CN105844365A - Optimization method and device of household energy management system - Google Patents
Optimization method and device of household energy management system Download PDFInfo
- Publication number
- CN105844365A CN105844365A CN201610217646.4A CN201610217646A CN105844365A CN 105844365 A CN105844365 A CN 105844365A CN 201610217646 A CN201610217646 A CN 201610217646A CN 105844365 A CN105844365 A CN 105844365A
- Authority
- CN
- China
- Prior art keywords
- optimization model
- electricity
- load
- family
- photovoltaic power
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000005457 optimization Methods 0.000 title claims abstract description 134
- 238000000034 method Methods 0.000 title claims abstract description 35
- 230000005611 electricity Effects 0.000 claims abstract description 100
- 230000007613 environmental effect Effects 0.000 claims abstract description 53
- 238000010248 power generation Methods 0.000 claims abstract description 34
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims abstract description 31
- 229910052799 carbon Inorganic materials 0.000 claims abstract description 31
- 210000000349 chromosome Anatomy 0.000 claims description 24
- 230000002068 genetic effect Effects 0.000 claims description 22
- 230000006872 improvement Effects 0.000 claims description 9
- 230000003993 interaction Effects 0.000 claims description 7
- 238000005265 energy consumption Methods 0.000 abstract description 5
- 238000005516 engineering process Methods 0.000 description 7
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 5
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 229910002092 carbon dioxide Inorganic materials 0.000 description 2
- 230000014759 maintenance of location Effects 0.000 description 2
- 230000006798 recombination Effects 0.000 description 2
- 238000005215 recombination Methods 0.000 description 2
- 238000005406 washing Methods 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 239000001569 carbon dioxide Substances 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000012790 confirmation Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A30/00—Adapting or protecting infrastructure or their operation
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Development Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Supply And Distribution Of Alternating Current (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses an optimization method and a device of a household energy management system, wherein the method comprises the following steps: classifying the family loads according to the using modes of the family loads to obtain the type of each family load; predicting the photovoltaic power consumption according to the meteorological data and the historical data of the household load power consumption; according to the photovoltaic power consumption, preset constraint conditions, electricity price data and the type of family load, obtaining an economic optimization model with the minimum cost and an environmental protection optimization model with the minimum carbon emission; wherein the optimization model is the working state of each family load in each period. According to the method, the photovoltaic power consumption of the user is predicted through the historical data of the meteorological data and the household load power consumption, and the relation between new energy power generation and the energy consumption demand of the user is combined; meanwhile, an economic optimization model and an environmental protection optimization model are obtained by combining real-time electricity price data, so that the aim of economic electricity utilization or environmental protection electricity utilization can be fulfilled to the maximum extent by the working states of all loads.
Description
Technical Field
The invention relates to the technical field of smart power grids, in particular to an optimization method and device of a household energy management system.
Background
At present, the development direction of the power grid worldwide is to construct a flexible, clean, safe, economic and friendly smart power grid. The strong intelligent power grid is an organic whole including all links of power generation, power transmission, power transformation, power distribution, power utilization, scheduling and the like, and is a complete intelligent power system. The household user serves as the tail end of the whole system, and an intelligent household energy management system is formed by utilizing an advanced measurement technology, a communication technology, an automatic control technology, a new energy technology and an intelligent optimization decision technology.
With the progress of technology, more and more loads in smart homes participate in the dispatching management of energy systems. When multiple loads on a line participate in the optimal scheduling, it is possible to generate an optimization result that these loads work simultaneously. This result can cause the line to be overloaded, resulting in trip protection of the home air circuit breaker, causing inconvenience to the normal electricity usage of the user, and threatening the home safety if the air circuit breaker fails to be disconnected for any reason. The home energy management system also loses its energy management meaning. With the addition of new energy power generation in a family, the implementation of real-time electricity price in a power grid and the adjustability of energy demand of a user, the user is a complicated matter if the user wants to realize the maximization of own benefits through the energy management of the user. The intelligent optimization decision-making technology in the intelligent home energy management system is used as the core of the system, the energy utilization behavior of a home is optimized, the system automatically controls the load operation, and the intelligent power utilization of a user is greatly facilitated.
However, the current household energy management system still cannot well combine the relationship between new energy power generation, real-time electricity price and energy demand of users to arrange the working states of all loads, so as to maximally achieve the purposes of economic power utilization or environmental protection power utilization.
