Control method for energy management and control system, apparatus, device and storage medium

Abstract

A control method for an energy management and control system, an apparatus, a device and a storage medium. The method comprises: acquiring weather data of a target date (110); on the basis of the target date, the weather data and a prediction model, generating first predicted electric power data and second predicted electric power data, the first predicted electric power data being data of electric power which can be converted by a renewable energy module on the target date, and the second predicted electric power data being data of electric power required by a load device to work on the target date (120); and, on the basis of the first predicted electric power data and the second predicted electric power data, generating a charging policy of the target date, the charging policy comprising: a first charging policy for charging an energy storage device on the basis of the renewable energy module, or a second charging policy for charging the energy storage device on the basis of the renewable energy module and another power supply module (130). The method achieves accurate control on power storage of energy storage devices, thereby improving the utilization efficiency of photovoltaic storage systems.

Description

Control method, device, equipment and storage medium of energy management and control system

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application is based on the Chinese patent application with application number 202311465150.5 and application date of November 3, 2023, and claims the priority of the Chinese patent application. The entire content of the Chinese patent application is hereby introduced into this application as a reference. Technical Field

[0003] The present application relates to the field of control technology, and in particular to a control method, device, equipment and storage medium for an energy management and control system. Background Art

[0004] With the increasing maturity of photovoltaic power generation and energy storage technologies, photovoltaic energy storage systems, consisting of photovoltaic power generation and energy storage equipment, have been widely used in many fields to reduce electricity costs. Photovoltaic energy storage systems use photovoltaic power generation to convert light energy into electrical energy and store excess energy in energy storage equipment for use in emergencies.

[0005] In practice, to ensure sufficient energy storage capacity, energy is stored in advance before unusual weather conditions occur. However, this approach alone is inflexible in addressing complex power usage environments, resulting in low PV-storage system utilization efficiency and a failure to maximize its value.

[0006] Summary of the Invention

[0007] In view of this, embodiments of the present application provide a control method, apparatus, device, and storage medium for an energy management and control system, aiming to improve the utilization efficiency of a photovoltaic storage system.

[0008] The technical solution of the embodiment of the present application is implemented as follows:

[0009] In a first aspect, an embodiment of the present application provides a control method for an energy management and control system, the method comprising:

[0010] Get weather data for the target date;

[0011] Based on the target date, the weather data, and the forecast model, first forecasted electricity data and second forecasted electricity data are generated; wherein the first forecasted electricity data is the electricity data that the renewable energy module can convert at the target date, and the second forecasted electricity data is the electricity data required for the load device to operate at the target date;

[0012] Based on the first predicted power data and the second predicted power data, a charging strategy for the target date is generated, wherein the charging strategy includes: a first charging strategy for charging the energy storage device based on the renewable energy module, or a second charging strategy for charging the energy storage device based on the renewable energy module and other power modules.

[0013] In some embodiments, the method further comprises:

[0014] determining that the target date has arrived and the charging strategy is the first charging strategy, generating first power supply instruction information, the first power supply instruction information being used to instruct the load device to start up within a first set time interval and instruct the renewable energy module to supply power to the load device within the first set time interval;

[0015] The first set time interval is a time interval corresponding to the rated operating power value of the renewable energy module.

[0016] In some embodiments, the method further comprises:

[0017] Determining that the target date has arrived and the charging strategy is the first charging strategy, generating first charging instruction information, wherein the first charging instruction information is used to instruct the renewable energy module to charge the energy storage device in a second set time interval;

[0018] The second set time interval is a time interval corresponding to when the renewable energy module is in an operating state.

[0019] In some embodiments, the other power supply module includes a grid power supply module, the load device includes a water heater, and the method further includes:

[0020] Determining that the target date has arrived and the charging strategy is the second charging strategy, and in response to the current time entering a third set time interval, obtaining a state of charge detection value of the energy storage device;

[0021] Determining a predicted state of charge value of the energy storage device based on the first predicted power data, the second predicted power data, the state of charge detection value, and the rated charge value of the energy storage device;

[0022] If it is determined that the predicted state of charge value is less than or equal to a set threshold, a target charging state of charge value of the energy storage device is generated based on the first predicted power data, the second predicted power data, the rated power value, and the lower limit of the state of charge of the energy storage device;

[0023] Based on the target charging state of charge value, second charging instruction information is generated, where the second charging instruction information is used to instruct the grid power supply module to charge the energy storage device to the target charging state of charge value in the third set interval.

[0024] In some embodiments, the method further comprises:

[0025] If it is determined that the predicted state of charge value is greater than a set threshold, generating first power supply indication information and / or first charging indication information;

[0026] The first power supply instruction information is used to instruct the water heater to start up within a first set time and to instruct the renewable energy module to supply power to the water heater within the first set time interval, and the first charging instruction information is used to instruct the renewable energy module to charge the energy storage device within a second set time interval;

[0027] The first set time is a time interval corresponding to the rated operating power of the renewable energy module, and the second set time interval is a time interval corresponding to when the renewable energy module is in an operating state.

[0028] In some embodiments, the method further comprises:

[0029] If it is determined that the predicted state of charge value is less than or equal to a set threshold, generating second power supply indication information, the second power supply indication information being used to instruct the grid power supply module to supply power to the water heater within the third set time interval;

[0030] The third set time interval includes: a low electricity price time interval of the grid power module.

[0031] In some embodiments, generating the charging strategy for the target date based on the first predicted power data and the second predicted power data includes:

[0032] determining whether the first predicted power data is greater than or equal to the second predicted power data, and if so, generating the first charging strategy;

[0033] If not, the second charging strategy is generated.

