Flexible Load Scheduling Method and System Based on Energy Management System

Abstract

The present invention provides a flexible load scheduling method and system based on an energy management system, which relates to the field of power technology. The specific implementation scheme is as follows: Based on the predicted photovoltaic power generation curve and the predicted non-adjustable load curve in the first time period of the power system, with the control temperature of the temperature-controlled load and the operation time of the adjustable time load in the power system as independent variables, and the average value of the net power fluctuation in the first time period of the power system as the dependent variable, a first objective function is constructed; Based on the constraint conditions of the control temperature of the temperature-controlled load and the constraint conditions of the operation time of the adjustable time load, the minimum value of the first objective function is solved to obtain the target temperature curve of the temperature-controlled load in the first time period and the target operation time curve of the adjustable time load in the first time period; Based on the target temperature curve and the target operation time curve, the temperature-controlled load and the adjustable time load in the power system are scheduled respectively in the first time period.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power, and in particular, to a flexible load scheduling method and system based on an energy management system. Background Art

[0002] Today, with the reduction of fossil fuel reserves, attention has been focused on investing in renewable energy. In order to address the poor durability of aging infrastructure and the power system stability issues brought about by intermittent renewable energy, there is an urgent need to reduce electricity demand. If properly planned, high renewable energy penetration can be achieved while maintaining a certain voltage quality. To this end, in the field of residential energy, various tools have been developed to simulate household appliances (such as refrigerators, air conditioners, light bulbs, and televisions) to help researchers investigate the impact of different energy-saving technologies on overall energy consumption. Smart home appliances, smart meters, and Internet of Things devices are popular topics in smart grid research. Currently, the main obstacle to the adoption of smart systems is cost. With ongoing research and development efforts, more cost-effective home energy management systems are expected to emerge soon.

[0003] The first step in an energy management system is the monitoring of power generation units and loads. Currently, advanced monitoring systems include solar photovoltaic power generation, wind power generation, and building load grouping data recording. These systems typically use single-board computers as data acquisition and processing units, wireless modules for data transmission, and voltage / current sensors connected to microcontrollers for data sampling. To achieve higher energy system efficiency, environmental sensors including humidity sensors, temperature sensors, and light sensors have been integrated into the energy management system. Some energy management systems have added Internet of Things functions, allowing users to remotely control loads and view recorded data. The network visualization of energy data also improves management transparency, which is crucial when multiple stakeholders are involved.

[0004] However, frequent load scheduling can cause trouble to users, and under a large number of operation requirements, the control strategies and information transmission methods of the energy management system have also been severely tested. Existing research has focused more on the theoretical exploration of energy management in multiple energy usage scenarios, but lacks key practical, general, and substantial method research. There are generally problems such as a long development cycle for control strategies and high system upgrade costs. There is an urgent need to develop a low-cost embedded energy management system controller that has both complete theoretical and technical support and high practicality. Summary of the Invention

[0005] The present invention provides a flexible load scheduling method, device, system, and storage medium based on an energy management system, which can solve at least one of the above technical problems.

[0006] According to one aspect of the present invention, there is provided a flexible load scheduling method based on an energy management system, which is applied to a controller of the energy management system and includes:

[0007] Based on the predicted photovoltaic power generation curve and the predicted non-adjustable load curve of the power system in the first time period, taking the control temperature of the temperature-controlled load and the operation time of the adjustable-time load in the power system as independent variables, and taking the average value of the net power fluctuation of the power system in the first time period as the dependent variable, a first objective function is constructed;

[0008] Based on the control temperature constraint condition of the temperature-controlled load and the constraint condition of the operation time of the adjustable-time load, the minimum value of the first objective function is solved to obtain the target temperature curve of the temperature-controlled load in the first time period and the target operation time curve of the adjustable-time load in the first time period;

[0009] Based on the target temperature curve and the target operation time curve, the temperature-controlled load and the adjustable-time load in the power system are respectively scheduled in the first time period.

[0010] According to another aspect of the present invention, there is provided a flexible load scheduling device based on an energy management system, which is applied to a controller of the energy management system and includes:

[0011] A first function construction module, configured to construct a first objective function based on the predicted photovoltaic power generation curve and the predicted non-adjustable load curve of the power system in the first time period, taking the control temperature of the temperature-controlled load and the operation time of the adjustable-time load in the power system as independent variables, and taking the average value of the net power fluctuation of the power system in the first time period as the dependent variable;

[0012] A first function solving module, configured to solve the minimum value of the first objective function based on the control temperature constraint condition of the temperature-controlled load and the constraint condition of the operation time of the adjustable-time load, to obtain the target temperature curve of the temperature-controlled load in the first time period and the target operation time curve of the adjustable-time load in the first time period;

[0013] A flexible load scheduling module, configured to schedule the temperature-controlled load and the adjustable-time load in the power system respectively in the first time period based on the target temperature curve and the target operation time curve.

[0014] According to another aspect of the present invention, there is provided an electronic device, including:

[0015] At least one processor; and

[0016] A memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute any of the flexible load scheduling methods based on an energy management system in the embodiments of the present invention.

[0018] According to another aspect of the present invention, there is provided a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to execute any of the flexible load scheduling methods based on an energy management system in the embodiments of the present invention.

