CN113077160A - Energy optimization control method and system for smart power grid - Google Patents

Energy optimization control method and system for smart power grid Download PDF

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CN113077160A
CN113077160A CN202110396505.4A CN202110396505A CN113077160A CN 113077160 A CN113077160 A CN 113077160A CN 202110396505 A CN202110396505 A CN 202110396505A CN 113077160 A CN113077160 A CN 113077160A
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高玉明
林勇
鞠文杰
王金友
陈晓东
王瑞琪
牛蔚然
王凯
张惠智
王志梁
汪东军
牟强
李咸安
吴小川
孟凡波
付静雯
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State Grid Shandong Integrated Energy Service Co ltd
State Grid Corp of China SGCC
Weifang Power Supply Co of State Grid Shandong Electric Power Co Ltd
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State Grid Corp of China SGCC
Weifang Power Supply Co of State Grid Shandong Electric Power Co Ltd
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Abstract

The invention discloses a method and a system for energy optimization control of a smart grid, wherein the method comprises the following steps: acquiring electricity price information, carbon dioxide emission duty, user number, total time slot number, electric appliance number and power consumption information of each electric appliance in a set area; establishing an intelligent power grid energy optimization control model by taking at least one of the minimum electric power cost and the minimum greenhouse gas emission penalty cost as a target on the basis of the information; the intelligent power grid energy optimization control model can take at least one of electric appliance operation time constraint, electric appliance operation continuity constraint, load shedding coefficient constraint and peak clipping constraint as a constraint condition according to actual needs; and solving the intelligent power grid energy optimization control model to obtain an optimal control scheme for intelligent power grid energy control. The invention considers various factors and parameters influencing the management efficiency of the demand side, and considers a plurality of performance indexes in a demand side management model together, thereby simplifying the calculation process and saving the cost.

Description

Energy optimization control method and system for smart power grid
Technical Field
The invention relates to the technical field of intelligent power grid energy optimization, in particular to an intelligent power grid energy optimization control method and system.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
In an electrical power system, the power demand of a consumer is a crucial parameter. The first challenge facing energy suppliers is to efficiently meet the peak power demand of the consumer, keeping the peak-to-average power ratio at a minimum.
As technology advances, intermittent weather conditions and rapidly changing lifestyles force consumers to use more power. Energy suppliers are not able to continuously increase the amount of electricity generated due to certain limitations, including the cost of electricity generation and the unavailability of other resources, to meet the ever-increasing demand of each consumer, and are a challenging task for the energy supplier.
The power demand of a consumer may be controlled through a demand-side management (DSM) scheme, in which the consumer manages its energy consumption by designing and implementing the demand-side management scheme on the consumer side. Demand-side management plays an important role in energy consumption. It can adjust the demand of electricity in the customer site according to the price of electricity provided by the electric power company, which helps the supplier manage the load during peak hours and stabilizes the electric power system. Demand side management systems are typically used at the customer site to meet power demand without the need for installation at an additional power plant site. Demand-side management systems narrow the gap between peak demand during peak hours and the available electrical energy produced. The increase in energy consumption during peak periods can generate severe surges, which can adversely affect the power grid station and further destabilize the power system. Thus, by reducing peak hour demand, the total energy consumption during peak hours will be reduced. Demand is reduced by reshaping the demand profile by shifting flexible loads from peak hours to off-peak hours, which in turn helps to improve the stability, reliability and efficiency of the power system.
However, the existing demand side management scheme usually only considers a single management target, and when a target domain needs to be expanded, the demand side management schemes of multiple single targets are operated in parallel, so that not only is the cost higher, but also the operation becomes more complicated.
Disclosure of Invention
In order to solve the problems, the invention provides an energy optimization control method and system for a smart grid, which jointly consider a plurality of performance indexes in a demand side management model, consider various factors and parameters influencing the demand side management efficiency, and can realize multi-objective optimization of energy management of the smart grid.