[0034] In a second aspect, an embodiment of the present application provides an energy management device, the energy management device comprising:

[0035] an acquisition module configured to acquire weather data for a target date;

[0036] a first generating module configured to generate first predicted power data and second predicted power data based on the target date, the weather data, and the prediction model; wherein the first predicted power data is power data that can be converted by the renewable energy module on the target date, and the second predicted power data is power data required for the load device to operate on the target date;

[0037] The second generation module is configured to generate a charging strategy for the target date based on the first predicted power data and the second predicted power data, wherein the charging strategy includes: a first charging strategy for charging the energy storage device based on the renewable energy module, or a second charging strategy for charging the energy storage device based on the renewable energy module and other power modules.

[0038] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor and a memory for storing a computer program that can be run on the processor, wherein:

[0039] The processor is configured to execute the steps of the method described in the first aspect when running a computer program.

[0040] In a fourth aspect, an embodiment of the present application provides a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in the first aspect are implemented.

[0041] The technical solution provided in an embodiment of the present application provides a control method for an energy management and control system, the method comprising: obtaining weather data for a target date; generating first predicted electricity data and second predicted electricity data based on the target date, the weather data and a prediction model; wherein the first predicted electricity data is the electricity data that can be converted by the renewable energy module on the target date, and the second predicted electricity data is the electricity data required for the load device to operate on the target date; based on the first predicted electricity data and the second predicted electricity data, generating a charging strategy for the target date, the charging strategy comprising: a first charging strategy for charging the energy storage device based on the renewable energy module, or a second charging strategy for charging the energy storage device based on the renewable energy module and other power modules.

[0042] In this way, based on the prediction model, the first predicted electricity data and the second predicted electricity data are predicted and generated under the target date and the weather data of the target date, thereby improving the accuracy of the electricity data prediction; and based on the first predicted electricity data and the second predicted electricity data, a charging strategy for the target date is generated, and the charging strategy includes: a first charging strategy for charging the energy storage device based on the renewable energy module, or a second charging strategy for charging the energy storage device based on the renewable energy module and other power modules, thereby realizing precise control of the energy storage of the energy storage device, thereby improving the utilization efficiency of the photovoltaic storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] FIG1 is a flow chart of a control method for an energy management and control system according to an embodiment of the present application;

[0044] FIG2 is a schematic diagram of the structure of a home solar storage system provided in an application example of the present application;

[0045] FIG3 is a flow chart of an energy scheduling solution based on power supply and demand forecasting and electricity prices provided in an application example of the present application;

[0046] FIG4 is a schematic diagram of a photovoltaic-load power curve provided in an application example of the present application;

[0047] FIG5 is a schematic diagram of the structure of an energy management device provided in an embodiment of the present application;

[0048] FIG6 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0049] The present application will be described in further detail below with reference to the accompanying drawings and embodiments.

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application pertains. The terms used herein in the specification of this application are for the purpose of describing specific embodiments only and are not intended to limit this application.

[0051] The present invention provides a method for controlling an energy management and control system, as shown in FIG1 , which includes the following steps:

[0052] Step 110: Obtain weather data for the target date.

[0053] Here, the target date includes but is not limited to weekdays and holidays. The weather data for the target date can be determined based on the current geographical location. The weather data includes but is not limited to: temperature parameters, wind parameters, sunny or rainy parameters, etc.

[0054] Step 120: Generate first predicted electricity data and second predicted electricity data based on the target date, weather data, and the forecast model; wherein the first predicted electricity data is the electricity data that the renewable energy module can convert on the target date, and the second predicted electricity data is the electricity data required for the load device to operate on the target date.

[0055] Here, the prediction model can be constructed after pre-training based on historical data. The historical data here includes: the convertible power data of the power module under different dates (weekdays / holidays) and different weather conditions, and the power data required for the operation of the load device.

[0056] Here, the power module is used to convert external energy into the electrical energy required by the device and output it. External energy sources include, but are not limited to, renewable energy and external power sources. The power module includes renewable energy modules and other power modules. Renewable energy sources include, but are not limited to, solar energy (i.e., photovoltaic energy), hydropower, wind energy, biomass energy, wave energy, tidal energy, ocean thermal energy, geothermal energy, and other sources. Renewable energy offers environmental and energy-saving advantages and holds unlimited potential. Furthermore, other power modules can power the load device based on the external power source.

[0057] Here, after constructing the prediction model, the target date and weather data are input into the prediction model, and the prediction model outputs and generates first predicted electricity data and second predicted electricity data; wherein, the first predicted electricity data is the electricity data that the renewable energy module can convert, and the second predicted electricity data is the electricity data required for the load equipment to work under the target date.

[0058] Step 130: Generate a charging strategy for the target date based on the first predicted power data and the second predicted power data. The charging strategy includes: a first charging strategy for charging the energy storage device based on the renewable energy module, or a second charging strategy for charging the energy storage device based on the renewable energy module and other power modules.

[0059] Here, an energy storage device is a device for storing and releasing energy, and the energy storage device may be an energy storage battery. The energy storage device is charged based on renewable energy and / or other power modules. The charging strategy corresponding to the target date may be generated based on the first predicted power data and the second predicted power data. The charging strategies include: a first charging strategy for charging the energy storage device based on the renewable energy module, or a second charging strategy for charging the energy storage device based on the renewable energy module and other power modules.

[0060] In this way, based on the prediction model, the first predicted electricity data and the second predicted electricity data are predicted and generated under the target date and the weather data of the target date, thereby improving the accuracy of the electricity data prediction; and based on the first predicted electricity data and the second predicted electricity data, a charging strategy for the target date is generated, and the charging strategy includes: a first charging strategy for charging the energy storage device based on the renewable energy module, or a second charging strategy for charging the energy storage device based on the renewable energy module and other power modules, thereby realizing precise control of the energy storage of the energy storage device, thereby improving the utilization efficiency of the photovoltaic storage system.

