CN111639788A - Electric water heater operation optimization scheduling algorithm considering cost and comfort - Google Patents

Electric water heater operation optimization scheduling algorithm considering cost and comfort Download PDF

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CN111639788A
CN111639788A CN202010356480.0A CN202010356480A CN111639788A CN 111639788 A CN111639788 A CN 111639788A CN 202010356480 A CN202010356480 A CN 202010356480A CN 111639788 A CN111639788 A CN 111639788A
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邢建旭
王伟
刘海峰
卢峰
郑松松
朱晓黎
石勇
周佩祥
项镭
刘强
李磊
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Huzhou Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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Abstract

The invention relates to the technical field of intelligent electricity utilization, in particular to an operation optimization scheduling algorithm of an electric water heater considering cost and comfort, which comprises the following steps: step S1, establishing an electric water heater water temperature discrete change thermodynamic model which is suitable for the directed acyclic graph and takes user preference into account; step S2, calculating a vertex aiming at the shortest path problem of the single-source directed acyclic graph of the optimized operation scheduling algorithm; step S3, establishing a minimized cost optimization scheduling model considering electricity consumption cost and comfort cost; and step S4, solving a scheduling model by using Dijkstra algorithm. The substantial effects of the invention are as follows: the directed acyclic graph is used for solving and calculating, the calculation complexity is reduced, the scheduling operation efficiency is improved, the scheduling flexibility is improved by dynamically weighting the power consumption and the comfort level, and the optimal scheduling of the power consumption of the water heater is achieved under the condition of considering the comfort level.

Description

Electric water heater operation optimization scheduling algorithm considering cost and comfort
Technical Field
The invention relates to the technical field of intelligent electricity utilization, in particular to an operation optimization scheduling algorithm of an electric water heater considering cost and comfort.
Background
The smart grid is a modern application system in the field of power energy management. It utilizes automatic control and modern communication technology to raise efficiency, reliability and safety of energy consumption. With the development of smart power grids, mass data resources are increasingly accumulated. One of the reasons is the increase in data recording frequency. The smart meters used in the smart grid may record the power load data of the user periodically, typically 1 hour, 30 minutes, 15 minutes or even 1 minute. For power consumers, these load data are helpful to obtain their consumption behavior patterns, also referred to as load patterns. This load pattern may be used for consumer classification. In recent years, demand response has substantially developed, and power suppliers can achieve efficient energy control, flexible pricing, and demand management according to load patterns and consumption categories. On the other hand, power terminal consumers can understand their load patterns by responding to fluctuations in electricity prices to reduce their electricity fee expenses. In the demand response research, temperature control devices are often selected as main research objects, on one hand, due to the variable participation degree and energy storage characteristics of the temperature control devices, and on the other hand, the temperature control devices represented by water heaters, air conditioners and refrigerators form the main power load of a family, so that the temperature control devices are convenient for practical application. The optimal scheduling of the user side electric water heater necessarily involves the optimization of the electricity utilization cost and the comfort cost of the user. While current research only targets electricity cost as an optimization goal, comfort cost is only one constraint of optimization. Leading to insufficient consideration of the comfort level of the user, it is necessary to develop a scheduling method that comprehensively considers the electricity cost and the comfort level cost.