In some embodiments, the following technical scheme is adopted:
a smart grid energy optimization control method comprises the following steps:
acquiring electricity price information, carbon dioxide emission duty, user number, total time slot number, electric appliance number and power consumption information of each electric appliance in a set area;
establishing an intelligent power grid energy optimization control model by taking at least one of the minimum electric power cost and the minimum greenhouse gas emission penalty cost as a target on the basis of the information;
the intelligent power grid energy optimization control model can take at least one of electric appliance operation time constraint, electric appliance operation continuity constraint, load shedding coefficient constraint and peak clipping constraint as a constraint condition according to actual needs;
and solving the intelligent power grid energy optimization control model to obtain an optimal intelligent power grid energy control scheme, and performing intelligent power grid energy control.
The intelligent power grid energy optimization control model specifically comprises the following steps:
Figure BDA0003018779390000031
wherein,
Figure BDA0003018779390000032
respectively representCost function of electric power and CO2As a function of the cost of the emissions,
Figure BDA0003018779390000033
a decision variable indicating that k the user selects the a device of the nth class consumer at time t,
Figure BDA0003018779390000034
the values of the load shedding system are related to the values of the load shedding system, the human-computer interaction factor vector, the peak clipping limiting coefficient, the continuous operation time of the equipment and the preference coefficient of the consumer; k is the total number of users, N is the load type, T is the total number of time slots, AnA group of devices representing an nth load type; total
Figure BDA0003018779390000035
Represents the total cost of energy consumed; alpha is alpha1Shown is a selection parameter for the operation of selection and priority setting.
The constraint conditions of the intelligent power grid energy optimization control model comprise: an appliance operating time constraint, an appliance operating continuity constraint, a consideration load shedding factor constraint, and a peak clipping constraint.
In other embodiments, the following technical solutions are adopted:
a smart grid energy optimization control system, comprising:
the data acquisition module is used for acquiring the electricity price information, the carbon dioxide emission tariff, the number of users, the total number of time slots, the number of electrical equipment and the power consumption information of each electrical appliance in a set area;
the model building module is used for building an intelligent power grid energy optimization control model by taking at least one of the minimum electric power cost and the minimum greenhouse gas emission penalty cost as a target based on the information;
the intelligent power grid energy optimization control model can take at least one of electric appliance operation time constraint, electric appliance operation continuity constraint, load shedding coefficient constraint and peak clipping constraint as a constraint condition according to actual needs;
and the model solving module is used for solving the intelligent power grid energy optimization control model to obtain an optimal intelligent power grid energy control scheme for intelligent power grid energy control.
In other embodiments, the following technical solutions are adopted:
a terminal device comprising a processor and a memory, the processor being arranged to implement instructions; the memory is used for storing a plurality of instructions, and the instructions are suitable for being loaded by the processor and executing the intelligent power grid energy optimization control method.
In other embodiments, the following technical solutions are adopted:
a computer readable storage medium, wherein a plurality of instructions are stored, and the instructions are suitable for being loaded by a processor of a terminal device and executing the intelligent power grid energy optimization control method.
Compared with the prior art, the invention has the beneficial effects that:
(1) the method considers various factors and parameters influencing the management efficiency of the demand side, considers a plurality of performance indexes in one demand side management model together, selects corresponding input parameters according to actual demands, and can simplify the calculation process and save the cost compared with the parallel operation of a single target model.
(2) The invention also considers the user demand curve distribution which influences the stability of the power system, and ensures that the whole energy demand does not exceed the safety limit from the aspect of power stability.
Additional features and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
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Fig. 1 is a flowchart of a smart grid energy optimization control method in an embodiment of the present invention.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Example one
In one or more embodiments, a smart grid energy optimization control method is disclosed, and with reference to fig. 1, includes the following processes:
(1) acquiring electricity price information, carbon dioxide emission duty, user number, total time slot number, electric appliance number and power consumption information of each electric appliance in a set area;
(2) establishing an intelligent power grid energy optimization control model by taking at least one of the minimum electric power cost and the minimum greenhouse gas emission penalty cost as a target on the basis of the information; the intelligent power grid energy optimization control model can take at least one of an electrical appliance operation time constraint, an electrical appliance operation continuity constraint, a load shedding coefficient constraint and a peak clipping constraint as a constraint condition;
(3) and solving the intelligent power grid energy optimization control model to obtain an optimal intelligent power grid energy control scheme, and performing intelligent power grid energy control.