[0061] In some embodiments, the method comprises:

[0062] If it is determined that the target date has arrived and the charging strategy is the first charging strategy, first power supply instruction information is generated, where the first power supply instruction information is used to instruct the load device to start up within a first set time interval and to instruct the renewable energy module to supply power to the load device within the first set time interval;

[0063] The first set time interval is a time interval corresponding to the rated operating power value of the renewable energy module.

[0064] Here, when the target date is reached and the charging strategy is the first charging strategy, it indicates that the renewable energy module has a high conversion efficiency and can both charge the energy storage device and power the load device. In response to the first strategy, first power supply instruction information is generated. The first power supply instruction information is used to instruct the load device to power on within a first set time interval and to instruct the renewable energy module to supply power to the load device within the first set time interval.

[0065] Here, the first set time interval is the time interval during which the renewable energy module operates at its rated power. The rated operating power can be the maximum operating power of the renewable energy module. For the renewable energy module, different operating power levels correspond to different time intervals during its operation. For example, taking photovoltaic power generation as an example, its operating principle is to convert sunlight into electrical energy, also known as the photovoltaic effect. Generally speaking, the midday time period corresponds to the rated operating power period for renewable energy, as sunlight is most intense during this period.

[0066] In this way, by generating the first power supply indication information for the load device under the first charging strategy, the renewable energy module is controlled to supply power to the load device during the time interval of the renewable energy module's operating power rating, thereby realizing energy scheduling control of the load device and improving the utilization efficiency of the renewable energy module.

[0067] In some embodiments, the method comprises:

[0068] Determining that the target date has arrived and the charging strategy is the first charging strategy, generating first charging instruction information, the first charging instruction information being used to instruct the renewable energy module to charge the energy storage device during the second set time interval;

[0069] The second set time interval is a time interval corresponding to when the renewable energy module is in an operating state.

[0070] Here, when the target date is determined and the charging strategy is the first charging strategy, the energy storage device can be charged based on the renewable energy module. In response to the first charging strategy, first charging indication information is generated. The first charging indication information is used to instruct the renewable energy module to charge the energy storage device within the second set time interval.

[0071] Here, the second set time interval corresponds to the time interval when the renewable energy module is in operation. The corresponding operating time varies for different renewable energy modules. Photovoltaic power generation works by converting sunlight into electrical energy, also known as the photovoltaic effect. Therefore, the corresponding operating time interval is 7:00 AM to 7:00 PM. Generally speaking, around noon is the peak time for photovoltaic power generation, and operating power is also at its highest during this time.

[0072] In this way, by generating the first charging instruction information under the first charging strategy, the renewable energy module is instructed to charge the energy storage device in the second set time interval, thereby achieving energy scheduling of the energy storage device and improving the utilization efficiency of the renewable energy power supply device.

[0073] In some embodiments, the other power modules further include: a grid power module, the load device includes a water heater, and the method further includes:

[0074] Determining that the target date has arrived and the charging strategy is the second charging strategy, and in response to the current time entering the third set time interval, obtaining a state of charge detection value of the energy storage device;

[0075] Determining a predicted state of charge value of the energy storage device based on the first predicted power data, the second predicted power data, the state of charge detection value, and the rated charge value of the energy storage device;

[0076] If it is determined that the predicted state of charge value is less than or equal to the set threshold, a target charging state of charge value of the energy storage device is generated based on the first predicted power data, the second predicted power data, the rated power value, and the lower limit of the state of charge of the energy storage device;

[0077] Based on the target charging state of charge value, second charging instruction information is generated, and the second charging instruction information is used to instruct the grid power module to charge the energy storage device to the target charging state of charge value in a third set interval.

[0078] Here, other power modules also include a grid power module, which provides power from the grid. If the target date is reached and the second charging strategy is selected, the energy storage device should be charged using the renewable energy module and the grid power module. The load device includes a water heater, which acts as a heat storage device and can store heat in advance to meet user hot water needs.

[0079] Here, in response to the current time entering the third set time interval, the state of charge detection value of the energy storage device is obtained. The state of charge value (SOC) is used to reflect the remaining capacity of the energy storage device, and is numerically defined as the ratio of the remaining power to the rated power of the energy storage device. When the current time enters the third set time interval, the state of charge value of the energy storage device is detected to obtain the SOC of the energy storage device. For energy storage devices, due to the different capacities of energy storage devices, the corresponding rechargeable rated power values ​​are different. The rated power value here can be the maximum rechargeable power value.

[0080] Here, the predicted state of charge value of the energy storage device is determined based on the first predicted power data, the second predicted power data, the state of charge detection value, and the rated charge value of the energy storage device. For example, assuming that the first predicted power data is Q1, the second predicted power data is Q2, the state of charge detection value is SOC-1, and the rated charge value of the energy storage device is Q3, the predicted state of charge value can be calculated based on the formula [Q3*SOC_1-(Q2-Q1)] / Q3 to determine the predicted state of charge value of the energy storage device.

[0081] Here, the set threshold value may be a lower limit value of the SOC of the energy storage device. For example, the set threshold value may be a minimum value of the SOC of the energy storage device. When it is determined that the predicted state of charge value of the energy storage device is less than or equal to the set threshold value, it indicates that the predicted state of charge value of the energy storage device is insufficient to support the operation of the energy storage device on the target date, and the energy storage device needs to be charged as soon as possible to ensure the normal operation of the energy storage device.

