CN111859242A - Household power energy efficiency optimization method and system - Google Patents
Household power energy efficiency optimization method and system Download PDFInfo
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Abstract
The invention provides a method and a system for optimizing household power energy efficiency, wherein the method comprises the following steps: comprehensively evaluating each household power energy efficiency index system by using a pre-constructed energy efficiency evaluation model based on the acquired power utilization information of each household user; obtaining the power consumption of all controllable loads in the household electric equipment in a set period based on the evaluation result of each household user, the operation parameters of each controllable load in the electric equipment of the household user and a pre-constructed controllable load model; based on the electricity consumption of all controllable loads in the household electricity consumption equipment in a set period, the household electricity consumption behavior is optimized by taking the minimum load peak-valley difference as a target. The invention can accurately evaluate the household energy efficiency and provides an electricity utilization optimization scheme for users with low electricity utilization efficiency.
Description
Technical Field
The invention relates to the field of power demand side management, in particular to a method and a system for optimizing household power energy efficiency.
Background
With the continuous development of social economy, the proportion of the residential electricity to the total electricity consumption of the whole society is gradually increased, but the comprehensive utilization efficiency of the electricity is still very low, and the energy conservation and emission reduction work of household users has received more and more attention. Meanwhile, due to the development of intelligent household power utilization equipment and a load monitoring technology, a power grid company can obtain a large amount of household power utilization information, and the power data of the user side provides data support for implementing a power demand side management method. How to utilize the information evaluates the power efficiency of the home user, and a personalized solution is formulated for the user with lower energy efficiency, so that the power utilization efficiency of the home user is improved, and the method has important significance for optimizing the resource allocation of the power industry.
Disclosure of Invention
In order to solve the above disadvantages in the prior art, the present invention provides a method for optimizing energy efficiency of household power, including:
comprehensively evaluating each household power energy efficiency index system by using a pre-constructed energy efficiency evaluation model based on the acquired power utilization information of each household user;
obtaining the power consumption of all controllable loads in the household electric equipment in a set period based on the evaluation result of each household user, the operation parameters of each controllable load in the electric equipment of the household user and a pre-constructed controllable load model;
based on the electricity consumption of all controllable loads in the household electricity consumption equipment in a set period, the household electricity consumption behavior is optimized by taking the minimum load peak-valley difference as a target.
Preferably, the energy efficiency evaluation model is constructed by:
establishing a multi-stage family electric power energy efficiency index system;
carrying out dimensionless pretreatment on indexes in the household power energy efficiency index system;
sequentially calculating index weight vectors of all levels in a family power energy efficiency index system;
and establishing an energy efficiency evaluation model by utilizing a hierarchical comprehensive evaluation method based on the weight vectors of all superior indexes and the weight vector of the subordinate index corresponding to each superior index in the multi-level family power energy efficiency index system.
Preferably, the index in the household power energy efficiency index system is subjected to dimensionless preprocessing according to the following formula:
in the formula: x is the number of*Representing the value of the index after dimensionless, xiRepresents the value of the ith index before non-dimensionalization,
represents the minimum value of the i-th index,
denotes the maximum value of the i-th index, and n denotes the total number of indexes.
Preferably, the sequentially calculating the index weight vectors of each level in the household power energy efficiency index system includes:
evaluating the primary indexes in the household power energy efficiency index system based on an analytic hierarchy process or a G1 group method to obtain weight vectors of the primary indexes;
acquiring a weight vector of a second-level index under each first-level index in the family power energy efficiency index system by using an entropy weight method;
the household power energy efficiency index system is a secondary index system.
Preferably, the first level in the household power energy efficiency index system comprises household user information, power utilization information of electric equipment and power consumption information of different household power utilization periods;
the second-level indexes of the family user information comprise: the average annual power consumption of the family and the annual power consumption of the unit area of the family;
the second-level indexes of the power utilization information of the electric equipment comprise: the annual power consumption of the mobile electric equipment, the annual power consumption of the air conditioning system and the annual power consumption of the lighting and entertainment equipment can be transferred;
The second-level indexes of the electricity consumption information of different electricity consumption periods of the family comprise: the household annual power consumption in the valley period, the household annual power consumption in the peak period and the household annual power consumption in the slow period.
