CN105739308A - Power optimization control method and system applied to temperature control electric appliance - Google Patents

Abstract

The invention provides a power optimization control method and a power optimization control system applied to a temperature control electric appliance, wherein the power optimization control method comprises the following steps: acquiring a required temperature parameter, a power price parameter and a working principle model parameter of the temperature control electric appliance which are preset in each preset time period within one day of working time; performing genetic algorithm optimization processing under a constraint condition set by adopting an economic index calculation formula and a satisfaction degree calculation formula according to a required temperature parameter, an electricity price parameter and a working principle model parameter of the temperature control electric appliance to obtain power values of the temperature control electric appliance in each preset time period of one day working time; and planning and setting the working power of the temperature control electric appliance according to the obtained power value. The scheme can dynamically and quantitatively analyze the relationship between the economy and the satisfaction of the temperature control electric appliance, and provides data support for the personalized operation of different users. The scheme has the function of peak clipping and valley filling by controlling the household temperature control electric appliance, and can alleviate the contradiction between supply and demand of electric energy to a certain extent.

Description

Power optimization control method and system applied to temperature control electric appliance

Technical Field

The invention relates to the technical field of electric appliance control, in particular to a power optimization control method and system applied to a temperature control electric appliance.

Background

With the popularization of advanced measurement systems on the electricity utilization side and the implementation of demand response mechanisms, the advantages of active loads are gradually shown in household electrical systems, and particularly in household photovoltaic systems with photovoltaic power generation equipment and household storage batteries, the electricity utilization mode is more flexible. In the household photovoltaic system, active load refers to that the electricity utilization time and the electricity utilization load size are changed to match the demand response of an electricity operator under the condition that the energy demand of a user is not changed or is slightly changed by intelligently controlling the electricity utilization equipment. Generally, the active load mainly includes a controllable load and a programmable load, wherein the controllable load usually runs for a long time, is greatly influenced by temperature and climate factors, and the power is adjustable and can even be intermittently interrupted; the planned load is generally not conveniently power adjustable, but the running time can be flexibly arranged within a certain range.

By integrating the weather information and the historical use information of the user, a day-ahead power utilization plan of the user can be made in advance aiming at the active load, so that the demand response is matched, and the power utilization cost of the user is reduced. At present, a method applied to a day-ahead scheduling plan of a home power system generally considers the home power consumption cost as a main control target on the premise of considering the time-of-use power price, and adds the room temperature difference or the water temperature difference representing the use satisfaction degree to the power consumption cost to obtain an objective function of optimal control. However, this method only adds the temperature difference and the electricity consumption when dealing with the two indexes of the use economy and the use satisfaction, and this processing method can make the optimization effect have one-sidedness, and for the user, sometimes needs to know how much satisfaction is compromised as a cost (or vice versa) to meet a certain economic goal when using a certain household appliance, so as to provide further data support for the diversified selection of the user, and therefore, needs to perform quantitative and dynamic analysis on the economy and satisfaction of each household appliance.

Disclosure of Invention

The invention provides a power optimization control method and a power optimization control system applied to temperature control electric appliances, which are used for solving the problem that the relationship between the use satisfaction degree and the power consumption expenditure of each temperature control electric appliance cannot be quantitatively and dynamically analyzed in the prior art.

In one aspect, the present invention provides a power optimization control method applied to a temperature control electrical appliance, including:

acquiring a required temperature parameter and an electricity price parameter preset in each preset time period within one day of working time of the temperature control electric appliance and a working principle model parameter of the temperature control electric appliance;

performing genetic algorithm optimization processing under a first constraint condition set by adopting an economic index calculation formula according to the required temperature parameter, the electricity price parameter and the working principle model parameter of the temperature control electric appliance to obtain a minimum economic value in one day;

performing genetic algorithm optimization processing under a second constraint condition group by adopting a satisfaction calculation formula according to the required temperature parameter, the electricity price parameter, the working principle model parameter of the temperature control electric appliance and the minimum economic value to obtain the corresponding satisfaction value and economic value under different electricity expenses;

determining an optimal economic value and an optimal satisfaction value by using a judgment condition according to the obtained economic value and the satisfaction value;

determining the power value of the temperature control electric appliance in each preset time period of the working time of one day according to the optimal economic value and the optimal satisfaction value;

and planning and setting the working power of the temperature control electric appliance according to the obtained power value.