[0082] Here, based on the first predicted power data, the second predicted power data, the rated power value and the lower limit of the state of charge of the energy storage device, a target charging state of charge value of the energy storage device can be generated. For example, it is assumed that the target charging state of charge value of the energy storage device is SOC_goal = (Q2-Q1) / Q3+20%, where Q1 is the first predicted power data, Q2 is the second predicted power data, Q3 is the rated power value, and the lower limit of the state of charge of the energy storage device is 20%. Based on the target charging state of charge value, a second charging indication information is generated, and the second charging indication information is used to instruct the grid power module to charge the energy storage device to the target charging state of charge value in the third set interval.

[0083] In this way, by determining that the preset state of charge value is less than or equal to the set threshold value based on the comparison result between the predicted state of charge and the set threshold value under the second strategy, the power module for charging the energy storage device is further judged, thereby achieving accurate selection of the power module with high flexibility. In addition, based on the grid power module charging the energy storage device to the target state of charge value in the third set interval, the charging efficiency of the energy storage device is improved, thereby enhancing the user experience.

[0084] In some embodiments, the method further comprises:

[0085] If it is determined that the predicted state of charge value is greater than a set threshold, generating first power supply indication information and / or first charging indication information;

[0086] The first power supply instruction information is used to instruct the water heater to start up within a first set time and to instruct the renewable energy module to supply power to the water heater within a first set time interval, and the first charging instruction information is used to instruct the renewable energy module to charge the energy storage device within a second set time interval;

[0087] The first set time is a time interval corresponding to the rated operating power of the renewable energy module, and the second set time interval is a time interval corresponding to when the renewable energy module is in an operating state.

[0088] Here, it is determined that the predicted state of charge value is greater than the set threshold, indicating that the state of charge value of the energy storage device can still support its operation on the target date, and then the first power supply indication information and / or the first charging indication information are generated.

[0089] Here, the first power supply indication information is used to instruct the water heater to start up within the first set time and to instruct the renewable energy module to supply power to the water heater within the first set time interval, and the first charging indication information is used to instruct the renewable energy module to charge the energy storage device within the second set time interval; the first set time is the time interval corresponding to the operating power rating of the renewable energy module, and the second set time interval is the time interval corresponding to when the renewable energy module is in operation.

[0090] In this way, by utilizing the comparison result between the predicted state of charge value and the set threshold, that is, determining that the predicted state of charge value is greater than the set threshold, the generation of the first power supply indication information and / or the first charging indication information is controlled, thereby realizing energy scheduling of the energy storage device and / or the water heater and improving the utilization rate of the renewable energy module.

[0091] In some embodiments, the method further comprises:

[0092] If it is determined that the predicted state of charge value is less than or equal to the set threshold, second power supply indication information is generated, where the second power supply indication information is used to instruct the grid power module to supply power to the water heater within a third set time interval;

[0093] The third set time interval includes: a low electricity price time interval of the grid power module.

[0094] Here, the third set time interval includes: the low electricity price time interval of the grid power module. For grid power supply, it generally adopts the peak-valley electricity price mechanism, that is, the State Grid generally divides electricity consumption time into peak hours, valley hours and flat hours. In order to encourage residents to use electricity on a time-sharing basis to alleviate peak electricity consumption, the electricity price during the valley hours is set to the lowest to ensure the safety of the urban power grid. The low electricity price time interval in the third set time interval is the same as the time interval corresponding to the valley hour. Generally, the early morning is the valley electricity price period, such as the valley electricity price period in Guangzhou is 0 o'clock (24 o'clock)-8 o'clock.

[0095] Here, if the state of charge value is determined to be less than or equal to a set threshold, second power supply instruction information is generated. The second power supply instruction information is used to instruct the grid power supply module to supply power to the water heater during a third set time interval. In this way, power is supplied to the water heater by the grid power supply module during low electricity price periods, which not only enables timely heat storage in the water heater but also reduces the water heater's electricity costs.

[0096] In some embodiments, generating a charging strategy for a target date based on the first predicted power data and the second predicted power data includes:

[0097] determining whether the first predicted power data is greater than the second predicted power data, and if so, generating a first charging strategy;

[0098] If not, a second charging strategy is generated.

[0099] Here, when it is determined that the first predicted power data is greater than or equal to the second predicted power data, the power data that can be converted by the renewable energy module is greater than the power data required by the load device, indicating that on the target date, the power converted by the power module can sufficiently supply power to the load device and can generate additional power. Therefore, at this time, a first strategy for charging the energy storage device based on the renewable energy module is generated. If it is determined that the first predicted power data is less than the second predicted power data, it indicates that at this time, the power converted by the power module is not sufficient to supply power to the load device. At this time, the power demand of the load device and the energy storage device cannot be met based solely on the renewable energy module. Therefore, a second charging strategy for charging the energy storage device based on the renewable energy module and other power modules is generated.

[0100] In this way, different charging strategies are generated based on the comparison between the first predicted power data and the second predicted power data, thereby greatly improving the charging efficiency of the energy storage device.

[0101] Below, the embodiment of the present application is described in detail with reference to an application example.

[0102] In this application example, the renewable energy module is a photovoltaic power generation module, the energy storage device is a storage battery, and the load device includes a water heater. The predicted photovoltaic power generation (i.e., the first predicted power data) is represented by Q1, the predicted load power consumption (i.e., the second predicted power data) is represented by Q2, the energy storage battery saturation capacity (i.e., the rated charge capacity of the energy storage device) is represented by Q3, and the battery backup SOC (i.e., the target state of charge of the energy storage device) is represented by SOC_goal.

[0103] In this application example, the power grid uses a peak-valley electricity pricing mechanism: Low electricity prices are generally applied in the early morning hours, such as in Guangzhou, where the low electricity price period is from midnight to 8 am. The first set time interval corresponds to the time period when the photovoltaic power generation module operates at maximum power. The photovoltaic power generation calculation period (i.e., the aforementioned second set time interval) is t1 = [τpv_0, τpv_1], where τpv_0 is the start time of photovoltaic power generation and τpv_1 is the end time of photovoltaic power generation.

