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

Abstract

The invention discloses a control method and device of an energy management and control system, equipment and a storage medium. The method comprises the following steps: acquiring weather data of a target date; generating first predicted electric quantity data and second predicted electric quantity data based on the target date, the weather data and the prediction model; wherein the first predicted electric quantity data is electric quantity data which can be converted by the renewable energy source module at the target date, and the second predicted electric quantity data is electric quantity data required by the load equipment at the target date; and based on the first predicted electric quantity data and the second predicted electric quantity data, generating a charging strategy of the target date, the charging strategy including 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. Therefore, the accuracy of electric quantity data prediction and the utilization efficiency of the optical storage system are improved.

Description

Technical Field

[0001] 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

[0002] At present, with the increasing maturity of photovoltaic power generation and energy storage technology, the photovoltaic storage system composed of photovoltaic power generation and energy storage equipment has been widely used in many fields to reduce electricity costs. The photovoltaic storage system uses photovoltaic power generation to convert light energy into electrical energy, and stores the excess electrical energy in the energy storage device for emergency use.

[0003] In actual applications, in order to ensure that the energy storage equipment can store sufficient electricity, electricity will be stored in advance before abnormal weather occurs. However, only using the above solution cannot flexibly cope with complex power consumption environments, resulting in low utilization efficiency of the photovoltaic storage system and failure to maximize the value of the photovoltaic storage system. Summary of the invention

[0004] In view of this, the embodiments of the present application provide a control method, device, equipment and storage medium of an energy management and control system, aiming to improve the utilization efficiency of a photovoltaic storage system.

[0005] The technical solution of the embodiment of the present application is implemented as follows:

[0006] In a first aspect, an embodiment of the present application provides a control method for an energy management and control system, the method comprising:

[0007] Get weather data for the target date;

[0008] 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;

[0009] 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.

[0010] In some embodiments, the method further comprises:

[0011] 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;

[0012] The first set time interval is a time interval corresponding to the rated operating power of the renewable energy module.

[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 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;

[0015] The second set time interval is a time interval corresponding to when the renewable energy module is in operation.

[0016] 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:

[0017] 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;

[0018] 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;

[0019] 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;

[0020] Based on the target charging state of charge value, second charging indication information is generated, where the second charging indication 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.

[0021] In some embodiments, the method further comprises:

[0022] 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;

[0023] 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;

[0024] 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.

[0025] In some embodiments, the method further comprises:

[0026] 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;

[0027] Wherein, the third set time interval includes: a low electricity price time interval of the grid power module.

[0028] 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:

[0029] 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;

[0030] If not, the second charging strategy is generated.

[0031] In a second aspect, an embodiment of the present application provides an energy management device, the energy management device comprising:

[0032] The acquisition module is used to obtain the weather data of the target date;

[0033] A first generating module is used 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 the power data that the renewable energy module can convert on the target date, and the second predicted power data is the power data required for the load device to work on the target date;

[0034] The second generating module is used to generate a charging strategy for the target date based on the first predicted power data and the second predicted power 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 supply modules.

[0035] 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:

[0036] The processor is used to execute the steps of the method described in the first aspect when running a computer program.

[0037] 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.

[0038] The technical solution provided in the embodiment of the present application provides a control method for an energy management and control system, the method comprising: obtaining weather data on a target date; generating first predicted electricity data and second predicted electricity data based on the target date, the weather data and the 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 work 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 based on charging the energy storage device with the renewable energy module, or a second charging strategy based on charging the energy storage device with the renewable energy module and other power modules.

[0039] 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 achieving 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

[0040] Figure 1 A schematic diagram of a flow chart of a control method of an energy management and control system provided in one embodiment of the present application;

[0041] Figure 2 A schematic diagram of the structure of a home solar storage system provided as an application example of this application;

[0042] Figure 3 A flow chart of an energy dispatching scheme based on power supply and demand forecasting and electricity price provided for an application example of this application;

[0043] Figure 4A schematic diagram of a photovoltaic-load power curve provided for an application example of this application;

[0044] Figure 5 A schematic diagram of the structure of an energy management device provided in an embodiment of the present application;

[0045] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0046] The present application is further described in detail below in conjunction with the accompanying drawings and embodiments.