Preferably, the evaluating the primary indexes in the household power energy efficiency index system based on an analytic hierarchy process or a G1 group method to obtain the weight vector of each primary index includes:
comparing every two first-level indexes in a household power energy efficiency index system to establish a judgment matrix;
when the consistency index of the judgment matrix meets the requirement, evaluating the primary indexes in the household power energy efficiency index system based on an analytic hierarchy process to obtain the weight vector of each primary index;
and otherwise, evaluating the primary indexes in the household power energy efficiency index system based on a G1 group method to obtain the weight vector of each primary index.
Preferably, the energy efficiency evaluation model is represented by the following formula:
in the formula: e is the electricity utilization efficiency of the household user; lambda [ alpha ]iA weight representing the ith primary index; n is the number of first-level indexes; etaijVector weight of the second-level index under the ith first-level index is represented; m is the number of the second-level indexes under the ith first-level index; z is a radical of ijIs the jth secondary index under the ith primary index.
Preferably, the construction of the controllable load model includes:
taking the state of the controllable load corresponding to each time interval in the set period as a target function;
the method comprises the steps that the condition that the time required for completing a task in a working time interval allowed by a user is required to be longer than the time required for completing the task in the working time interval allowed by the controllable load during the state adjustment of the controllable load, and the total working state of the controllable load is equal to the total working time required for completing the task in the working time interval allowed by the user is taken as a constraint condition;
when the controllable load is an electric automobile, the constraint conditions are required to be satisfied:
the charging power of the electric automobile is smaller than the maximum charging power of the electric automobile, and the state of charge of the electric automobile is maintained between the minimum state of charge and the maximum state of charge;
wherein the controllable load comprises a washing machine, an air conditioner, a water heater and an electric automobile.
Preferably, the obtaining of the power consumption of all the controllable loads in the household electrical equipment in a set period based on the evaluation result of each household user, the operating parameters of each controllable load in the electrical equipment of the household user, and a pre-constructed controllable load model includes:
Based on the evaluation results of all the family users, the family users with the electric energy efficiency evaluation scores lower than the threshold value in the obtained evaluation results;
based on the time that the household user allows to arrange the electricity consumption of the controllable load and a pre-constructed controllable load model, the state of all controllable loads in the household electricity consumption equipment at each time interval in a set period is obtained;
and obtaining the power consumption of all the controllable loads in the household electrical equipment at each time interval in the set period based on the states of all the controllable loads in the household electrical equipment at each time interval in the set period and the power of the controllable loads.
Preferably, the household electricity consumption behavior is optimized according to the following formula:
min D＝max(Tnew)-min(Tnew)
in the formula: d is the load peak-to-valley difference, TnewAnd setting a load value vector in a period after optimization, wherein the load value vector consists of the electricity consumption of the controllable load in each time interval.
Based on the same inventive concept, the invention also provides a family electric power energy efficiency optimization system, which comprises:
the evaluation module is used for comprehensively evaluating each household power energy efficiency index system by utilizing a pre-constructed energy efficiency evaluation model based on the acquired power utilization information of each household user;
the calculation module is used for obtaining the power consumption of all controllable loads in the household electric equipment in a set period based on the evaluation result of each household user, the operation parameters of each controllable load in the electric equipment of the household user and a pre-constructed controllable load model;
And the optimization module is used for optimizing the household electricity consumption behavior by taking the minimum load peak-valley difference as a target based on the electricity consumption of all controllable loads in the household electricity consumption equipment in a set period.
