A load control system and method based on smart energy unit

Abstract

The present invention discloses a load control system and method based on a smart energy unit, and relates to the technical field of power load control. The method includes: real-time collection of load-related data, and real-time transmission to the smart energy unit; the smart energy unit receives the transmitted real-time data, and calculates the urgency of each load according to a preset urgency algorithm; based on the urgency calculation result, formulates and optimizes the load control strategy, and generates corresponding load control instructions; issues load control instructions, and controls the corresponding area, thereby realizing flexible load control. By introducing an urgency algorithm, the present invention can give priority to regulating high-urgency equipment and reduce the energy consumption of low-urgency equipment according to real-time power demand, environmental parameters and electricity costs, thereby ensuring user comfort while minimizing energy consumption and electricity costs, and significantly improving the response speed and flexibility of the system when dealing with peak loads.

Description

Technical Field

[0001] The present invention relates to the technical field of power load regulation and control, and in particular to a load regulation and control system based on a smart energy unit and a method thereof. Background Art

[0002] Power load regulation is one of the core tasks of the energy management system. Especially in the large-scale use scenarios of high-energy consumption equipment such as air conditioners, how to effectively regulate power load and reduce peak power demand has become a key issue of concern in the industry.

[0003] Traditional load regulation technologies mostly rely on fixed time periods or simple temperature threshold settings, and lack the ability to respond to changes in user demand and real-time environmental conditions. Such systems are usually rigid in load control and fail to dynamically adjust according to actual demand, resulting in a large amount of power waste or a decline in user experience. For example, air conditioning systems usually run at maximum power, especially during peak hours, which increases the burden on the power system, increases electricity costs, and increases the risk of energy waste.

[0004] Although some load management systems based on the Internet of Things, sensors and big data technologies have emerged in recent years, achieving a certain degree of optimization control by real-time monitoring of equipment operating status and environmental conditions, these systems still have shortcomings when facing complex dynamic changes in loads. A significant problem is that the existing load management system is not flexible enough to respond to peak power demand and cannot effectively distinguish the urgency of the load, resulting in inefficient allocation of the overall load of the system. In addition, these systems fail to fully consider the fluctuation of electricity prices and the priority relationship between different loads, resulting in an inability to balance costs and user comfort in power distribution.

[0005] Therefore, a control system is needed that can be dynamically optimized according to the urgency of equipment load to achieve more efficient energy management, especially to achieve optimal utilization of power resources when dealing with peak loads and complex power usage scenarios. Summary of the invention

[0006] In view of the problems existing in the existing load control technology in terms of intelligence level, data processing depth and real-time response capability, the present invention proposes a load control system and method based on a smart energy unit, which realizes more intelligent and autonomous load control with the support of big data, artificial intelligence and multi-source data fusion technology.

[0007] In order to achieve the above object, the present invention is implemented by the following technical solutions:

[0008] A load control system based on a smart energy unit, the system comprising:

[0009] A data acquisition unit, including a variety of sensors and data acquisition equipment, is used to collect real-time data related to the load and transmit it to the smart energy unit in real time. The real-time data related to the load includes electrical quantity data and operating parameters of the air conditioner, environmental data, and multi-way switch status quantities;

[0010] The smart energy unit is used to receive the real-time data transmitted by the data acquisition unit, and calculate the urgency of each load according to a preset urgency algorithm, formulate and optimize the load control strategy based on the urgency calculation results, and generate corresponding load control instructions; the control execution unit is used to receive the load control instructions issued by the smart energy unit, and control the corresponding area according to the load control instructions, including the start and stop of the air conditioner, mode switching, temperature setting, wind speed adjustment, and control of the switch state quantity, so as to realize flexible load control.

[0011] As a preferred embodiment of the present invention, the electrical quantity data includes instantaneous values ​​and cumulative values, the instantaneous values ​​include A, B, C phase voltage and current, total and phase apparent power, total and phase active power, total and phase reactive power, and total and phase power factor, and the cumulative values ​​include positive active energy indication, reverse active energy indication, inductive reactive energy indication, and inductive reactive energy indication;

[0012] The operating parameters are determined according to the type of air conditioner:

[0013] For multi-split air-conditioning units, the operating parameters include the start / stop status of the indoor and outdoor units, the operating mode, the air-conditioning set temperature, the indoor temperature, the indoor humidity, the set wind speed, the fault code, the outdoor unit rated power and the compressor operating frequency; for water circulation central air-conditioning systems, the operating parameters include the cold water or hot water outlet temperature and the set value, the cold water or hot water inlet temperature and the set value, the current value of the host operating current and the ratio between the set value; for split air-conditioning units, the operating parameters include the start / stop status, the operating mode, the air-conditioning set temperature, the indoor temperature and the set wind speed;

[0014] The environmental data specifically refers to the ambient temperature and humidity at the end of the air-conditioning system.