[0104] The load device's power consumption calculation period, i.e., the load device's power consumption time interval t2, is [τvalley_1, τvalley_0]. The third set time interval is the grid's valley price period, interval t3 = [τvalley_0, τvalley_1], where τvalley_0 valley price starts and τvalley_1 valley price ends. The battery SOC (i.e., the energy storage device's detected state of charge) at the valley price start time is SOC_valley.

[0105] The basic operating principle of existing energy storage batteries is that if photovoltaic power generation exceeds the load's power consumption, the excess photovoltaic power is stored. When photovoltaic power generation is insufficient, the energy storage battery discharges the power to the load. In addition, the energy storage battery has a backup power function, which can be fully charged before abnormal weather occurs. The existing solution does not comprehensively consider battery and load scheduling strategies based on photovoltaic power supply, load power consumption, and electricity pricing mechanisms. As a result, the value of home photovoltaic storage systems is not maximized, and the economic benefits are low. Currently, the battery backup power mode is manually set by the user, which is not intelligent and is mainly used for pre-emptive power backup in abnormal weather conditions, so its frequency of use is low.

[0106] This application example uses the predicted photovoltaic power generation and load power consumption of a household photovoltaic storage system on different dates (weekdays / holidays) and in different weather conditions, and combines this with electricity prices to control battery power storage and water heater heat storage to improve the utilization efficiency of the photovoltaic storage system.

[0107] First, this application example provides a structural diagram of a home solar-to-storage system, as shown in Figure 2. Specifically, it includes:

[0108] 1. Power generation components. The power generation components include photovoltaic power generation modules, which are used to convert photovoltaic energy into electrical energy for output.

[0109] 2. Energy storage unit. The energy storage unit is connected to the power generation component and includes energy storage batteries, which are used to store the electrical energy output by the power generation component.

[0110] 3. Inverter. The inverter is connected to the energy storage unit and power generation components to convert the direct current of the photovoltaic panel into alternating current. That is, the solar panel converts solar energy into electrical energy and outputs it to the inverter, which then outputs the AC power required by the equipment.

[0111] 4. Distribution Cabinet. Distribution cabinets are used to distribute electrical energy. By distributing and controlling the power load, they achieve energy distribution and protection, ensuring the normal operation of each appliance and improving the reliability and safety of the power grid.

[0112] 5. Water heater. The power distribution cabinet is connected to the load equipment, including the water heater. As a heat storage device, the water heater can provide hot water to the family in a timely and convenient manner, meeting the hot water needs of family members at any time.

[0113] 6. Power sensor: The power sensor can detect the power of the photovoltaic power generation module and the power of the load device.

[0114] 7. Power grid (i.e. the aforementioned power grid power module).

[0115] Based on the aforementioned solar-to-storage system, this application example provides an energy scheduling solution based on power supply and demand forecasts and electricity prices, as shown in Figure 3. The specific implementation steps are as follows:

[0116] Step 301: Based on the historical operation data of the home photovoltaic storage system, a photovoltaic power generation and load power consumption prediction model is established on different dates (weekdays / holidays) and in different weather conditions.

[0117] Here, the operating history data of the household photovoltaic storage system includes photovoltaic power generation data and load power consumption data on different dates (weekdays / holidays) and different weather conditions. Based on this historical data, a photovoltaic power generation and load power consumption prediction model is constructed on different dates (weekdays / holidays) and different weather conditions.

[0118] In this way, the present application can realize the control of battery power storage and water heater heat storage based on the photovoltaic power generation and load power consumption predicted by the home photovoltaic storage system on different dates (weekdays / holidays) and in different weather conditions, combined with electricity prices.

[0119] Step 302: Predict the photovoltaic power generation Q1 and load power consumption Q2 for the current day based on the current date and the current weather.

[0120] In practical applications, weather data for a target date is obtained; based on the target date, weather data, and a prediction model, first predicted power data and second predicted power data are generated;

[0121] Here, the current date (ie, the aforementioned target date) and the weather of the day (ie, the aforementioned weather data) are input into the prediction model, and the photovoltaic power generation Q1 and load power consumption Q2 of the day can be output and predicted.

[0122] The calculation period for photovoltaic power generation Q1 is t1 = [τpv_0, τpv_1], where τpv_0 is the start time of photovoltaic power generation and τpv_1 is the end time of photovoltaic power generation. The calculation period for load power consumption Q2 is t2 = [τvalley_1, τvalley_0], where τvalley_0 is the start time of the valley price and τvalley_1 is the end time of the valley price. For example, if the off-peak electricity price period in Guangzhou is 0-8, the load power consumption calculation period is from 8:00 AM to 12:00 AM.

[0123] Figure 4 shows a schematic diagram of the PV-load power curve. For PV power generation, the start and end times are τpv_0 and τpv_1. In the figure, τpv_0 is at 7 o'clock and τpv_1 is at 19 o'clock. For the load device, its power generation can be calculated at [τvalley_1, τvalley_0]. τvalley_1 is at 8 o'clock and τvalley_0 is at 24 o'clock, or 0 o'clock. Generally speaking, the grid's valley price period is t3 = [τvalley_0, τvalley_1].

[0124] Step 303: Determine whether Q1 ≥ Q2. If not, execute step 304; if so, execute step 305.

[0125] Here, it is determined whether the first predicted power data is greater than the second predicted power data. If so, a first charging strategy is generated and step 305 is executed; if not, a second charging strategy is generated and step 304 is executed.