Preferably, the system further comprises a construction module for constructing an energy efficiency evaluation model; the building module is specifically configured to:
establishing a multi-stage family electric power energy efficiency index system;
carrying out dimensionless pretreatment on indexes in the household power energy efficiency index system;
sequentially calculating index weight vectors of all levels in a family power energy efficiency index system;
and establishing an energy efficiency evaluation model by utilizing a hierarchical comprehensive evaluation method based on the weight vectors of all superior indexes and the weight vector of the subordinate index corresponding to each superior index in the multi-level family power energy efficiency index system.
The technical scheme provided by the invention has the following beneficial effects:
according to the technical scheme provided by the invention, the comprehensive evaluation is carried out on the power efficiency index system of each family by utilizing the pre-constructed energy efficiency evaluation model based on the acquired power consumption information of each family user; obtaining the power consumption of all controllable loads in the household electric equipment in a set period based on the evaluation result of each household user, the operation parameters of each controllable load in the electric equipment of the household user and a pre-constructed controllable load model; based on the electricity consumption of all controllable loads in the household electricity consumption equipment in a set period, the household electricity consumption behavior is optimized by taking the minimum load peak-valley difference as a target. According to the technical scheme, the household energy efficiency evaluation index system is comprehensively and comprehensively evaluated through the established energy efficiency evaluation model, subjective and objective evaluation information is considered, the evaluation result of the household energy efficiency can be accurately obtained, meanwhile, a power utilization optimization scheme is provided for users with low power utilization efficiency, and a basis is provided for evaluating the power utilization efficiency of power customers, formulating a user energy-saving scheme and mining the energy-saving potential of the users.
Drawings
Fig. 1 is a flowchart of a method for optimizing energy efficiency of household power provided by the present invention;
fig. 2 is a detailed flowchart of a method for optimizing energy efficiency of household power according to an embodiment of the present invention;
FIG. 3 is a two-stage family energy efficiency evaluation index system established by the invention.
Detailed Description
For a better understanding of the present invention, reference is made to the following description taken in conjunction with the accompanying drawings and examples.
Example 1: as shown in fig. 1, the present invention provides a method for optimizing energy efficiency of home power, including:
s1, comprehensively evaluating each household power energy efficiency index system by using a pre-constructed energy efficiency evaluation model based on the acquired power consumption information of each household user;
s2 obtaining the power consumption of all controllable loads in the household electric equipment in a set period based on the evaluation result of each household user, the operation parameters of each controllable load in the electric equipment of the household user and a pre-constructed controllable load model;
and S3, optimizing the household electricity consumption behavior with the load peak-valley difference as the target based on the electricity consumption of all controllable loads in the household electricity consumption equipment in a set period.
The technical solution shown in fig. 1 is specifically described by using fig. 2:
Step 1), establishing a household power energy efficiency evaluation index system shown in fig. 3, wherein the first level comprises household user information, power utilization information of electric equipment and power consumption information of different household power utilization periods, and in the second level index, the household user information comprises: the average annual power consumption of the family is the total annual power consumption of the family divided by the number of the family, and the unit annual power consumption of the family is the total annual power consumption of the family divided by the area of the family; the power utilization information of the electric equipment comprises: the transferable power utilization equipment refers to power utilization equipment with flexibly selectable power utilization time, and comprises a washing machine, a water heater and an electric automobile, and the entertainment equipment comprises a computer and a television. The electricity consumption information of different electricity consumption periods of the family comprises the annual electricity consumption in the low valley period, the annual electricity consumption in the peak period and the annual electricity consumption in the slow period, and the time-of-use electricity price information of China is referred to, and the peak period is as follows: 9:00-11:59, 15: 00-21: 59; the flat period is as follows: 6:00-8:59, 12: 00-14:59, 22: 00-23: 59; the low valley period is: 0:00-5: 59.
Step 2), in order to make the index data of different units or magnitudes capable of being weighted and compared, the indexes are subjected to non-dimensionalization processing by a non-dimensionalization processing formula, wherein the formula is as follows:
wherein x*Representing the value of the index after dimensionless, xiRepresents the value of the ith index before non-dimensionalization,
represents the minimum value of the i-th index,
indicates the maximum value of the i-th index.
