Flexible load scheduling method and system based on energy management system

Abstract

The invention provides a flexible load scheduling method and system based on an energy management system, and relates to the technical field of electric power. According to the specific implementation scheme, on the basis of a photovoltaic generating capacity prediction curve and a non-adjustable load prediction curve of a power system in a first time period, the control temperature of a temperature control load and the operation time of a time-adjustable load in the power system are used as independent variables; taking the net power fluctuation average value of the power system in the first time period as a dependent variable to construct a first objective function; based on the control temperature constraint condition of the temperature control load and the constraint condition of the operation time of the adjustable time load, the minimum value of the first objective function is solved, and 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 are obtained; and based on the target temperature curve and the target operation time curve, a temperature control load and an adjustable time load in the power system are respectively scheduled in the first time period.

Description

Technical Field

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

[0002] Nowadays, with the decreasing reserves of fossil fuels, attention is focused on investing in renewable energy sources. There is an urgent need to reduce the demand for electricity in order to cope with the poor durability of aging infrastructure and the stability issues of the power system caused by intermittent renewable energy sources. If planned properly, 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 TVs) to help researchers investigate the impact of different energy-saving technologies on overall energy consumption. Energy-saving appliances, smart meters, and IoT devices are popular topics in smart grid research. Currently, the main obstacle to the adoption of smart systems is cost. With the ongoing research and development work, more cost-effective home energy management systems are expected to emerge soon.

[0003] The first step of an energy management system is the monitoring of generators and loads. Current advanced monitoring systems include grouped data recording for solar photovoltaic power generation, wind power generation, and building loads. 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. In order to achieve higher energy system efficiency, environmental sensors including humidity sensors, temperature sensors, and light sensors have been integrated into energy management systems. Some energy management systems have added IoT capabilities that allow users to remotely control loads and view recorded data. Web visualization of energy data also improves management transparency, which is critical when multiple stakeholders are involved.

[0004] However, frequent load scheduling will cause troubles to users, and under a large number of operational requirements, the control strategy and information transmission method of the energy management system are also severely tested. Existing studies have explored the theory of energy control in various energy scenarios, but lack key practical, universal, and substantive method research. There are generally problems such as long control strategy development cycle and high system upgrade cost. 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, a flexible load scheduling method based on an energy management system is provided, which is applied to a controller of the energy management system, 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, the temperature-controlled load and the adjustable time load in the power system are respectively scheduled within the first time period.

[0007] According to another aspect of the present invention, a flexible load scheduling device based on an energy management system is provided, which is applied to a controller of the energy management system, 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 is used 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.

[0008] According to another aspect of the present invention, there is provided an electronic device, comprising: at least one processor; and 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 so that the at least one processor can execute any flexible load scheduling method based on an energy management system in the embodiments of the present invention.

[0009] According to another aspect of the present invention, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute any flexible load scheduling method based on an energy management system according to an embodiment of the present invention.

[0010] Adopting the technical solution of the present invention, 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, 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, construct a first objective function; based on the control temperature constraint of the temperature-controlled load and the operation time constraint 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; based on the target temperature curve and the target operation time curve, dispatch the temperature-controlled load and the adjustable time load in the power system in the first time period respectively. 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 dispatch the temperature-controlled load and the adjustable time load of the power system to reduce the power fluctuation of the power grid.

[0011] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The accompanying drawings are used to better understand the present invention and do not constitute a limitation of the present invention. Figure 1 is a flow chart of a flexible load scheduling method based on an energy management system according to an embodiment of the present invention; Figure 2 is a structural block diagram of an energy management system according to an embodiment of the present invention; Figure 3 is a flow chart of a slave controller of an energy management system according to an embodiment of the present invention; Figure 4 It is a structural block diagram of a flexible load dispatching device based on an energy management system according to an embodiment of the present invention; Figure 5 The block diagram is a block diagram of an electronic device for implementing the method according to the embodiment of the present invention. DETAILED DESCRIPTION

[0013] The following is a description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be appreciated by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present invention. Similarly, for clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0014] Figure 1 It is a flow chart of a flexible load scheduling method based on an energy management system according to an embodiment of the present invention.

[0015] like Figure 1 As shown, the flexible load scheduling method based on the energy management system is applied to the controller of the energy management system and may include: S110, constructing a first objective function based on a photovoltaic power generation prediction curve and a non-adjustable load prediction curve of the power system in the first time period, taking a control temperature of a temperature-controlled load and an operation time of an adjustable time load in the power system as independent variables, and taking an average value of a net power fluctuation of the power system in the first time period as a dependent variable; S120, based on the control temperature constraint of the temperature control load and the operation time constraint of the adjustable time load, solving the minimum value of the first objective function 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; S130 , based on the target temperature curve and the target operation time curve, respectively dispatching the temperature-controlled load and the adjustable time load in the power system within a first time period.