Preferably, the economic indicator calculation formula is:wherein, C_ehIs an economic value, eb (h) is a power price parameter, P, for each preset time period_eh(h) Working power of each preset time period;

preferably, the first restriction condition set includes:

0≤P_eh(h)≤P_eh.maxwherein P is_eh(h) For operating power, P, of each predetermined time period_eh.maxPresetting maximum working power for the temperature control electric appliance;

$T_{eh}^s\left(h\right)-10\≤T_{eh}\left(h\right)\≤T_{eh.max}^s,$ wherein,is a required temperature parameter within a preset time period,maximum temperature value, T, allowed for temperature-controlled electric appliance_eh(h) Setting the actual temperature parameter in a preset time period;

and working principle model parameters of the temperature control electric appliance.

Preferably, the satisfaction calculation formula is:wherein S is_ehIn order to be a satisfactory value,is a desired temperature parameter, T, within a predetermined time period_eh(h) Is the actual temperature within a preset time period,a quadratic function of the difference between the actual temperature and the demanded temperature;

preferably, the second set of restriction conditions comprises:

0≤P_eh(h)≤P_eh.maxwherein P is_eh(h) For operating power, P, of each predetermined time period_eh.maxPresetting maximum working power for the temperature control electric appliance;

$T_{eh}^s\left(h\right)-10\≤T_{eh}\left(h\right)\≤T_{eh.max}^s,$ wherein,is a required temperature parameter within a preset time period,maximum temperature value, T, allowed for temperature-controlled electric appliance_eh(h) Setting the actual temperature parameter in a preset time period;

wherein eb (h) is the electricity price parameter P in each preset time period_eh(h) For operating power of each predetermined time period, C_eh.minFor the minimum economic value, α is to relax the constraint coefficient of economic indicator, α is (α)₁,α₂…,α_n)；

And working principle model parameters of the temperature control electric appliance.

Preferably, the judgment condition is: if max (C-C) is satisfied_eh.max)(S-S_eh.min) Then the current economic value and the current satisfaction value are the optimal economic value and the optimal satisfaction value.

Preferably, the first and second electrodes are formed of a metal,

$f_{eh}\left(T_{eh}^s\right(h)-T_{eh}(h\left)\right)=f\left({\mathrm{\&Delta;T}}_{eh}\right)=\left\{\begin{array}{c}-0.1\&CenterDot;({\left({\mathrm{\&Delta;T}}_{eh}\right)}^2-100),{\mathrm{\&Delta;T}}_{eh}\&GreaterEqual;0\\ 10,{\mathrm{\&Delta;T}}_{eh}<0\end{array}\right..$

in another aspect, the present invention provides a power optimization control system for a temperature-controlled electrical appliance, including:

the parameter acquisition module is used for acquiring a required temperature parameter and an electricity price parameter preset in each preset time period within one day of working time of the temperature control electric appliance and a working principle model parameter of the temperature control electric appliance;

the first optimization module is used for performing genetic algorithm optimization processing under a first constraint condition group by adopting an economic index calculation formula according to the required temperature parameter, the electricity price parameter and the working principle model parameter of the temperature control electric appliance to obtain a minimum economic value in one day;

the second optimization module is used for performing genetic algorithm optimization processing under a second constraint condition group by adopting a satisfaction calculation formula according to the required temperature parameter, the electricity price parameter, the working principle model parameter of the temperature control electric appliance and the minimum economic value to obtain the corresponding satisfaction value and economic value under different electricity prices;

the judging module is used for determining an optimal economic value and an optimal satisfaction value by utilizing a judging condition according to the obtained economic value and the satisfaction value;

the power acquisition module is used for determining the power value of the temperature control electric appliance in each preset time period of one day working time according to the optimal economic value and the optimal satisfaction value;

and the regulating and controlling module is used for planning and setting the working power of the temperature control electric appliance according to the acquired power value.

