CN116544955B

CN116544955B - Load regulation and control method, device and system

Info

Publication number: CN116544955B
Application number: CN202310813883.7A
Authority: CN
Inventors: 宋诗; 陈潇
Original assignee: Sunshine Hui Carbon Technology Co ltd
Current assignee: Sunshine Hui Carbon Technology Co ltd
Priority date: 2023-07-03
Filing date: 2023-07-03
Publication date: 2023-11-24
Anticipated expiration: 2043-07-03
Also published as: CN116544955A

Abstract

The invention discloses a load regulation and control method, a load regulation and control device and a load regulation and control system. The load regulation method comprises the following steps: acquiring a power grid dispatching instruction and power consumption data of a power user, and calculating the total task of the system according to the power grid dispatching instruction and the power consumption data; acquiring equipment information, equipment characteristic models and equipment operation data of a system, and dividing the adjustable load in the total task into a plurality of subtasks by taking an optimization constraint condition as an optimization target; wherein, the combination mode of the subtasks comprises a time dimension and/or an amplitude dimension, and one subtask comprises a regulation strategy for one or more devices; and generating a control instruction of the equipment according to the regulation strategy of the subtask so as to perform operation control on each piece of equipment. The embodiment of the invention can improve the efficiency of load regulation by providing the high-frequency complementary load regulation method.

Description

Load regulation and control method, device and system

Technical Field

The present invention relates to the field of energy technologies, and in particular, to a load regulation method, device, and system.

Background

Various loads exist in energy systems such as a virtual power plant system, a zero-carbon system, a comprehensive energy system and the like, and in order to realize reasonable utilization of energy, the loads need to be regulated and controlled, so that the energy system can be also called a load regulation and control system. Among various loads, the load of high-power equipment such as air conditioners and heat pumps is the key point of regulation. Along with the development of energy technology, the load types in the energy system are more complex, and the application scenes are also various, however, in the prior art, when the load is regulated and controlled, a single regulation and control mode is still adopted, and the problem of low regulation and control efficiency exists.

Disclosure of Invention

The invention provides a load regulation and control method, a load regulation and control device and a load regulation and control system so as to improve the efficiency of load regulation and control.

According to an aspect of the present invention, there is provided a load regulation method including:

acquiring a power grid dispatching instruction and power consumption data of a power user, and calculating the total task of the system according to the power grid dispatching instruction and the power consumption data;

acquiring equipment information, equipment characteristic models and equipment operation data of a system, and dividing the adjustable load in the total task into a plurality of subtasks by taking an optimization constraint condition as an optimization target; wherein, the combination mode of the subtasks comprises a time dimension and/or an amplitude dimension, and one subtask comprises a regulation strategy for one or more devices;

and generating a control instruction of the equipment according to the regulation strategy of the subtask so as to perform operation control on each piece of equipment.

Optionally, after the operation control is performed on each device, the method further includes:

collecting operation data of equipment in the subtasks in real time, and adjusting the control instruction of the corresponding equipment according to the operation data so as to enable the regulation strategy of the subtasks to form a closed loop;

and according to the overall operation data of the operation data computing system, adjusting the combination mode of the overall tasks and/or the subtasks according to the overall operation data so as to enable the overall task regulation strategy to form a closed loop.

Optionally, the combining manner of the plurality of subtasks in the time dimension specifically includes:

dividing the total task into a plurality of time periods in a whole demand response period; each time period is matched with a corresponding task amplitude;

the time period comprises a plurality of time periods, and each time period is used for executing one subtask; the task amplitude of the time period is the sum of amplitude vectors of a plurality of subtasks.

Optionally, the combination manner of the subtasks in the amplitude dimension specifically includes:

and in the time period, a plurality of subtasks are simultaneously executed, and the task amplitude in the time period is the sum of the amplitudes of the subtasks.

Optionally, the combination manner of the plurality of subtasks in the time dimension and the amplitude dimension specifically includes:

the time period comprises a plurality of time periods, a plurality of subtasks are executed in each time period, the task amplitude in the time period is the sum of the amplitudes of the subtasks corresponding to the time period, and the task amplitude in the time period is the sum of the amplitude vectors of the time periods corresponding to the time period.

Optionally, the optimization constraint includes: at least one of the lowest energy consumption after combination, the highest energy efficiency after combination, the lowest carbon emission coefficient after combination and the optimal efficiency after combination.

Optionally, the control instruction includes: at least one of a regulation profile for the device, a regulation amplitude for the device, and a regulation cycle for the device.

Optionally, the device feature model includes: at least one of an operation characteristic curve, a regulation characteristic curve, a cost characteristic curve and a carbon bank characteristic curve;

the operation characteristic curve represents the relation between equipment power and efficiency, the regulation characteristic curve represents the relation between equipment power, environment temperature and comfort, the cost characteristic curve represents the relation between cost and time, and the carbon row characteristic curve represents the relation between carbon row and time.

Optionally, the equipment characteristic model is divided into a plurality of types according to different working conditions and temperatures and humidities;

and/or the device feature model is divided into a plurality of types according to the degree of refinement.

Optionally, the device information includes: at least one of device geographic location, microscopic location, parameters, cost, task and task history data.

Optionally, the method for generating the control instruction of the device according to the regulation strategy of the subtask specifically includes:

determining an efficiency optimization combination of each device according to the device characteristic model of each device under the subtask;

and determining a control instruction of the corresponding equipment according to the efficiency optimization combination.

Optionally, the number of the plurality of devices determines a standard, which specifically includes:

if the demand response quantity of the subtask is smaller, the quantity of enabled devices in the subtask is smaller;

and if the demand response quantity of the subtask is larger, the number of enabled devices in the subtask is larger.

Optionally, the system is a virtual power plant system, a zero-carbon system or a comprehensive energy system;

the load includes: at least one of an air conditioner and a heat pump.

Optionally, the step of calculating the total task is performed in a main controller, and the step of calculating the subtasks is performed in a co-controller; the subtasks are in one-to-one correspondence with the cooperative controllers;

or, the calculation steps of the total task and the subtask are executed in the main controller; and the cooperative controller is used for sending control instructions to the corresponding equipment according to the respective subtasks.