Disclosure of Invention
The invention provides an optimization method and device of a household energy management system, which solve the problems that the existing household energy management system still cannot well combine the relationship between new energy power generation, real-time electricity price and energy demand of users, arrange the working states of all loads and maximally realize the purposes of economic electricity utilization or environment-friendly electricity utilization.
In a first aspect, the present invention provides a method for optimizing a home energy management system, including:
classifying the family loads according to the using modes of the family loads to obtain the type of each family load; predicting the photovoltaic power consumption according to the meteorological data and the historical data of the household load power consumption;
according to the photovoltaic power consumption, preset constraint conditions, electricity price data and the type of family load, obtaining an economic optimization model with the minimum cost and an environmental protection optimization model with the minimum carbon emission;
wherein the optimization model is the working state of each family load in each period.
Preferably, the obtaining an economic optimization model with the minimum cost and an environmental protection optimization model with the minimum carbon emission according to the photovoltaic power consumption, the preset constraint condition, the electricity price data and the type of the household load further includes:
an improved genetic algorithm is adopted to obtain an economic optimization model with the minimum cost and an environmental protection optimization model with the minimum carbon emission;
wherein, the improvement of the genetic algorithm is to move the chromosome to the right to generate a new chromosome.
Preferably, after obtaining the economic optimization model with the minimum cost and the environmental protection optimization model with the minimum carbon emission according to the photovoltaic power consumption, the preset constraint condition, the electricity price data and the type of the household load, the method further comprises:
and selecting the economic optimization model or the environmental protection optimization model to carry out household energy management according to a user selection instruction.
Preferably, the economic optimization model is as follows:
wherein,
C2(t)＝G(t)×PGformula three
Wherein Cost is the total Cost of a user in one day; c1(t) settling the total cost for buying and selling the electricity by the user and the power grid; c2(t) is a subsidy of the state for photovoltaic power generation; pb(t) the electricity purchase price in the real-time electricity prices; ps(t) in real-time electricity pricesA price to sell electricity; p (t) is the interaction electric quantity between the household and the power grid, P (t)>When 0, the family buys power from the power grid, P (t)<When 0, the family sells electricity to the power grid; t is the length of a time interval, T is 1h, the day is divided into 24 time intervals, and T is an integer from 1 to 24; g (t) is predicted photovoltaic power generation; pGThe price is subsidized for the state of the full electric quantity of the photovoltaic power generation.
Preferably, the environmental protection optimization model has the following formula four:
wherein F is the carbon emission generated by purchasing electricity in one day; carbon emission generated by first-degree electricity generation of firepower; p (t) is the purchase of electricity from the grid; a set of epochs with Ω of P (t) > 0; t is the length of one period, T ═ 1h, divides the day into 24 periods, and T is an integer from 1 to 24.
In a second aspect, the present invention further provides an optimization apparatus for a home energy management system, including:
the load classification and photovoltaic prediction module is used for classifying the family loads according to the using modes of the family loads to obtain the type of each family load; predicting the photovoltaic power consumption according to the meteorological data and the historical data of the household load power consumption;
the model acquisition module is used for acquiring an economic optimization model with the minimum cost and an environmental protection optimization model with the minimum carbon emission according to the photovoltaic power consumption, preset constraint conditions, electricity price data and the type of the family load;
wherein the optimization model is the working state of each family load in each period.
Preferably, the model obtaining module further adopts an improved genetic algorithm to obtain an economic optimization model with the minimum cost and an environmental protection optimization model with the minimum carbon emission;
wherein, the improvement of the genetic algorithm is to move the chromosome to the right to generate a new chromosome.
Preferably, the method further comprises the following steps:
and the model determining module is used for selecting the economic optimization model or the environmental protection optimization model to carry out family energy management according to a user selection instruction.
Preferably, the economic optimization model is as follows:
wherein,
C2(t)＝G(t)×PGformula three
Wherein Cost is the total Cost of a user in one day; c1(t) settling the total cost for buying and selling the electricity by the user and the power grid; c2(t) is a subsidy of the state for photovoltaic power generation; pb(t) the electricity purchase price in the real-time electricity prices; ps(t) a selling price among the real-time electricity prices; p (t) is the interaction electric quantity between the household and the power grid, P (t)>When 0, the family buys power from the power grid, P (t)<When 0, the family sells electricity to the power grid; t is the length of a time interval, T is 1h, the day is divided into 24 time intervals, and T is an integer from 1 to 24; g (t) is predicted photovoltaic power generation; pGThe price is subsidized for the state of the full electric quantity of the photovoltaic power generation.