[0126] Here, the predicted supply and demand relationship can be determined by comparing Q1 and Q2. If Q1 ≥ Q2, it indicates that the supply is greater than the demand at this time, the photovoltaic power generation is sufficient, and the energy storage battery can be charged (i.e., the first charging strategy mentioned above), and step 304 is executed; if Q1 < Q2, it indicates that the supply is less than the demand at this time, the power generation of the photovoltaic power generation module is insufficient, and the grid power module, i.e., the grid and the photovoltaic power generation module, are required to charge the energy storage battery (i.e., the second strategy mentioned above), and step 305 is executed.

[0127] Step 304 : Determine whether [Q3*SOC_valley-(Q2-Q1)] / Q3≥20%. If so, execute step 305 ; if not, execute step 306 .

[0128] In actual applications, it is determined that the target date has been reached and the charging strategy is the second charging strategy. In response to the current time entering the third set time interval, the state of charge detection value of the energy storage device is obtained; and based on the first predicted power data, the second predicted power data, the state of charge detection value and the rated charge value of the energy storage device, the predicted state of charge value of the energy storage device is determined.

[0129] It should be noted that if Q1 < Q2, it is necessary to re-determine whether the battery power can support daytime load operation. In response to the current time entering the low electricity price time period, the SOC_valley corresponding to the current time is obtained, and the predicted state of charge value of the energy storage battery (i.e., the predicted state of charge value of the energy storage device mentioned above) is determined based on the formula [Q3 * SOC_valley - (Q2 - Q1)] / Q3. Here, 20% is the set threshold, i.e., the minimum state of charge value of the energy storage device.

[0130] Example: Battery saturation capacity Q3 = 10kW.h, battery SOC_valley = 40% at the start of valley price; Q1 = 15kW.h, Q2 = 10kW.h, no backup power is required; Q1 = 15kW.h, Q2 = 16kW.h, [Q3*SOC_valley-(Q2-Q1)] / Q3 = 30%>20%, no backup power is required.

[0131] Here, [Q3*SOC_valley-(Q2-Q1)] / Q3 is compared with 20% to determine whether the energy storage battery's power level can support daytime load operation. If so, the current predicted SOC value of the energy storage battery can still support its daytime operation, and step 305 is executed. If not, the current predicted SOC value of the energy storage battery is no longer sufficient to support its daytime operation, and the energy storage device needs to be charged as soon as possible to ensure normal operation of the energy storage battery, and step 306 is executed.

[0132] Step 305: Generate first charging instruction information and first power supply instruction information.

[0133] In actual applications, when it is determined that the target date has arrived and the charging strategy is the first charging strategy, first power supply indication information is generated. The first power supply indication information is used to instruct the load device to start up within a first set time interval and to instruct the renewable energy module to supply power to the load device within the first set time interval; wherein the first set time interval is the time interval corresponding to the operating power rating of the renewable energy module.

[0134] Exemplarily, if Q1≥Q2, first power supply indication information is generated, which is used to instruct the water heater to shut down, instruct the water heater to start running within a first set time interval, and instruct the photovoltaic power generation module to supply power to it when the photovoltaic power generation power is large.

[0135] In actual applications, it is determined that the target date has arrived and the charging strategy is the first charging strategy, and first charging indication information is generated. The first charging indication information is used to instruct the renewable energy module to charge the energy storage device in the second set time interval; wherein the second set time interval is the time interval corresponding to when the renewable energy module is in an operating state.

[0136] For example, if Q1≥Q2, the energy storage battery does not need backup power during the off-peak electricity price period and can store photovoltaic power during the day, that is, first charging instruction information is generated, indicating that the energy storage device is charged based on the photovoltaic power generation module within the second set time interval, that is, t1=[τpv_0, τpv_1].

[0137] In addition, if [Q3*SOC_valley-(Q2-Q1)] / Q3≥20%, the energy storage battery does not need to backup power during the off-peak electricity price period and can store photovoltaic power during the day, that is, generating first charging instruction information, indicating that the energy storage device should be charged based on the photovoltaic power generation module within the second set time interval, that is, t1=[τpv_0, τpv_1].

[0138] Step 306: Generate second charging indication information and second power supply indication information.

[0139] In actual applications, if it is determined that the predicted state of charge value is less than or equal to the set threshold, a target charging state of charge value of the energy storage device is generated based on the first predicted power data, the second predicted power data, the rated power value and the lower limit of the state of charge of the energy storage device; and based on the target charging state of charge value, second charging indication information is generated, and the second charging indication information is used to instruct the grid power module to charge the energy storage device to the target charging state of charge value within a third set interval.

[0140] If [Q3*SOC_valley-(Q2-Q1)] / Q3 is less than 20%, the energy storage battery will start to provide backup power during the off-peak electricity price period, and use the low electricity price (i.e., during the low electricity price time period) to charge the energy storage battery to support daytime load operation.

[0141] And determine the backup SOC setting value SOC_goal of the energy storage battery = (Q2-Q1) / Q3+20%, Q3 is the battery saturation power, SOC_valley is the battery SOC at the start of the valley price (that is, the state of charge detection value of the energy storage device mentioned above).

[0142] Based on the SOC_goal, second charging instruction information is generated, and the second charging instruction information is used to instruct the grid (ie, the aforementioned grid power module) to charge the energy storage battery to the SOC_goal during the low electricity price time period.

[0143] For example, if Q1 = 15 kW.h, Q2 = 18 kW.h, [Q3*SOC_valley-(Q2-Q1)] / Q3 = 10% < 20%, then the standby SOC setting value SOC_goal = (Q2-Q1) / Q3 + 20% = 50%.

[0144] In actual applications, if it is determined that the predicted state of charge value is less than or equal to the set threshold, a second power supply indication information is generated, and the second power supply indication information is used to instruct the grid power module to supply power to the water heater within a third set time interval; wherein the third set time interval includes: the low electricity price time interval of the grid power module.