Optionally, the load regulation method further includes:

the total tasks are updated in real time according to the power grid dispatching instruction and the power consumer electricity consumption data;

and the subtasks are updated in real time according to the total tasks, the equipment information, the equipment feature model, the equipment operation data and the optimization constraint conditions.

Optionally, the load regulation method further includes:

and the calculation strategy of the total task and the calculation strategy of the subtasks are updated according to the set conditions.

According to another aspect of the present invention, there is provided a load control device comprising:

the task management module is used for acquiring power grid dispatching instructions, power user power consumption data, equipment information of the system, equipment characteristic models and equipment operation data; according to a power grid dispatching instruction and a total task of the power consumer electricity consumption data computing system, and with optimization constraint conditions as optimization targets, dividing an adjustable load in the total task into a plurality of subtasks; wherein, the combination mode of the subtasks comprises a time dimension and/or an amplitude dimension, and one subtask comprises a regulation strategy for one or more devices;

The strategy management module is used for generating a control instruction of the equipment according to the regulation strategy of the subtasks;

and the operation monitoring module is used for performing operation control on each device according to the control instruction.

Optionally, the operation monitoring module is further configured to: and collecting the operation data of the equipment in the subtasks in real time, and adjusting the control instruction of the corresponding equipment according to the operation data so as to enable the regulation strategy of the subtasks to form a closed loop.

Optionally, the load control device further comprises:

and the monitoring evaluation module is used for calculating the overall operation data of the system according to the operation data, and adjusting the combination mode of the overall tasks and/or the subtasks according to the overall operation data so as to enable the overall task regulation strategy to form a closed loop.

Optionally, the load control device further comprises:

and the equipment library is used for storing the equipment information and carrying out full life cycle management on the equipment of the adjustable resource so as to be beneficial to matching the power grid dispatching instruction with corresponding equipment.

Optionally, the load control device further comprises:

and the model library is used for storing the equipment characteristic model and carrying out full life cycle management on the equipment characteristic model so as to facilitate the calculation of the combination of a plurality of subtasks.

According to another aspect of the present invention, there is provided a load regulation system comprising: a main controller, a co-controller and a device;

wherein the main controller is connected with a plurality of auxiliary controllers, and the auxiliary controllers are connected with one or a plurality of devices; the main controller and the auxiliary controller are used for executing the load regulation method according to any embodiment of the invention.

The embodiment of the invention provides a high-frequency complementary load regulation and control method by researching and analyzing various complex data and various complex scenes in an energy system. Specifically, the data to be acquired are divided into power grid dispatching instructions, power consumer electricity data, equipment information of a system, equipment characteristic models, equipment operation data and optimization constraint conditions. In the execution process, the total task of the system can be calculated according to the power grid dispatching instruction and the power consumption data of the power consumer; according to the equipment information, the equipment characteristic model, the equipment operation data and the optimization constraint conditions of the system, the adjustable load in the total task can be divided into a plurality of subtasks; and further converting the regulation strategy of the subtasks into control instructions of the equipment. The equipment characteristic model is participated in load regulation, so that characteristic differences of different equipment and scenes are taken into consideration, the regulation is performed more pertinently, and the regulation is more accurate. The subtasks may be combined in a time dimension and/or an amplitude dimension. The combination of the time dimension reduces the operation of the equipment in the time dimension, and realizes the regulated high-frequency characteristic; the combination of amplitude dimensions complements the amplitudes of the operation of different devices, realizes the complementation characteristic of regulation and control, and has higher regulation and control efficiency and finer regulation and control. In summary, the embodiment of the invention can improve the efficiency of regulating and controlling loads such as an air conditioner, a heat pump and the like, and realize fine regulation and control, thereby reducing the energy cost and being beneficial to accelerating the construction of a novel energy system taking new energy as a main body.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of a load control method according to an embodiment of the present invention;

FIG. 2 is a flowchart of another load control method according to an embodiment of the present invention;

FIG. 3 is a flowchart of another load control method according to an embodiment of the present invention;

FIG. 4 is a schematic diagram of a sub-task combination in a time dimension according to an embodiment of the present invention;

FIG. 5 is a schematic diagram of a subtask combination in the amplitude dimension according to an embodiment of the present invention;

FIG. 6 is a schematic diagram of a subtask in a combination of time dimension and amplitude dimension according to an embodiment of the present invention;

FIG. 7 is a schematic diagram of another combination of time dimension and amplitude dimension of a subtask according to an embodiment of the present invention;

FIG. 8 is a flowchart of another load control method according to an embodiment of the present invention;

FIG. 9 is a schematic diagram of a load control system according to an embodiment of the present invention;

fig. 10 is a schematic structural diagram of a load control device according to an embodiment of the present invention;

FIG. 11 is a schematic diagram of another load control device according to an embodiment of the present invention;

FIG. 12 is a schematic view of a load control device according to an embodiment of the present invention;

FIG. 13 is a schematic view of a load control device according to an embodiment of the present invention;

fig. 14 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.

Detailed Description

In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.

It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

The embodiment of the invention provides a load regulation and control method which can be applied to various levels of governments, various organizations, various enterprises, families or individuals and the like, is suitable for regulating and controlling any one or more loads in a load regulation and control system, and is particularly suitable for regulating and controlling the loads of high-power equipment such as air conditioners or heat pumps and the like. The load regulation method may be performed by a load regulation device, which may be implemented in software and/or hardware, which may be configured in a controller of a load regulation system. The load control system may be, for example, a virtual power plant system, a zero-carbon system, or a comprehensive energy system, etc.