[0019] Adopting the technical solution of the present invention, based on the predicted photovoltaic power generation curve and the predicted non-adjustable load curve in the first time period of the power system, taking the control temperature of the temperature-controlled load and the operation time of the adjustable-time load in the power system as independent variables, and taking the average value of the net power fluctuation in the first time period of the power system as the dependent variable, a first objective function is constructed; based on the constraint conditions of the control temperature of the temperature-controlled load and the constraint conditions of the operation time of the adjustable-time load, the minimum value of the first objective function is solved to obtain the target temperature curve of the temperature-controlled load in the first time period and the target operation time curve of the adjustable-time load in the first time period; based on the target temperature curve and the target operation time curve, the temperature-controlled load and the adjustable-time load in the power system are respectively scheduled in the first time period. In this way, the temperature-controlled load and the adjustable-time load that can minimize the net power fluctuation of the power system can be used to schedule the temperature-controlled load and the adjustable-time load of the power system, reducing the power grid power fluctuation.

[0020] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. Description of the Drawings

[0021] The drawings are used to better understand the solution and do not constitute a limitation to the present invention. Among them:

[0022] Figure 1 is a flowchart of a flexible load scheduling method based on an energy management system according to an embodiment of the present invention;

[0023] Figure 2 is a structural block diagram of an energy management system according to an embodiment of the present invention;

[0024] Figure 3 is a flowchart of a slave controller of an energy management system according to an embodiment of the present invention;

[0025] Figure 4It is a structural block diagram of a flexible load scheduling device based on an energy management system according to an embodiment of the present invention;

[0026] Figure 5 It is a block diagram of an electronic device for implementing the method according to an embodiment of the present invention. Detailed implementation manners

[0027] The following describes exemplary embodiments of the present invention with reference to the accompanying drawings. Various details of the embodiments of the present invention are included to facilitate understanding, and they should be considered merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope of the present invention. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0028] Figure 1 It is a flowchart of a flexible load scheduling method based on an energy management system according to an embodiment of the present invention.

[0029] As Figure 1 shown, the flexible load scheduling method based on the energy management system is applied to a controller of the energy management system and may include:

[0030] S110, based on the predicted photovoltaic power generation curve and the predicted non-adjustable load curve in the first time period of the power system, taking the control temperature of the temperature-controlled load and the operation time of the adjustable time load in the power system as independent variables, and taking the average value of the net power fluctuation in the first time period of the power system as the dependent variable, to construct a first objective function;

[0031] S120, based on the constraint conditions of the control temperature of the temperature-controlled load and the constraint conditions of the operation time of the adjustable time load, solve the minimum value of the first objective function to obtain the target temperature curve of the temperature-controlled load in the first time period and the target operation time curve of the adjustable time load in the first time period;

[0032] S130, based on the target temperature curve and the target operation time curve, schedule the temperature-controlled load and the adjustable time load in the power system respectively in the first time period.

[0033] It can be understood that the flexible load includes a temperature-controlled load and an adjustable time load.

[0034] Exemplarily, the first time period can be a future day or several days, or a week, etc.

[0035] Exemplarily, the predicted photovoltaic power generation curve is used to describe the photovoltaic power generation changing with time. The predicted non-adjustable load curve is used to describe the non-adjustable load changing with time.

[0036] Exemplarily, for the predicted photovoltaic power generation curve of the power system within the first time period, it can be obtained by calculating the environmental temperature information and solar radiation in the first time period according to the photovoltaic power generation prediction model.

[0037] Exemplarily, the photovoltaic power generation prediction model can adopt the following formula:

[0038] ;

[0039] Wherein, represents the photovoltaic power generation power at time t, represents the total irradiance of sunlight incident on the photovoltaic array plane at time t, represents the area of the photovoltaic array plane, represents the efficiency of the photovoltaic module under standard test conditions, represents the loss of photovoltaic efficiency caused by temperature change at time t, represents the conversion efficiency of the inverter.

[0040] Exemplarily, the temperature-controlled load can include air conditioners and electric water heaters, etc. Air conditioners and electric water heaters usually have on / off states, and the power consumption is constant in the on and off states. The air conditioner has different power consumptions in the on and off states according to the set temperature and the environmental temperature.

[0041] Exemplarily, the adjustable time load has a fixed operation cycle and can adjust the start time.

[0042] Exemplarily, the control temperature constraint conditions of the temperature-controlled load can include that the control temperature is less than the upper temperature limit value and greater than the lower temperature limit value.

[0043] Exemplarily, the constraint conditions for the operation time of the adjustable time load include the optional operation time of the adjustable time load.

[0044] Exemplarily, the particle swarm algorithm or the genetic algorithm can be used to solve the minimum value of the first objective function to obtain the target temperature curve of the temperature-controlled load within the first time period and the target operation time curve of the adjustable time load within the first time period.

[0045] Exemplarily, based on the target temperature curve, the temperature of the temperature-controlled load in the power system is set within the first time period, and based on the target operation time curve, the operation time of the adjustable time load in the power system is set within the first time period.