Preferably, the environmental protection optimization model has the following formula four:
wherein F is the carbon emission generated by purchasing electricity in one day; carbon emission generated by first-degree electricity generation of firepower; p (t) is the purchase of electricity from the grid; a set of epochs with Ω of P (t) > 0; t is the length of one period, T ═ 1h, divides the day into 24 periods, and T is an integer from 1 to 24.
According to the technical scheme, the photovoltaic power consumption of the user is predicted through the meteorological data and the historical data of the household load power consumption, and the relation between the new energy power generation and the energy consumption demand of the user is combined; meanwhile, an economic optimization model and an environmental protection optimization model are obtained by combining real-time electricity price data, so that the aim of economic electricity utilization or environmental protection electricity utilization can be fulfilled to the maximum extent by the working states of all loads.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic flowchart of an optimization method of a home energy management system according to an embodiment of the present invention;
fig. 2 is a schematic circuit diagram of a household power supply according to an embodiment of the present invention;
fig. 3 is a schematic flow chart of another optimization method for a home energy management system according to an embodiment of the present invention;
FIG. 4 is a schematic flow chart of a genetic algorithm provided in accordance with an embodiment of the present invention;
fig. 5 is a schematic diagram of load ratios of lines after optimization according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of an optimization apparatus of a home energy management system according to an embodiment of the present invention.
Detailed Description
The following further describes embodiments of the invention with reference to the drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
Fig. 1 is a flowchart illustrating an optimization method of a home energy management system according to an embodiment of the present invention, including:
s1, classifying the family loads according to the using modes of the family loads to obtain the type of each family load; predicting the photovoltaic power consumption according to the meteorological data and the historical data of the household load power consumption;
the dispatching objects of the household energy management system are mainly various loads in a household. Load models need to be built by classifying loads, and loads in a family are generally divided into non-dispatchable loads and dispatchable loads. The non-scheduling load is that the load such as refrigerator, lighting and the like must be operated, and the use state of the load influences the convenience of electricity utilization of users, so the load does not participate in scheduling and only serves as the basis of scheduling. Schedulable loads are divided into interruptible loads, uninterruptable loads and temperature controlled loads. The load such as an electric fan can be interrupted, and the operation can be interrupted. Uninterruptible loads, such as those of washing machines and the like, whose operating characteristics do not allow interruption. The operating state of temperature-controlled loads, such as water heaters and air conditioners, is mainly determined by the temperature of water or room temperature to be controlled. When the temperature meets the requirement, the temperature control load can not work; when the temperature does not meet the requirements, the temperature control load needs to work. The classification mode considers the load in the family more comprehensively and manages the load more finely.
S2, obtaining an economic optimization model with the minimum cost and an environmental protection optimization model with the minimum carbon emission according to the photovoltaic power consumption, preset constraint conditions, electricity price data and the type of the family load;
wherein the optimization model is the working state of each family load in each period.
In the embodiment, an energy management mode of daily planned scheduling is adopted, one day is divided into a plurality of time intervals, after photovoltaic power generation and load power consumption on the next day are predicted, load is optimally scheduled by combining electricity price information and user setting, and the operation of the load is arranged in a proper time interval so as to meet the power consumption requirement of a user and realize the intelligent optimization decision function of the household energy management system. The intelligent optimization decision obtains the optimal energy utilization plan of the user by solving the established optimization model by adopting a mathematical optimization algorithm or a biological evolution algorithm. The objectives of the optimization model are roughly divided into: the energy consumption cost is minimized, the satisfaction is maximized, the comfort is maximized, and the environmental protection is optimized. The model can be used for modeling aiming at one target or a plurality of models, and the use energy of the user can be optimized.
According to the method, the photovoltaic power consumption of the user is predicted through the historical data of the meteorological data and the household load power consumption, and the relation between the new energy power generation and the energy consumption demand of the user is combined; meanwhile, an economic optimization model and an environmental protection optimization model are obtained by combining real-time electricity price data, so that the aim of economic electricity utilization or environmental protection electricity utilization can be fulfilled to the maximum extent by the working states of all loads.