Specifically, the present embodiment considers a residential area having many houses. Each house has different types of loads (mobile, seasonal and base loads). Residential offers price of electricity, carbon dioxide emission penalty duty, availability of electricity supply, durability of load shedding, and daily maximum demand. Some household appliances require the presence of a person to operate, such as washing machines, irons and dishwashers. Thus, the presence of a person of the operator of a given device is also assumed. Household appliances of a residential area are arranged in a uniform manner to collectively achieve a minimum electric power cost, a low peak-to-average power ratio, less greenhouse gas emissions, and a flat demand load curve, thereby stabilizing the electric power system of the residential area. Furthermore, in achieving the above goals, certain limitations need to be met, including consideration of consumer preferences, peak clipping, appliance priority, and consumer preferences, among others.
Table 1 shows the meaning of the parameters involved in this example.
TABLE 1 description of the meanings of the parameters
Figure BDA0003018779390000061
Let K be the total number of consumers with N types of load per household in the residential community. Each load type includes An energy source configuration set by
Figure BDA0003018779390000062
Describing, the load shedding coefficient LS and the human-computer interaction factor vector HIF are respectively LtAnd
Figure BDA0003018779390000063
typically, the scheduling time is divided into T time slots, and the operation duration and the starting time of the electric appliance are respectively used
Figure BDA0003018779390000064
And
Figure BDA0003018779390000065
represents;
cost is one of the important objective functions in demand side management objectives. Once the cost of electricity is minimized, the peak to average power ratio will also decrease, which will result in a flattening of the consumer demand curve. In addition, the reduction in power costs also reduces peak hour demand, which in turn reduces high currents in the distribution lines and reduces transmission/distribution losses.
Thus, in this embodiment, there are two objective functions: the power cost is minimum and the greenhouse gas emission punishment cost is minimum;
the cost of electricity may be calculated by multiplying the total energy consumed by all consumers by the price of electricity provided by the supplier. Two tariffs are provided to the consumer, namely energy costs and carbon dioxide emission costs. The cost function for both costs is calculated as follows:
Figure BDA0003018779390000071
Figure BDA0003018779390000072
in a specific energy optimization control process, at least one of the optimization targets may be selected according to actual needs, such as geographical properties of residential areas, government policies, and the like.
Before using the smart grid energy optimization control model for calculation, we need to obtain the following parameters in advance: total duration, electricity price information in a set area, carbon dioxide emission duty, user number, total time slot number, electric appliance number, power consumption information of each electric appliance, limited time range of electric appliance use, price rate related to time, greenhouse gas emission penalty, load curve and the like.
In addition, at least one of a load shedding system, a human-computer interaction factor vector, a peak clipping limit coefficient, equipment continuous operation time and a consumer preference coefficient can be used as a selection input parameter of the intelligent power grid energy optimization control model according to actual needs; different input parameters can be selected according to different demand side management strategies.