Step 3), determining the weight value of the first-level index by combining an analytic hierarchy process and a G1 group method, and specifically comprising the following steps:
firstly, pairwise comparison is carried out on the energy efficiency indexes of the first stage, and a judgment matrix B is established.
In the formula, ωijAnd the importance degree of the ith index relative to the jth index in the first-level evaluation index system is determined.
Determining eigenvector gamma of formula (2) by using matrix product square root methodi。
Based on the determined feature vector gammaiFinding the maximum eigenvalue λmax。
According to λmaxAnd calculating the consistency index beta of the judgment matrix B.
According to the ratio of the consistency index beta of the matrix B to the average random consistency index of the same order, if the ratio is less than 0.1, the matrix B is judged to meet the consistency, and the feature vector gammaiIs the weight of the first level indicator. Otherwise, determining the first-level index weight according to the G1 group method.
The G1 group method is an index weight determination method which is an improved analytic hierarchy process and does not need consistency check, is a subjective experience judgment method, does not need to construct a judgment matrix, has small calculated amount and does not limit the number of indexes in the same level;
The steps of the group G1 method in this example are as follows:
firstly, determining the order relation between indexes: if a certain index x in the energy efficiency evaluation of the home useriGreater than xjThen is marked as xi＞xj. If the index x1,x2,...,xmWith respect to the evaluation index set X ═ X1,x2,...,xmTheir sequence relationship can be determined as follows: firstly, the most important index of m indexes is selected from an index set X and marked as Xi(ii) a Then, the most important index is selected from the rest indexes and marked as xj(ii) a And so on; thus, the unique index order relationship can be determined.
Then, the degree of importance between the two indexes is judged, and two evaluation indexes x are assumedk-1And xkThe ratio of the degrees of importance of is κk-1/κkIs recorded as rkSee the formula:
rk＝κk-1/κk(7)
wherein k ═ m, m-1, m-2,.., 3,2, κkAnd the weight corresponding to the k index in the index set is shown.
ωijAnd riThe reference table for values of (A) is as follows:
TABLE 1 omegaijAnd riReference table of values of
| ωijOr ri | Description of the invention |
| 1.0 | The two indexes have equal importance |
| 1.2 | The former index is slightly more important than the latter index |
| 1.4 | The former index is significantly more important than the latter index |
| 1.6 | The former index is more important than the latter index |
| 1.8 | The former index is extremely important than the latter index |
| 1.1，1.3，1.5，1.7 | Correspond to the two or more Intermediate case of adjacent judgment |
There are also the formulas:
recursion can be obtained:
κk-1＝κkrk(9)
wherein k is m, m-1, m-2.
Step 4), setting m secondary indexes y under the ith primary index1,y2,...,ymCarrying out weight configuration on the m secondary indexes by using an entropy weight method, which specifically comprises the following steps:
calculating the information entropy of each index, and setting the information entropy of each index as EjThe calculation formula is as follows:
and finally, calculating the weight of each index, wherein the calculation formula is as follows:
step 5), setting 3 primary energy efficiency index weight vectors of the home users as lambda ═ lambda1,λ2,...,λn]The vector weight of the second-level energy efficiency index under the ith first-level energy efficiency index is etai＝[ηi1,ηi2,...,ηim]Then, the family power energy efficiency evaluation model established based on the hierarchical comprehensive evaluation method is as follows:
in the formula, e is the electricity utilization efficiency of a household user; lambda [ alpha ]iA weight representing the ith primary index; n is the number of first-level indexes; etaijVector weight of the second-level index under the ith first-level index is represented; m is the number of the second-level indexes under the ith first-level index; z is a radical ofijIs the jth secondary index under the ith primary index.