The virtual power plant system is a power coordination management system for realizing the aggregation and coordination optimization of DGs (distributed power supplies), energy storage systems, controllable loads, DERs (distributed energy sources) of electric vehicles and the like through advanced information communication technology and software and hardware systems, and is used as a special power plant to participate in the power market and the power grid operation. The core of the virtual power plant concept can be summarized as "communication" and "aggregation". Key technologies of the virtual power plant mainly comprise a coordination control technology, an intelligent metering technology and an information communication technology. The most attractive function of virtual power plants is to be able to aggregate the DER participation in the power market and auxiliary service market operations, providing management and auxiliary services for distribution and transmission networks. The virtual power plant can be used as a positive power plant for supplying power and peak shaving to the system, and can be used as a negative power plant for responding through a load side to cooperate with the system to fill the valley. Specifically, when electricity utilization is intense, the virtual power plant can play the role of a positive power plant, and from a supply side, distributed energy DERs such as a distributed new energy source, an energy storage system, an electric automobile and the like are aggregated and optimized to supply power to a power grid, so that the load demand of traditional power is met, meanwhile, the virtual power plant can play the role of a negative power plant, and from the demand side, the controllable load peak-shifting electricity utilization is coordinated, and the problem of the shortage of power supply and demand is relieved.

The comprehensive energy system is characterized in that advanced physical information technology and innovation management mode are utilized in a certain area, and multiple energy sources such as coal, petroleum, natural gas, electric energy and heat energy in the area are integrated, so that coordinated planning, optimized operation, collaborative management, interactive response and complementary interaction among multiple heterogeneous energy subsystems are realized. The comprehensive energy system can effectively improve the energy utilization efficiency and promote the sustainable development of energy while meeting the diversified energy utilization requirements in the system. In the process of planning, building, running and the like, the comprehensive energy system forms an energy production, supply and marketing integrated system by organically coordinating and optimizing links such as energy generation, transmission and distribution (energy network), conversion, storage, consumption and the like. The integrated energy system comprises: the energy supply network (such as power supply, air supply, cold supply/heat supply and other networks), the energy exchange link (such as a CCHP unit, a generator set, a boiler, an air conditioner, a heat pump and the like), the energy storage link (such as electricity storage, air storage, heat storage, cold storage and the like), the terminal comprehensive energy supply unit (such as a micro-grid) and a large number of terminal users.

Fig. 1 is a flowchart of a load control method according to an embodiment of the present invention. Referring to fig. 1, the load regulation method includes the steps of:

S110, acquiring a power grid dispatching instruction and power user electricity consumption data, and calculating the total task of the system according to the power grid dispatching instruction and the power user electricity consumption data.

The virtual power plant system is taken as an example, and can be used for a scene that one to a plurality of devices of a single power consumer participate in the virtual power plant, and can also be used for a scene that an aggregator aggregates a plurality of devices of a plurality of power consumers to participate in the virtual power plant. The grid dispatching instructions may be based on bid data in demand responses determined by the virtual power plant aggregator or the power consumer and the virtual power plant operator. The power grid dispatching instruction comprises power grid dispatching task data D (t), for example, whether a load regulation system is required to fill valleys or stagger peaks and the specific electric quantity wattage value, wherein the power grid dispatching task data D (t) is controllable data. The electricity consumer electricity consumption data L (t) is a baseline load based on virtual power plant rules, which needs to ensure supply, not to be regulated.

Illustratively, the calculation of the overall task T (T) may employ the following formula:

T(t)=L(t)+D(t)+ꝺ(t)

wherein ꝺ (t) is a function of the same direction as D (t) for setting the target margin.

S120, acquiring equipment information, equipment characteristic models and equipment operation data of a system, and dividing the adjustable load in the total task into a plurality of subtasks by taking an optimization constraint condition as an optimization target; wherein the combination of the plurality of subtasks comprises a time dimension and/or an amplitude dimension, and one subtask comprises a regulation strategy for one or more devices.

Wherein the device information is basic data of the device with the regulated load, such as geographical location, microscopic location, parameters, cost, tasks and/or task history data of the device, etc. The plant characterization model refers to various characterization curves of the plant for characterizing various characteristics of the plant, such as operating, regulatory, cost, and/or carbon line characteristics, etc. The operation characteristic curve represents the relation between the power and the efficiency of the equipment, the regulation characteristic curve represents the relation between the power, the ambient temperature and the comfort level of the equipment, the cost characteristic curve represents the relation between the cost and the time, and the carbon row characteristic curve represents the relation between the carbon row and the time. Device operational data is various parameters of the device operation, such as power, efficiency, voltage and/or current, etc., at a historical or current time.

The equipment characteristic models are divided into a plurality of types according to different working conditions and temperatures and humidity; and the equipment characteristic models are divided into a plurality of types according to different degrees of refinement so as to adapt to different scenes.

The optimization constraints are goals that require additional implementation in achieving the overall task, such as lowest combined energy consumption, highest combined energy efficiency, lowest combined carbon-emission coefficient, and/or optimal combined efficiency. The setting of the optimization constraint condition is beneficial to optimizing the regulation and control result. The setting basis of the optimization constraint condition can be policies, regulations, plans, standards, business scope and/or processes and the like of the government, various enterprises and public institutions, families, aggregators and the like participating in the virtual power plant to develop the virtual power plant work.

Taking air conditioners (or heat pumps) in a virtual power plant system as an example, the controllable load in the total task is a combination of sub-tasks Ci (t) of N air conditioners (or heat pumps), namely，i=[1,N]，t=[t1,t2]T1 to t2 are demand response periods.

The combination of the time dimensions is to divide one time period in the prior art into a plurality of time periods, for example, two devices (device a and device B) have a power XX kw in 1min, the divided subtask 1 is a power XX1 kw in the first 0.5min of device a, and the divided subtask 2 is a power XX2 kw in the last 0.5min of device B, xx= (xx1+xx2)/2.

The combination mode of the amplitude dimension is to divide a period of time into a plurality of subtasks to be executed simultaneously, for example, the power consumption of two devices (a device a and a device B) in 1min is ZZ kilojoules, the divided subtask 1 is the power consumption of the device a in 1min is ZZ3 kilojoules, the divided subtask 2 is the power consumption of the device B in 1min is ZZ4 kilojoules, and zz=zz3+z4.

S130, generating control instructions of the equipment according to the regulation and control strategies of the subtasks so as to perform operation control on each piece of equipment.

Each subtask can comprise not only one device but also a plurality of devices, and for the situation of the plurality of devices, the regulation strategy of the subtask can be regarded as the overall goal of the plurality of devices, and the regulation strategy, namely the control instruction, of each device can be obtained through optimization. Such as a regulatory profile for the device, a regulatory amplitude for the device, and/or a regulatory period for the device, etc.