[0016] It can be understood that the flexible load includes a temperature-controlled load and an adjustable time load.

[0017] Exemplarily, the first time period may be one day or several days in the future, or one week, etc.

[0018] For example, the photovoltaic power generation prediction curve is used to describe the photovoltaic power generation that changes with time. The non-adjustable load prediction curve is used to describe the non-adjustable load that changes with time.

[0019] Exemplarily, the photovoltaic power generation prediction curve of the power system in the first time period can be obtained by calculating the ambient temperature information and solar radiation in the first time period according to the photovoltaic power generation prediction model.

[0020] For example, the photovoltaic power generation prediction model may adopt the following formula: ; in, represents the photovoltaic power generation at time t, It represents the total irradiance of sunlight incident on the photovoltaic array plane at time t. represents the plane area of ​​the photovoltaic array, Indicates the efficiency of photovoltaic modules under standard test conditions. represents the photovoltaic efficiency loss caused by temperature change at time t, Indicates the conversion efficiency of the inverter.

[0021] Exemplarily, the temperature control load may include air conditioners and electric water heaters, etc. Air conditioners and electric water heaters usually have an on / off state, and the power consumption is constant in the on and off states. The air conditioner has different power consumption in the on and off states according to the set temperature and the ambient temperature.

[0022] Exemplarily, the adjustable time load has a fixed operation period and the start time can be adjusted.

[0023] Exemplarily, the control temperature constraint condition of the temperature control load may include that the control temperature is less than an upper temperature limit value and greater than a lower temperature limit value.

[0024] Exemplarily, the constraint condition of the operation time of the adjustable time load includes the selectable operation time of the adjustable time load.

[0025] Exemplarily, a particle swarm algorithm or a genetic algorithm may be used to solve the minimum value of the first objective function to obtain a target temperature curve of the temperature-controlled load in the first time period and a target operating time curve of the adjustable time load in the first time period.

[0026] 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.

[0027] According to the above implementation, 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, the control temperature of the temperature-controlled load and the operation time of the adjustable time load in the power system are used as independent variables, and the average value of the net power fluctuation of the power system in the first time period is used as the dependent variable to construct the first objective function; based on the control temperature constraint of the temperature-controlled load and the operation time constraint 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 to reduce the power fluctuation of the power grid.

[0028] In one implementation, the first objective function may be: ; ; ; in, 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 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.

[0029] For example, the temperature control load Can include air conditioning load and water heater load .

[0030] For example, the air conditioning load The model can be as follows: ; ; ; ; in, represents the air conditioning input power at time t, Indicates the working status of the air conditioner. When the value is 1, it means it is on, and when the value is 0, it means it is off. Indicates the power when the air conditioner is turned on. Indicates the power when the air conditioner is turned off. Indicates the set temperature of the air conditioner. represents the indoor temperature of the air-conditioning environment at time t, Indicates that the air conditioner's thermostat is dead. 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 outdoor temperature .

[0031] For example, the electric water heater load The model can be as follows: ; ; in, represents the switch state of the electric water heater at time t, Indicates the on / off state of the electric water heater at time t, where a value of 1 indicates on and a value of 0 indicates off. Indicates the set temperature of the electric water heater. Indicates that the water heater's thermostat is dead. represents the thermal efficiency constant of the electric water heater, Indicates the indoor temperature of the water heater at time t. Indicates the power of the electric heater.

[0032] For example, for different adjustable time loads, the powers in the same time window may be different or the same. For the same adjustable time load, the powers in different time windows may be the same or different.

[0033] According to the above implementation, a first objective function can be constructed 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 of the power system in the first time period as the dependent variable.

[0034] In one embodiment, based on the control temperature constraints of the temperature-controlled load and the operating time constraints 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 operating time curve of the adjustable time load in the first time period, including: determining an initialized first population, wherein the first population includes multiple first individuals, and the first individual corresponds to the temperature curve of the temperature-controlled load in the first time period and the operating time curve of the adjustable time load in the first time period; performing the following first iterative operation starting from the initialized first population: based on the first objective function, calculating the average net power fluctuation corresponding to each first individual in the first population of the current iteration in the first time period; calculating the average net power fluctuation corresponding to each first individual in the first time period When the maximum value among the means is greater than the preset first net power fluctuation threshold, cross-mutation is performed on the first population to obtain the first population for the next iteration to continue the first iterative operation; when the maximum value among the average values ​​of net power fluctuations corresponding to each first individual in the first time period is less than the first net power fluctuation threshold, the first iterative operation is stopped, and the target first individual is determined based on the first individual corresponding to the minimum value among the average values ​​of net power fluctuations corresponding to each first individual 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, 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 are determined respectively.