The domestic electric water heater optimized dispatching method based on demand side response, disclosed in Chinese patent CN109409610A, published in 2019, 3, 1, comprises the following steps: when only the influence of heat dissipation on the water temperature is considered, a thermodynamic model formula of the electric water heater is obtained; when only the heat loss brought by heat exchange is considered, a thermodynamic model formula of the electric water heater is obtained; comprehensively deducing an explicit equation of the electric water heater; obtaining a comfortable interval type of the electric water heater set by a user; obtaining the water temperature constraint satisfying the electric water heater scheduling requirement; the objective function of the optimized scheduling problem of the household electric water heater based on the response of the demand side is to solve the minimum value of the electricity price of the scheduling scheme; the optimized scheduling model of the electric water heater comprises the following steps: an objective function and a constraint; performing optimized scheduling on the household electric water heater based on an optimized scheduling model of the electric water heater; the optimized scheduling of the electric water heater is processed into a linear integer programming problem, so that an optimal scheduling scheme can be obtained in a short time. Although the technical problem of its technical scheme solution is similar with this patent, it can not be with comfort level cost quantization, can not compromise power consumption cost and comfort level cost.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the current water heater dispatching method cannot give consideration to the technical problems of electricity utilization cost and comfort cost. The algorithm dynamically weights the power consumption and the comfort level, improves the scheduling flexibility and achieves the optimal scheduling of the power consumption of the water heater.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows: an optimal scheduling algorithm for electric water heater operation considering cost and comfort comprises the following steps: step S1, establishing an electric water heater water temperature discrete change thermodynamic model which is suitable for the directed acyclic graph and takes user preference into account; step S2, calculating the peak aiming at the shortest path problem of the single-source directed acyclic graph of the optimized operation scheduling algorithm, namely a decision stage SnWater temperature state T at the beginning and end of timei，n、Tj，n+1And corresponding time period tnAverage power of
Figure BDA0002473646840000021
Step S3, establishing a minimized cost optimization scheduling model considering electricity consumption cost and comfort cost; step S4, solving the minimum cost by using Dijkstra algorithm*(u, v), shortest Path p*Optimal water temperature set point
Figure BDA0002473646840000022
And optimum average power
Figure BDA0002473646840000023
The complexity of a scheduling algorithm is reduced through the directed acyclic graph, the calculation efficiency is improved, the power consumption cost and the comfort cost are taken as optimization targets, and the power consumption cost can be reduced while the comfort is considered.
Preferably, step S1 includes: the directed acyclic graph comprises N decision stages SnEach decision stage S of the graphnCorresponding time period tnEach decision stage SnThe specific decision content is to determine the optimal real-time water temperature set point T in the periodi，nAll decision stages SnCorresponding water temperature set point Ti，nThe vertex forming the directed acyclic graph, the edge E of the directed acyclic graphi，j，nConnecting two adjacent vertices, i.e. Ti，nAnd Tj，n+1Edge E of directed acyclic graphi，j，nThe weight of (D) represents the water temperature from Ti，nTransition to Tj，n+1Normalized cost of (2), the normalized cost comprisingElectricity usage costs and comfort costs. And dividing a decision stage, and associating the directed acyclic graph with the decision stage, so that the calculation efficiency is improved.
Preferably, in step S2, the period tnAverage power of
Figure BDA0002473646840000024
The calculating method comprises the following steps: the thermodynamic model of the electric water heater is as follows:
Figure BDA0002473646840000025
discretizing the above formula to obtain a calculation formula:
Figure BDA0002473646840000026
wherein m iswThe water quality contained in the electric water heater, CpThe specific heat capacity of the water is,
Figure BDA0002473646840000027
is a period of time tnAverage mass flow rate of water, TinletFor inlet water temperature, U.AwhThe electric energy consumed to raise the unit temperature per unit mass of water, TambThe current temperature in the electric water heater is,
Figure BDA0002473646840000028
the calculation method comprises the following steps:
Figure BDA0002473646840000029
Figure BDA00024736468400000210
wherein the content of the first and second substances,
Figure BDA00024736468400000211
in order to prefer the water temperature,
Figure BDA00024736468400000212
is a period of time tnThe average water temperature of the water is,
Figure BDA00024736468400000213
is the water mass flow rate of the water heater. And a thermodynamic model of the electric water heater is established, and the scheduling accuracy is improved.