As an alternative to this embodiment, S2 further includes:
s21, obtaining an economic optimization model with the minimum cost and an environmental protection optimization model with the minimum carbon emission by adopting an improved genetic algorithm;
wherein, the improvement of the genetic algorithm is to move the chromosome to the right to generate a new chromosome.
The improvement of the genetic algorithm specifically comprises: the model is designed for 0-1 integer, and adopts binary coding mode to make individual bodyAnd (6) coding is carried out. Due to the fact that the constraint conditions in the model are more, if a penalty function processing mode is adopted, a feasible solution is difficult to obtain. The shortest continuous operation time constraint on the load takes the form of individual improvements generated for each generation. In each generation of newly generated individuals, the chromosomes are shifted to the right by positions 1 and 2 … (L)ij-1) position, accumulating the newly generated chromosome with the original chromosome to generate a new chromosome. Thereby ensuring that the chromosome codes carried by the individual are continuous. And a penalty function mode is adopted for safety constraint, load working time constraint and temperature constraint of the temperature control load. In the face of the situation that the optimal solution is missed in the genetic algorithm, the optimal retention algorithm is adopted in the genetic process, and the possible optimal solution is prevented from being damaged by variation and recombination. And finally, selecting a final Pareto optimal solution in a Pareto optimal filter.
Further, after S2, the method further includes:
and S3, selecting the economic optimization model or the environmental protection optimization model to perform household energy management according to a user selection instruction.
Specifically, the economic optimization model is as follows:
wherein,
C2(t)＝G(t)×PGformula three
Wherein Cost is the total Cost of a user in one day; c1(t) settling the total cost for buying and selling the electricity by the user and the power grid; c2(t) is a subsidy of the state for photovoltaic power generation; pb(t) the electricity purchase price in the real-time electricity prices; ps(t) a selling price among the real-time electricity prices; p (t) is homeAmount of interaction between the household and the grid, P (t)>When 0, the family buys power from the power grid, P (t)<When 0, the family sells electricity to the power grid; t is the length of a time interval, T is 1h, the day is divided into 24 time intervals, and T is an integer from 1 to 24; g (t) is predicted photovoltaic power generation; pGThe price is subsidized for the state of the full electric quantity of the photovoltaic power generation.
Further, the environmental protection optimization model has the following formula four:
wherein F is the carbon emission generated by purchasing electricity in one day; carbon emission generated by first-degree electricity generation of firepower; p (t) is the purchase of electricity from the grid; a set of epochs with Ω of P (t) > 0; t is the length of one period, T ═ 1h, divides the day into 24 periods, and T is an integer from 1 to 24.
The following describes in detail the method for optimizing the home energy management system according to the present embodiment. In the embodiment, the security is introduced into an optimization model of the household energy management system, the security is introduced as an optimization target in the intelligent optimization decision process, a multi-target optimization model is established, and a genetic algorithm can be used for solving.
A1, establishing a basic model of the household energy management system;
the dispatching objects of the household energy management system are mainly various loads in a household, such as an electric automobile, a cabinet air conditioner, a wall-mounted air conditioner, lighting, a kitchen, a toilet and a socket in fig. 2. Load models need to be built by classifying loads, and loads in a family are generally divided into non-dispatchable loads and dispatchable loads. The non-scheduling load is that the load such as lighting and the like must be operated, and the use state of the load influences the convenience of electricity utilization of users, so that the load does not participate in scheduling and is only used as the basis of scheduling. Schedulable loads are divided into interruptible loads, uninterruptable loads and temperature controlled loads. The operation of the air conditioner can be interrupted by interrupting the load such as a cabinet air conditioner, a wall-mounted air conditioner and the like. Uninterruptible loads, such as those of a washing machine or the like (not shown in fig. 2), the operating characteristics of which do not allow interruption. The operating state of temperature-controlled loads, such as water heaters and air conditioners, is mainly determined by the temperature of water or room temperature to be controlled. When the temperature meets the requirement, the temperature control load can not work; when the temperature does not meet the requirements, the temperature control load needs to work. The classification mode considers the load in the family more comprehensively and manages the load more finely.