Suppose demand-side management is done during "peak" hours and time-of-use electricity prices are employed. Each hour is divided into several time periods. Duration of the time period in minutes is denoted by ΓiAnd (4) showing. The number of time segments in one hour can be calculated as follows:
Γh＝60/Γi (1)
the total number of time slots T is equal to the product of the number of time slots per hour and the total number of hours; i.e. T ═ h Γite {1,2, … T } represents a time period, similarly, forThe man-machine interaction factor vector of each electric appliance of each type of load in the h hour of the kth customer can be converted into a test value of each time slot, which is expressed by the following formula:
Figure BDA0003018779390000081
the shedding factor is also converted into a T value per slot as shown in equation (3):
Figure BDA0003018779390000082
similarly, the consumer's willingness to operate a particular appliance at his or her own will is also represented by the T value for each time slot, given by equation (4):
Figure BDA0003018779390000083
we have several types of household appliances, which have different powers. The power characteristic is expressed as:
Figure BDA0003018779390000084
wherein a ∈ An,，
Figure BDA0003018779390000085
Representing the power consumption of the nth load of the ath household appliance in the kth time slot by the kth customer. If the power consumption of the kth user's n-type load of the a-th device per hour is
Figure BDA0003018779390000086
Then the energy consumption of the t-th slot of device a is represented by:
Figure BDA0003018779390000087
in the power curve calculation, the rated power of the device and its duration of operation must be known. To better explain this concept we take four appliances as an example, i.e. washing machine, dryer, dishwasher, electric car. The washing machine was allowed to run for 30 minutes, the dryer for 10 minutes, the dishwasher for 18 minutes, and the electric vehicle for 60 minutes. The operating time is expressed in terms of the number of time slots. If the span of one time slot is 5 minutes, the operation time is 6 time slots for the washing machine, 2 time slots for the dryer, 4 time slots for the dishwasher, and 12 time slots for the electric vehicle.
Generally, the operation time of the a-th device of the n-th type of the k-th consumer is determined by the number of slots
Figure BDA0003018779390000091
The mathematical model that conforms to the operation of a given device over t time slots is shown as:
Figure BDA0003018779390000092
wherein,
Figure BDA0003018779390000093
is the decision variable for the t-th slot of the consumer k to turn on the n-type a-th device.
Another very important concept to be understood is the continuous operation of a particular appliance. For example: if the washing machine starts to operate, it will continue to operate until the last time slot is allocated. To ensure that a particular device is continuously operating for a desired period of time, we have set the following constraints:
Figure BDA0003018779390000094
wherein,
Figure BDA0003018779390000095
is the a-th setting of the k-th user n typeThe start-up time of the device.
The user profile consists of different types of loads, some of which should be served/run before others. Such as the inability to operate the dryer without washing out the clothes, we therefore group these types of loads into different groups, denoted by β I, where I ═ 1,2,3 … I.
The optimization model proposed in this embodiment is a unified framework in which multiple performance indicators can be achieved under various constraints imposed by both the consumer and the supplier. In addition to performance indicators, the unified model built may also choose to turn on and off related constraints. To select the desired result and associated constraints, we use certain selection parameters shown in table 2. The administrator has the right to select the desired goals and constraints. The choice of goals and constraints depends on the geographical nature of the residential area and government policies. For example, in a region where there is a shortage of electricity, a load shedding factor will be selected. One of the parameters selected is alpha1∈[0，1]Selection and priority setting for operations; it has a range of [0, 1]If equal to α 10, then the model only considers the emission cost of carbon dioxide, others do not, if α is1Being equal to some value of 0-1, such as 0.5, means that the model can take into account the effects of various other factors.
In addition, many household appliances cannot be frequently switched on and off. For example, if a dishwasher is turned on/off before the end of its operating time, it may shorten its life and may affect its efficiency. This property of the appliance is modeled as the continuity constraint given in equation (7). We use alpha5To decide whether to guarantee continuous operation of the plant. Similarly, we use the selection parameter α for consumer preference, peak clipping, human-computer interaction factor and load shedding, respectively6、α4、α3And alpha2。
We can be summarized by the following equation:
Figure BDA0003018779390000101
α2、α3and alpha6Selection parameters representing load shedding, human-computer interaction factor and consumer preferences, respectively. In order for the optimization model to be cost effective and to ensure reliability of the power system, the total energy consumption of each customer should not exceed a given threshold. The threshold limit may be set by the utility company or the customer. Based on the meteorological data and generator set type, utility companies as well as consumers can predict the maximum energy demand of the consumer.
Setting a threshold value gamma in each time slotk,tThe clipping constraint is as follows:
Figure BDA0003018779390000102
wherein alpha is4Is a selection parameter for selecting the peak clipping option or not. The peak limit should satisfy:
Figure BDA0003018779390000103
table 2 lists the values of the selection variables used to select the different constraints in the model.