[0047] 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 belongs. The terms used herein in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0048] The present application provides a control method for an energy management system. Figure 1 As shown, the method comprises the following steps:

[0049] Step 110: Obtain weather data for the target date.

[0050] Here, the target date includes but is not limited to working days and holidays. The weather data of 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 and rainy state parameters, etc.

[0051] Step 120: Generate first predicted electricity data and second predicted electricity data based on the target date, weather data and 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.

[0052] 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 (working days / holidays) and different weather conditions and the power data required for the load device to work.

[0053] Here, the power module is used to convert external energy into the electrical energy required by the device and output it. External energy includes but is not limited to: renewable energy and external power supply. The power module includes: renewable energy module and other power modules, and renewable energy includes but is not limited to: solar energy, photovoltaic, hydropower, wind energy, biomass energy, wave energy, tidal energy, ocean temperature difference energy, geothermal energy, etc. Renewable energy has the advantages of environmental protection and energy saving, and has unlimited potential. In addition, other power modules can supply power to load devices based on external power supplies.

[0054] 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 on the target date.

[0055] 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 including: 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.

[0056] Here, the energy storage device is a device for storing and releasing energy, and the energy storage device can be an energy storage battery. The energy storage device is charged based on renewable energy and / or other power modules, and the charging strategy corresponding to the target date can be generated 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.

[0057] 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 achieving precise control of the energy storage of the energy storage device, thereby improving the utilization efficiency of the photovoltaic storage system.

[0058] In some embodiments, the method comprises:

[0059] 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, where 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;

[0060] The first set time interval is a time interval corresponding to the rated value of the operating power of the renewable energy module.

[0061] Here, when it is determined that the target date has been reached and the charging strategy is the first charging strategy, it indicates that the conversion efficiency of the renewable energy module is high, and it can both charge the energy storage device and supply power to the load device. In response to the first strategy, the first power supply indication information is generated, and the first power supply indication information is used to instruct the load device to start up within the first set time interval and to instruct the renewable energy module to supply power to the load device within the first set time interval.

[0062] Here, the first set time interval is the time interval of the rated operating power of the renewable energy module, and the rated operating power may be the maximum value of the operating power of the renewable energy module. For the renewable energy module, in the time interval of its operation, the operating power corresponding to different time intervals is different. For example, taking photovoltaic power generation as an example, its working principle is to convert light energy into electrical energy through the irradiation of sunlight, which is also called the photovoltaic effect. Generally speaking, the noon time period is the time interval corresponding to the rated operating power of the renewable energy, because the sunlight is the strongest during this period.

[0063] 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 rated operating power of the renewable energy module, thereby realizing energy scheduling control of the load device and improving the utilization efficiency of the renewable energy module.

[0064] In some embodiments, the method comprises:

[0065] 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 in the second set time interval;

[0066] The second set time interval is a time interval corresponding to when the renewable energy module is in an operating state.

[0067] 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 in the second set time interval.

[0068] Here, the second set time interval is the time interval corresponding to when the renewable energy module is in operation. The corresponding operation time is different for different renewable energy modules. For photovoltaic power generation, its working principle is to convert light energy into electrical energy through the irradiation of sunlight, also known as the photovoltaic effect, so its corresponding operation time interval is 7 am to 7 pm. Generally speaking, around noon is the best time period for photovoltaic power generation, and the operating power during this time period is also the largest.

[0069] 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 realizing energy scheduling of the energy storage device and improving the utilization efficiency of the renewable energy power supply device.

[0070] 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:

[0071] Determining that the target date is reached and the charging strategy is the second charging strategy, in response to the current time entering the third set time interval, obtaining a charge state detection value of the energy storage device;

[0072] 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 power value that can be charged by the energy storage device;

[0073] Determining that the predicted state of charge value is less than or equal to the 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;

[0074] 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 in a third set interval.

[0075] Here, other power modules also include: a grid power module, which is used to provide power to the outside based on the grid. When the target date is determined and the charging strategy is the second charging strategy, the energy storage device should be charged based on the renewable energy module and the grid power module. The load equipment includes: a water heater, which is a heat storage device that can store heat in advance to meet the user's hot water demand.