Fig. 2 is a flowchart of another load control method according to an embodiment of the present invention. Referring to fig. 2, on the basis of the above embodiments, optionally, after S130, performing operation control on each device, the method further includes:

s140, collecting operation data of the equipment in the subtasks in real time, and adjusting control instructions of the corresponding equipment according to the operation data so as to enable the regulation strategy of the subtasks to form a closed loop.

The running data of the equipment comprise index data such as power, load and the like, and the execution condition of the subtasks on the equipment can be obtained through the running data. And comparing the operation data with the control instruction to obtain whether the actual operation of the equipment meets the requirement of the control instruction, and adjusting the operation of the equipment in real time according to the difference between the operation data and the control instruction so as to realize the closed-loop control of the subtasks.

S150, calculating overall operation data of the system according to the operation data, and adjusting a combination mode of the overall tasks and/or the subtasks according to the overall operation data so as to enable the overall task regulation strategy to form a closed loop.

The overall operation data is the sum of the operation data of a plurality of subtasks, and the health states of the subtasks and the equipment can be calculated and evaluated according to the execution condition of each subtask on the equipment, so that the effect of the whole regulation strategy is evaluated. The overall operation data is compared with the overall tasks, so that data is provided for optimization regulation; if the monitoring and evaluation data exceeds the preset threshold, the regulation strategy is correspondingly adjusted according to the difference between the monitoring and evaluation data, so that closed-loop management of the total task is realized.

Fig. 3 is a flowchart illustrating yet another load control method according to an embodiment of the present invention. Referring to fig. 3, the load regulation method includes the steps of:

s210, acquiring a power grid dispatching instruction and power user electricity consumption data, and calculating the total task of the system according to the power grid dispatching instruction and the power user electricity consumption data.

S220, acquiring equipment information, equipment characteristic models and equipment operation data of the system, and dividing the adjustable load in the total task into a plurality of subtasks by taking the optimization constraint condition as an optimization target.

S230, determining an efficiency optimization combination of each device according to the device characteristic model of each device under the subtask; and determining the control instruction of the corresponding equipment according to the efficiency optimization combination.

S240, collecting operation data of the equipment in the subtasks in real time.

S250, judging whether the operation data of the equipment is consistent with the control instruction; if yes, executing S260; otherwise, return to S230.

And S260, calculating the overall operation data of the system according to the operation data in real time.

S270, judging whether the overall operation data of the system accords with the overall task; if yes, executing S280; otherwise, return to S220.

S280, ending. The regulation of the full cycle of the response ends.

The embodiment of the invention further improves the accuracy and efficiency of the regulation strategy by realizing closed-loop control on subtasks and total tasks.

The sub-tasks in the above embodiments may be combined in a time dimension and an amplitude dimension in various ways, and several of them are described below, but the present invention is not limited thereto.

Fig. 4 is a schematic diagram of a subtask combination in a time dimension according to an embodiment of the present invention. Referring to fig. 4, in an embodiment of the present invention, optionally, a combination manner of multiple subtasks in a time dimension specifically includes: the total task is divided into a plurality of time periods (T1-Tx) within the demand response full period (T1-T2). One time period is one time slice, and the subtasks are repeatedly executed in a plurality of time periods.

Each time period is matched with a corresponding task amplitude P, and illustratively, in the time period T1, the task amplitude is P1, and the task amplitudes of the time periods are equal. In other embodiments, the task amplitude for each time period may also be set to be unequal.

The time period comprises a plurality of time periods, corresponding to a plurality of subtasks (C1-Cm), and executing one subtask in each time period; the task amplitude of the time period is the sum of the amplitude vectors of the plurality of subtasks.

More specifically, taking load adjustment of m air conditioners (or heat pumps) as an example, one air conditioner (or heat pump) corresponds to one sub-task. In a time period, the task amplitude is set to be P0, the number of the subtasks is m, the m subtasks are ordered according to time, the first subtask is executed first, the power of the air conditioner (or heat pump) under the subtask is P1, and the last subtask is executed last, and the power of the air conditioner (or heat pump) under the subtask is Pm. Then the total power of the air conditioner (or heat pump) is p0=p1+ … +pm during this time period. In other embodiments, it may also be configured that the powers of the air conditioners (or heat pumps) under the respective subtasks are different, specifically determined according to the device information, the device feature model, the device operation data, and the like of the air conditioners (or heat pumps).

Fig. 5 is a schematic diagram of a subtask combination in an amplitude dimension according to an embodiment of the present invention. Referring to fig. 5, in an embodiment of the present invention, optionally, a combination manner of a plurality of subtasks in an amplitude dimension specifically includes: during the time period, a plurality of subtasks are simultaneously executed, and for example, during the time period T1, subtasks C1 through Cm are simultaneously executed. The task amplitude during this time period T1 is the sum of the amplitudes of the plurality of subtasks.

More specifically, taking load adjustment of m air conditioners (or heat pumps) as an example, one air conditioner (or heat pump) corresponds to one sub-task. In a time period, the task amplitude is set to be that the power of an air conditioner (or a heat pump) is P1, the number of the subtasks is m, the m subtasks are executed simultaneously, the powers of the air conditioner (or the heat pump) under each subtask are P11-Pm 1 respectively, and then P1=P11+ … +Pm1. Illustratively, the powers of the air conditioners (or heat pumps) of the m subtasks are set equal or unequal, and are specifically determined according to the equipment information, the equipment characteristic model, the equipment operation data and the like of the air conditioners (or heat pumps).

In the above embodiments, optionally, when the number of subtasks is fixed, if the demand response amount of the total task is smaller, the magnitude of the subtasks divided in the magnitude dimension is smaller; if the demand response of the total task is larger, the subtasks divided in the amplitude dimension have larger amplitudes. When the amplitude of the subtasks is fixed, if the demand response quantity of the total task is smaller, the number of the divided subtasks is smaller; if the demand response of the total task is larger, the number of divided subtasks is larger. The load can be adaptively adjusted according to the application scene and different specific working conditions, and the precision and the efficiency of the load regulation and control are considered.