Preferably, in step S2, the method for calculating the vertex includes:
Figure BDA0002473646840000031
preferably, in step S3, the method for creating a least cost optimized scheduling model that takes into account electricity cost and comfort cost includes: each edge Ei，j，nCorresponding electricity costs ECi，j，nComprises the following steps:
Figure BDA0002473646840000032
total electricity cost ECtotalComprises the following steps:
Figure BDA0002473646840000033
wherein, PnIs a period tnAverage water temperature of the electric water heater. And establishing a scheduling model considering both the electricity cost and the comfort cost, thereby realizing scheduling considering both the electricity cost and the comfort cost from the technical aspect.
Preferably, in step S3, the normalized electricity consumption cost is:
Figure BDA0002473646840000034
wherein, ECmaxAnd ECminThe upper limit and the lower limit of the electricity consumption are respectively set manually;
the normalized comfort cost is:
Figure BDA0002473646840000035
wherein the content of the first and second substances,
Figure BDA0002473646840000036
the minimum temperature of the water is used,
Figure BDA0002473646840000037
the maximum water mass flow rate of the water heater. The normalized cost is used, so that the calculation is convenient, and the scheduling accuracy is improved.
Preferably, in step S3, the period tnThe cost of (A) is as follows:
Figure BDA0002473646840000038
wherein λ iscostAnd λcomfortWeight of electricity cost and comfort cost, λcost+λcomfort1, when the current temperature T in the electric water heaterambDeviation from a preferred water temperature
Figure BDA0002473646840000039
The larger the value, λcomfortThe larger.
As a preference, the first and second liquid crystal compositions are,
Figure BDA00024736468400000310
. Dynamic adjustment of lambdacostAnd λcomfortThe scheduling of the electric water heater is more flexible.
Preferably, in step S3, the minimum cost optimized scheduling model is established as follows:
an objective function:
Figure BDA0002473646840000041
constraint conditions are as follows:
(1) and (4) operation constraint:
Figure BDA0002473646840000042
(2) cost deviation tolerance constraints:
Figure BDA0002473646840000043
Figure BDA0002473646840000044
wherein the content of the first and second substances,
Figure BDA0002473646840000045
the deviation between the total cost of electricity and the minimum cost of electricity;
(3) and (3) parameter constraint:
λcost+λcomfort＝1
(4) comfort level restraint:
Tmin≤Tn≤Tmax
Figure BDA0002473646840000046
wherein the content of the first and second substances,
Figure BDA0002473646840000047
the lowest acceptable water temperature.
Preferably, in step S4, the shortest path from the initial point u to the final vertex v using Dijkstra' S algorithm is:
Figure BDA0002473646840000048
solving a minimum cost according to the operation constraint, the cost deviation tolerance constraint, the parameter constraint and the comfort constraint*(u, v), shortest Path p*Optimal water temperature set point
Figure BDA0002473646840000049
And optimum average power
Figure BDA00024736468400000410
The substantial effects of the invention are as follows: the directed acyclic graph is used for solving and calculating, the calculation complexity is reduced, the scheduling operation efficiency is improved, the scheduling flexibility is improved by dynamically weighting the power consumption and the comfort level, and the optimal scheduling of the power consumption of the water heater is achieved under the condition of considering the comfort level.
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Fig. 1 is a flow chart of an optimized operation scheduling algorithm of an electric water heater according to an embodiment.
FIG. 2 shows the result of optimizing an optimal water temperature set point according to an embodiment.
Detailed Description
The following provides a more detailed description of the present invention, with reference to the accompanying drawings.