[0046] Based on the above embodiments, on the basis of the predicted photovoltaic power generation curve and the predicted non-adjustable load curve of the power system within the first time period, taking the control temperature of the temperature-controlled load and the operation time of the adjustable time load in the power system as independent variables, and taking the average value of the net power fluctuation of the power system within the first time period as the dependent variable, a first objective function is constructed; based on the control temperature constraint condition of the temperature-controlled load and the constraint condition of the operation time of the adjustable time load, the minimum value of the first objective function is solved to obtain the target temperature curve of the temperature-controlled load within the first time period and the target operation time curve of the adjustable time load within the first time period; based on the target temperature curve and the target operation time curve, the temperature-controlled load and the adjustable time load in the power system are scheduled respectively within the first time period. In this way, the temperature-controlled load and the adjustable time load that can minimize the net power fluctuation of the power system can be used to schedule the temperature-controlled load and the adjustable time load of the power system, reducing the power grid power fluctuation.

[0047] In one embodiment, the first objective function can be:

[0048] ;

[0049] ;

[0050] ;

[0051] Wherein, represents the first objective function, represents the net power of the power system at time t, represents the net power of the power system at time t - 1, represents the photovoltaic power generation of the power system at time t, represents the load power of the power system at time t, represents the load power of the temperature-controlled load at time t, represents the load power of the non-adjustable load of the power system at time t, represents the load power of the adjustable time load at time t, represents the first time period.

[0052] Exemplarily, the temperature-controlled load may include an air-conditioning load and a water heater load .

[0053] Exemplarily, the model of the air-conditioning load can be as follows:

[0054] ;

[0055] ;

[0056] ;

[0057] ;

[0058] Among them, represents the air conditioner input power at time t of the air conditioner, represents the working state of the air conditioner. When the value is 1, it means it is turned on, and when the value is 0, it means it is turned off. represents the power when the air conditioner is turned on, represents the power when the air conditioner is turned off, represents the set temperature of the air conditioner, represents the indoor temperature of the environment where the air conditioner is located at time t, represents the thermostat dead band of the air conditioner, represents the energy input ratio of the air conditioner at time t, which depends on the rated energy input ratio of the air conditioner and the outdoor temperature .

[0059] Exemplarily, the model of the electric water heater load can be as follows:

[0060] ;

[0061] ;

[0062] Among them, represents the switch state of the electric water heater at time t, represents the switch state of the electric water heater at time t, where when the value is 1, it means it is turned on, and when the value is 0, it means it is turned off. represents the set temperature of the electric water heater, represents the thermostat dead band of the water heater, represents the thermal efficiency constant of the electric water heater, represents the indoor temperature of the environment where the water heater is located at time t. represents the power of the electric heater.

[0063] Exemplarily, for different adjustable time loads, their powers within the same time window can be different or the same. For the same adjustable time load, the powers in different time windows can be the same or different.

[0064] According to the above embodiments, a first objective function can be constructed with the control temperature of the temperature control load and the operation time of the adjustable time load in the power system as independent variables and the average value of the net power fluctuation in the first time period of the power system as the dependent variable.

[0065] In one embodiment, based on the control temperature constraint conditions of the temperature control load and the constraint conditions of the operation time of the adjustable time load, the minimum value of the first objective function is solved to obtain the target temperature curve of the temperature control load in the first time period and the target operation time curve of the adjustable time load in the first time period, including: determining an initialized first population, where the first population includes a plurality of first individuals, and each first individual corresponds to the temperature curve of the temperature control load in the first time period and the operation time curve of the adjustable time load in the first time period; starting from the initialized first population, performing the following first iterative operation: based on the first objective function, calculating the average value of the net power fluctuations corresponding to each first individual in the first time period in the current iteration of the first population; in the case where the maximum value among the average values of the net power fluctuations corresponding to each first individual in the first time period is greater than a preset first net power fluctuation threshold, performing crossover and mutation on the first population to obtain the first population for the next iteration, and continuing to perform the first iterative operation; in the case where the maximum value among the average values of the net power fluctuations corresponding to each first individual in the first time period is less than the first net power fluctuation threshold, stopping the execution of the first iterative operation, determining the target first individual based on the first individual corresponding to the minimum value among the average values of the net power fluctuations corresponding to each first individual in the first time period, and based on the temperature curve of the temperature control load in the first time period and the operation time curve of the adjustable time load in the first time period corresponding to the target first individual, respectively determining the target temperature curve of the temperature control load in the first time period and the target operation time curve of the adjustable time load in the first time period.

[0066] Exemplarily, each first individual in the first population can be subjected to crossover and mutation with a preset crossover and mutation probability to obtain a new first population for the next iteration.

[0067] Exemplarily, in each iteration, each first individual in the first population needs to meet the following conditions: the target temperature curve of the temperature control load in the first time period needs to meet the control temperature constraint conditions of the temperature control load, and the target operation time curve of the adjustable time load in the first time period needs to meet the constraint conditions of the operation time of the adjustable time load.

[0068] According to the above embodiment, the minimum value of the first objective function can be quickly and accurately solved, and the target temperature curve of the temperature control load in the first time period and the target operation time curve of the adjustable time load in the first time period can be output.

[0069] In one embodiment, the above method may further include: determining a temperature control load prediction curve and an adjustable time load prediction curve of the power system in the first time period based on the target temperature curve of the temperature control load and the target operation time curve of the adjustable time load in the first time period respectively; determining a net power prediction curve of the power system in the first time period based on the photovoltaic power generation prediction curve, the non-adjustable load prediction curve, the temperature control load prediction curve and the adjustable time load prediction curve; and adjusting the charging and discharging of the battery in the power system in the first time period based on the net power prediction curve.