[0076] 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 (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.

[0077] 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 power value of the energy storage device that can be charged. Exemplarily, 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 power value of the energy storage device that can be charged is Q3, the predicted state of charge value can be calculated based on the formula [Q3*SOC_1-(Q2-Q1)] / Q3, thereby determining the predicted state of charge value of the energy storage device.

[0078] 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 on the target date, the predicted state of charge value of the energy storage device is not sufficient to support the operation of the energy storage device, and the energy storage device needs to be charged as soon as possible to ensure the normal operation of the energy storage device.

[0079] 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, the target charging state of charge value of the energy storage device can be generated. Exemplarily, 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, the 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.

[0080] 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, and 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 improving the user experience.

[0081] In some embodiments, the method further comprises:

[0082] 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;

[0083] 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;

[0084] The first set time is a time interval corresponding to the rated value of the 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.

[0085] Here, it is determined that the predicted state of charge value is greater than a set threshold value, 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.

[0086] 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.

[0087] 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.

[0088] In some embodiments, the method further comprises:

[0089] 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 supply module to supply power to the water heater within a third set time interval;

[0090] The third set time interval includes: a low electricity price time interval of the grid power module.

[0091] 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 a 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 power 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.

[0092] Here, when it is determined that the state of charge value is less than or equal to the set threshold, the second power supply indication information is generated, and 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. In this way, supplying power to the water heater based on the grid power supply module during the low electricity price time interval can not only realize the timely heat storage of the water heater, but also reduce the electricity cost of the water heater.

[0093] 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:

[0094] Determine whether the first predicted power data is greater than the second predicted power data, and if so, generate a first charging strategy;

[0095] If not, a second charging strategy is generated.

[0096] 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 the renewable energy module can convert 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, a first strategy for charging the energy storage device based on the renewable energy module is generated at this time. 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 cannot sufficiently supply power to the load device. At this time, only based on the renewable energy module, the power demand of the load device and the energy storage device cannot be met. Therefore, a second charging strategy for charging the energy storage device based on the renewable energy module and other power modules is generated.

[0097] 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.

[0098] Below, the embodiment of the present application is described in detail with reference to an application example.

[0099] In this application example, the renewable energy module is a photovoltaic power generation module, the energy storage device is an energy storage battery, and the load device includes a water heater. The predicted photovoltaic power generation (i.e., the aforementioned first predicted power data) is represented by Q1, the predicted load power consumption (i.e., the aforementioned second predicted power data) is represented by Q2, the saturated power of the energy storage battery (i.e., the rated power value of the aforementioned energy storage device that can be charged) is represented by Q3, and the battery backup SOC (i.e., the target charging state value of the aforementioned energy storage device) is represented by SOC_goal.

[0100] In this application example, the power grid uses a peak-valley electricity price mechanism: generally, the early morning is the low-valley electricity price period, such as the low-valley electricity price period in Guangzhou from 0:00 to 8:00. The first set time interval is the time interval corresponding to the maximum operating power of the photovoltaic power generation module. The photovoltaic power generation calculation time period (i.e., the aforementioned second set time interval) 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.

[0101] The power consumption calculation time period of the load device, i.e., the power consumption time interval t2 of the load device, is [τvalley_1, τvalley_0]. The third set time interval is the low valley price time interval t3 of the power grid = [τvalley_0, τvalley_1], where τvalley_0 is the valley price start time and τvalley_1 is the valley price end time. The battery SOC at the valley price start time of SOC_valley (i.e., the detected state of charge value of the energy storage device mentioned above).

[0102] The basic operation mode of the energy storage battery in the existing solution is that when the photovoltaic power generation power exceeds the load power consumption, the photovoltaic surplus power is stored. When the photovoltaic power generation is insufficient, the energy storage battery is discharged to the load for use. In addition, the energy storage battery has a backup power function, which can charge the battery with sufficient power before abnormal weather occurs. The existing solution does not comprehensively consider the battery and load scheduling strategy in combination with photovoltaic power supply, load power consumption, and electricity price mechanism. The household photovoltaic storage system has not maximized its value and has low economic benefits. At present, the battery backup power mode is manually set by the user, which is not intelligent enough and is mostly used for advance backup power in abnormal weather, and the application frequency is low.