[0035] Exemplarily, a crossover mutation may be performed on each first individual in the first population using a preset crossover mutation probability to obtain a new first population for the next iteration.

[0036] Exemplarily, at each iteration, each first individual in the first population needs to meet the following conditions: the target temperature curve of the temperature-controlled load in the first time period needs to meet the control temperature constraints of the temperature-controlled load, and the target operation time curve of the adjustable time load in the first time period needs to meet the operation time constraints of the adjustable time load.

[0037] According to the above implementation, the first objective function can be quickly and accurately solved to obtain the minimum value, 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.

[0038] In one embodiment, the above method may also include: determining the temperature control load prediction curve and the 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 in the first time period and the target operation time curve of the adjustable time load in the first time period; determining the 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 batteries in the power system in the first time period based on the net power prediction curve.

[0039] Exemplarily, the target temperature curve is input into the above-mentioned temperature control load model to obtain the temperature control load prediction curve output by the model.

[0040] 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, so that the adjustable time load prediction curve can be obtained.

[0041] Exemplarily, the net power prediction curve of the power system in the first time period can be obtained by 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.

[0042] Exemplarily, during a time period when the net power of the net power prediction curve is relatively large, the battery is charged, and during a time period when the net power of the net power prediction curve is relatively small, the battery is discharged.

[0043] For example, common energy storage battery types include lead-acid batteries and lithium batteries, etc. These batteries usually have three working states: charging, discharging, and standby. The relationship between the battery's storage capacity and its charge and discharge power can be described by the following formula: ; Among them, S b (t+1) represents the capacity of the battery at time t+1, S b (t) represents the capacity of the battery at time t; P b,c (t) and P b,d (t) represent the charging power and discharging power of the battery at time t respectively; σ b is the battery loss constant; η b,c and η b,d Respectively represent the charging and discharging efficiency of the battery; Δt is the unit time step.

[0044] The operation of the battery includes the following four constraints: state of charge constraint, power upper and lower limit constraints, operating state constraint and climbing constraint.

[0045] ; ; ; ; ; in, Indicates the state of charge of the battery at time t; and They are the upper and lower limits of the battery state of charge respectively; , , and They are the upper and lower limits of battery charge and discharge power respectively; and Respectively represent the charging and discharging status of the battery at time t, taking 0 or 1, 1 means charging, Taking 1 means discharge; Indicates the operating state of the battery, which constrains the battery from being charged and discharged at the same time; Indicates the upper limit of battery ramp constraint. represents the transmission power of the battery at time t, Represents the transmission power of the battery at time t+1.

[0046] The SoC of the battery at time t can be calculated using the following formula: ; in, Indicates the charging and discharging efficiency of the battery. Indicates the state of charge of the battery at time t-1; SoH indicates the health state of the battery. SoH is usually in the range of 0 to 1, reflecting the health level of the battery. The lower the SoH, the higher the degree of battery aging; is the rated capacity of the battery.

[0047] According to the above implementation, 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.

[0048] In one embodiment, based on the net power prediction curve, the charging and discharging of batteries in the power system are adjusted within a first time period, including: determining the net power peak period and the net power trough period based on the net power prediction curve; constructing a second objective function with the charging power during the net power peak period and the discharging power during the net power trough period as independent variables and 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 trough period; based on the target charging power during the net power peak period and the target discharging power during the net power trough period, the charging and discharging of batteries in the power system are adjusted within the first time period.

[0049] Exemplarily, a continuous interval in which the net power is greater than a preset first threshold is regarded as a net power peak period, and a continuous interval in which the net power is less than a preset second threshold is regarded as a net power valley period.

[0050] Exemplarily, the second objective function is: ; ; in, represents the second objective function, It represents the net power of the power system after charge and discharge adjustment at time t, It represents the net power of the power system after charge and discharge adjustment at time t-1. It represents the charging power or discharging power of the power system at time t.

[0051] Exemplarily, a particle swarm algorithm or a genetic algorithm may be used to solve 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.