[0015] As a preferred solution of the present invention, the smart energy unit is divided into a main control part and an expansion part;

[0016] The main control part specifically includes:

[0017] The core processing module includes the central processing unit, memory, clock and security chip;

[0018] Human-computer interaction module, including USB interface, LCD screen, buttons, indicator lights and buzzer, used to realize interactive operation, information feedback and status prompts between users and equipment;

[0019] A remote communication module, including a wireless module communication interface and a network communication interface 1, for realizing a remote communication connection, wherein the wireless module communication interface is used to access a wireless public network communication module, a wireless private network communication module and other remote communication modules;

[0020] A local communication module, including a bus communication interface and a network communication interface 2, for realizing a local communication connection;

[0021] The power module includes a main power supply and a backup power supply, which are used to power the system; the backup power supply uses a supercapacitor as an energy storage element. When the main power supply is insufficient or disappears, the backup power supply is automatically put into use, and reset and restart are not allowed; during the backup power supply period, when the smart energy unit remains continuously online, it communicates with the main station for no less than 3 times, and when the smart energy unit is not continuously online, it re-establishes a normal connection with the main station for no less than 1 time;

[0022] The extension part is connected to the main control part for communication, has an independent microprocessor, and is used to expand the local IoT communication and load management auxiliary functions, including RS485 communication module, HPLC communication module, dual-mode communication module, wireless communication module, state quantity input module and switch quantity output module;

[0023] Among them, the dual-mode communication module is specifically HPLC+HRF dual-mode communication.

[0024] As a preferred solution of the present invention, the core processing module includes:

[0025] The load control submodule is used to calculate the urgency of each load and formulate and optimize the load control strategy, as well as generate corresponding load control instructions;

[0026] Data processing submodule, used for data storage, air conditioner operation status identification and air conditioner data statistical analysis;

[0027] Data query and upload submodule, used for data query, event record query and active data upload;

[0028] The event recording submodule is used to record events related to demand response, changes in air-conditioning operating parameters, and abnormal operation events;

[0029] The parameter setting and query submodule is used for setting, querying, clearing and resetting clock call and synchronization, terminal configuration parameters, communication parameters, event recording parameters, collection and upload task parameters, equipment parameters, and air conditioning unit control parameters;

[0030] Data forwarding submodule, used for transparent forwarding of DL / T698.45 protocol data between the master station and field equipment;

[0031] The upgrade protection submodule is used for local or remote software upgrades and hardware or software security protection.

[0032] As a preferred solution of the present invention, the urgency algorithm specifically includes:

[0033] The change of air conditioning system over time is described by the thermodynamic equation:

[0034] ;

[0035] In the formula, express Moment The indoor temperature of the area for About the moment The derivative of The rate of change of indoor temperature in each area over time; express The outdoor temperature at the time; For the The heat loss rate of the area, For the The power conversion efficiency of each region; For air conditioning system Moment The power input of each zone is positive for heating and negative for cooling;

[0036] Calculate minimum heating or cooling time , the calculation formula is:

[0037] ;

[0038] In the formula, For the Target temperature set for each zone; is the maximum heating power, is the maximum cooling power; is the air conditioning system parameter; It is a function of time calculated according to the air conditioning system parameters and the thermodynamic equations;

[0039] The formula for calculating urgency is:

[0040] ;

[0041] In the formula, For the The urgency of the area; For the The start time of the regional load regulation request, For the The expected end time of the regional load regulation request;

[0042] The system comprehensive power is expressed as:

[0043] ;

[0044] In the formula, Indicates the total power supply. represents the power from the grid, represents the power from photovoltaic power generation, Indicates the power from the energy storage unit;

[0045] Use reinforcement learning algorithm to calculate the current urgency and the system comprehensive power, calculate the optimal power that should be allocated to each load at each moment. The goal of reinforcement learning is to optimize energy allocation so that energy costs are minimized while meeting the load regulation requirements. The reward function of the reinforcement learning algorithm is The definition is as follows:

[0046] ;

[0047] In the formula, for The electricity price at the time; For the The load control requirements of each region , Both are weights to balance user comfort and energy costs; for The set of all regions requesting load regulation at the moment;

[0048] The reinforcement learning controller optimizes the load control at each moment according to the reward function. When allocating the total power output by the reinforcement learning controller to each area, the minimum urgency priority rule is adopted. Areas with low urgency will be given priority in power support to ensure that emergency tasks are handled in a timely manner.

[0049] ;

[0050] In the formula, express Moment The power allocated to each area;

[0051] The system dynamically adjusts the load control strategy according to the actual situation at each moment, continuously updates the urgency, and optimizes the load control strategy through feedback from historical data.

[0052] As a preferred solution of the present invention, the reinforcement learning controller adopts a deep deterministic policy gradient algorithm for training and decision-making, and the algorithm includes:

[0053] An actor network: used to map system states to deterministic load control instructions;

[0054] A critic network: used to evaluate the action value of the load control instructions generated by the actor network in the current state, and guide the optimization direction of the load control strategy by calculating the expected return (taking into account energy costs and user comfort);

[0055] Policy optimization mechanism: The policy gradient method is used to iteratively update the parameters of the actor network based on the evaluation results of the critic network to minimize the loss function of the critic network, thereby achieving learning and adaptation of the optimal load regulation strategy.