[0070] Exemplarily, inputting the target temperature curve into the above model of the temperature control load, the temperature control load prediction curve output by the model can be obtained.

[0071] Exemplarily, based on each operation time in the target operation time curve, the corresponding load power is searched in the model of the adjustable time load, and thus, the adjustable time load prediction curve can be obtained.

[0072] Exemplarily, subtracting the non-adjustable load prediction curve, the temperature control load prediction curve and the adjustable time load prediction curve from the photovoltaic power generation prediction curve, the net power prediction curve of the power system in the first time period can be obtained.

[0073] Exemplarily, the battery is charged during the time period when the net power in the net power prediction curve is larger, and the battery is discharged during the time period when the net power in the net power prediction curve is smaller.

[0074] Exemplarily, common types of energy storage batteries include lead-acid batteries and lithium batteries, etc. These batteries usually have three working states: charging, discharging and standby. The relationship between the stored power of the battery and its charging and discharging power can be described by the following formula:

[0075] ;

[0076] where, S b (t + 1) represents the capacity of the battery at the (t + 1)-th moment, and S b (t) represents the capacity of the battery at the t-th moment; P b,c (t) and P b,d (t) respectively represent the charging power and discharging power of the battery at the t-th moment; σ b is the loss constant of the battery; η b,c and η b,d respectively represent the charging and discharging efficiencies of the battery; Δt is the unit time step.

[0077] The operation of the battery includes the following four constraints: state of charge constraint, power upper and lower limit constraint, operation state constraint and ramp rate constraint.

[0078] ;

[0079] ;

[0080] ;

[0081] ;

[0082] ;

[0083] wherein, represents the state of charge of the battery at time t; and are the upper and lower limits of the state of charge of the battery, respectively; , , and are the upper and lower limits of the charge and discharge power of the battery, respectively; and represent the charging and discharging states of the battery at time t, taking 0 or 1, taking 1 means charging, taking 1 means discharging; represents the operating state of the battery, that is, it restricts that the battery cannot charge and discharge simultaneously; represents the upper limit of the battery ramp constraint. represents the transmission power of the battery at time t, represents the transmission power of the battery at time t + 1.

[0084] The SoC of the battery at time t can be calculated by the following formula:

[0085] ;

[0086] wherein, represents the charge and discharge efficiency of the battery, represents the state of charge of the battery at time t - 1; SoH represents the state of health of the battery. SoH is usually in the range of 0 to 1, reflecting the health level of the battery. The lower SoH is, the higher the degree of battery aging; is the rated capacity of the battery.

[0087] According to the above embodiments, by charging and discharging the battery, the net power curve is further smoothed, the power fluctuation amplitude of the power system is reduced, and the operation of the power system is optimized.

[0088] In one embodiment, based on the net power prediction curve, the charge and discharge of the battery in the power system are adjusted within the first time period, including: determining the peak net power period and the valley net power period based on the net power prediction curve; constructing a second objective function with the charging power during the peak net power period and the discharging power during the valley net power period as independent variables and the average value of the net power fluctuation after charge and discharge adjustment as the dependent variable; solving the minimum value of the second objective function to obtain the target charging power during the peak net power period and the target discharging power during the valley net power period; and adjusting the charge and discharge of the battery in the power system within the first time period based on the target charging power during the peak net power period and the target discharging power during the valley net power period.

[0089] Exemplarily, for a continuous interval where the net power is greater than a preset first threshold, it is regarded as the peak net power period. For a continuous interval where the net power is less than a preset second threshold, it is regarded as the valley net power period.

[0090] Exemplarily, the second objective function is:

[0091] ;

[0092] ;

[0093] where represents the second objective function, represents the net power of the power system after charge and discharge adjustment at time t, represents the net power of the power system after charge and discharge adjustment at time t - 1, represents the charging power or discharging power of the power system at time t.

[0094] Exemplarily, the particle swarm optimization algorithm or the genetic algorithm can be used to solve the minimum value of the second objective function to obtain the target charging power during the peak net power period and the target discharging power during the valley net power period.

[0095] In one embodiment, solving the minimum value of the objective function to obtain the target charging power during the peak net power period and the target discharging power during the low net power period includes: determining an initialized second population, where the second population includes a plurality of second individuals, and the second individuals correspond to the charging power during the peak net power period and the discharging power during the low net power period; starting from the initialized second population, performing the following second iterative operation: based on the second objective function, calculating the average value of the net power fluctuations after charge and discharge adjustment for each second individual in the current iteration of the second population during the first time period; in the case where the maximum value among the average values of the net power fluctuations after charge and discharge adjustment for each second individual during the first time period is greater than a preset second net power fluctuation threshold, performing crossover and mutation on the second population to obtain the second population for the next iteration, and continuing to perform the second iterative operation; in the case where the maximum value among the average values of the net power fluctuations after charge and discharge adjustment for each second individual during the first time period is less than the second net power fluctuation threshold, stopping the execution of the second iterative operation, determining the target second individual based on the second individual corresponding to the minimum value among the average values of the net power fluctuations after charge and discharge adjustment for each second individual, and determining the target charging power during the peak net power period and the target discharging power during the low net power period respectively based on the charging power during the peak net power period and the discharging power during the low net power period corresponding to the target second individual.