[0145] It should be noted that if [Q3*SOC_valley-(Q2-Q1)] / Q3 is less than 20%, a second power supply indication information is generated during the low electricity price time period. For example, the second power supply indication information is used to instruct the water heater to start up and increase the set temperature to reach the desired temperature during the low electricity price time period to meet the hot water usage demand on that day.

[0146] This application example reduces the cost of hot water by utilizing free photovoltaic power or utilizing off-peak electricity prices to heat the water heater. Furthermore, battery storage is controlled based on predicted photovoltaic power generation and load power consumption, in conjunction with electricity prices, significantly improving the efficiency of the photovoltaic storage system.

[0147] As shown in Figure 5, the energy management device 500 includes: an acquisition module 510 and a first generation module 520 and a second generation module 530, the acquisition module 510 is configured to obtain weather data of the target date; the first generation module 520 is configured to generate first predicted electricity data and second predicted electricity data based on the target date, weather data and the prediction model; wherein the first predicted electricity data is the electricity data that the renewable energy module can convert on the target date, and the second predicted electricity data is the electricity data required for the load device to work on the target date; the second generation module 530 is also configured to generate a charging strategy for the target date based on the first predicted electricity data and the second predicted electricity data, and the charging strategy includes: a first charging strategy for charging the energy storage device based on the renewable energy module, or a second charging strategy for charging the energy storage device based on the renewable energy module and other power modules.

[0148] In some embodiments, the energy management device further includes a determination module 540 configured to, when determining that the target date has arrived and the charging strategy is the first charging strategy, generate first power supply indication information, the first power supply indication information being used to instruct the load device to start up within a first set time interval and to instruct the renewable energy module to supply power to the load device within the first set time interval;

[0149] The first set time interval is a time interval corresponding to the rated operating power value of the renewable energy module.

[0150] In some embodiments, the determination module 540 is further configured to determine that the target date has arrived and the charging strategy is the first charging strategy, and generate first charging instruction information, the first charging instruction information being used to instruct the renewable energy module to charge the energy storage device in the second set time interval;

[0151] The second set time interval is a time interval corresponding to when the renewable energy module is in an operating state.

[0152] In some embodiments, other power modules also include: a grid power module, the load device includes a water heater, the determination module 540 is also configured to determine that the target date has been entered and the charging strategy is the second charging strategy, and the acquisition module 510 is also configured to obtain the charge state detection value of the energy storage device in response to the current time entering the third set time interval; the determination module 540 is also configured to determine the predicted charge state value of the energy storage device based on the first predicted power data, the second predicted power data, the charge state detection value and the rated charge value of the energy storage device; if it is determined that the predicted charge state value is less than or equal to the set threshold, then based on the first predicted power data, the second predicted power data, the rated power value and the charge state lower limit value of the energy storage device, a target charging charge state value of the energy storage device is generated; the energy management device also includes a third generation module 550, configured to generate second charging indication information based on the target charging charge state value, the second charging indication information is used to instruct the grid power module to charge the energy storage device to the target charging charge state value in the third set interval.

[0153] In some embodiments, the determination module 540 is further configured to generate first power supply indication information and / or first charging indication information if it is determined that the predicted state of charge value is greater than a set threshold;

[0154] The first power supply instruction information is used to instruct the water heater to start up within a first set time and to instruct the renewable energy module to supply power to the water heater within a first set time interval, and the first charging instruction information is used to instruct the renewable energy module to charge the energy storage device within a second set time interval;

[0155] The first set time is a time interval corresponding to the rated operating power of the renewable energy module, and the second set time interval is a time interval corresponding to when the renewable energy module is in an operating state.

[0156] In some embodiments, the determination module 540 is further configured to generate second power supply indication information if it is determined that the predicted state of charge value is less than or equal to a set threshold, the second power supply indication information being used to instruct the grid power module to supply power to the water heater within a third set time interval;

[0157] The third set time interval includes: a low electricity price time interval of the grid power module.

[0158] In some embodiments, the second generating module 530 is further configured to determine whether the first predicted power data is greater than or equal to the second predicted power data, and if so, generate a first charging strategy;

[0159] If not, a second charging strategy is generated.

[0160] In actual application, the acquisition module 510, the first generation module 520, the second generation module 530, the determination module 540 and the third generation module 550 can be implemented by a processor in the energy management device. Of course, the processor needs to run the computer program in the memory to implement its functions.

[0161] It should be noted that the energy management device provided in the above embodiments, when controlling the energy management system, is illustrated only by the division of the above-mentioned program modules. In actual applications, the above-mentioned processing can be assigned to different program modules as needed, that is, the internal structure of the device can be divided into different program modules to complete all or part of the above-described processing. In addition, the energy management device provided in the above embodiments and the control method embodiment of the energy management system are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0162] Based on the hardware implementation of the above program modules and in order to implement the method of the embodiment of the present application, the embodiment of the present application further provides an electronic device. Figure 6 only shows an exemplary structure of the electronic device rather than the entire structure. Part or all of the structure shown in Figure 6 can be implemented as needed.

[0163] As shown in Figure 6, an electronic device 600 provided in an embodiment of the present application includes: at least one processor 601, a memory 602, a user interface 603, and at least one network interface 604. The various components in the electronic device 600 are coupled together via a bus system 605. It will be understood that the bus system 605 is used to implement connections and communications between these components. In addition to including a data bus, the bus system 605 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clarity, in Figure 6, various buses are labeled as the bus system 605.

[0164] The user interface 603 may include a display, a keyboard, a mouse, a trackball, a click wheel, keys, buttons, a touch pad or a touch screen.

[0165] The memory 602 in the embodiment of the present application is used to store various types of data to support the operation of the electronic device. Examples of such data include: any computer program used to operate on the electronic device.