Step 6), controllable loads in the family are modeled, the loads have the common characteristic that the electricity utilization time can be flexibly arranged in a time range allowed by a user, and the electricity utilization mathematical model is as follows:
αh≤t≤βhh∈H (14)
In the formula, H represents a controllable electric device set, and H represents a certain controllable electric device; [ alpha ] toh,βh]Represents the h user allowed work time of the controllable load;
the controllable load h allows the working time interval to need more time than it needs to complete the task, as shown in the following formula:
βh-αh≥dh(15)
in the formula (d)hRepresenting the working time required by the controllable equipment to complete the required task;
the state adjustment of the controllable electric equipment needs to be within the working time interval [ alpha ] allowed by the userh,βh]And (b) as shown in the following formula:
in the formula, sh,tThe variable is a working state variable of the controllable load, when the variable is 1, the variable represents that the controllable electric equipment is in a working state, and when the variable is 0, the variable represents that the controllable electric equipment is in a closed state;
the sum of the operating states of the controllable electric devices in the operating time interval allowed by the user is equal to the total operating time required for the controllable electric devices to complete the task, as shown in the following formula:
in addition, the operating characteristics of a charging device such as an electric vehicle also need to satisfy the following constraints:
charging power of electric automobile is less than its maximum charging power
As shown in the following formula:
the state of charge of the electric vehicle is maintained at a minimum state of charge SminAnd maximum state of charge SmaxAs shown in the following formula:
Smin≤S(t)≤Smax(20)
in the formula, S (t +1) and S (t) are the charge states of the electric vehicle at the t +1 moment and the t moment respectively; p chCharging power for the electric vehicle; and E is the rated capacity of the battery of the electric automobile.
Aiming at the users with lower power consumption energy efficiency evaluation scores, power consumption optimization is carried out on the users with the load peak-valley difference as a target, and the power consumption energy efficiency of the users is improved, wherein an optimization target formula is shown as the following formula:
min D＝max(Tnew)-min(Tnew) (21)
wherein D is the load peak-to-valley difference, TnewAnd in order to optimize the load value vector of 24 hours in one day, the load value vector consists of the state of the controllable load in each hour and the electricity consumption calculated by the power of the controllable load.
Optimizing the time of use of a controllable load in a household with the aim of equation (21) to obtain a state variable s of the controllable loadh,tAs a variable, the controllable load is transferred to the low-ebb period from the peak period as far as possible for use, so that the electricity utilization cost of the user is reduced, and the difference of the peak and the valley of the electricity utilization is reduced.
The technical scheme provided by the invention is as follows: firstly, according to the power consumption information of a household user, establishing a household power energy efficiency index system comprising two levels, carrying out dimensionless pretreatment on indexes in the energy efficiency index system of the user, then evaluating a first-level index by using an analytic hierarchy process or a G1 group method, evaluating a second-level index by using an entropy weight method, and finally constructing an energy efficiency evaluation model based on a hierarchical comprehensive evaluation method to carry out comprehensive evaluation on the power energy efficiency condition of the household; according to the evaluation result, mathematical modeling is carried out on the controllable load in the household electric equipment, and power utilization optimization is carried out on the user with lower energy efficiency evaluation by taking the minimum load peak-valley difference as a target, so that the power utilization energy efficiency of the user is improved. According to the technical scheme, a comprehensive family energy efficiency evaluation index system is established, subjective and objective evaluation information is comprehensively considered in the established comprehensive evaluation model, the family energy efficiency can be accurately evaluated, an energy-saving scheme is formulated for a user by a power company, and a basis is provided for fully mining the energy-saving potential of the user.
Example 2: based on the same inventive concept, the embodiment of the invention also provides a family electric power energy efficiency optimization system, which comprises:
the evaluation module is used for comprehensively evaluating each household power energy efficiency index system by utilizing a pre-constructed energy efficiency evaluation model based on the acquired power utilization information of each household user;
the calculation module is used for obtaining the power consumption of all controllable loads in the household electric equipment in a set period based on the evaluation result of each household user, the operation parameters of each controllable load in the electric equipment of the household user and a pre-constructed controllable load model;
and the optimization module is used for optimizing the household electricity consumption behavior by taking the minimum load peak-valley difference as a target based on the electricity consumption of all controllable loads in the household electricity consumption equipment in a set period.