[0103] This application example is based on the predicted photovoltaic power generation and load power consumption of the household photovoltaic storage system on different dates (weekdays / holidays) and different weather conditions, and combines electricity prices to control battery power storage and water heater heat storage to improve the utilization efficiency of the photovoltaic storage system.

[0104] First, this application example provides a structural diagram of a home solar storage system. Figure 2 As shown, specifically including:

[0105] 1. Power generation components. The power generation components include photovoltaic power generation modules, which are used to convert photovoltaics into electrical energy for output.

[0106] 2. Energy storage unit. The energy storage unit is connected to the power generation component and includes an energy storage battery for storing the electrical energy output by the power generation component.

[0107] 3. Inverter. The inverter is connected to the energy storage unit and the power generation component, and is used to invert 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 alternating current required by the device.

[0108] 4. Distribution cabinet. The distribution cabinet is used to distribute electric energy. By distributing and controlling the power load, the distribution and protection of electric energy are achieved, ensuring the normal operation of each electrical appliance and improving the reliability and safety of the power grid.

[0109] 5. Water heater. The power distribution cabinet is connected to the load equipment, which includes the water heater. As a heat storage device, the water heater can provide hot water for the family in a timely manner, which is convenient and fast and can meet the hot water needs of family members at any time.

[0110] 6. Power sensor. The power sensor can detect the power of the photovoltaic power generation module and the power of the load equipment.

[0111] 7. Power grid (i.e. the aforementioned power grid power module).

[0112] Based on the above solar energy storage system, this application example provides an energy scheduling solution based on power supply and demand forecast and electricity price, such as Figure 3 As shown, the specific implementation steps are as follows:

[0113] Step 301: Based on the historical operation data of the household 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.

[0114] 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 in 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 in different weather conditions.

[0115] In this way, the present application can control battery power storage and water heater heat storage based on the predicted photovoltaic power generation and load power consumption of the household photovoltaic storage system on different dates (weekdays / holidays) and in different weather conditions and combined with electricity prices.

[0116] Step 302: Predict the photovoltaic power generation Q1 and load power consumption Q2 for the day based on the date and weather of the day.

[0117] In practical applications, weather data of 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;

[0118] 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.

[0119] Among them, the calculation time period of photovoltaic power generation Q1 is t1 = [τpv_0, τpv_1], τpv_0 is the start time of photovoltaic power generation, and τpv_1 is the end time of photovoltaic power generation. The calculation time period of load power consumption Q2 is t2 = [τvalley_1, τvalley_0], τvalley_0 is the start time of valley price, and τvalley_1 is the end time of valley price. If the low-valley electricity price period in Guangzhou is 0-8, then the calculation time period of load power consumption is from 8 am to 0 am.

[0120] like Figure 4 As shown, Figure 4 It is a schematic diagram of the power curve of photovoltaic-load. For photovoltaic power generation, the start and end time of photovoltaic power generation is τpv_0 to τpv_1. In the figure, τpv_0 is 7 points and τpv_1 is 19 points. For load equipment, its power generation can be calculated at [τvalley_1, τvalley_0]. τvalley_1 is 8 points and τvalley_0 is 24 points, i.e. 0 points. Generally speaking, the low valley price time interval of the power grid is t3 = [τvalley_0, τvalley_1].

[0121] Step 303: Determine whether Q1≥Q2, if not, execute step 304; if yes, execute step 305.

[0122] 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.

[0123] 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 power generation of photovoltaic power generation is sufficient, and the energy storage battery can be charged (that is, 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, and the power generation of the photovoltaic power generation module is insufficient, then the grid power module, that is, the grid and the photovoltaic power generation module are required to charge the energy storage battery (that is, the second strategy mentioned above), and step 305 is executed.

[0124] Step 304: Determine whether [Q3*SOC_valley-(Q2-Q1)] / Q3≥20%. If so, execute step 305; if not, execute step 306.

[0125] 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 charge state detection value of the energy storage device is obtained; and based on the first predicted power data, the second predicted power data, the charge state detection value and the rated power value of the energy storage device that can be charged, the predicted charge state value of the energy storage device is determined.