Fig. 6 is a schematic diagram of a subtask combination in a time dimension and an amplitude dimension according to an embodiment of the present invention. Referring to fig. 6, in an embodiment of the present invention, optionally, a combination of a plurality of subtasks in a time dimension and an amplitude dimension specifically includes: the total task is divided into a plurality of time periods (T1-Tx) within the demand response full period (T1-T2). One time period is one time slice, and the subtasks are repeatedly executed in a plurality of time periods.

The time period includes a plurality of time periods in each of which a plurality of subtasks are performed. Illustratively, taking load adjustment of n×m air conditioners (or heat pumps) as an example, one air conditioner (or heat pump) corresponds to one sub-task. One time period includes m time periods, each of which performs n subtasks. Specifically, subtasks C11 to Cn1 are executed simultaneously in the first time period, and subtasks C1m to Cnm are executed simultaneously in the mth time period, ….

The task amplitude in the time period is the sum of the amplitudes of the plurality of subtasks corresponding to the time period, and the task amplitude in the time period is the average task amplitude of the plurality of time periods corresponding to the time period. For example, the subtasks C11 to Cn1 are simultaneously executed in the first period, and the powers of the air conditioners (or heat pumps) under the subtasks are respectively P111 to Pn11, so that the task amplitude p11=p111+ … +pn11 in the first period. The subtasks C1m to Cnm are simultaneously executed in the nth time period, and the powers of the air conditioners (or heat pumps) under the subtasks are respectively P1m1 to Pnm1, so that the task amplitude P1 n=p1m1+ … +pnm1 in the nth time period. The task amplitude of the time period T1 is the sum of the amplitude vectors of the m time periods, i.e., p1=p11+ … +p1n.

Therefore, the embodiment of the invention can complement each other in amplitude and time through different subtask combinations, in particular longitudinal amplitude superposition and transverse time superposition, accurately regulate and control the demand side, fully utilize different load characteristics and different load regulation and control, and realize accurate flexible regulation and control.

On the basis of the above embodiments, optionally, S130, a method for generating a control instruction of a device according to a subtask regulation policy specifically includes: and determining the efficiency optimization combination of each device according to the device characteristic model of each device under the subtask. Fig. 7 is a schematic diagram of another combination of time dimension and amplitude dimension of a subtask according to an embodiment of the present invention. Referring to fig. 7, different shapes represent feature models of different devices, each of which is combined in the time and amplitude dimensions by efficiency optimization. In a period of time, the devices with the same characteristic model can be used for combination, and the devices with different characteristic models can also be used for combination. Then, according to the efficiency optimization combination, the control instruction of the corresponding equipment is determined. Specifically, corresponding control instructions are generated according to the feature models of the devices.

In the foregoing embodiments, optionally, the number determining criteria of the plurality of devices specifically include: if the demand response quantity of the subtask is smaller, the quantity of enabled devices in the subtask is smaller; if the demand response amount of the subtask is larger, the number of enabled devices in the subtask is larger. The load can be adaptively adjusted according to the application scene and different specific working conditions, and the precision and the efficiency of the load regulation and control are considered.

The following describes the specific implementation of the embodiment of the present invention further by taking an air conditioning (or heat pump) load as an example. By adopting the mode of combining the sub-tasks in the embodiments, the peak clipping or valley filling of a plurality of air conditioners (or heat pumps) can be staggered according to the human body induction model. Because of the use of a multi-tasking combination, such as a combination in the time dimension, multiple air conditioners (or heat pumps) in the same space may not simultaneously demand response. Since a multitasking combination is employed, for example in the amplitude dimension, the regulation depth for each power consumer device can be exchanged in number. Therefore, the embodiment of the invention can realize multiple times (time dimension), multiple quantities (amplitude dimension) and low amplitude (amplitude dimension), has higher regulation frequency (time dimension), has finer regulation (time dimension and amplitude dimension), and reduces the electricity utilization influence on the power users.

In the embodiment of the invention, the regulation frequency (time dimension combination) is variable, the time frequency of the demand response is high, the period is short, and the time frequency of the demand response is low, the period is long; the depth is variable (amplitude dimension combination), the amplitude is shallow when the demand response is small, and the amplitude is deep when the demand response is large; the number of devices participating in demand response is variable (the number of subtasks or the number of devices under one subtask), the number of hours of demand response is small, and the number of hours of demand response is large. Namely, when the demand response is small, the subtasks are small, the number of combined equipment is small, the amplitude is low and the period is short; when the demand response is large, the subtasks are large, the number of combined devices is large, the amplitude is high and the period is long.

In the regulation and control process, the amplitude and the period can be continuously increased until the limit value is reached according to the difference of the demand response quantity, and the peak clipping maximum potential is that all the equipment of the system is operated at the minimum or is shut down completely, and the period is the longest to the full period of the demand response; the maximum potential of valley filling is that the equipment is all operated at the highest limit, and the period is longest to the full period of demand response. For example, multiple devices to be controlled in a space (e.g., a room) may be combined, so that one or more devices can bear the total adjustment range of the space devices, thereby improving energy efficiency and avoiding the devices from running in a low-efficiency working condition. The core logic of the embodiment of the invention is to optimally regulate and control a plurality of air conditioners (or heat pumps) to achieve the virtual power plant demand response instruction under the optimal conditions of overall economy, comfort of users and low carbon emission.

Fig. 8 is a flowchart of another load control method according to an embodiment of the present invention. Referring to fig. 8, on the basis of the above embodiments, optionally, the load regulation method further includes:

s160, the total tasks are updated in real time according to the power grid dispatching instruction and the power consumption data of the power consumers; and the subtasks are updated in real time according to the total tasks, the equipment information, the equipment characteristic model, the equipment operation data and the optimization constraint conditions.

By the arrangement, dynamic regulation and control of the load are realized. The change of the total tasks is determined by a power grid dispatching instruction and power consumption data of power users; the subtasks are varied depending on the overall task, the device information, the device feature model device operational data, and the optimization constraints. Therefore, when the power grid dispatching instruction, the power consumer power consumption data, the equipment information, the equipment characteristic model, the equipment operation data and the optimization constraint condition are changed, the total tasks and the subtasks can be updated, and the optimization is achieved again.