In an embodiment, the system further includes a construction module configured to construct an energy efficiency assessment model; the building module is specifically configured to:
establishing a multi-stage family electric power energy efficiency index system;
carrying out dimensionless pretreatment on indexes in the household power energy efficiency index system;
sequentially calculating index weight vectors of all levels in a family power energy efficiency index system;
And establishing an energy efficiency evaluation model by utilizing a hierarchical comprehensive evaluation method based on the weight vectors of all superior indexes and the weight vector of the subordinate index corresponding to each superior index in the multi-level family power energy efficiency index system.
In an embodiment, the sequentially calculating the index weight vectors of each level in the household power energy efficiency index system specifically includes:
evaluating the primary indexes in the household power energy efficiency index system based on an analytic hierarchy process or a G1 group method to obtain weight vectors of the primary indexes;
acquiring a weight vector of a second-level index under each first-level index in the family power energy efficiency index system by using an entropy weight method;
the household power energy efficiency index system is a secondary index system.
In the embodiment, a first level in the household power energy efficiency index system comprises household user information, power utilization information of electric equipment and power consumption information of different household power utilization periods;
the second-level indexes of the family user information comprise: the average annual power consumption of the family and the annual power consumption of the unit area of the family;
the second-level indexes of the power utilization information of the electric equipment comprise: the annual power consumption of the mobile electric equipment, the annual power consumption of the air conditioning system and the annual power consumption of the lighting and entertainment equipment can be transferred;
The second-level indexes of the electricity consumption information of different electricity consumption periods of the family comprise: the household annual power consumption in the valley period, the household annual power consumption in the peak period and the household annual power consumption in the slow period.
In an embodiment, the evaluating the primary indexes in the household power energy efficiency index system based on an analytic hierarchy process or a G1 group method to obtain a weight vector of each primary index includes:
comparing every two first-level indexes in a household power energy efficiency index system to establish a judgment matrix;
when the consistency index of the judgment matrix meets the requirement, evaluating the primary indexes in the household power energy efficiency index system based on an analytic hierarchy process to obtain the weight vector of each primary index;
and otherwise, evaluating the primary indexes in the household power energy efficiency index system based on a G1 group method to obtain the weight vector of each primary index.
In an embodiment, the constructing of the controllable load model includes:
taking the state of the controllable load corresponding to each time interval in the set period as a target function;
the method comprises the steps that the condition that the time required for completing a task in a working time interval allowed by a user is required to be longer than the time required for completing the task in the working time interval allowed by the controllable load during the state adjustment of the controllable load, and the total working state of the controllable load is equal to the total working time required for completing the task in the working time interval allowed by the user is taken as a constraint condition;
When the controllable load is an electric automobile, the constraint conditions are required to be satisfied:
the charging power of the electric automobile is smaller than the maximum charging power of the electric automobile, and the state of charge of the electric automobile is maintained between the minimum state of charge and the maximum state of charge;
wherein the controllable load comprises a washing machine, an air conditioner, a water heater and an electric automobile.
In an embodiment, the calculation module includes:
the selection unit is used for obtaining the household users with the electric energy efficiency evaluation scores lower than the threshold value in the evaluation results based on the evaluation results of the household users;
the first calculating unit is used for obtaining the states of all controllable loads in the household electric equipment at each time interval in a set period based on the time for allowing the household user to arrange the controllable loads to use electricity and a pre-constructed controllable load model;
and the second calculating unit is used for obtaining the power consumption of all the controllable loads in the household electric equipment at each time interval in the set period based on the states of all the controllable loads in the household electric equipment at each time interval in the set period and the power of the controllable loads.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The present invention is not limited to the above embodiments, and any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are included in the scope of the claims of the present invention which are filed as the application.