[0126] It should be noted that if Q1<Q2, it is necessary to determine again whether the battery power can support the daytime load operation. In response to the current time entering the low electricity price time interval, 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 aforementioned energy storage device) is determined based on the formula [Q3*SOC_valley-(Q2-Q1)] / Q3, where 20% is the set threshold, i.e. the minimum state of charge value of the energy storage device.

[0127] 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.

[0128] Here, [Q3*SOC_valley-(Q2-Q1)] / Q3 is compared with 20% to determine whether the energy storage battery power can support the daytime load operation. If so, it indicates that the current state of charge value of the energy storage battery can still support its daytime operation, and then step 305 is executed; if not, it indicates that the current predicted state of charge value of the energy storage battery is not enough to support the daytime operation of the energy storage battery, and the energy storage device needs to be charged as soon as possible to ensure the normal operation of the energy storage battery, and then step 306 is executed.

[0129] Step 305: Generate first charging indication information and first power supply indication information.

[0130] In actual applications, when it is determined that the target date has been reached and the charging strategy is the first charging strategy, the first power supply indication information is generated, and the first power supply indication information is used to instruct the load device to start up within the 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.

[0131] Exemplarily, if Q1≥Q2, first power supply indication information is generated, and the first power supply indication information 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.

[0132] 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 operation.

[0133] Exemplarily, 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, generate first charging indication information, indicating that the energy storage device is charged based on the photovoltaic power generation module within the second set time interval, i.e., t1=[τpv_0, τpv_1].

[0134] In addition, if [Q3*SOC_valley-(Q2-Q1)] / Q3≥20%, the energy storage battery does not need to back up power during the off-peak electricity price period, and can store photovoltaic power during the day, that is, generate the first charging indication information, indicating that the energy storage device is charged based on the photovoltaic power generation module within the second set time interval, i.e., t1=[τpv_0, τpv_1].

[0135] Step 306: Generate second charging indication information and second power supply indication information.

[0136] In actual applications, 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 charge state 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.

[0137] If [Q3*SOC_valley-(Q2-Q1)] / Q3<20%, ​​the energy storage battery will start to back up 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.

[0138] 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 (ie, the charge state detection value of the aforementioned energy storage device).

[0139] Based on SOC_goal, second charging instruction information is generated, and the second charging instruction information is used to instruct the power grid (ie, the aforementioned power grid power module) to charge the energy storage battery to SOC_goal during the low electricity price time period.

[0140] 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%.

[0141] 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.

[0142] It should be noted that if [Q3*SOC_valley-(Q2-Q1)] / Q3<20%, ​​the 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.

[0143] In this way, this application example reduces the cost of hot water by using photovoltaic power for free heating or using off-peak electricity prices to heat water heaters. At the same time, based on the predicted photovoltaic power generation and load power consumption and combined with electricity prices, the battery storage is controlled, greatly improving the utilization efficiency of the photovoltaic storage system.

[0144] like Figure 5 As shown, 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 used to acquire weather data of the target date; the first generation module 520 is used to generate first predicted electricity data and second predicted electricity data based on the target date, weather data and 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 work on the target date; the second generation module 530 is also used to generate a charging strategy for the target date based on the first predicted electricity data and the second predicted electricity data, the charging strategy including: 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.

[0145] In some embodiments, the energy management device further includes a determination module 540, which is used to determine that when the target date is reached 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;

[0146] The first set time interval is a time interval corresponding to the rated value of the operating power of the renewable energy module.

[0147] In some embodiments, the determination module 540 is further used to determine that the target date has arrived and the charging strategy is the first charging strategy, and to generate first charging indication information, where 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;

[0148] The second set time interval is a time interval corresponding to when the renewable energy module is in an operating state.

[0149] In some embodiments, other power modules also include: a power grid power module, the load device includes a water heater, the determination module 540 is also used 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 used to respond to the current time entering the third set time interval to obtain the charge state detection value of the energy storage device; the determination module 540 is also used 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 power value of the energy storage device that can be charged; 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 lower limit of the charge state of the energy storage device, generate a target charging charge state value of the energy storage device; the energy management device also includes a third generation module 550, which is used to generate a second charging indication information based on the target charging charge state value, and the second charging indication information is used to instruct the power grid module to charge the energy storage device to the target charging charge state value in the third set interval.