S170, the calculation strategy of the total task and the calculation strategy of the subtasks are updated according to the set conditions.

By the arrangement, the sustainable regulation and control of the load are realized. The setting condition may be that the periodic upgrade is performed according to the setting time, and the setting condition may also be that an upgrade instruction is received. The basis for computing the policy upgrade may be history, changes in real-time data, learning algorithms to monitor the evaluation data, etc.

Fig. 9 is a schematic structural diagram of a load control system according to an embodiment of the present invention. Referring to fig. 9, optionally, on the basis of the above embodiments, the load regulation system further includes: a main controller and a plurality of auxiliary controllers. Wherein, many cooperated controllers all are connected to the master controller, and a cooperated controller is connected with one or more equipment. Illustratively, there are n co-controllers, including co-controller 1 to co-controller n, each of which is connected to the master controller. M pieces of equipment are controlled under each auxiliary controller, and the 1 st auxiliary controller controls the equipment 11 to 1m; …; the nth cooperative controller controls the devices n1 to nm.

The calculation steps of the overall tasks and the subtasks are performed in the main controller or the co-controller in a plurality of different ways, and are specifically described below, but the present invention is not limited thereto. In one embodiment, optionally, the step of calculating the total task is performed in the master controller and the step of calculating the subtasks is performed in the slave controller; the subtasks are in one-to-one correspondence with the co-controllers.

The main controller receives a power grid dispatching instruction, judges whether the power grid dispatching instruction is in the maximum response capacity range, if so, calculates an optimized operation strategy of each device according to the overall operation data and the state uploaded by each co-controller and the device information, the device characteristic model and the device operation data of each device, and divides the overall task into a plurality of subtasks, wherein the subtasks do not comprise the regulation strategy of each device. The cooperative controller receives the regulation and control strategy of the corresponding subtask, issues a control instruction to the equipment according to the regulation and control strategy, collects the equipment operation data and calculates the overall operation data, and feeds back to the main controller. The main controller performs closed-loop control with the scheduling instruction as a target. The device executes the control instructions and uploads the operational data and status to the co-controller.

In another embodiment of the present invention, optionally, the calculating steps of the total task and the subtask are both performed in the main controller; and the cooperative controller is used for sending control instructions to the corresponding equipment according to the respective sub-tasks.

The main controller receives the power grid dispatching instruction, judges whether the power grid dispatching instruction is in the maximum response capacity range, if so, calculates and issues a subtask of each co-controller according to the overall operation data uploaded by each co-controller, and the subtask also comprises a regulation strategy of each device. And the cooperative controller receives the subtasks, calculates a control instruction of each device according to the operation data and the feature model of each device, and sends the control instruction to the device. The cooperative controller also collects the operation data of the equipment and performs closed-loop control by taking the subtasks as targets; and calculating overall operation data, and uploading the equipment operation data and the overall operation data to the main controller together. The device executes the control instructions and uploads the operational data and status to the co-controller.

In the above embodiments, the demand response service of the virtual power plant system is taken as an example to describe the embodiments of the present invention, but the embodiments are not limited to the demand response.

The embodiment of the invention also provides a load regulation device. Fig. 10 is a schematic structural diagram of a load control device according to an embodiment of the present invention. Referring to fig. 10, the load regulation device includes:

the task management module 100 is used for acquiring power grid dispatching instructions, power consumer electricity consumption data, equipment information of a system, an equipment characteristic model and equipment operation data; according to the power grid dispatching instruction and the total tasks of the power consumer electricity consumption data computing system, and with optimization constraint conditions as optimization targets, dividing the adjustable load in the total tasks into a plurality of subtasks; wherein, the combination mode of a plurality of subtasks comprises a time dimension and/or an amplitude dimension, and one subtask comprises a regulation strategy for one or more devices;

the policy management module 200 is configured to generate a control instruction of the device according to a regulation policy of the subtask;

and the operation monitoring module 300 is used for performing operation control on each device according to the control instruction.

The embodiment of the invention provides a high-frequency complementary load regulation and control device by researching and analyzing various complex data and various complex scenes in an energy system. Specifically, the data to be acquired are divided into power grid dispatching instructions, power consumer electricity data, equipment information of a system, equipment characteristic models, equipment operation data and optimization constraint conditions. In the execution process, the total task of the system can be calculated according to the power grid dispatching instruction and the power consumption data of the power consumer; according to the equipment information, the equipment characteristic model, the equipment operation data and the optimization constraint conditions of the system, the adjustable load in the total task can be divided into a plurality of subtasks; and further converting the regulation strategy of the subtasks into control instructions of the equipment. The equipment characteristic model is participated in load regulation, so that characteristic differences of different equipment and scenes are taken into consideration, the regulation is performed more pertinently, and the regulation is more accurate. The subtasks may be combined in a time dimension and/or an amplitude dimension. The combination of the time dimension reduces the operation of the equipment in the time dimension, and realizes the regulated high-frequency characteristic; the combination of amplitude dimensions complements the amplitudes of the operation of different devices, realizes the complementation characteristic of regulation and control, and has higher regulation and control efficiency and finer regulation and control. In summary, the embodiment of the invention can improve the efficiency of regulating and controlling loads such as an air conditioner, a heat pump and the like, and realize fine regulation and control, thereby reducing the energy cost and being beneficial to accelerating the construction of a novel energy system taking new energy as a main body.

Optionally, the operation monitoring module is further configured to: and acquiring the operation data of the equipment in the subtasks in real time, and adjusting the control instruction of the corresponding equipment according to the operation data so as to form a closed loop by the regulation strategy of the subtasks. The operation monitoring module can conduct data interaction with the equipment through technical means such as a communication interface and an API.