According to the technical scheme, the relation between the economical efficiency and the satisfaction degree of the temperature control electric appliance can be analyzed dynamically and quantitatively, and data support is provided for personalized operation of different users. Aiming at the time-of-use electricity price, the scheme can effectively adjust the working period of the temperature control electric appliance to be transferred to the low electricity price period, properly reduces the power of the electric water heater in the peak period of the hot water temperature and the hot water quantity demand at night, and sacrifices a part of using satisfaction degree so as to achieve the aim of reducing the electricity consumption. The scheme has the function of peak clipping and valley filling by controlling the household temperature control electric appliance, and can alleviate the contradiction between supply and demand of electric energy to a certain extent.

Drawings

Fig. 1 is a schematic flow chart of a power optimization control method according to embodiment 1 of the present invention;

fig. 2 is a schematic structural diagram of a power optimization control system according to embodiment 2 of the present invention.

Detailed Description

The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.

Fig. 1 shows a power optimization control method applied to a temperature control electrical appliance according to embodiment 1 of the present invention, including:

and S1, acquiring a required temperature parameter and an electricity price parameter preset in each preset time period within one day of working time of the temperature control electric appliance, and working principle model parameters of the temperature control electric appliance. In this step, the required temperature parameter may be pre-stored operation process history data. The electricity price parameter is market data according to a standard charging regime. The working principle model parameters of the temperature control electric appliance are parameters obtained by establishing an operation model of the temperature control electric appliance according to the working principle of the temperature control electric appliance and are known parameters. The establishment of its parameters will not be described in detail here. In this embodiment, an electric water heater is taken as an example, and an operation model of the electric water heater is established according to a working principle of the electric water heater, as follows:

cρV₀T(h+1)＝(1-β_w){ψP_eh(h)Δh+cρ[(V₀-F_eh(h))T(h)+F_eh(h)T_r]}(1)

$F_{a.eh}\left(h\right)=F_{eh}\left(h\right)\&CenterDot;min\{\frac{T_{s.eh}\left(h\right)}{T\left(h\right)},1\}---\left(2\right)$

wherein c represents the specific heat capacity of water; ρ represents the density of water; v₀Indicating the volume of the water tank; t (h) represents the water temperature value in the water tank in the h time period; t is_s.eh(h) β for user to set water temperature in time period h_wRepresents the energy dissipation coefficient of the water tank, psi represents the conversion coefficient between electric energy (kilowatt-hour) and heat energy (Joule), and has a value of 3.6 × 10⁶；P_eh(h) Represents the power of the tank heater (for an electric water heater with constant operating power, this value can be considered as the average power over an hour); t is_rIs to be notedThe temperature of the tap water entering the water tank; f_eh(h) The required water consumption of the user in the time period h is achieved; f_a.eh(h) The actual water usage for the user over time period h.

The real-time electricity price parameter during the time of day is shown in table 1:

table 2 shows the relevant parameters of the electric water heater:

power range	0～3kW
		Setting the water temperature	Ts.eh＝[0 0 0 0 0 0 30 30 0 0 0 30 30 30 0 0 0 0 0 0 50 50 50 0]
Set water consumption L	Feh＝[0 0 0 0 0 0 20 20 0 0 0 25 10 15 0 0 0 0 0 0 50 60 20 0]
		Other parameters	V0＝80L,Tmax＝75℃,Tr＝15℃,βw＝5％

As can be seen from tables 1 and 2, this embodiment uses each hour as one time period for setting, i.e., 24 time periods, during the time of day.

And S2, according to the required temperature parameter, the electricity price parameter and the working principle model parameter of the temperature control electric appliance, optimizing under the first constraint condition group by using an economic index calculation formula to obtain the minimum economic value in one day. In this step, first, optimization processing is performed solely with the economic indicator as an optimization target. The economic index calculation formula is as follows:

$C_{eh}=\underset{h=1}{\overset{24}{\&Sigma;}}eb\left(h\right)P_{eh}\left(h\right)---\left(3\right)$

wherein, C_ehIs an economic value, eb (h) is a power price parameter, P, for each preset time period_eh(h) The working power of each preset time period. In the optimization process, a genetic algorithm is required to perform optimization under a first constraint set.

The first set of constraints includes:

0≤P_eh(h)≤P_eh.max(4)

wherein, P_eh(h) For operating power, P, of each predetermined time period_eh.maxThe preset maximum working power of the temperature control electric appliance.