Claims (12)
1. A household power energy efficiency optimization method is characterized by comprising the following steps:
comprehensively evaluating each household power energy efficiency index system by using a pre-constructed energy efficiency evaluation model based on the acquired power utilization information of each household user;
obtaining the power consumption of all controllable loads in the household electric equipment in a set period based on the evaluation result of each household user, the operation parameters of each controllable load in the electric equipment of the household user and a pre-constructed controllable load model;
Based on the electricity consumption of all controllable loads in the household electricity consumption equipment in a set period, the household electricity consumption behavior is optimized by taking the minimum load peak-valley difference as a target.
2. The method according to claim 1, wherein the energy efficiency assessment model is constructed by:
establishing a multi-stage family electric power energy efficiency index system;
carrying out dimensionless pretreatment on indexes in the household power energy efficiency index system;
sequentially calculating index weight vectors of all levels in a family power energy efficiency index system;
and establishing an energy efficiency evaluation model by utilizing a hierarchical comprehensive evaluation method based on the weight vectors of all superior indexes and the weight vector of the subordinate index corresponding to each superior index in the multi-level family power energy efficiency index system.
3. The method according to claim 2, wherein the index in the family electricity energy efficiency index system is subjected to dimensionless preprocessing according to the following formula:
in the formula: x is the number of*Representing the value of the index after dimensionless, xiRepresents the value of the ith index before non-dimensionalization,
represents the minimum value of the i-th index,
denotes the maximum value of the i-th index, and n denotes the total number of indexes.
4. The method according to claim 2, wherein the sequentially calculating the index weight vectors of each level in the household power energy efficiency index system comprises:
Evaluating the primary indexes in the household power energy efficiency index system based on an analytic hierarchy process or a G1 group method to obtain weight vectors of the primary indexes;
acquiring a weight vector of a second-level index under each first-level index in the family power energy efficiency index system by using an entropy weight method;
the household power energy efficiency index system is a secondary index system.
5. The method according to claim 4, wherein the first level of the family power energy efficiency index system comprises family user information, electric equipment power consumption information and household power consumption information in different power consumption periods;
the second-level indexes of the family user information comprise: the average annual power consumption of the family and the annual power consumption of the unit area of the family;
the second-level indexes of the power utilization information of the electric equipment comprise: the annual power consumption of the mobile electric equipment, the annual power consumption of the air conditioning system and the annual power consumption of the lighting and entertainment equipment can be transferred;
the second-level indexes of the electricity consumption information of different electricity consumption periods of the family comprise: the household annual power consumption in the valley period, the household annual power consumption in the peak period and the household annual power consumption in the slow period.
6. The method of claim 4, wherein the evaluating the primary indexes in the family power energy efficiency index system based on an analytic hierarchy process or a G1 group method to obtain a weight vector of each primary index comprises:
Comparing every two first-level indexes in a household power energy efficiency index system to establish a judgment matrix;
when the consistency index of the judgment matrix meets the requirement, evaluating the primary indexes in the household power energy efficiency index system based on an analytic hierarchy process to obtain the weight vector of each primary index;
and otherwise, evaluating the primary indexes in the household power energy efficiency index system based on a G1 group method to obtain the weight vector of each primary index.
7. The method of claim 2, wherein the energy efficiency assessment model is represented by the following equation:
in the formula: e is the electricity utilization efficiency of the household user; lambda [ alpha ]iA weight representing the ith primary index; n is the number of first-level indexes; etaijVector weight of the second-level index under the ith first-level index is represented; m is the number of the second-level indexes under the ith first-level index; z is a radical ofijIs the jth secondary index under the ith primary index.