[0150] In some embodiments, the determination module 540 is further configured to determine that the predicted state of charge value is greater than a set threshold, and then generate first power supply indication information and / or first charging indication information;

[0151] 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;

[0152] The first set time is a time interval corresponding to the rated value of the 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.

[0153] In some embodiments, the determination module 540 is further used to determine that the predicted state of charge value is less than or equal to a set threshold, and then generate second power supply indication information, the second power supply indication information is used to instruct the grid power supply module to supply power to the water heater within a third set time interval;

[0154] The third set time interval includes: a low electricity price time interval of the grid power module.

[0155] In some embodiments, the second generating module 530 is further used 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;

[0156] If not, a second charging strategy is generated.

[0157] 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 function.

[0158] It should be noted that: the energy management device provided in the above embodiment only uses the division of the above program modules as an example when controlling the energy management system. In actual applications, the above 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 processing described above. In addition, the energy management device provided in the above embodiment and the control method embodiment of the energy management system belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0159] 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 also provides an electronic device. Figure 6 Only an exemplary structure of the electronic device is shown, not all structures, and it can be implemented as needed. Figure 6 Partial or complete structure shown.

[0160] like Figure 6 As shown, the electronic device 600 provided in the 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 through a bus system 605. It can be understood that the bus system 605 is used to realize the connection and communication between these components. In addition to the 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 bus system 605 .

[0161] 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.

[0162] 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.

[0163] The control method of the energy management and control system disclosed in the embodiment of the present application can be applied to the processor 601, or implemented by the processor 601. The processor 601 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the control method of the energy storage management and control system can be completed by the hardware integrated logic circuit or software instructions in the processor 601. The above-mentioned processor 601 can be a general-purpose processor, a digital signal processor (DSP, DigitalSignal Processor), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The processor 601 can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiment of the present application. The general-purpose processor can be a microprocessor or any conventional processor, etc. In combination with the steps of the method disclosed in the embodiment of the present application, it can be directly embodied as a hardware decoding processor to execute, or it can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium, which is located in the memory 602. The processor 601 reads the information in the memory 602 and completes the steps of the control method of the energy management and control system provided in the embodiment of the present application in combination with its hardware.

[0164] In an exemplary embodiment, the electronic device may be implemented by one or more application specific integrated circuits (ASIC), DSP, programmable logic device (PLD), complex programmable logic device (CPLD), field programmable gate array (FPGA), general processor, controller, microcontroller (MCU), microprocessor, or other electronic components to execute the aforementioned method.

[0165] It can be understood that the memory 602 can be a volatile memory or a non-volatile memory, and can also include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disk, or a compact disc read-only memory (CD-ROM); the magnetic surface memory can be a disk memory or a tape memory. The volatile memory can be a 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, SyncLink Dynamic Random Access Memory), and direct RAM bus random access memory (DRRAM, Direct Rambus Random Access Memory).The memories described in the embodiments of the present application are intended to include, but are not limited to, these and any other suitable types of memories.

[0166] 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, a memory 602 storing a computer program, and 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, a PROM, an EPROM, an EEPROM, a Flash Memory, a magnetic surface memory, an optical disk, or a CD-ROM.

[0167] 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.

[0168] In addition, the technical solutions described in the embodiments of the present application can be combined arbitrarily without conflict.

[0169] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A control method for an energy management and control system, characterized in that: The method further comprises: 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, characterized in that 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, characterized in that 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, characterized in that: 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 indication information is generated, where the second charging indication 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.

5. The method according to claim 4, characterized in that 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, characterized in that 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, characterized in that 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, characterized in that: The energy management device comprises: The acquisition module is used to obtain the weather data of the target date; A first generating module is used 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 the power data that the renewable energy module can convert on the target date, and the second predicted power data is the power data required for the load device to work on the target date; The second generating module is used to generate a charging strategy for the target date based on the first predicted power data and the second predicted power 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 supply modules.

9. An electronic device, characterized in that: include: A processor and a memory for storing a computer program that can be executed on the processor, wherein: The processor is used 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, characterized in that: 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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