[0056] As a preferred solution of the present invention, for event records related to demand response, event record types include load resource registration events, load resource cancellation events, demand response notification events, demand response user confirmation events, air conditioning operation parameter change events, and operation abnormality events, among which operation abnormality events include equipment failure records, clock deviations, system time modifications, meter reading failures, lower-level equipment loss, multi-split air conditioning unit equipment report changes, monthly communication traffic exceeds the limit, external power supply interruption or recovery, software upgrade or version change, hardware reset, data initialization, factory parameter restoration and event initialization.

[0057] A load control method based on a smart energy unit, the method comprising:

[0058] Collect load-related data in real time, including electrical quantity data, operating parameters, environmental data, and multi-way switch status of air conditioners, and transmit them to the smart energy unit in real time;

[0059] The smart energy unit receives the transmitted real-time data and calculates the urgency of each load according to the preset urgency algorithm;

[0060] Based on the emergency calculation results, formulate and optimize the load control strategy and generate corresponding load control instructions;

[0061] The load control instructions are issued to control the corresponding area, including the start and stop of the air conditioner, mode switching, temperature setting, wind speed adjustment, and control of the switch state quantity, so as to achieve flexible load control.

[0062] As a preferred solution of the present invention, the step of calculating the urgency of each load according to a preset urgency algorithm specifically includes:

[0063] Describe the changes of the air conditioning system over time through thermodynamic equations and calculate the minimum heating or cooling time;

[0064] Calculate the urgency of load regulation in each area based on the minimum heating or cooling time;

[0065] Using the reinforcement learning algorithm, the optimal power that should be allocated to each load at each moment is calculated based on the current urgency and system comprehensive power. The goal of reinforcement learning is to optimize energy distribution so that energy costs are minimized while meeting load control requirements. The reward function of the reinforcement learning algorithm is The definition is as follows:

[0066] ;

[0067] In the formula, for The electricity price at the time; For the The load control requirements of each region , Both are weights to balance user comfort and energy costs; for The set of all regions requesting load regulation at the moment;

[0068] The reinforcement learning controller optimizes the load control at each moment according to the reward function. When allocating the total power output by the reinforcement learning controller to each area, the minimum urgency priority rule is adopted. Areas with low urgency will be given priority in power support to ensure that emergency tasks are handled in a timely manner.

[0069] The system dynamically adjusts the load control strategy according to the actual situation at each moment, continuously updates the urgency, and optimizes the load control strategy through feedback from historical data.

[0070] As a preferred embodiment of the present invention, the training and decision-making steps of the reinforcement learning algorithm include:

[0071] Using actor networks to map system states to deterministic load control instructions;

[0072] The critic network evaluates the action value of the load control instructions generated by the actor network in the current state;

[0073] According to the evaluation results of the critic network, the policy gradient method is used to iteratively update the parameters of the actor network;

[0074] By minimizing the loss function of the critic network, the optimal load regulation strategy is learned and adapted.

[0075] The beneficial effects of the present invention are as follows: by introducing an emergency algorithm, precise control of power load is achieved, significantly improving the response speed and flexibility of the system in dealing with peak loads. The algorithm can prioritize the control of high-emergency equipment and reduce the energy consumption of low-emergency equipment based on real-time power demand, environmental parameters and electricity costs, thereby minimizing energy consumption and electricity costs while ensuring user comfort. The energy utilization rate of the overall system is improved, and the pressure on the power system during peak power consumption is effectively alleviated. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0077] Figure 1 It is a system software architecture diagram of the present invention;

[0078] Figure 2 It is a schematic diagram of the modular structure of the system of the present invention;

[0079] Figure 3 The following is a block diagram of the hardware composition of the smart energy unit in an embodiment of the present invention;

[0080] Figure 4 This is a structural block diagram of a core processing module in an embodiment of the present invention;

[0081] Figure 5 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0082] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0083] like Figure 1 As shown, it is an embodiment of the present invention, which provides a load control system based on a smart energy unit, which is divided into a system layer and an application layer. Physical hardware is located at the bottom layer of the software architecture, is the foundation of the system, and supports all upper-layer software, including electronic devices such as central processing units, memory, and communication devices.

[0084] The system layer includes:

[0085] Embedded operating system: provides basic support for upper-level applications;

[0086] Hardware driver: responsible for communicating with physical hardware and providing an abstract interface of hardware to the upper layer;

[0087] System interface layer: The operating system provides a system call interface to the application layer through the system interface layer, so that the upper layer application can call system functions;

[0088] Hardware Abstraction Layer (HAL): Provides access to the underlying hardware devices, shields the specific implementation details of the underlying hardware, and enhances the hardware compatibility of the system.

[0089] The application layer includes:

[0090] Multiple APP modules, such as basic APP, edge computing APP, advanced business APP, and other APP. Basic APP is responsible for executing basic functional logic, edge computing APP is used to perform complex calculations on local devices, and advanced business APP is a business function for high-end users. Multiple APPs interact with each other through the message bus engine to ensure that data between applications can be smoothly transmitted and processed.

[0091] like Figure 2 As shown, the system is divided into functional modules, including a data acquisition unit, a smart energy unit and a control execution unit.