TABLE 2 matrix selection variable alpha for various propertiesi∈[0,1]
Figure BDA0003018779390000104
Figure BDA0003018779390000111
To better understand table 2, we show some random cases in table 3. The table gives three different scenarios, namely simplified demand-side management, moderate demand-side management and unified demand-side management.
In simplified demand-side management, all constraints are closed (since all selection parameters are assigned zero values), and the objective function is to reduce emissions costs. Moderate demand side management represents the objective functionThe method is a model of emission reduction cost, and the selected constraint conditions are household appliance priority constraint and household appliance continuous operation constraint. Whereas in unified demand-side management, the objective function is the cost of electricity, all relevant constraints are through α1、α2、α3、α4、α5、α6One of them is designated for selection.
Table 3 detailed description of selected parameters
Figure BDA0003018779390000112
Figure BDA0003018779390000121
To minimize the final total cost of power, it is calculated as follows:
Figure BDA0003018779390000122
constraints that may be considered include:
electrical appliance operating time constraints:
Figure BDA0003018779390000123
and (3) electric appliance operation continuity constraint:
Figure BDA0003018779390000131
considering the load shedding factor constraint:
Figure BDA0003018779390000132
clipping peak and constraining:
Figure BDA0003018779390000133
wherein,
Figure BDA0003018779390000134
and alpha2、α3、α4、α5、α6The values of (a) are related, the specific relationship is shown in table 3, and the following description is divided into 3 cases:
case 1: simplified demand side optimization mode
α 1 is 0, in this case
Figure BDA0003018779390000135
α2＝α3＝α4＝α5＝α 60, this control is simplest; at this time, if the operation of the apparatus is ensured for the entire time interval:
Figure BDA0003018779390000136
case 2: moderate demand side optimization mode
α 1 is 0.5, in this case
Figure BDA0003018779390000137
Figure BDA0003018779390000138
However, α2＝α4＝α6＝1；α3＝α 50; selecting load shedding and consumer preference constraints, and selecting peak clipping constraints; at this time, the process of the present invention,
Figure BDA0003018779390000139
case 3: unified demand side optimization model
In this mode, the most comprehensive factor to consider is α1＝α2＝α3＝α4＝α5＝α6＝1
Since α 1 is 1, the power consumption rate is selected,
Figure BDA00030187793900001310
at this time, the following cases are included:
if it is ensured that the equipment is operating within a certain time interval,
Figure BDA0003018779390000141
due to alpha 51, a continuous operation constraint is selected,
Figure BDA0003018779390000142
③ since α 2 ═ α 4 ═ α 6 ═ 1, the load shedding and consumer preferences and the peak clipping constraint are chosen,
Figure BDA0003018779390000143
finally, the sum is obtained
Figure BDA0003018779390000144
And substituting the formula to solve.
After collecting the required input parameters, an integer linear programmable solver is used to obtain the minimum power cost required for a given set of appliances for the parameters.
Example two
In one or more embodiments, a smart grid energy optimization control system is disclosed, comprising:
the data acquisition module is used for acquiring the electricity price information, the carbon dioxide emission tariff, the number of users, the total number of time slots, the number of electrical equipment and the power consumption information of each electrical appliance in a set area;
the model building module is used for building an intelligent power grid energy optimization control model by taking at least one of the minimum electric power cost and the minimum greenhouse gas emission penalty cost as a target based on the information;
the intelligent power grid energy optimization control model can take at least one of electric appliance operation time constraint, electric appliance operation continuity constraint, load shedding coefficient constraint and peak clipping constraint as a constraint condition according to actual needs;
and the model solving module is used for solving the intelligent power grid energy optimization control model to obtain an optimal intelligent power grid energy control scheme for intelligent power grid energy control.
It should be noted that the specific implementation manner of each module is implemented by using the method disclosed in the first embodiment, and is not described again.
EXAMPLE III
In one or more embodiments, a terminal device is disclosed, which includes a server including a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the computer program to implement the smart grid energy optimization control method in the first embodiment. For brevity, no further description is provided herein.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include both read-only memory and random access memory, and may provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.
In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software.