[0096] Exemplarily, each second individual in the second population can be subjected to crossover and mutation through a preset crossover and mutation probability to obtain a new second population.

[0097] It can be understood that the charging power during the peak net power period and the discharging power during the low net power period corresponding to each individual in the second population for each iteration respectively need to satisfy the corresponding upper and lower limits of the charging power and the upper and lower limits of the discharging power.

[0098] According to the above embodiment, the minimum value of the second objective function can be calculated quickly and accurately, and the target charging power during the peak net power period and the target discharging power during the low net power period can be output.

[0099] Figure 2 It is a structural block diagram of an energy management system according to an embodiment of the present disclosure.

[0100] As Figure 2 shown, the energy management system can execute any method described in the embodiments of the present disclosure.

[0101] The energy management system may include a load monitoring unit, a photovoltaic-battery monitoring unit, and a data processing unit.

[0102] As the controller of the subordinate energy management system, the load monitoring unit has the function of sampling the load voltage and current, and packs and sends the data to the main energy management system controller. If required by the user, it can also execute the load switching command. For the sampling of the line AC voltage, a 1.2 VA PCB type transformer is used to isolate the current, and then the voltage is reduced to 12Vrms. Since the sampling microcontroller can only operate at a DC voltage of 5V, a bridge rectifier and a resistor divider are used to convert the AC 12 Vrms into the maximum input voltage of the microcontroller or 5 V DC power. For the sampling of the line AC current, a low-cost Hall effect based linear current sensor (ACS712 integrated circuit) is adopted. It can directly provide a 0-5V DC output proportional to the current value. The typical sensitivity of this module is 185 mV / a, and the error is +-1.5%. At the same time, to improve the measurement accuracy, it needs to be calibrated.

[0103] Generally speaking, the load monitoring unit collects AC voltage data through a voltage sensor, collects AC current data through a Hall effect based current sensor, and calculates the power based on the collected voltage and current data. At the same time, according to whether the device is turned on or off, especially the load switching command involving user instructions, the load status is collected. Finally, these data are packaged and sent to the main EMS controller for data processing.

[0104] The working mode of the PV-battery monitoring unit is almost the same as that of the load monitoring unit, except that the voltage and current measurements are carried out on the DC side of the PV and the battery, which can minimize the errors caused by the low efficiency of the PV system components. For example, by measuring the voltage closest to the battery, the input / output energy of the battery can be better estimated. In practice, modern inverters can provide digital data of the PV system, including AC / DC side voltage / current, battery state of charge (SoC), temperature, etc. In actual situations, if the PV system is not connected to the distribution board, additional connection wires are required, or a wireless data transmission module needs to be added to the energy management system.

[0105] The PV-battery monitoring unit directly collects the DC voltage and current of the PV system from the DC side of the PV system, minimizing the errors caused by component inefficiencies. By measuring the voltage on the battery side, the input / output energy of the battery is estimated to obtain the charge and discharge state (SoC) of the battery. At the same time, the battery temperature data is collected, which helps to avoid battery damage caused by excessive temperature during the optimization process. Finally, the power generation power of the PV system is calculated based on the voltage and current data. The above data is transmitted to the data processing unit, and the optimization algorithm optimizes the charge and discharge operation of the battery based on these data.

[0106] The data processing unit mainly manages the power optimization function and also serves as a user interface for collecting load switching commands and other system measurement and status parameters. As an embedded single-board computer, the Raspberry Pi 4 1gb version is used to run the optimization algorithm. The master and slave controllers of the energy manager communicate with each other through a serial communication protocol. Due to changes in environmental conditions or the location of the unit, wireless communication may sometimes be interrupted. Compared with wireless communication, this communication method ensures the stability of operation. This unit obtains the input data of current / voltage from the slave controller of the energy management system, optimizes the charging and discharging of the battery, and then sends back the battery power command in the form of PWM for execution.

[0107] Furthermore, the battery power command acquisition step includes: obtaining current and voltage data from the slave EMS controller; calculating the optimal battery charging and discharging profile using the embedded optimization algorithm; and converting the calculated charging and discharging commands into PWM values.

[0108] The recipient of the battery power command is the slave EMS controller. This controller is responsible for receiving the battery power command in the form of PWM and performing the corresponding charging and discharging operations.

[0109] After the slave EMS controller receives the battery power command, the following actions are performed:

[0110] Charging: When the command indicates charging, the EMS controller controls the charging circuit through a PWM signal to store electrical energy in the battery;

[0111] Discharging: When the command indicates discharging, the EMS controller controls the discharging circuit through a PWM signal to release the stored electrical energy in the battery to the home power grid.

[0112] A computer is used to wirelessly access the master controller of the energy management system for programming and data visualization. It is also possible to choose to use a mobile phone to interact with the master controller of the energy management system.