[0092] (1) Data acquisition unit

[0093] It includes a variety of sensors and data acquisition equipment to collect real-time data related to the load and transmit it to the smart energy unit in real time. The real-time data related to the load includes the electrical quantity data and operating parameters of the air conditioner, environmental data, multi-way switch status, etc.;

[0094] The collection interval of electrical quantity data can be set within 1-60 minutes, and the default collection interval is 1 minute. The data content is shown in Table 1:

[0095] Table 1 Electrical quantity data content

[0096]

[0097] The collection interval of operating parameters can be set within 1-60 minutes, and the default collection interval is 5 minutes. According to the specific type of air conditioner, the data content is shown in Table 2:

[0098] Table 2 Operation parameter data content

[0099]

[0100] The air conditioning set temperature is defined as the target value of the ambient temperature in the relatively closed space where the air conditioning system terminal (indoor unit) is located. The water circulation central air conditioning system needs to use an algorithm to convert the inlet and outlet water set values ​​of the cold / warm water pipeline into the indoor ambient set temperature.

[0101] The environmental data specifically refers to the ambient temperature and humidity at the end of the air-conditioning system. The collection interval can be set within 1 to 60 minutes, and the default interval is 5 minutes.

[0102] (2) Smart Energy Unit

[0103] It is used to receive real-time data transmitted by the data acquisition unit, calculate the urgency of each load according to the preset urgency algorithm, formulate and optimize the load control strategy based on the urgency calculation results, and generate corresponding load control instructions;

[0104] Specifically, Figure 3 As shown in the figure, the smart energy unit is divided into a main control part and an expansion part. The main control part includes:

[0105] Core processing module: including central processing unit, memory, clock and security chip;

[0106] The central processing unit (CPU) is the control center of the system, responsible for executing program codes and instructions, and calculating and processing various types of data. The computing power of the CPU directly affects the speed and efficiency of the system's load control processing; the memory includes random access memory (RAM) and read-only memory (ROM), which are used to store temporary data and solidified operating programs during system operation. The size of RAM determines the system's ability to process large amounts of real-time data, and ROM is used to store the system's firmware and preset control strategies; the clock module is used to maintain system time synchronization and ensure that the work of each module is carried out in the correct time sequence, especially when receiving and processing real-time data; the security chip is responsible for encrypting and decrypting data to ensure that the transmitted data is not stolen or tampered with, especially instructions involving energy load control, to ensure the security of data and instructions.

[0107] Human-computer interaction module: including USB interface, LCD screen, buttons, indicator lights and buzzer, used to realize interactive operation, information feedback and status prompts between users and equipment;

[0108] The USB interface is used to connect external devices, which is convenient for importing or exporting data from the system, or for system maintenance and upgrading through external devices; the LCD screen provides a visual interface to display information such as the system's operating status, load control strategy, and execution of control instructions, so that operators can intuitively understand the system's working conditions; the buttons are the main input device for users to interact with the system, and users can enter instructions or select different functional operations of the system through buttons; indicator lights of different colors are used to display the system's operating status, such as normal operation, fault, standby, etc., to quickly provide users with system status feedback; when the system is abnormal or fails, the buzzer will sound an alarm to remind users to pay attention to the problem and perform necessary maintenance or inspection.

[0109] Remote communication module: including wireless module communication interface and network communication interface 1, used to realize remote communication connection, wherein the wireless module communication interface is used to access wireless public network communication module (4G / 5G network standard), wireless private network communication module (1.8GHz or 230MHz power wireless private network), other remote communication modules, etc.;

[0110] Local communication module: including bus communication interface and network communication interface 2 (RJ45 network port), used to realize local communication connection;

[0111] Power module: includes main power supply and backup power supply, used to power the system;

[0112] The backup power supply uses supercapacitors as energy storage elements and is integrated inside the smart energy unit. When the main power supply is insufficient or disappears, the backup power supply is automatically switched on and reset and restart are not allowed. During the backup power supply period, when the smart energy unit remains continuously online, it communicates with the main station for no less than 3 times (reporting power outage alarms), and when the smart energy unit is not continuously online, it re-establishes a normal connection with the main station for no less than 1 time.

[0113] The expansion part is connected and communicated with the main control part, and has an independent microprocessor for expanding local IoT communication and load management auxiliary functions. It is selected according to the actual needs of on-site load management on the user side. The structure, appearance and color of the expansion function module are compatible with the main control part.

[0114] The extensions include:

[0115] RS485 communication module: supports at least 4 RS485 buses, adopts multi-channel parallel communication to improve communication efficiency, and supports transparent forwarding communication; the baud rate and other communication parameters of each channel are controlled by the main control part, and its communication performance meets the following requirements:

[0116] a) Supports transmission rates of 1200bps to 115200bps, and the signal is not distorted within this transmission speed range;

[0117] b) When the A and B terminals of RS-485 are unloaded, the output differential mode voltage |VAB| shall be no less than 1.5V and no more than 5.5V;

[0118] c) In the receiving state, a differential voltage of -200mV to +200mV is applied between the A and B terminals, and the absolute value of the line current should not exceed 1.7mA;

[0119] d) Add a load resistor of 375Ω between the RS-485A and B terminals, and the output differential mode voltage |VAB| should be no less than 1.5V;

[0120] e) The receiving sensitivity of the RS-485 communication interface is ±200mV (allowable deviation is 5%);

[0121] f) RS-485 interface has long-term overload protection and self-recovery capability;

