and has a data visualization interface that can be used for user routine system start and stop operations, prompting the user of the current operating mode, and when an optimizable operating strategy appears and the current strategy needs to be changed, prompting the user to select a new operating strategy, and An information transmission module (60) is connected to the data acquisition module (10) and the calculation module (20) by signal, and is capable of building a local area network for equipment in the building and connecting the building with an external network.

10. A flexible building energy management method, characterized in that: The following steps are involved: Starting the building energy flexible management system according to any one of claims 1 to 9; Obtain the next day's real-time electricity price curve and next day's weather forecast data; On the previous day, a first predicted power generation curve for the next day is generated based on the weather forecast data for the next day, a first predicted power consumption curve for the next day is generated based on the weather forecast data for the next day and the stored historical power consumption curve of the building, and a full-day predicted electricity price curve for the next day is generated based on the real-time electricity price curve for the next day and the first predicted power consumption curve for the next day; At the i-th moment of the next day, compare whether the first predicted power generation is greater than the first predicted power consumption at that moment. If the judgment result is yes, the first cold and heat storage operation strategy and the first electricity storage operation strategy are executed, and the full-day predicted electricity rate curve is adjusted in real time. If the judgment result is no, the second power storage operation strategy and the second cold and heat storage operation strategy are executed, and the full-day predicted electricity rate curve is adjusted in real time; as well as Real-time operation data including real-time electricity charges at the i-th moment is obtained as training data, and at the i+1-th moment, the first predicted power generation is continued to be compared to see whether it is greater than the first predicted power consumption at the moment.

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