With continued reference to fig. 10, on the basis of the above embodiments, optionally, the load control device further includes:

the monitoring and evaluating module 400 is configured to calculate overall operation data of the system according to the operation data, and adjust a combination mode of the subtasks according to the overall operation data, so as to further optimize a regulation strategy of the subtasks. The monitoring and evaluation module 400 may include a data interface, a monitoring sub-module, and an evaluation sub-module, among others. The data interface is used for data transmission, the monitoring submodule is used for monitoring the overall operation data, and the evaluation submodule is used for adjusting the overall tasks and/or the combination modes of the subtasks according to the overall operation data.

Fig. 11 is a schematic structural diagram of another load control device according to an embodiment of the present invention. Referring to fig. 11, optionally, the monitoring and evaluating module 400 is further configured to adjust the combination of the total tasks according to the overall operation data, so that the total task regulation strategy forms a closed loop.

Illustratively, the monitoring and evaluating module 400 obtains the execution condition of each subtask on the device, outputs data to the task management module 100 and the policy management module 200, forms closed-loop management, and provides data for optimizing regulation. If the data is monitored and evaluated to exceed the preset threshold, the task management module 100 and the policy management module 200 may make corresponding adjustments.

Fig. 12 is a schematic structural diagram of another load control device according to an embodiment of the present invention. Referring to fig. 12, on the basis of the above embodiments, optionally, the load control device further includes:

the device library 500 is used for storing device information and performing full life cycle management on devices of the adjustable resources so as to facilitate matching of power grid dispatching instructions with corresponding devices.

Illustratively, the equipment library 500 can perform full life cycle management on adjustable resources such as air conditioning (or heat pump) equipment of the virtual power plant, and the equipment library 500 can store data such as equipment information, equipment feature models, and equipment operation data. Specifically, the method can be a geographic position, a microscopic position (a room or space where the device is located), parameters (indexes such as rated power and energy efficiency), cost, carbon emission, an adjusting range, tasks, task history data, evaluation data, communication parameters and the like.

The equipment library 500 may provide virtual power plant air conditioning (or heat pump) equipment resource data for one or more of the task management module 100, the policy management module 200, and the operation monitoring module 300. This arrangement makes it easier for the task management module 100 to search the equipment library 500 for resources matching the grid dispatching instructions; the task management module 100 and the strategy management module 200 are facilitated to optimize the regulation strategy; the operation monitoring module 300 is beneficial to monitoring different devices, and the device resource regulation and control efficiency and quality are improved. Based on the data of the equipment library 500, the equipment combination (such as economic, energy efficiency, carbon emission and other indexes) reaching the total virtual power plant task target can be optimized, and an optimized regulation strategy is output.

Fig. 13 is a schematic structural diagram of another load control device according to an embodiment of the present invention. Referring to fig. 13, on the basis of the above embodiments, optionally, the load control device further includes:

the model library 600 is used for storing the device feature models and performing full life cycle management on the device feature models to facilitate the calculation of a combination of multiple subtasks.

The model library 600 is illustratively capable of full lifecycle management of device feature models, storing device information for devices, device feature models, and device operational data. Specifically, an operation characteristic curve (power-efficiency curve), a regulation characteristic curve (power-room temperature-comfort curve), a cost characteristic curve (cost-time curve), a carbon-emission characteristic curve (carbon-emission-time curve), and the like may be mentioned.

Model library 600 can provide device characteristic data for task management module 200 and/or policy management module 300. The arrangement is beneficial to the task management module 200 to screen out the virtual power plant air conditioning (or heat pump) equipment combination with optimized energy efficiency, cost and carbon emission more easily; the policy management module 300 is beneficial to optimizing the device regulation and control instruction more easily and improving the device resource regulation and control efficiency and quality. The data based on the model library 600 may be optimized (e.g., economic, energy efficiency, carbon emissions, etc.) to achieve the total virtual plant mission objective for the equipment combination, outputting an optimized regulatory strategy.

Optionally, in the task management module 100, a combination manner of the plurality of subtasks in the time dimension specifically includes:

dividing the total task into a plurality of time periods in the whole demand response period; each time period is matched with a corresponding task amplitude;

the time period includes a plurality of time periods, and each time period is used for executing one subtask; the task amplitude of the time period is the average amplitude of the plurality of subtasks.

Optionally, in the task management module 100, the dividing criteria of the multiple time periods specifically include:

if the demand response of the total task is smaller, the number of sub-tasks divided in the time dimension is larger;

If the demand response of the total task is larger, the number of subtasks divided in the time dimension is smaller.

Optionally, in the task management module 100, a combination manner of the plurality of subtasks in the amplitude dimension specifically includes:

in the time period, a plurality of subtasks are simultaneously executed, and the task amplitude in the time period is the sum of the amplitudes of the plurality of subtasks.

Optionally, in the task management module 100, the number of simultaneous execution of multiple subtasks determines criteria, specifically including:

if the demand response of the total task is smaller, the subtasks divided in the amplitude dimension have smaller amplitudes;

if the demand response of the total task is larger, the subtasks divided in the amplitude dimension have larger amplitudes.

Optionally, in the task management module 100, a combination manner of the plurality of subtasks in a time dimension and an amplitude dimension specifically includes:

the time period comprises a plurality of time periods, a plurality of subtasks are executed in each time period, the task amplitude in the time period is the sum of the amplitudes of the subtasks corresponding to the time period, and the task amplitude in the time period is the average task amplitude of the time periods corresponding to the time period.

Optionally, in the task management module 100, the optimization constraint includes: at least one of the lowest energy consumption after combination, the highest energy efficiency after combination, the lowest carbon emission coefficient after combination and the optimal efficiency after combination.

Optionally, in the policy management module 200, the control instruction includes: at least one of a regulation profile for the device, a regulation amplitude for the device, and a regulation cycle for the device.

the operation characteristic curve represents the relation between the power and the efficiency of the equipment, the regulation characteristic curve represents the relation between the power, the ambient temperature and the comfort level of the equipment, the cost characteristic curve represents the relation between the cost and the time, and the carbon row characteristic curve represents the relation between the carbon row and the time.

Optionally, the equipment characteristic model is divided into a plurality of types according to different working conditions and temperatures and humidity;

and/or the device feature model is classified into a plurality of types according to the degree of refinement.