[0122] g) The terminals of the RS-485 interface can withstand 380V AC for 5 minutes without damage;

[0123] HPLC communication module: It is compatible with the HPLC communication module for branch measurement to achieve interconnection and meet the following requirements:

[0124] a) Power consumption: static power consumption ≤ 0.5W, dynamic power consumption ≤ 2.5W;

[0125] b) Withstand voltage: When powered by a three-phase four-wire power supply, it has the ability to resist single-phase grounding faults in a three-phase four-wire distribution network and withstand 1.9 times the nominal voltage for 4 hours;

[0126] c) HPLC communication meets:

[0127] Working frequency band: The working frequency band of high-speed power line carrier is 2MHz~12MHz, 2.4MHz~5.6MHz, 0.7MHz~3MHz, 1.7MHz~3MHz, which can be switched;

[0128] Communication rate: When the power supply is isolated, the space is shielded, there is no competition, the master-slave node ratio is 1:1, and the physical layer data packet size is tested at 512 and 1024 bytes, the communication rate shall not be less than 1Mbps.

[0129] Dual-mode communication module: specifically, HPLC+HRF dual-mode communication. In addition to meeting the above HPLC communication requirements, HRF communication must also meet the following requirements:

[0130] Working frequency band: The basic communication frequency band of high-speed wireless communication meets 470MHz~510MHz;

[0131] Modulation mode: Supports OFDM modulation mode, and subcarrier mapping mode supports BPSK, QPSK, and 16QAM;

[0132] The transmitter performance complies with the requirements of Table 3:

[0133] Table 3 Transmission performance requirements

[0134]

[0135] The receiver performance should comply with the requirements of Table 4:

[0136] Table 4 Receiving performance requirements

[0137]

[0138] Wireless communication module: meet the requirements of operating frequency range and transmission power stipulated by the national radio regulatory agency;

[0139] State quantity input (remote signal) module: supports at least 4 switch state quantity inputs. The state quantity input is an open / closed switching contact without power. When each state quantity is input with a stable DC 5V voltage, its power consumption is ≤0.2W;

[0140] Switching output (remote control) module: provides at least 4 normally open switching outputs (control signal outputs). The output circuit is equipped with safety measures to prevent false operation and facilitate on-site testing. The control output mode can be configured as pulse or level. The default is pulse with a pulse width of 300ms±100ms. The contact breaking capacity meets the pure resistance load of AC 250V / 5A, DC 110V / 0.4A or DC 30V / 2A.

[0141] Furthermore, if Figure 4 As shown, the core processing module includes:

[0142] The load control submodule is used to calculate the urgency of each load and formulate and optimize the load control strategy, as well as generate corresponding load control instructions;

[0143] Data processing submodule: used for data storage, air conditioning operation status identification and air conditioning data statistical analysis;

[0144] The scope of data storage includes: real-time data collected by the data acquisition module, electrical quantity data, operating parameters, ambient temperature and humidity data for the latest month, operating parameters of air-conditioning equipment before load flexible adjustment, and various event records, with no less than 100 records for each event;

[0145] Air conditioning operation status identification includes the on / off status and communication status of the air conditioning unit; the total load, load adjustment capacity, set temperature average, ambient temperature average, etc. of the air conditioning unit are calculated based on the collected data; the rationality of the collected data and statistical analysis results is judged to avoid abnormal data generation;

[0146] Data query and upload submodule: used for data query, event record query and active data upload;

[0147] Event recording submodule: used for demand response-related event recording, air conditioning operation parameter change recording and abnormal operation event recording;

[0148] Event record types include load resource registration events, load resource cancellation events, demand response notification events, demand response user confirmation events, air conditioning operation parameter change events, and abnormal operation events. Abnormal operation events include equipment failure records, clock deviation, system time modification, meter reading failure, lower-level equipment disconnection (communication interruption / recovery), multi-split air conditioning unit equipment report changes, monthly communication traffic limit exceeded, external power supply interruption / recovery, software upgrade / version change, hardware reset, data initialization, factory parameter restoration, event initialization, etc.

[0149] Parameter setting and query submodule: used for setting, querying, clearing and resetting clock call and synchronization, terminal configuration parameters, communication parameters, event recording parameters, collection and upload task parameters, equipment parameters, and air conditioning unit control parameters;

[0150] Data forwarding submodule: used for transparent forwarding of DL / T698.45 protocol data between the master station and field equipment;

[0151] Upgrade protection submodule: used for local / remote software upgrade and hardware / software security protection;

[0152] Local software upgrades update system files and applications through local maintenance interfaces such as USB. Remote software upgrades enable online software downloads through remote communication channels. Upgrades must be approved and certified by ESAM before they can be performed. When downloading remote software, the software should have the ability to resume downloading from breakpoints. If an exception occurs during the upgrade process, the application or operating system should be able to be restored to the previous version.