$T_{eh}^s\left(h\right)-10\&le;T_{eh}\left(h\right)\&le;T_{eh.max}^s---\left(5\right)$

Wherein,is a required temperature parameter within a preset time period,maximum temperature value, T, allowed for temperature-controlled electric appliance_eh(h) The actual temperature parameter is the actual temperature parameter in the preset time period.

Meanwhile, the first constraint condition set further comprises working principle model parameters of the temperature control electric appliance. Because the electric appliance is a temperature control electric appliance, the working principle model parameters of the electric appliance can relate to temperature parameters, and the working principle model parameters of the temperature control electric appliance can be used as constraint conditions to play constraint conditions on the temperature parameters.

It should be noted that, the above formula (3) is used as an objective function, the formula (4), the formula (5) and the working principle model of the temperature control electric appliance to be measured are used as constraint conditions, and a genetic algorithm is adopted to optimize the economic value index, so as to finally obtain the minimum economic value. In the optimization process of the step, the difference from the prior art lies in the difference of the economic value index calculation formula and the constraint condition. How to perform the optimization process by using genetic algorithm is a mature process for those skilled in the art, and is not described herein again.

And S3, according to the required temperature parameters, the electricity price parameters, the working principle model parameters of the temperature control electric appliance and the minimum economic value, optimizing by adopting a satisfaction degree calculation formula under a second constraint condition group to obtain the corresponding satisfaction values and economic values under different electricity expenses. In the step, the use satisfaction of the temperature control electric appliance is taken as an optimization target, the minimum economic value and other parameters obtained in the step S2 are taken as constraints, the constraints on the economic performance are gradually relaxed according to a certain step length, and the use satisfaction and the corresponding economic value of the temperature control electric appliance in each preset time period at different electricity consumption can be obtained by utilizing a genetic algorithm. In the optimization process, a satisfaction calculation formula and a second set of constraints are required, as follows:

the satisfaction calculation formula is as follows:

$S_{eh}=\underset{h=1}{\overset{24}{\&Sigma;}}{f}_{eh}\left(T_{eh}^s\right(h)-T_{eh}(h\left)\right)---\left(6\right)$

wherein S is_ehIn order to be a satisfactory value,is a desired temperature parameter, T, within a predetermined time period_eh(h) Is an actual temperature parameter within a preset time period,as a quadratic function of the difference between the actual temperature and the desired temperature. The function reaches a maximum when the actual water temperature reaches (or exceeds) the required water temperature, and the function reaches a maximum when the actual water temperature is lower than the minimum water temperature requirement at that momentWhen the function reaches the minimum value 0, for example, the function may be set to the following formula in actual calculation:

$f\left({\mathrm{\&Delta;T}}_{eh}\right)=\left\{\begin{array}{c}-0.1\&CenterDot;({\left({\mathrm{\&Delta;T}}_{eh}\right)}^2-100),{\mathrm{\&Delta;T}}_{eh}\&GreaterEqual;0\\ 10,{\mathrm{\&Delta;T}}_{eh}<0\end{array}\right.---\left(7\right)$

when the actual water temperature is equal to the set water temperature (i.e., Δ T)_eh0) or the actual water temperature is greater than the set water temperature (in this case, the outlet water temperature can be adjusted by adding cold water), the satisfaction index reaches the maximum value of 10; when the actual water temperature is less than the set water temperature, the satisfaction degree is less than 10, and along with the reduction of the water temperature, the satisfaction degree is reduced in a quadratic function mode when delta T_ehWhen 10, the satisfaction reaches the minimum value of 0.

The second set of constraints comprises:

0≤P_eh(h)≤P_eh.max(8)

wherein, P_eh(h) For operating power, P, of each predetermined time period_eh.maxThe preset maximum working power of the temperature control electric appliance.

$T_{eh}^s\left(h\right)-10\&le;T_{eh}\left(h\right)\&le;T_{eh.max}^s---\left(9\right)$

Wherein,is a required temperature parameter within a preset time period,the maximum temperature value is preset for the temperature control electric appliance.