[0052] In one embodiment, solving the minimum value of the 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: determining an initialized second population, 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; performing the following second iteration operation starting from the initialized second population: based on the second objective function, calculating the average value of the net power fluctuation after charging and discharging adjustment in the first time period corresponding to each second individual in the second population of the current iteration; when the maximum value of the average value of the net power fluctuation after charging and discharging adjustment corresponding to each second individual in the first time period is greater than a preset second net power fluctuation When the dynamic threshold is reached, the second population is cross-mutated to obtain the second population for the next iteration, so as to continue to execute the second iterative operation; when the maximum value of the average value of the net power fluctuation adjusted by charge and discharge corresponding to each second individual in the first time period is less than the second net power fluctuation threshold, the second iterative operation is stopped, and 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 adjusted by charge and discharge corresponding to each second individual in the first time period, and the target charging power during the net power peak period and the target discharging power during the net power valley period are determined respectively based on the charging power during the net power peak period and the discharging power during the net power valley period corresponding to the target second individual.

[0053] Exemplarily, a new second population may be obtained by performing crossover mutation on each second individual in the second population using a preset crossover mutation probability.

[0054] It can be understood that the charging power during the net power peak period and the discharging power during the net power valley period corresponding to each individual in the second population of each iteration need to satisfy the corresponding upper and lower limit conditions of charging power and the upper and lower limit conditions of discharging power respectively.

[0055] According to the above implementation, the minimum value of the second objective function can be quickly and accurately calculated, and the target charging power during the net power peak period and the target discharging power during the net power valley period can be output.

[0056] Figure 2 It is a structural block diagram of an energy management system according to an embodiment of the present disclosure.

[0057] like Figure 2 As shown, the energy management system can execute any of the methods described in the embodiments of the present disclosure.

[0058] The energy management system may include a load monitoring unit, a photovoltaic-battery monitoring unit and a data processing unit.

[0059] As the controller of the slave energy management system, the load monitoring unit has the function of sampling the load voltage and current, and sends the data in a package to the master energy management system controller. It can also execute load switching commands if the user needs it. For line AC voltage sampling, a 1.2 VA PCB type transformer is used to isolate the current and then reduce the voltage to 12Vrms. Since the sampling microcontroller can only work at a DC voltage of 5V, a bridge rectifier and a resistor divider are used to convert the AC 12 Vrms to the microcontroller maximum input voltage or 5 V DC. For line AC current sampling, a low-cost linear current sensor based on the Hall effect (ACS712 integrated circuit) is used. It can directly provide a 0-5V DC output proportional to the current value. The typical sensitivity of the module is 185 mV / a, and the error is +-1.5%. At the same time, in order to improve the accuracy of the measurement, it needs to be calibrated.

[0060] In general, the load monitoring unit collects AC voltage data through a voltage sensor, collects AC current data through a current sensor based on the Hall effect, and calculates the power based on the collected voltage and current data. At the same time, the load status is collected based on whether the device is turned on or off, especially the load switching command involving user instructions. Finally, these data are packaged and sent to the main EMS controller for data processing.

[0061] The PV-battery monitoring unit works in almost the same way as the load monitoring unit, except that the voltage and current are measured on the DC side of the PV and battery, which minimizes the errors caused by the inefficiency 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 lines are required, or a wireless data transmission module needs to be added to the energy management system.

[0062] The photovoltaic-battery monitoring unit directly collects the DC voltage and current of the photovoltaic system from the DC side of the photovoltaic system to minimize the error caused by low component efficiency. By measuring the voltage on the battery side, the input / output energy of the battery is estimated to obtain the battery charge and discharge state (SoC). At the same time, the battery temperature data is collected to help avoid battery damage due to excessive temperature during the optimization process. Finally, the power generation of the photovoltaic 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 battery charge and discharge operation based on this data.

[0063] The data processing unit manages the power optimization functions and also serves as a user interface for collecting load shedding commands and other system measurements and status parameters. A Raspberry Pi 4 1gb version is used as an embedded single board computer to run the optimization algorithm. The energy manager master and slave controllers communicate with each other via a serial communication protocol. This communication method ensures the stability of operation compared to wireless communication, which may sometimes be interrupted due to changes in environmental conditions or unit locations. The unit obtains data on current / voltage inputs from the energy management system slave controller, optimizes battery charge and discharge, and then sends back battery power commands in the form of PWM for execution.

[0064] Furthermore, the battery power command acquisition step includes: acquiring current and voltage data from a slave EMS controller; calculating an optimal battery charge and discharge profile using an embedded optimization algorithm; and converting the calculated charge and discharge commands into PWM values.

[0065] The receiver of the battery power command is the slave EMS controller, which is responsible for receiving the battery power command in the form of PWM and performing the corresponding charging and discharging operations.