Optionally, in the policy management module 200, according to the regulation policy of the subtask, the method for generating the control instruction of the device specifically includes:

and determining the control instruction of the corresponding equipment according to the efficiency optimization combination.

Optionally, in the policy management module 200, the number of the plurality of devices determines criteria, specifically including:

if the demand response amount of the subtask is larger, the number of enabled devices in the subtask is larger.

Optionally, the system is a virtual power plant system, a zero-carbon system or a comprehensive energy system; the load includes: at least one of an air conditioner and a heat pump.

Optionally, the load control device further comprises: the updating module is used for updating the total tasks in real time according to the power grid dispatching instruction and the power consumption data of the power users; and the subtasks are updated in real time according to the total tasks, the equipment information, the equipment characteristic model, the equipment operation data and the optimization constraint conditions.

Optionally, the load control device further comprises: and the upgrading module is used for upgrading the calculation strategy of the total task and the calculation strategy of the subtasks according to the set conditions.

The load regulation device provided by the embodiment of the invention can execute the load regulation method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

The embodiment of the invention also provides a load regulation and control system. The load regulation system includes: a main controller, a co-controller and a device; the main controller is connected with a plurality of auxiliary controllers, and the auxiliary controllers are connected with one or a plurality of devices; the main controller and the auxiliary controller are used for executing the load regulation method provided by any embodiment of the invention. The load regulation and control system provided by the embodiment of the invention can execute the load regulation and control method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

The embodiment of the invention also provides electronic equipment which is arranged in the load regulation and control system. Fig. 14 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.

As shown in fig. 14, the electronic device 10 includes at least one processor 11, and a memory such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.

Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as the load regulation method.

In some embodiments, the load regulation method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the load regulation method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the load regulation method in any other suitable way (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

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

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

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.

The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.

The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.

Claims

1. A load regulation method, characterized in that it is applied to a virtual power plant system, a zero-carbon system or a comprehensive energy system, the load regulation method comprising:

acquiring a power grid dispatching instruction and power consumption data of a power user, and calculating the total task of the system according to the power grid dispatching instruction and the power consumption data; the power grid dispatching instruction comprises bid-winning data in a demand response;

acquiring equipment information, equipment characteristic models and equipment operation data of a system, and dividing the adjustable load in the total task into a plurality of subtasks by taking an optimization constraint condition as an optimization target; wherein, the combination mode of the subtasks comprises a time dimension and/or an amplitude dimension, and one subtask comprises a regulation strategy for one or more devices; the optimization constraint includes: at least one of the lowest energy consumption after combination, the highest energy efficiency after combination, the lowest carbon-emission coefficient after combination and the optimal efficiency after combination;

Generating control instructions of the equipment according to the regulation and control strategies of the subtasks so as to perform operation control on each piece of equipment;

the combination mode of the plurality of subtasks in the time dimension specifically comprises the following steps:

the time period comprises a plurality of time periods, and each time period is used for executing one subtask; the task amplitude of the time period is the sum of amplitude vectors of a plurality of subtasks;

the combination mode of the subtasks in the amplitude dimension specifically comprises the following steps:

2. The load control method according to claim 1, characterized by further comprising, after said operation control of each device:

3. The load regulation method according to claim 1, wherein the combination of the plurality of subtasks in the time dimension and the amplitude dimension specifically comprises:

4. The load control method according to claim 1, wherein the control instruction includes: at least one of a regulation profile for the device, a regulation amplitude for the device, and a regulation cycle for the device.

5. The load regulation method of claim 1, wherein the plant feature model comprises: at least one of an operation characteristic curve, a regulation characteristic curve, a cost characteristic curve and a carbon bank characteristic curve;

6. The load control method according to claim 5, wherein the equipment characteristic model is divided into a plurality of types according to different working conditions and temperatures and humidity;

7. The load regulation method of claim 1, wherein the device information comprises: at least one of device geographic location, microscopic location, parameters, cost, task and task history data.

8. The load regulation method according to claim 1, wherein the method for generating the control command of the device according to the regulation strategy of the subtask specifically comprises:

9. The load control method of claim 8 wherein the number of devices determines a standard, comprising:

10. The load regulation method of claim 1, wherein the load comprises: at least one of an air conditioner and a heat pump.

11. The load control method according to claim 1, wherein the calculation step of the total task is performed in a main controller, and the calculation step of the sub task is performed in a cooperative controller; the subtasks are in one-to-one correspondence with the cooperative controllers;

12. The load control method according to claim 1, characterized by further comprising:

13. The load control method according to claim 1, characterized by further comprising:

14. A load regulation device for use in a virtual power plant system, a zero-carbon system, or a comprehensive energy system, the load regulation device comprising:

the task management module is used for acquiring power grid dispatching instructions, power user power consumption data, equipment information of the system, equipment characteristic models and equipment operation data; according to a power grid dispatching instruction and a total task of the power consumer electricity consumption data computing system, and with optimization constraint conditions as optimization targets, dividing an adjustable load in the total task into a plurality of subtasks; wherein, the combination mode of the subtasks comprises a time dimension and/or an amplitude dimension, and one subtask comprises a regulation strategy for one or more devices; the power grid dispatching instruction comprises bid-winning data in a demand response; the optimization constraint includes: at least one of the lowest energy consumption after combination, the highest energy efficiency after combination, the lowest carbon-emission coefficient after combination and the optimal efficiency after combination;

in the time period, a plurality of subtasks are executed simultaneously, and the task amplitude in the time period is the sum of the amplitudes of the subtasks;

15. The load regulation device of claim 14, wherein the operation monitoring module is further configured to: and collecting the operation data of the equipment in the subtasks in real time, and adjusting the control instruction of the corresponding equipment according to the operation data so as to enable the regulation strategy of the subtasks to form a closed loop.

16. The load modulation device of claim 14, further comprising:

17. The load modulation device of claim 14, further comprising:

18. The load modulation device of claim 14, further comprising:

19. A load regulation system, comprising: a main controller, a co-controller and a device;

wherein the main controller is connected with a plurality of auxiliary controllers, and the auxiliary controllers are connected with one or a plurality of devices; the master controller and the co-controller are configured to perform the load regulation method of any one of claims 1 to 13.