[0153] As a preferred embodiment of the present invention, the urgency algorithm specifically includes:

[0154] The change of air conditioning system over time is described by the thermodynamic equation:

[0155] ;

[0156] In the formula, express Moment The indoor temperature of the area for About the moment The derivative of The rate of change of indoor temperature in each area over time; express The outdoor temperature at the time; For the The heat loss rate of the area, For the The power conversion efficiency of each region; For air conditioning system Moment The power input of each zone is positive for heating and negative for cooling;

[0157] Calculate minimum heating or cooling time , which will be the current temperature Adjust to target temperature The minimum time required, assuming maximum heating / cooling rate, is a measure of the relationship between energy demand and the number of time steps and is calculated as:

[0158] ;

[0159] In the formula, For the Target temperature set for each zone; is the maximum heating power, is the maximum cooling power; are air conditioning system parameters, such as heat capacity, thermal resistance, etc.; It is a function of time calculated according to the air conditioning system parameters and the thermodynamic equations;

[0160] The formula for calculating urgency is:

[0161] ;

[0162] In the formula, For the The urgency of the area; For the The start time of the regional load regulation request, For the The expected end time of the regional load regulation request;

[0163] The system comprehensive power is expressed as:

[0164] ;

[0165] In the formula, Indicates the total power supply. represents the power from the grid, represents the power from photovoltaic power generation, Indicates the power from the energy storage unit;

[0166] Integrate multiple energy sources for comprehensive power supply during load regulation. By dynamically dispatching different energy sources, the system can purchase electricity from the grid when electricity prices are low, and give priority to energy storage and photovoltaic power generation when electricity prices are high.

[0167] Using reinforcement learning (RL) algorithms, we can The optimal power that should be allocated to each load at each moment is calculated based on the system comprehensive power. The goal of reinforcement learning is to optimize energy allocation so that energy costs are minimized while meeting the load regulation requirements. The reward function of the reinforcement learning algorithm is as follows:

[0168] ;

[0169] In the formula, For the moment electricity price; For the The load control requirements of each region , Both are weights to balance user comfort and energy costs; for The set of all regions requesting load regulation at the moment;

[0170] The reinforcement learning controller optimizes the load regulation at each moment according to the reward function, minimizes energy costs, and ensures that the needs of the equipment are met. In this way, the system can learn an efficient load regulation strategy.

[0171] When allocating the total power output by the reinforcement learning controller to each area, the minimum urgency priority rule is adopted, and loads with less urgency will be given priority in power support to ensure that urgent tasks are handled in a timely manner.

[0172] ;

[0173] In the formula, express Moment The power allocated to each area;

[0174] The system dynamically adjusts the load control strategy according to the actual situation (electricity price, temperature, energy storage status, etc.) at each moment, and continuously updates the urgency. At the same time, it optimizes the load control strategy through feedback from historical data, making the scheduling more intelligent and efficient.

[0175] (3) Control execution unit

[0176] It is used to receive load control instructions issued by the smart energy unit, and regulate the corresponding area according to the load control instructions, including the start and stop of the air conditioner, mode switching, temperature setting, wind speed adjustment, and control of the switch state quantity, so as to achieve flexible load regulation.

[0177] like Figure 5 FIG. 1 is another embodiment of the present invention, which provides a load control method based on a smart energy unit, comprising the following steps:

[0178] S1: Collect load-related data in real time, including electrical quantity data, operating parameters, environmental data, and multi-way switch status of the air conditioner, and transmit it to the smart energy unit in real time;

[0179] S2: The smart energy unit receives the transmitted real-time data and calculates the urgency of each load according to a preset urgency algorithm;

[0180] The urgency of each load is calculated according to a preset urgency algorithm, specifically including:

[0181] Describe the changes of the air conditioning system over time through thermodynamic equations and calculate the minimum heating or cooling time;

[0182] Calculate the urgency of load regulation in each area based on the minimum heating or cooling time;

[0183] Using the reinforcement learning algorithm, the optimal power that should be allocated to each load at each moment is calculated based on the current urgency and system comprehensive power. The goal of reinforcement learning is to optimize energy distribution so that energy costs are minimized while meeting load control requirements. The reward function of the reinforcement learning algorithm is The definition is as follows:

[0184] ;

[0185] In the formula, for The electricity price at the time; For the The load control requirements of each region , Both are weights to balance user comfort and energy costs; for The set of all regions requesting load regulation at the moment;

[0186] The reinforcement learning controller optimizes the load control at each moment according to the reward function. When allocating the total power output by the reinforcement learning controller to each area, the minimum urgency priority rule is adopted. Areas with low urgency will be given priority in power support to ensure that emergency tasks are handled in a timely manner.

[0187] The system dynamically adjusts the load control strategy according to the actual situation at each moment, continuously updates the urgency, and optimizes the load control strategy through feedback from historical data.

[0188] S3: Based on the emergency calculation results, formulate and optimize the load control strategy and generate corresponding load control instructions;

[0189] S4: issuing the load control instruction to control the corresponding area, including starting and stopping the air conditioner, mode switching, temperature setting, wind speed adjustment, and control of the switch state quantity, so as to achieve flexible load control.

[0190] In one embodiment, the training and decision-making steps of the reinforcement learning algorithm include:

[0191] Using actor networks to map system states to deterministic load control instructions;

[0192] The critic network evaluates the action value of the load control instructions generated by the actor network in the current state;

[0193] According to the evaluation results of the critic network, the policy gradient method is used to iteratively update the parameters of the actor network;

[0194] By minimizing the loss function of the critic network, the optimal load regulation strategy is learned and adapted.