[0113] The main function of the firmware of the slave controller of the energy manager is to collect and transmit data such as load, battery, photovoltaic electric power, current, and voltage and transmit them to the master controller to run the optimization algorithm. The flowchart of the operation of the slave controller is as Figure 3As shown. At the beginning, set I / O (input / output) and declare variables, sample the current / voltage input, initialize the real-time clock RTC and the electrically erasable programmable read-only memory EEPROM. In the code loop, the sampling is continuous. If the refresh period of the relay setting or the battery power command expires, read the date from the RTC, then copy the corresponding new data from the memory and set the on / off of the relay. Since the relay setting and the battery power are available in a set of 24 hours at a time, the refresh period can be at most 24 hours. If the data storage period expires, read the date, complete the data aggregation and store it in the EEPROM. The data storage is implemented every 10 minutes. If a message is received from the main controller of the energy management system, extract the message ID and execute the subsequent code. There are three valid code responses to the incoming message: send back data, update the relay setting, update the battery power command.

[0114] Figure 4 It is a structural block diagram of a flexible load scheduling device based on an energy management system according to an embodiment of the present invention.

[0115] As Figure 4 shown, the flexible load scheduling device based on the energy management system is applied to the controller of the energy management system, and the device includes:

[0116] The first function construction module 410 is used to construct a first objective function based on the predicted power generation curve of photovoltaic power generation and the predicted curve of non-adjustable load in the power system during the first time period, with the control temperature of the temperature-controlled load and the operation time of the adjustable time load in the power system as independent variables, and the average value of the net power fluctuation in the power system during the first time period as the dependent variable;

[0117] The first function solving module 420 is used to solve the minimum value of the first objective function based on the control temperature constraint condition of the temperature-controlled load and the constraint condition of the operation time of the adjustable time load, and obtain the target temperature curve of the temperature-controlled load and the target operation time curve of the adjustable time load in the first time period;

[0118] The flexible load scheduling module 430 is used to schedule the temperature-controlled load and the adjustable time load in the power system respectively during the first time period based on the target temperature curve and the target operation time curve.

[0119] In one embodiment, the above device further includes:

[0120] A load forecasting module, configured to determine a temperature control load forecasting curve and an adjustable time load forecasting curve of the power system in the first time period based on a target temperature curve of the temperature control load in the first time period and a target operation time curve of the adjustable time load in the first time period, respectively;

[0121] A net power forecasting module, configured to determine a net power forecasting curve of the power system in the first time period based on the photovoltaic power generation forecasting curve, the non-adjustable load forecasting curve, the temperature control load forecasting curve, and the adjustable time load forecasting curve;

[0122] A charge and discharge adjustment module, configured to adjust the charge and discharge of a battery in the power system in the first time period based on the net power forecasting curve.

[0123] In one embodiment, the charge and discharge adjustment module includes:

[0124] A time determination unit, configured to determine a net power peak period and a net power trough period based on the net power forecasting curve;

[0125] A second function construction unit, configured to construct a second objective function with the charging power in the net power peak period and the discharging power in the net power trough period as independent variables and the average value of the net power fluctuation after charge and discharge adjustment as the dependent variable;

[0126] A second function solving unit, configured to solve the minimum value of the second objective function to obtain the target charging power in the net power peak period and the target discharging power in the net power trough period;

[0127] A charge and discharge adjustment unit, configured to adjust the charge and discharge of a battery in the power system in the first time period based on the target charging power in the net power peak period and the target discharging power in the net power trough period.

[0128] In one embodiment, the first objective function is:

[0129] ;

[0130] ;

[0131] ;

[0132] Wherein, represents the first objective function, represents the net power of the power system at time t, represents the photovoltaic power generation of the power system at time t, represents the load power of the power system at time t, represents the load power of the temperature-controlled load at time t, represents the load power of the non-adjustable load of the power system at time t, represents the load power of the adjustable time load at time t, represents the first time period.

[0133] In one implementation, the first function solving module 420 includes:

[0134] A first initialization unit, configured to determine an initialized first population, where the first population includes a plurality of first individuals, and the first individuals correspond to the temperature curve of the temperature-controlled load within the first time period and the operation time curve of the adjustable time load within the first time period;

[0135] A first iteration unit, configured to perform the following first iteration operation starting from the initialized first population:

[0136] Based on the first objective function, calculate the average value of the net power fluctuations of each of the first individuals in the first population corresponding to the first time period;

[0137] In the case where the maximum value among the average values of the net power fluctuations of each of the first individuals corresponding to the first time period is greater than a preset first net power fluctuation threshold, perform crossover and mutation on the first population to obtain the first population for the next iteration, so as to continue to perform the first iteration operation;

[0138] A first iteration stop unit, configured to stop performing the first iteration operation in the case where the maximum value among the average values of the net power fluctuations of each of the first individuals corresponding to the first time period is less than the first net power fluctuation threshold, determine a target first individual based on the first individual corresponding to the minimum value among the average values of the net power fluctuations of each of the first individuals corresponding to the first time period, and determine the target temperature curve of the temperature-controlled load within the first time period and the target operation time curve of the adjustable time load within the first time period respectively based on the temperature curve of the temperature-controlled load within the first time period and the operation time curve of the adjustable time load within the first time period corresponding to the target first individual.