[0166] The control method for the energy management and control system disclosed in the embodiments of this application can be applied to or implemented by processor 601. Processor 601 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the control method for the energy storage management and control system can be completed by hardware integrated logic circuits or software instructions in processor 601. The processor 601 may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, etc. Processor 601 can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of this application. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module may be located in a storage medium located in memory 602. Processor 601 reads information from memory 602 and, in conjunction with its hardware, completes the steps of the control method for the energy management and control system provided in the embodiments of this application.

[0167] In an exemplary embodiment, the electronic device may be implemented by one or more application specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers (MCUs), microprocessors, or other electronic components to perform the aforementioned method.

[0168] It is understood that memory 602 can be volatile memory or non-volatile memory, or can include both volatile and non-volatile memory. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), ferromagnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disk, or compact disc read-only memory (CD-ROM); magnetic surface memory can be magnetic disk memory or tape memory. Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct rambus random access memory (DRRAM).The memories described in the embodiments of this application are intended to include, but are not limited to, these and any other suitable types of memories.

[0169] In an exemplary embodiment, the present application also provides a storage medium, namely, a computer storage medium, which may be a computer-readable storage medium, for example, including a memory 602 storing a computer program. The computer program may be executed by a processor 601 of an electronic device to complete the steps of the method of the present application. The computer-readable storage medium may be a memory such as a ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface storage, optical disk, or CD-ROM.

[0170] It should be noted that: "first", "second", etc. are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0171] In addition, the technical solutions described in the embodiments of the present application can be combined arbitrarily without conflict.

[0172] The above are only specific embodiments of the present application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by any person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A control method for an energy management and control system, the method further comprising: Get weather data for the target date; Based on the target date, the weather data and the forecast model, a first forecasted electricity data and a second forecasted electricity data are generated; wherein the first forecasted electricity data is the electricity data that can be converted by the renewable energy module on the target date, and the second forecasted electricity data is the electricity data required for the load device to work on the target date; Based on the first predicted electricity data and the second predicted electricity data, a charging strategy for the target date is generated, and the charging strategy includes: a first charging strategy for charging the energy storage device based on the renewable energy module, or a second charging strategy for charging the energy storage device based on the renewable energy module and other power modules.

2. The method according to claim 1, wherein: The method further comprises: Determining that the target date has arrived and the charging strategy is the first charging strategy, generating first power supply indication information, wherein the first power supply indication information is used to instruct the load device to start up within a first set time interval and to instruct the renewable energy module to supply power to the load device within the first set time interval; The first set time interval is a time interval corresponding to the rated operating power of the renewable energy module.

3. The method according to claim 2, wherein: The method further comprises: Determining that the target date has arrived and the charging strategy is the first charging strategy, generating first charging indication information, wherein the first charging indication information is used to instruct the renewable energy module to charge the energy storage device in a second set time interval; The second set time interval is a time interval corresponding to when the renewable energy module is in operation.

4. The method according to claim 1, wherein: The other power supply module includes: a power supply module of a power grid, the load device includes a water heater, and the method further includes: Determining that the target date has arrived and the charging strategy is the second charging strategy, in response to the current time entering a third set time interval, obtaining a charge state detection value of the energy storage device; Determine a predicted state of charge value of the energy storage device based on the first predicted power data, the second predicted power data, the state of charge detection value, and the rated charge value of the energy storage device; Determining that the predicted state of charge value is less than or equal to a set threshold, generating a target charging state of charge value of the energy storage device based on the first predicted power data, the second predicted power data, the rated power value and the lower limit of the state of charge of the energy storage device; Based on the target charging state of charge value, second charging instruction information is generated, and the second charging instruction information is used to instruct the grid power module to charge the energy storage device in the third setting interval. The battery is charged to the target state of charge value.

5. The method according to claim 4, wherein: The method further comprises: If it is determined that the predicted state of charge value is greater than a set threshold, generating first power supply indication information and / or first charging indication information; The first power supply indication information is used to instruct the water heater to start up within a first set time and to instruct the renewable energy module to supply power to the water heater within the first set time interval, and the first charging indication information is used to instruct the renewable energy module to charge the energy storage device within a second set time interval; The first set time is a time interval corresponding to the rated operating power of the renewable energy module, and the second set time interval is a time interval corresponding to when the renewable energy module is in an operating state.

6. The method according to claim 4, wherein: The method further comprises: If it is determined that the predicted state of charge value is less than or equal to a set threshold, second power supply indication information is generated, where the second power supply indication information is used to instruct the grid power supply module to supply power to the water heater within the third set time interval; Wherein, the third set time interval includes: a low electricity price time interval of the grid power module.

7. The method according to claim 1, wherein: The generating the charging strategy for the target date based on the first predicted power data and the second predicted power data includes: determining whether the first predicted power data is greater than or equal to the second predicted power data, and if so, generating the first charging strategy; If not, the second charging strategy is generated.

8. An energy management device, comprising: an acquisition module configured to acquire weather data for a target date; A first generating module is configured to generate first predicted power data and second predicted power data based on the target date, the weather data and the prediction model; wherein the first predicted power data is power data that can be converted by the renewable energy module on the target date, and the second predicted power data is power data required for the load device to work on the target date; The second generating module is configured to generate a charging strategy for the target date based on the first predicted power data and the second predicted power data, wherein the charging strategy includes: a first charging strategy for charging the energy storage device based on the renewable energy module, or a second charging strategy for charging the energy storage device based on the renewable energy module and other power supply modules.

9. An electronic device, comprising: A processor and a memory for storing a computer program that can be executed on the processor, wherein: The processor is configured to execute the steps of the method according to any one of claims 1 to 7 when running a computer program.

10. A computer storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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