[0195] In summary, the present invention achieves precise control of power load by introducing an emergency algorithm, significantly improving the response speed and flexibility of the system in dealing with peak loads. The algorithm can prioritize the control of high-emergency equipment and reduce the energy consumption of low-emergency equipment based on real-time power demand, environmental parameters and electricity costs, thereby minimizing energy consumption and electricity costs while ensuring user comfort. The energy utilization rate of the overall system is improved, and the pressure on the power system during peak power consumption is effectively alleviated.

[0196] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or other arbitrary combinations. When implemented using software, it can be implemented in whole or in part in the form of a computer program product, which includes one or more computer instructions. When loading and executing computer program instructions on a computer, the process or function according to the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium.

[0197] In addition, each functional unit in each embodiment of the present application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into one module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of a software functional module. If the above-mentioned integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. The storage medium can be a read-only memory, a disk or an optical disk, etc.

[0198] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A load control system based on a smart energy unit, characterized in that: The system comprises: A data acquisition unit, including a variety of sensors and data acquisition equipment, is used to collect real-time data related to the load and transmit it to the smart energy unit in real time. The real-time data related to the load includes electrical quantity data and operating parameters of the air conditioner, environmental data, and multi-way switch status quantities; The smart energy unit is used to receive the real-time data transmitted by the data acquisition unit, and calculate the urgency of each load according to the preset urgency algorithm, formulate and optimize the load control strategy based on the urgency calculation result, and generate corresponding load control instructions; the control execution unit is used to receive the load control instructions issued by the smart energy unit, and control the corresponding area according to the load control instructions, including the start and stop of the air conditioner, mode switching, temperature setting, wind speed adjustment, and control of the switch state quantity, so as to realize flexible control of the load; The urgency algorithm specifically includes: The change of air conditioning system over time is described by the thermodynamic equation: ； In the formula, express Moment The indoor temperature of the area for About the moment The derivative of The rate of change of indoor temperature in each area over time; express The outdoor temperature at the time; For the The heat loss rate of the area, For the The power conversion efficiency of each region; For air conditioning system Moment The power input of each zone is positive for heating and negative for cooling; Calculate minimum heating or cooling time , the calculation formula is: ； In the formula, For the Target temperature set for each zone; is the maximum heating power, is the maximum cooling power; is the air conditioning system parameter; It is a function of time calculated according to the air conditioning system parameters and the thermodynamic equations; The formula for calculating urgency is: ； In the formula, For the The urgency of the area; For the The start time of the regional load regulation request, For the The expected end time of the regional load regulation request; The system comprehensive power is expressed as: ； In the formula, Indicates the total power supply. represents the power from the grid, represents the power from photovoltaic power generation, Indicates the power from the energy storage unit; Using reinforcement learning algorithms, according to the current urgency and the system comprehensive power, calculate the optimal power that should be allocated to each load at each moment. The goal of reinforcement learning is to optimize energy allocation so that energy costs are minimized while meeting the load regulation requirements. The reward function of the reinforcement learning algorithm is The definition is as follows: ； In the formula, for The electricity price at the time; For the The load control requirements of each region , Both are weights to balance user comfort and energy costs; for The set of all regions requesting load regulation at the moment; The reinforcement learning controller optimizes the load control at each moment according to the reward function. When allocating the total power output by the reinforcement learning controller to each area, the minimum urgency priority rule is adopted. Areas with low urgency will be given priority in power support to ensure that emergency tasks are handled in a timely manner. ； In the formula, express Moment The power allocated to each area; The system dynamically adjusts the load control strategy according to the actual situation at each moment, continuously updates the urgency, and optimizes the load control strategy through feedback from historical data.

2. According to claim 1, a load control system based on a smart energy unit is characterized in that: The electrical quantity data includes instantaneous values ​​and cumulative values. The instantaneous values ​​include A, B, and C phase voltages and currents, total and phase apparent power, total and phase active power, total and phase reactive power, and total and phase power factor. The cumulative values ​​include positive active energy indication, reverse active energy indication, inductive reactive energy indication, and inductive reactive energy indication. The operating parameters are determined according to the type of air conditioner: For multi-split air-conditioning units, the operating parameters include the start / stop status of the indoor and outdoor units, the operating mode, the air-conditioning set temperature, the indoor temperature, the indoor humidity, the set wind speed, the fault code, the outdoor unit rated power and the compressor operating frequency; for water circulation central air-conditioning systems, the operating parameters include the cold water or hot water outlet temperature and the set value, the cold water or hot water inlet temperature and the set value, the current value of the host operating current and the ratio between the set value; for split air-conditioning units, the operating parameters include the start / stop status, the operating mode, the air-conditioning set temperature, the indoor temperature and the set wind speed; The environmental data specifically refers to the ambient temperature and humidity at the end of the air-conditioning system.