8. The method of claim 1, wherein the constructing of the controllable loading model comprises:
taking the state of the controllable load corresponding to each time interval in the set period as a target function;
the method comprises the steps that the condition that the time required for completing a task in a working time interval allowed by a user is required to be longer than the time required for completing the task in the working time interval allowed by the controllable load during the state adjustment of the controllable load, and the total working state of the controllable load is equal to the total working time required for completing the task in the working time interval allowed by the user is taken as a constraint condition;
When the controllable load is an electric automobile, the constraint conditions are required to be satisfied:
the charging power of the electric automobile is smaller than the maximum charging power of the electric automobile, and the state of charge of the electric automobile is maintained between the minimum state of charge and the maximum state of charge;
wherein the controllable load comprises a washing machine, an air conditioner, a water heater and an electric automobile.
9. The method as claimed in claim 1, wherein the obtaining of the power consumption of all the controllable loads in the household electrical equipment in a set period based on the evaluation result of each household user, the operation parameters of each controllable load in the electrical equipment of the household user and a pre-constructed controllable load model comprises:
based on the evaluation results of all the family users, the family users with the electric energy efficiency evaluation scores lower than the threshold value in the obtained evaluation results;
based on the time that the household user allows to arrange the electricity consumption of the controllable load and a pre-constructed controllable load model, the state of all controllable loads in the household electricity consumption equipment at each time interval in a set period is obtained;
and obtaining the power consumption of all the controllable loads in the household electrical equipment at each time interval in the set period based on the states of all the controllable loads in the household electrical equipment at each time interval in the set period and the power of the controllable loads.
10. The method of claim 9, wherein the home power usage behavior is optimized as follows:
minD＝max(Tnew)-min(Tnew)
in the formula: d is the load peak-to-valley difference, TnewFor optimizing rear deviceThe load value vector in the fixed period is composed of the electricity consumption of the controllable load in each time interval.
11. A home electricity energy efficiency optimization system, comprising:
the evaluation module is used for comprehensively evaluating each household power energy efficiency index system by utilizing a pre-constructed energy efficiency evaluation model based on the acquired power utilization information of each household user;
the calculation module is used for obtaining the power consumption of all controllable loads in the household electric equipment in a set period based on the evaluation result of each household user, the operation parameters of each controllable load in the electric equipment of the household user and a pre-constructed controllable load model;
and the optimization module is used for optimizing the household electricity consumption behavior by taking the minimum load peak-valley difference as a target based on the electricity consumption of all controllable loads in the household electricity consumption equipment in a set period.
12. The system of claim 11, further comprising a construction module for constructing an energy efficiency assessment model; the building module is specifically configured to:
Establishing a multi-stage family electric power energy efficiency index system;
carrying out dimensionless pretreatment on indexes in the household power energy efficiency index system;
sequentially calculating index weight vectors of all levels in a family power energy efficiency index system;
and establishing an energy efficiency evaluation model by utilizing a hierarchical comprehensive evaluation method based on the weight vectors of all superior indexes and the weight vector of the subordinate index corresponding to each superior index in the multi-level family power energy efficiency index system.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112467736A (en) * | 2020-12-01 | 2021-03-09 | 江苏博沃汽车电子系统有限公司 | Energy-saving control method and device for household power circuit |
| CN113253672A (en) * | 2021-06-16 | 2021-08-13 | 深圳市黑电平科技有限公司 | Load management system of intelligent home based on PLC |
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2020
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112467736A (en) * | 2020-12-01 | 2021-03-09 | 江苏博沃汽车电子系统有限公司 | Energy-saving control method and device for household power circuit |
| CN112467736B (en) * | 2020-12-01 | 2023-03-10 | 江苏博沃汽车电子系统有限公司 | Energy-saving control method and device for household power circuit |
| CN113253672A (en) * | 2021-06-16 | 2021-08-13 | 深圳市黑电平科技有限公司 | Load management system of intelligent home based on PLC |
| CN113253672B (en) * | 2021-06-16 | 2023-11-24 | 深圳市黑电平科技有限公司 | Intelligent household load management system based on PLC |
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