An optimal scheduling algorithm for electric water heater operation considering cost and comfort is shown in fig. 1, and the embodiment includes the following steps: and step S1, establishing a thermodynamic model of the water temperature discrete change of the electric water heater, which is suitable for the directed acyclic graph and takes the user preference into account. Step S1 includes: the directed acyclic graph comprises N decision stages SnEach decision stage S of the graphnCorresponding time period tnEach decision stage SnThe specific decision content is to determine the optimal real-time water temperature set point T in the periodi，nAll decision stages SnCorresponding water temperature set point Ti，nThe vertex forming the directed acyclic graph, the edge E of the directed acyclic graphi，j，nConnecting two adjacent vertices, i.e. Ti，nAnd Tj，n+1Edge E of directed acyclic graphi，j，nThe weight of (D) represents the water temperature from Ti，nTransition to Tj，n+1Normalized costs including electricity costs and comfort costs. And dividing a decision stage, and associating the directed acyclic graph with the decision stage, so that the calculation efficiency is improved.
Step S2, calculating the peak aiming at the shortest path problem of the single-source directed acyclic graph of the optimized operation scheduling algorithm, namely a decision stage SnWater temperature state at the beginning and end of the time ofTi，n、Tj，n+1And corresponding time period tnAverage power of
Figure BDA0002473646840000051
Time period tnAverage power of
Figure BDA0002473646840000052
The calculating method comprises the following steps: the thermodynamic model of the electric water heater is as follows:
Figure BDA0002473646840000053
discretizing the above formula to obtain a calculation formula:
Figure BDA0002473646840000054
wherein m iswThe water quality contained in the electric water heater, CpThe specific heat capacity of the water is,
Figure BDA0002473646840000055
is a period of time tnAverage mass flow rate of water, TinletFor inlet water temperature, U.AwhThe electric energy consumed to raise the unit temperature per unit mass of water, TambThe current temperature in the electric water heater is,
Figure BDA0002473646840000056
the calculation method comprises the following steps:
Figure BDA0002473646840000057
Figure BDA0002473646840000058
wherein the content of the first and second substances,
Figure BDA0002473646840000059
in order to prefer the water temperature,
Figure BDA00024736468400000510
is a period of time tnThe average water temperature of the water is,
Figure BDA00024736468400000511
is the water mass flow rate of the water heater. And a thermodynamic model of the electric water heater is established, and the scheduling accuracy is improved.
The method for calculating the vertex comprises the following steps:
Figure BDA00024736468400000512
step S3, a minimum cost-optimized scheduling model is established that takes into account electricity costs and comfort costs.
The method for establishing the minimized cost optimization scheduling model considering the electricity consumption cost and the comfort cost comprises the following steps: each edge Ei，j，nCorresponding electricity costs ECi，j，nComprises the following steps:
Figure BDA0002473646840000061
total electricity cost BCtotalComprises the following steps:
Figure BDA0002473646840000062
wherein, PnIs a period tnAverage water temperature of the electric water heater. And establishing a scheduling model considering both the electricity cost and the comfort cost, thereby realizing scheduling considering both the electricity cost and the comfort cost from the technical aspect.
The normalized electricity cost is:
Figure BDA0002473646840000063
wherein, ECmaxAnd ECminThe upper limit and the lower limit of the electricity consumption are respectively set manually;
the normalized comfort cost is:
Figure BDA0002473646840000064
wherein the content of the first and second substances,
Figure BDA0002473646840000065
the minimum temperature of the water is used,
Figure BDA0002473646840000066
the maximum water mass flow rate of the water heater. The normalized cost is used, so that the calculation is convenient, and the scheduling accuracy is improved.
Time period tnThe cost of (A) is as follows:
Figure BDA0002473646840000067
wherein λ iscostAnd λcomfortWeight of electricity cost and comfort cost, λcost+λcomfort1, when the current temperature T in the electric water heaterambDeviation from a preferred water temperature
Figure BDA0002473646840000068
The larger the value, λcomfortThe larger.
Figure BDA0002473646840000069
. Dynamic adjustment of lambdacostAnd λcomfortThe scheduling of the electric water heater is more flexible.