3. A load control system based on a smart energy unit according to claim 1, characterized in that: The smart energy unit is divided into a main control part and an expansion part; The main control part specifically includes: The core processing module includes the central processing unit, memory, clock and security chip; Human-computer interaction module, including USB interface, LCD screen, buttons, indicator lights and buzzer, used to realize interactive operation, information feedback and status prompts between users and equipment; A remote communication module, including a wireless module communication interface and a network communication interface 1, for realizing a remote communication connection, wherein the wireless module communication interface is used to access a wireless public network communication module, a wireless private network communication module and other remote communication modules; A local communication module, including a bus communication interface and a network communication interface 2, for realizing a local communication connection; The power module includes a main power supply and a backup power supply, which are used to power the system; the backup power supply uses a supercapacitor as an energy storage element. When the main power supply is insufficient or disappears, the backup power supply is automatically put into use, and reset and restart are not allowed; during the backup power supply period, when the smart energy unit remains continuously online, it communicates with the main station for no less than 3 times, and when the smart energy unit is not continuously online, it re-establishes a normal connection with the main station for no less than 1 time; The extension part is connected to the main control part for communication, has an independent microprocessor, and is used to expand the local IoT communication and load management auxiliary functions, including RS485 communication module, HPLC communication module, dual-mode communication module, wireless communication module, state quantity input module and switch quantity output module; Among them, the dual-mode communication module is specifically HPLC+HRF dual-mode communication.

4. A load control system based on a smart energy unit according to claim 3, characterized in that: The core processing module includes: The load control submodule is used to calculate the urgency of each load and formulate and optimize the load control strategy, as well as generate corresponding load control instructions; Data processing submodule, used for data storage, air conditioner operation status identification and air conditioner data statistical analysis; Data query and upload submodule, used for data query, event record query and active data upload; The event recording submodule is used to record demand response-related events, air conditioning operation parameter change records, and abnormal operation event records; The parameter setting and query submodule is used for setting, querying, clearing and resetting clock call and synchronization, terminal configuration parameters, communication parameters, event recording parameters, collection and upload task parameters, equipment parameters, and air conditioning unit control parameters; Data forwarding submodule, used for transparent forwarding of DL / T698.45 protocol data between the master station and field equipment; The upgrade protection submodule is used for local or remote software upgrades and hardware or software security protection.

5. A load control system based on a smart energy unit according to claim 1, characterized in that: The reinforcement learning controller uses a deep deterministic policy gradient algorithm for training and decision-making, which includes: An actor network: used to map system states to deterministic load control instructions; A critic network: used to evaluate the action value of the load control instructions generated by the actor network in the current state, and guide the optimization direction of the load control strategy by calculating the expected return; Policy optimization mechanism: The policy gradient method is used to iteratively update the parameters of the actor network based on the evaluation results of the critic network to minimize the loss function of the critic network, thereby achieving learning and adaptation of the optimal load regulation strategy.

6. A load control system based on a smart energy unit according to claim 4, characterized in that: For event records related to demand response, the event record types include load resource registration events, load resource cancellation events, demand response notification events, demand response user confirmation events, air conditioning operation parameter change events, and operation abnormality events. Among them, operation abnormality events include equipment failure records, clock deviation, system time modification, meter reading failure, lower-level equipment loss, multi-split air conditioning unit equipment report changes, monthly communication traffic excess, external power supply interruption or recovery, software upgrade or version change, hardware reset, data initialization, factory parameter restoration and event initialization.

7. A load control method of a load control system based on a smart energy unit according to any one of claims 1 to 6, characterized in that: The method comprises: Collect load-related data in real time, including electrical quantity data, operating parameters, environmental data, and multi-way switch status of air conditioners, and transmit them to the smart energy unit in real time; The smart energy unit receives the transmitted real-time data and calculates the urgency of each load according to the preset urgency algorithm, including: Describe the changes of air conditioning system over time through thermodynamic equations and calculate the minimum heating or cooling time; Calculate the urgency of load regulation in each area based on the minimum heating or cooling time; Using the reinforcement learning algorithm, the optimal power that should be allocated to each load at each moment is calculated based on the current urgency and system comprehensive power. The goal of reinforcement learning is to optimize energy distribution so that energy costs are minimized while meeting load control requirements. The reward function of the reinforcement learning algorithm is The definition is as follows: ； In the formula, for The electricity price at the time; For the The load control requirements of each region , Both are weights to balance user comfort and energy costs; for The set of all regions requesting load regulation at the moment; The reinforcement learning controller optimizes the load control at each moment according to the reward function. When allocating the total power output by the reinforcement learning controller to each area, the minimum urgency priority rule is adopted. Areas with low urgency will be given priority in power support to ensure that emergency tasks are handled in a timely manner. The system dynamically adjusts the load control strategy according to the actual situation at each moment, and continuously updates the urgency, while optimizing the load control strategy through the feedback of historical data; Based on the emergency calculation results, formulate and optimize the load control strategy and generate corresponding load control instructions; The load control instructions are issued to control the corresponding area, including the start and stop of the air conditioner, mode switching, temperature setting, wind speed adjustment, and control of the switch state quantity, so as to achieve flexible load control.

8. The load control method according to claim 7, characterized in that: The training and decision-making steps of the reinforcement learning algorithm include: Using actor networks to map system states to deterministic load control instructions; The critic network evaluates the action value of the load control instructions generated by the actor network in the current state; According to the evaluation results of the critic network, the policy gradient method is used to iteratively update the parameters of the actor network; By minimizing the loss function of the critic network, the optimal load regulation strategy is learned and adapted.

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