A2, establishing an optimization model of the family energy management system;
in the home energy management system, first, weather data and real-time electricity rate data of the next day are obtained from the network. And predicting the photovoltaic power generation amount and the power consumption of the necessary operation load every hour on the next day according to the meteorological data and the historical data of the power consumption of the user. And then, setting the next-day schedulable load operation requirement by the user on a human-computer interaction interface of the system. And finally, solving an optimal solution meeting the minimization of user cost or the minimization of carbon emission according to the prediction data, the setting requirements and the constraint conditions. And after the system electricity buying and selling state and the operation state of each load in each time period are obtained, generating an electricity utilization plan of the next day. After the user confirmation, the home energy management terminal controls the system to operate according to the plan, as shown in fig. 3.
The optimization model of the household energy management system considers three targets of economy, environmental protection and safety. The different user emphasis is different between the economy and the environmental protection, and the two modes of setting the economy and the environmental protection are selected by the user. The safety optimization model must be considered whether the user emphasizes economy or environmental protection. In the economic mode, an economic optimization model and a safety optimization model need to be considered simultaneously. In the environmental protection mode, an environmental protection optimization model and a safety optimization model need to be considered at the same time. Both modes are multiobjective optimization.
Since the security objective need only be to reach the safe limit. A main target method in multi-target optimization is adopted, the economy and the environmental protection are respectively used as main targets, a safety target is processed as the constraint of a model, and a processing mode of setting the maximum value is adopted.
(1) User electricity economy optimization model
The cost of the home user is divided into three main aspects: firstly, the electricity purchasing cost generated by purchasing electricity from a power grid; secondly, generating electricity selling income for selling electricity to the power grid; and thirdly, the state subsidies on photovoltaic power generation. The user's economic optimization model is also considered from these three aspects. The model is shown in formula one.
(2) User power consumption environment-friendly optimization model
In the embodiment, the electricity in the power grid is the traditional thermal power generation, and the thermal power generation can generate the emission of carbon dioxide. Under the condition that the total amount of household electricity is not changed, the more photovoltaic power generation is used, so that the electricity purchasing amount from the power grid can be reduced. The environmental protection optimization model is shown in formula four.
(3) The constraints for both types of models are the same:
the whole system should keep power balance during operation:
in the formula: wijThe power of the jth electrical appliance on the ith line; a. theij(t) is the working state of the jth electric appliance on the ith line in the time period of t, Aij(t) ═ 1 denotes the operation of the appliance, Aij(t) ═ 0 indicates that the appliance is not operating; mi(t) is the necessary operating load power on the ith line; p (t)>When 0, the electricity buying and selling state S (t) is 0; p (t)<At 0, the electricity trading state s (t) is 1.
And (3) power utilization safety restraint:
the safety of consumer electricity usage is the load conditions of the various lines in the home.
The safety of a home energy management system is mainly concerned with the wiring from the switchboard to the load. Active power flow on the ith line:
line i load rate:
wherein, Pli(t) is the active power flow on the ith line; plimaxThe rated load of the ith line.
Converting a safety target into an electricity utilization safety constraint according to a main target method in multi-target optimization, and setting a maximum value a:
Riformula eight with (t) less than or equal to a
According to the user setting, the working time length constraint of the load is as follows:
wherein N isijThe total length of time that the work is required for this load.
And (3) limiting the shortest continuous working time of the load:
wherein L isijThe shortest continuous working time length of the load is; b isijFor the period when the load starts to operate.
Temperature control load restraint:
the temperature control model controls the temperature to a set temperature Ts of the user, allowing the temperature to have a user acceptable deviation Δ T around the set temperature. The constraint is expressed as:
T＝Ts+ -Delta T formula eleven
Temperature control load model (air conditioner as an example):
wherein, TroomIs the indoor temperature; t isairIs the outdoor temperature; r is equivalent thermal resistance; c is equivalent thermal capacitance; q is the equivalent heat ratio; a. theijAnd (t) is the air conditioner running state. The water heater model is the same as the model.
In the embodiment, the load working period, the working duration and whether the load working period and the working duration are continuously required by the user are taken as the standards satisfied by the user; whether the temperature of the temperature controlled load is within the temperature range required by the user is taken as a standard for the comfort of the user. The standards are represented in the model in a constraint mode, so that the satisfaction degree and the comfort degree of a user are guaranteed.