$\underset{h=1}{\overset{24}{\&Sigma;}}eb\left(h\right)P_{eh}\left(h\right)\&le;(1+\mathrm{\&alpha;})C_{eh.min}---\left(10\right)$

Wherein eb (h) is the electricity price parameter P in each preset time period_eh(h) For operating power of each predetermined time period, C_eh.minFor minimum economic value, α is to relax the constraint on economic indexCoefficient, α ═ (α)₁,α₂…,α_n) It should be noted that the number n of α can be specifically determined according to the specific situation, because the α value is changed every time, according to S-maxS_ehTheoretically, the more α, the more accurate the result obtained in the subsequent step is, but in the actual calculation, considering that the time for solving is reduced and the accuracy requirement of the optimization problem is not very high, the α values with proper number are selected according to the required problem.

Meanwhile, the first constraint condition set further comprises working principle model parameters of the temperature control electric appliance. Because the electric appliance is a temperature control electric appliance, the working principle model parameters of the electric appliance can relate to temperature parameters, and the working principle model parameters of the temperature control electric appliance can be used as constraint conditions to play constraint conditions on the temperature parameters.

It should be noted that, the above formula (6) is used as an objective function, the formula (7), the formula (8), the formula (9), the formula (10) and the working principle model of the temperature control electrical appliance to be measured are used as constraint conditions, and a genetic algorithm is adopted to optimize the economic value index, so as to finally obtain the use satisfaction and the corresponding economic value of the temperature control electrical appliance in each preset time period. In the optimization process of the step, the difference from the prior art lies in the difference of the economic value index calculation formula and the constraint condition. How to perform the optimization process by using genetic algorithm is a mature process for those skilled in the art, and is not described herein again.

And S4, determining the optimal economic value and the optimal satisfaction value by using the judgment condition according to the obtained economic value and the satisfaction value. In this step, it is necessary to utilize max (C-C) according to the economic value and the satisfactory value obtained in step S3_eh.max)(S-S_eh.min) A determination is made to determine an optimal economic value and an optimal satisfaction value.

And S5, determining the power value of the temperature control electric appliance in each preset time period of the working time of one day according to the optimal economic value and the optimal satisfaction value. It should be noted that, in the optimization process in step S3, the power values for the respective preset time periods corresponding to a set of economic value and satisfactory value are already determined before the economic value and satisfactory value are obtained. That is to say a pair of an economic value and a satisfactory value is calculated from the determined power values.

And S6, planning and setting the working power of the temperature control electric appliance according to the acquired power value. In this step, the temperature control electric appliance is set according to the obtained power value, so that the temperature control electric appliance works according to the power value in each time period.

It should be noted that, in all the above steps, the actual temperature parameters involved are obtained according to a calculation formula in the step execution process, and do not involve real-time temperature acquisition.

Fig. 2 shows a power optimization control system applied to a temperature control electrical appliance according to embodiment 2 of the present invention, which includes:

the parameter acquisition module is used for acquiring a required temperature parameter and an electricity price parameter preset in each preset time period within one day of working time of the temperature control electric appliance and a working principle model parameter of the temperature control electric appliance.

And the first optimization module is used for performing genetic algorithm optimization processing under a first constraint condition group by adopting an economic index calculation formula according to the required temperature parameter, the electricity price parameter and the working principle model parameter of the temperature control electric appliance to obtain a minimum economic value in one day.

And the second optimization module is used for performing genetic algorithm optimization processing under a second constraint condition group by adopting a satisfaction calculation formula according to the required temperature parameter, the electricity price parameter, the working principle model parameter of the temperature control electric appliance and the minimum economic value to obtain the corresponding satisfaction value and economic value under different electricity prices.

And the judging module is used for determining the optimal economic value and the optimal satisfaction value by utilizing the judging condition according to the obtained economic value and the satisfaction value.

And the power acquisition module is used for determining the power value of the temperature control electric appliance in each preset time period of the working time of one day according to the optimal economic value and the optimal satisfaction value.

And the regulating and controlling module is used for planning and setting the working power of the temperature control electric appliance according to the acquired power value.

Because the system is based on the control method, the working principle of the system is the same as that of the control method, and the detailed description is omitted.

Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.

It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.

Those of ordinary skill in the art will understand that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions and scope of the present invention as defined in the appended claims.