[0139] In one implementation, the second objective function is:

[0140] ;

[0141]

[0142] Wherein, denote the second objective function denote the net power of the power system after charge and discharge adjustment at time t denote the charging power or discharging power of the power system at time t

[0143] In one implementation, the second function solving unit is specifically configured to

[0144] determine an initialized second population, where the second population includes a plurality of second individuals, and the second individuals correspond to the charging power during the peak period of the net power and the discharging power during the valley period of the net power

[0145] start the following second iteration operation from the initialized second population

[0146] Based on the second objective function, calculate the average value of the net power fluctuations of each of the second individuals in the current iteration of the second population after charge and discharge adjustment during the first time period

[0147] When the maximum value among the average values of the net power fluctuations of each of the second individuals after charge and discharge adjustment during the first time period is greater than a preset second net power fluctuation threshold, perform crossover and mutation on the second population to obtain the second population for the next iteration, so as to continue to perform the second iteration operation

[0148] When the maximum value among the average values of the net power fluctuations of each of the second individuals after charge and discharge adjustment during the first time period is less than the second net power fluctuation threshold, determine a target second individual based on the second individual corresponding to the minimum value among the average values of the net power fluctuations of each of the second individuals after charge and discharge adjustment during the first time period, and based on the charging power during the peak period of the net power and the discharging power during the valley period of the net power corresponding to the target second individual, respectively determine the target charging power during the peak period of the net power and the target discharging power during the valley period of the net power

[0149] For the specific functions and examples of the modules and sub-modules of the device according to the embodiments of the present invention, reference may be made to the relevant descriptions of the corresponding steps in the above method embodiments, which will not be elaborated herein

[0150] In the technical solution of the present invention, the acquisition, storage, and application of the user's personal information involved all comply with the provisions of relevant laws and regulations and do not violate public order and good customs

[0151] According to the embodiments of the present invention, the present invention also provides a system and a readable storage medium

[0152] Figure 5FIG. 0 shows a schematic block diagram of an exemplary electronic device 800 that can be used to implement an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as, for example, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, for example, personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely exemplary and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0153] As Figure 5 shown, the device 800 includes a computing unit 801 that can perform various appropriate actions and processes in accordance with a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0154] A plurality of components in the device 800 are connected to the I / O interface 805, including: an input unit 806, such as, for example, a keyboard, a mouse, etc.; an output unit 807, such as, for example, various types of displays, speakers, etc.; a storage unit 808, such as, for example, a magnetic disk, an optical disk, etc.; and a communication unit 809, such as, for example, a network card, a modem, a wireless communication transceiver, etc. The communication unit 809 allows the device 800 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0155] The computing unit 801 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 executes the various methods and processes described above, such as the flexible load scheduling method based on the energy management system. For example, in some embodiments, the flexible load scheduling method based on the energy management system can be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as the storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed onto the device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the flexible load scheduling method based on the energy management system described above can be executed. Alternatively, in other embodiments, the computing unit 801 can be configured to execute the flexible load scheduling method based on the energy management system by any other suitable means (e.g., by means of firmware).

[0156] Various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGA), application-specific integrated circuits (ASIC), application-specific standard products (ASSP), systems-on-chip (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a dedicated or general-purpose programmable processor, receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0157] The program code for implementing the methods of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to the processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing devices, such that when the program codes are executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program codes can be executed entirely on the machine, partially on the machine, as an independent software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0158] In the context of the present invention, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0159] To provide for interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can also be used to provide for interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0160] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0161] A computer system may include a client and a server. The client and the server are generally far away from each other and usually interact via a communication network. The client-server relationship is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.

[0162] It should be understood that various forms of the processes shown above may be used, steps may be reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in the present invention can be achieved, and no limitations are imposed herein.

[0163] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A flexible load scheduling method based on an energy management system, characterized in that: A controller applied to an energy management system, the method comprising: On the basis of the photovoltaic power generation prediction curve and the non-adjustable load prediction curve of the power system in the first time period, a first objective function is constructed with the control temperature of the temperature control load and the operation time of the adjustable time load in the power system as independent variables, and the average value of the net power fluctuation of the power system in the first time period as the dependent variable; Based on the control temperature constraint of the temperature control load and the operation time constraint of the adjustable time load, solving the first objective function for a minimum value, and obtaining a target temperature curve of the temperature control load in the first time period and a target operation time curve of the adjustable time load in the first time period; Based on the target temperature curve and the target operation time curve, respectively scheduling the temperature-controlled load and the adjustable time load in the power system within the first time period; Based on the target temperature curve of the temperature-controlled load in the first time period and the target operation time curve of the adjustable time load in the first time period, respectively determine the temperature-controlled load prediction curve and the adjustable time load prediction curve of the power system in the first time period; Determine a net power prediction curve of the power system in the first time period based on the photovoltaic power generation prediction curve, the non-adjustable load prediction curve, the temperature control load prediction curve and the adjustable time load prediction curve; Based on the net power prediction curve, determine a net power peak period and a net power valley period; The second objective function is constructed by taking the charging power during the net power peak period and the discharging power during the net power valley period as independent variables and taking the average value of the net power fluctuation after charging and discharging adjustment as the dependent variable; Solving the minimum value of the second objective function to obtain the target charging power during the net power peak period and the target discharging power during the net power valley period; Based on the target charging power during the net power peak period and the target discharging power during the net power valley period, charging and discharging of the battery in the power system are adjusted within the first time period.