The energy optimization control method for the smart grid in the first embodiment may be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, among other storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor. To avoid repetition, it is not described in detail here.
Those of ordinary skill in the art will appreciate that the various illustrative elements, i.e., algorithm steps, described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
Example four
In one or more embodiments, a computer-readable storage medium is disclosed, in which a plurality of instructions are stored, the instructions being adapted to be loaded by a processor of a terminal device and implementing the smart grid energy optimization control method described in the first embodiment.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.

Claims (9)

1. The intelligent power grid energy optimization control method is characterized by comprising the following steps:
acquiring electricity price information, carbon dioxide emission duty, user number, total time slot number, electric appliance number and power consumption information of each electric appliance in a set area;
establishing an intelligent power grid energy optimization control model by taking at least one of the minimum electric power cost and the minimum greenhouse gas emission penalty cost as a target on the basis of the information;
the intelligent power grid energy optimization control model is used for taking at least one of an electrical appliance operation time constraint, an electrical appliance operation continuity constraint, a load shedding coefficient constraint and a peak clipping constraint as a constraint condition;
and solving the intelligent power grid energy optimization control model to obtain an optimal intelligent power grid energy control scheme, and controlling the intelligent power grid energy according to the control scheme.
2. The smart grid energy optimization control method according to claim 1, wherein the total number of time slots is equal to a product of the number of time slots per hour and the total number of hours; wherein the time slot number in each hour is obtained according to the time segment number in each hour.
3. The smart grid energy optimization control method and model as claimed in claim 1, wherein load shedding factor constraints are considered while consumer preference factors, human-computer interaction factors and load shedding factor limitations are considered.
4. The energy optimization control method and model for the smart grid according to claim 1, wherein the customer preference factor, the man-machine interaction factor and the load shedding factor are respectively expressed by a test value of each time slot.
5. The smart grid energy optimization control method according to claim 1, wherein the smart grid energy optimization control model specifically comprises:
Figure FDA0003018779380000011
wherein,
Figure FDA0003018779380000012
representing the power cost function and CO, respectively2As a function of the cost of the emissions,
Figure FDA0003018779380000013
indicating k users are at timeTime t selects the decision variables of the a device of the nth type consumer,
Figure FDA0003018779380000022
the values of the load shedding system are related to the values of the load shedding system, the human-computer interaction factor vector, the peak clipping limiting coefficient, the continuous operation time of the equipment and the preference coefficient of the consumer; k is the total number of users, N is the load type, T is the total number of time slots, AnA group of devices representing an nth load type;
Figure FDA0003018779380000021
represents the total cost of energy consumed; alpha is alpha1Shown is a selection parameter for the operation of selection and priority setting.
6. The smart grid energy optimization control method according to claim 1, wherein the smart grid energy optimization control model is solved by an integer linear programmable solver.
7. A smart grid energy optimization control system, comprising:
the data acquisition module is used for acquiring the electricity price information, the carbon dioxide emission tariff, the number of users, the total number of time slots, the number of electrical equipment and the power consumption information of each electrical appliance in a set area;
the model building module is used for building an intelligent power grid energy optimization control model by taking at least one of the minimum electric power cost and the minimum greenhouse gas emission penalty cost as a target based on the information;
the intelligent power grid energy optimization control model can take at least one of electric appliance operation time constraint, electric appliance operation continuity constraint, load shedding coefficient constraint and peak clipping constraint as a constraint condition according to actual needs;
and the model solving module is used for solving the intelligent power grid energy optimization control model to obtain an optimal intelligent power grid energy control scheme for intelligent power grid energy control.
8. A terminal device comprising a processor and a memory, the processor being arranged to implement instructions; the memory is used for storing a plurality of instructions, wherein the instructions are suitable for being loaded by the processor and executing the smart grid energy optimization control method according to any one of claims 1 to 6.
9. A computer-readable storage medium having stored thereon a plurality of instructions, wherein the instructions are adapted to be loaded by a processor of a terminal device and to perform the smart grid energy optimization control method according to any one of claims 1 to 6.
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