The established minimum cost optimization scheduling model is as follows:
an objective function:
Figure BDA0002473646840000071
constraint conditions are as follows:
(1) and (4) operation constraint:
Figure BDA0002473646840000072
(2) cost deviation tolerance constraints:
Figure BDA0002473646840000073
Figure BDA0002473646840000074
wherein the content of the first and second substances,
Figure BDA0002473646840000075
the deviation between the total cost of electricity and the minimum cost of electricity;
(3) and (3) parameter constraint:
λcost+λcomfort＝1
(4) comfort level restraint:
Tmin≤Tn≤Tmax
Figure BDA0002473646840000076
wherein the content of the first and second substances,
Figure BDA0002473646840000077
the lowest acceptable water temperature.
Step S4, solving the minimum cost by using Dijkstra algorithm*(u, v), shortest Path p*Optimal water temperature set point
Figure BDA0002473646840000078
And optimum average power
Figure BDA0002473646840000079
The complexity of a scheduling algorithm is reduced through the directed acyclic graph, the calculation efficiency is improved, the power consumption cost and the comfort cost are taken as optimization targets, and the power consumption cost can be reduced while the comfort is considered.
The shortest path from the initial point u to the final vertex v using Dijkstra's algorithm is:
Figure BDA00024736468400000710
solving a minimum cost according to the operation constraint, the cost deviation tolerance constraint, the parameter constraint and the comfort constraint*(u, v), shortest Path p*Optimal water temperature set point
Figure BDA00024736468400000711
And optimum average power
Figure BDA00024736468400000712
Table 1 shows thermodynamic parameters of the electric water heater in this embodiment, and the optimal water temperature set points obtained by the algorithm simulation in this embodiment are shown in fig. 2.
TABLE 1 thermodynamic parameter settings for electric water heaters
Figure BDA0002473646840000081
The beneficial effect of this embodiment is: the directed acyclic graph is used for solving and calculating, the calculation complexity is reduced, the scheduling operation efficiency is improved, the scheduling flexibility is improved by dynamically weighting the power consumption and the comfort level, and the optimal scheduling of the power consumption of the water heater is achieved under the condition of considering the comfort level.
The above-described embodiments are only preferred embodiments of the present invention, and are not intended to limit the present invention in any way, and other variations and modifications may be made without departing from the spirit of the invention as set forth in the claims.

Claims (9)

1. An electric water heater operation optimization scheduling algorithm considering cost and comfort is characterized in that,
the method comprises the following steps:
step S1, establishing an electric water heater water temperature discrete change thermodynamic model which is suitable for the directed acyclic graph and takes user preference into account;
step S2, single-source directed acyclic for optimizing operation scheduling algorithmThe shortest path problem of the graph computes the vertices, decision stage SnWater temperature state T at the beginning and end of timei，n、Tj，n+1And corresponding time period tnAverage power of
Figure FDA0002473646830000011
Step S3, establishing a minimized cost optimization scheduling model considering electricity consumption cost and comfort cost;
step S4, solving the minimum cost by using Dijkstra algorithm*(u, v), shortest Path p*Optimal water temperature set point
Figure FDA0002473646830000012
And optimum average power
Figure FDA0002473646830000013
2. The optimal scheduling algorithm for electric water heater operation considering cost and comfort as claimed in claim 1, wherein the step S1 comprises:
the directed acyclic graph comprises N decision stages SnEach decision stage S of the graphnCorresponding time period tnEach decision stage SnThe specific decision content is to determine the optimal real-time water temperature set point T in the periodi，nAll decision stages SnCorresponding water temperature set point Ti，nThe vertex forming the directed acyclic graph, the edge E of the directed acyclic graphi，j，nConnecting two adjacent vertices, i.e. Ti，nAnd Tj，n+1Edge E of directed acyclic graphi，j，nThe weight of (D) represents the water temperature from Ti，nTransition to Tj，n+1The normalized cost includes a power usage cost and a comfort cost.