[0066] After receiving the battery level command, the slave EMS controller performs the following actions: Charging: When the command indicates charging, the EMS controller controls the charging circuit through PWM signals to store electrical energy in the battery; Discharge: When the command indicates discharge, the EMS controller controls the discharge circuit through a PWM signal to release the stored electrical energy in the battery into the home grid.

[0067] A computer is used to wirelessly access the EMS master controller for programming and data visualization. There is also the option of using a cell phone to interact with the master EMS controller.

[0068] The main function of the slave controller firmware of the energy manager is to collect and transmit data such as load, battery, photovoltaic power, current, voltage, etc. and transmit them to the main controller to run the optimization algorithm. The slave controller operation flow chart is as follows Figure 3As shown. At the beginning, set up I / O (input / output) and declare variables, current / voltage sampling input, initialize real-time clock RTC and electrically erasable programmable read-only memory EEPROM. In the code loop, sampling is continuous. If the refresh cycle of the relay setting or battery power command expires, the date is read from the RTC, and then the corresponding new data is copied from the memory and the relay is set on / off. Since the relay setting and battery power are available for a group of 24 hours at a time, the refresh cycle can be up to 24 hours. If the data storage cycle expires, the date is read, data aggregation is completed and stored in the EEPROM. Data storage is achieved every 10 minutes. If a message is received from the main controller of the energy management system, the information ID is extracted and the subsequent code is executed. There are three valid codes to respond to incoming messages: send back data, update relay settings, and update battery power commands.

[0069] 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.

[0070] like Figure 4 As 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: A first function construction module 410 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 420 is used to solve the first objective function for a minimum value based on the control temperature constraint of the temperature control load and the operation time constraint of the adjustable time load, so as 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; The flexible load scheduling module 430 is used 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.

[0071] In one embodiment, the above device further comprises: 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 is used 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.

[0072] In one embodiment, 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.

[0073] In one embodiment, 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.

[0074] In one implementation, the first function solving module 420 includes: A first initialization unit, configured to determine an initialized first population, wherein the first population includes a plurality of first individuals, the first individuals corresponding 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; A first iteration unit is configured to perform the following first iteration operation starting from the initialized first population: 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; A first iteration stopping unit is used to stop executing 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 value, determine a 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 determine 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 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.

[0075] In one embodiment, the second objective function is: ; in, represents the second objective function, 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.

[0076] In one implementation, the second function solving unit is specifically configured to: 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.

[0077] For the description of specific functions and examples of each module and submodule of the device in the embodiment of the present invention, reference can be made to the relevant description of the corresponding steps in the above method embodiment, which will not be repeated here.

[0078] In the technical solution of the present invention, the acquisition, storage and application of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0079] According to an embodiment of the present invention, the present invention also provides a system and a readable storage medium.

[0080] Figure 5 A schematic block diagram of an example electronic device 800 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as 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 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 examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0081] like Figure 5As shown, the device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 to 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.

[0082] A number of components in the device 800 are connected to the I / O interface 805, including: an input unit 806, such as a keyboard, a mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a disk, an optical disk, etc.; and a communication unit 809, such as 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 through a computer network such as the Internet and / or various telecommunication networks.

[0083] The computing unit 801 may be a variety of general and / or special 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, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 801 performs the various methods and processes described above, such as a flexible load scheduling method based on an energy management system. For example, in some embodiments, the flexible load scheduling method based on an energy management system may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed on 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 may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to execute the flexible load scheduling method based on the energy management system in any other appropriate manner (for example, by means of firmware).

[0084] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations 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 special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0085] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0086] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0087] To provide 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 pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the 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).

[0088] The systems and techniques described herein may be implemented in a computing system that includes back-end 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 front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may 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.

[0089] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server 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 combined with a blockchain.

[0090] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in the present invention can be achieved, and this document does not limit this.

[0091] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the principles of the present invention should be included in 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, the temperature-controlled load and the adjustable time load in the power system are respectively scheduled within the first time period.

2. The method according to claim 1, characterized in that Also includes: 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, charging and discharging of batteries in the power system are adjusted within the first time period.

3. The method according to claim 2, characterized in that The step of adjusting the charging and discharging of the battery in the power system within the first time period based on the net power prediction curve includes: 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.

4. The method according to claim 3, 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 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 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.

5. The method according to claim 4, 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.

6. The method according to claim 4, characterized in that The second objective function is: ； ； in, 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.

7. The method according to claim 3, 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.

8. 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 is used 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.

9. 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 7.

10. 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 1-7.

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