Claims (8)

1. A power optimization control method applied to a temperature control electric appliance is characterized by comprising the following steps:

acquiring a required temperature parameter and an electricity price parameter preset in each preset time period within one day of working time of the temperature control electric appliance and a working principle model parameter of the temperature control electric appliance;

performing genetic algorithm optimization processing under a first constraint condition set by adopting an economic index calculation formula according to the required temperature parameter, the electricity price parameter and the working principle model parameter of the temperature control electric appliance to obtain a minimum economic value in one day;

performing genetic algorithm optimization processing under a second constraint condition group by adopting a satisfaction calculation formula according to the required temperature parameter, the electricity price parameter, the working principle model parameter of the temperature control electric appliance and the minimum economic value to obtain the corresponding satisfaction value and economic value under different electricity expenses;

determining an optimal economic value and an optimal satisfaction value by using a judgment condition according to the obtained economic value and the satisfaction value;

determining the power value of the temperature control electric appliance in each preset time period of the working time of one day according to the optimal economic value and the optimal satisfaction;

and planning and setting the working power of the temperature control electric appliance according to the obtained power value.

2. The power optimization control method according to claim 1, wherein the economic indicator calculation formula is:wherein, C_ehIs an economic value, eb (h) is a power price parameter, P, for each preset time period_eh(h) The working power of each preset time period.

3. The power optimized control method of claim 1, wherein the first set of constraints comprises:

0≤P_eh(h)≤P_eh.maxwherein P is_eh(h) For operating power, P, of each predetermined time period_eh.maxPresetting maximum working power for the temperature control electric appliance;

wherein,is a required temperature parameter within a preset time period,maximum temperature value, T, allowed for temperature-controlled electric appliance_eh(h) Setting the actual temperature parameter in a preset time period;

and working principle model parameters of the temperature control electric appliance.

4. The power optimization control method of claim 1, wherein the satisfaction calculation formula is:wherein S is_ehIn order to be a satisfactory value,is a desired temperature parameter, T, within a predetermined time period_eh(h) Is an actual temperature parameter within a preset time period,is a quadratic function with respect to the difference between the actual temperature and the required temperature.

5. The power optimized control method of claim 1, wherein the second set of constraints comprises:

0≤P_eh(h)≤P_eh.maxwherein P is_eh(h) For operating power, P, of each predetermined time period_eh.maxPresetting maximum working power for the temperature control electric appliance;

wherein,is a required temperature parameter within a preset time period,is warmPresetting a maximum temperature value, T, for the electric controller_eh(h) Setting the actual temperature parameter in a preset time period;

wherein eb (h) is the electricity price parameter P in each preset time period_eh(h) For operating power of each predetermined time period, C_eh.minFor the minimum economic value, α is to relax the constraint coefficient of economic indicator, α is (α)₁,α₂…,α_n)；

And working principle model parameters of the temperature control electric appliance.

6. The power optimization control method according to claim 1, wherein the determination condition is: if max (C-C) is satisfied_eh.max)(S-S_eh.min) Then the current economic value and the current satisfaction value are the optimal economic value and the optimal satisfaction value.

7. The power optimization control method of claim 4,

8. a power optimization control system applied to a temperature control electric appliance is characterized by comprising:

the parameter acquisition module is used for acquiring a required temperature parameter, an electricity price parameter and a working principle model parameter of the temperature control electric appliance, which are preset in each preset time period within one day of working time;

the first optimization module is used for performing genetic algorithm optimization processing under a first constraint condition group by adopting an economic index calculation formula according to the required temperature parameter, the electricity price parameter and the working principle model parameter of the temperature control electric appliance to obtain a minimum economic value in one day;

the second optimization module is used for performing genetic algorithm optimization processing under a second constraint condition group by adopting a satisfaction calculation formula according to the required temperature parameter, the electricity price parameter, the working principle model parameter of the temperature control electric appliance and the minimum economic value to obtain the corresponding satisfaction value and economic value under different electricity expenses;

the judging module is used for determining an optimal economic value and an optimal satisfaction value by utilizing a judging condition according to the obtained economic value and the satisfaction value;

the power acquisition module is used for determining the power value of the temperature control electric appliance in each preset time period of one day working time according to the optimal economic value and the optimal satisfaction value;

and the regulating and controlling module is used for planning and setting the working power of the temperature control electric appliance according to the acquired power value.