3. The optimal scheduling algorithm for electric water heater operation considering cost and comfort as claimed in claim 2, wherein in step S2, the time isSegment tnAverage power of
Figure FDA0002473646830000014
The calculating method comprises the following steps:
the thermodynamic model of the electric water heater is as follows:
Figure FDA0002473646830000015
discretizing the above formula to obtain a calculation formula:
Figure FDA0002473646830000016
wherein m iswThe water quality contained in the electric water heater, CpThe specific heat capacity of the water is,
Figure FDA0002473646830000017
is a period of time tnAverage mass flow rate of water, TinletFor inlet water temperature, U.AwhThe electric energy consumed to raise the unit temperature per unit mass of water, TambThe current temperature in the electric water heater is,
Figure FDA0002473646830000021
the calculation method comprises the following steps:
Figure FDA0002473646830000022
Figure FDA0002473646830000023
wherein the content of the first and second substances,
Figure FDA0002473646830000024
in order to prefer the water temperature,
Figure FDA0002473646830000025
is a period of time tnThe average water temperature of the water is,
Figure FDA0002473646830000026
is the water mass flow rate of the water heater.
4. The optimal scheduling algorithm for electric water heater operation considering cost and comfort as claimed in claim 3, wherein in step S2, the method for calculating the vertex includes:
Figure FDA0002473646830000027
5. the optimal scheduling algorithm for electric water heater operation considering cost and comfort as claimed in claim 3, wherein in step S3, the method for establishing the minimized cost optimal scheduling model considering electricity cost and comfort cost comprises:
each edge Ei，j，nCorresponding electricity costs ECi，j，nComprises the following steps:
Figure FDA0002473646830000028
total electricity cost ECtotalComprises the following steps:
Figure FDA0002473646830000029
wherein, PnIs a period tnAverage water temperature of the electric water heater.
6. The optimal scheduling algorithm for electric water heater operation considering cost and comfort as claimed in claim 5, wherein in step S3, the normalized electricity cost is:
Figure FDA00024736468300000210
wherein, ECmaxAnd ECminThe upper limit and the lower limit of the electricity consumption are respectively set manually;
the normalized comfort cost is:
Figure FDA0002473646830000031
wherein the content of the first and second substances,
Figure FDA0002473646830000032
the minimum temperature of the water is used,
Figure FDA0002473646830000033
the maximum water mass flow rate of the water heater.
7. The optimal scheduling algorithm for electric water heater operation considering cost and comfort as claimed in claim 6, wherein in step S3, the time period t isnThe cost of (A) is as follows:
Figure FDA0002473646830000034
wherein λ iscostAnd λcomfortWeight of electricity cost and comfort cost, λcost+λcomfort＝1。
8. The algorithm for optimizing and dispatching electric water heater based on cost and comfort as claimed in claim 7, wherein in step S3, the minimum cost optimization dispatching model is established as:
an objective function:
Figure FDA0002473646830000035
constraint conditions are as follows:
(1) and (4) operation constraint:
Figure FDA0002473646830000036
(2) cost deviation tolerance constraints:
Figure FDA0002473646830000037
Figure FDA0002473646830000038
wherein the content of the first and second substances,
Figure FDA0002473646830000039
the deviation between the total cost of electricity and the minimum cost of electricity;
(3) and (3) parameter constraint:
λcost+λcomfort＝1
(4) comfort level restraint:
Tmin≤Tn≤Tmax
Figure FDA0002473646830000041
wherein the content of the first and second substances,
Figure FDA0002473646830000042
the lowest acceptable water temperature.
9. A cost and comfort considered optimal scheduling algorithm for electric water heater operation according to claim 8,
in step S4, the shortest path from the initial point u to the final vertex v using Dijkstra' S algorithm is:
Figure FDA0002473646830000043
solving according to the operation constraint, the cost deviation tolerance constraint, the parameter constraint and the comfort constraintOut of minimum cost*(u, v), shortest Path p*Optimal water temperature set point
Figure FDA0002473646830000044
And optimum average power
Figure FDA0002473646830000045
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