2. The method according to claim 1, characterized in that The first objective function is: ； ； ； in, represents the first objective function, represents the net power of the power system at time t, represents the photovoltaic power generation of the power system at time t, represents the load power of the power system at time t, represents the load power of the temperature control load at time t, represents the load power of the non-adjustable load of the power system at time t, represents the load power of the adjustable time load at time t, Indicates the first time period.

3. The method according to claim 2, characterized in that The step of solving the first objective function for a minimum value based on the control temperature constraint condition of the temperature control load and the operation time constraint condition of the adjustable time load to obtain a target temperature curve of the temperature control load in the first time period and a target operation time curve of the adjustable time load in the first time period includes: Determine a first population to be initialized, wherein the first population includes a plurality of first individuals, and the first individuals correspond to a temperature curve of the temperature-controlled load in the first time period and an operation time curve of the adjustable time load in the first time period; Starting from the first initialized population, the following first iteration operation is performed: Based on the first objective function, calculating the average value of the net power fluctuation of each of the first individuals in the first population in the current iteration corresponding to the first time period; When the maximum value of the average values ​​of the net power fluctuations of the first individuals in the first time period is greater than a preset first net power fluctuation threshold, cross-mutation is performed on the first population to obtain the first population for the next iteration, so as to continue to perform the first iteration operation; When the maximum value of the net power fluctuation average values ​​corresponding to each of the first individuals in the first time period is less than the first net power fluctuation threshold, stop executing the first iterative operation, determine the target first individual based on the first individual corresponding to the minimum value of the net power fluctuation average values ​​corresponding to each of the first individuals in the first time period, and based on the temperature curve of the temperature-controlled load in the first time period and the operation time curve of the adjustable time load in the first time period corresponding to the target first individual, determine the target temperature curve of the temperature-controlled load in the first time period and the target operation time curve of the adjustable time load in the first time period respectively.

4. The method according to claim 2, characterized in that: The second objective function is: ； ； in, represents the net power of the power system after charge and discharge adjustment at time t, represents the charging power or discharging power of the power system at time t.

5. The method according to claim 1, characterized in that Solving the minimum value of the second objective function to obtain the target charging power during the net power peak period and the target discharging power during the net power valley period includes: Determine a second population to be initialized, wherein the second population includes a plurality of second individuals, and the second individuals correspond to the charging power during the net power peak period and the discharging power during the net power valley period; The following second iteration operation is performed starting from the initialized second population: Based on the second objective function, calculating the average net power fluctuation of each second individual in the second population of the current iteration after charge and discharge adjustment in the first time period; When the maximum value of the net power fluctuation average values ​​after charge and discharge adjustment corresponding to each of the second individuals in the first time period is greater than a preset second net power fluctuation threshold, cross-mutate the second population to obtain the second population of the next iteration, so as to continue to perform the second iteration operation; When the maximum value of the average value of the net power fluctuation after charging and discharging adjusted for each of the second individuals in the first time period is less than the second net power fluctuation threshold, the target second individual is determined based on the second individual corresponding to the minimum value of the average value of the net power fluctuation after charging and discharging adjusted for each of the second individuals in the first time period, and the target charging power of the net power peak period and the target discharging power of the net power valley period are determined respectively based on the charging power of the net power peak period and the discharging power of the net power valley period corresponding to the target second individual.

6. A flexible load dispatching device based on an energy management system, characterized in that: A controller applied to an energy management system, the device comprising: A first function construction module is used to construct a first objective function based on a photovoltaic power generation prediction curve and a non-adjustable load prediction curve of the power system in a first time period, with a control temperature of the temperature control load and an operation time of the adjustable time load in the power system as independent variables, and with an average value of net power fluctuation of the power system in the first time period as a dependent variable; a first function solving module, configured to solve the first objective function for a minimum value based on a control temperature constraint of the temperature-controlled load and an operation time constraint of the adjustable time load, so as to obtain a target temperature curve of the temperature-controlled load in the first time period and a target operation time curve of the adjustable time load in the first time period; a flexible load scheduling module, configured to schedule the temperature-controlled load and the adjustable time load in the power system respectively within the first time period based on the target temperature curve and the target operation time curve; a load prediction module, configured to determine a temperature-controlled load prediction curve and an adjustable time load prediction curve of the power system in the first time period, based on a target temperature curve of the temperature-controlled load in the first time period and a target operation time curve of the adjustable time load in the first time period; A net power prediction module, configured to determine a net power prediction curve of the power system within the first time period based on the photovoltaic power generation prediction curve, the non-adjustable load prediction curve, the temperature control load prediction curve and the adjustable time load prediction curve; A charge and discharge adjustment module, configured to adjust the charge and discharge of the battery in the power system within the first time period based on the net power prediction curve; Wherein, the charge and discharge adjustment module includes: A time determination unit, configured to determine a net power peak period and a net power valley period based on the net power prediction curve; A second function construction unit is used to construct a second objective function by taking the charging power during the net power peak period and the discharging power during the net power valley period as independent variables and taking the average value of the net power fluctuation after charge and discharge adjustment as a dependent variable; A second function solving unit, used for solving the minimum value of the second objective function to obtain the target charging power during the net power peak period and the target discharging power during the net power valley period; The charge and discharge adjustment unit is used to adjust the charge and discharge of the battery in the power system within the first time period based on the target charging power during the net power peak period and the target discharging power during the net power valley period.

7. A flexible load dispatching system based on an energy management system, comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-5.

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