A3, optimizing a household energy management system;
the genetic algorithm is improved according to the model, and the specific flow is shown in figure 4. The model is designed for 0-1 integer, and the individual is coded by adopting a binary coding mode. Due to the fact that the constraint conditions in the model are more, if a penalty function processing mode is adopted, a feasible solution is difficult to obtain. The shortest continuous operation time constraint on the load takes the form of individual improvements generated for each generation. In each generation of newly generated individuals, the chromosomes are shifted to the right by positions 1 and 2 … (L)ij-1) position, accumulating the newly generated chromosome with the original chromosome to generate a new chromosome. Thereby ensuring that the chromosome codes carried by the individual are continuous. Using penalty functions for safety constraints, load operating time constraints and temperature constraints of temperature controlled loadsThe method. In the face of the situation that the optimal solution is missed in the genetic algorithm, the optimal retention algorithm is adopted in the genetic process, and the possible optimal solution is prevented from being damaged by variation and recombination. And finally, selecting a final Pareto optimal solution in a Pareto optimal filter.
In this embodiment, the proposed optimization method of the home energy management system is subjected to simulation verification in a certain home intelligent management system. The specific parameters and user settings are shown in tables 1-4 below.
TABLE 1 day-of-day real-time electricity rate information and coefficients
TABLE 2 user's work request and coefficient for the next day of temperature control load
TABLE 3 Home routing information
TABLE 4 user Next day work requirement for schedulable load
The method provided by the embodiment can ensure that the result obtained by optimization is safe, and cannot cause potential safety hazard to users. It can be seen from comparison of fig. 5 that the optimization result considering safety effectively avoids the occurrence of overload, where fig. 5(a) is the load rate of each line after economic optimization considering safety; FIG. 5(B) is a graph showing the load ratios of the respective lines after the optimization of the environmental protection without considering the safety; fig. 5(C) is a load factor of each line after economic optimization in consideration of safety; fig. 5(D) shows the load factor of each line after optimization of the environmental protection in consideration of safety.
The scheme can be realized through Matlab programming, and the optimization results of the economic mode and the environmental protection mode are respectively obtained according to different requirements of users. In the economic mode, the optimization result moves some loads to the late night with lower electricity price so as to reduce the energy cost for users; in the environmental protection mode, the optimization result moves some loads to the time period of photovoltaic power generation so as to fully utilize the photovoltaic power generation and reduce the power purchase from the power grid, thereby reducing the carbon emission.
Table 5 shows the comparison between the optimization effect of the present technical solution and the conventional optimization effect.
TABLE 5 comparison of the optimization results
| Optimization of non-use | Economic optimization | Environmental protection optimization | |
| Cost/dollar | 28.03 | 21.72 | 24.34 |
| CO2 emission/kg | 64.50 | 58.22 | 56.88 |
In the energy optimization process of the household energy management system, the load rates of all lines of a household are incorporated into an optimization model, and the optimization result of the model can ensure the safety of the electricity utilization of a user while optimizing the target. When the genetic algorithm is used for solving, the genetic algorithm is improved, and the chromosome of an individual is subjected to right shift and then accumulation, so that the chromosome coding is continuous, and the obtained optimal solution can ensure continuous load work.
Fig. 6 shows a schematic structural diagram of an optimization device of a home energy management system provided in this embodiment, the device includes a load classification and photovoltaic prediction module 11 and a model acquisition module 12, wherein,
the load classification and photovoltaic prediction module 11 is used for classifying the household loads according to the using mode of the household loads to obtain the type of each household load; predicting the photovoltaic power consumption according to the meteorological data and the historical data of the household load power consumption;
the model obtaining module 12 is configured to obtain an economic optimization model with the minimum cost and an environmental protection optimization model with the minimum carbon emission according to the photovoltaic power consumption, preset constraint conditions, electricity price data and the type of the family load;
wherein the optimization model is the working state of each family load in each period.
The load classification and photovoltaic prediction module 11 classifies the household loads according to the using mode of the household loads to obtain the type of each household load; predicting the photovoltaic power consumption according to the meteorological data and the historical data of the household load power consumption; the model obtaining module 12 obtains an economic optimization model with the minimum cost and an environmental protection optimization model with the minimum carbon emission according to the photovoltaic power consumption, the preset constraint conditions, the electricity price data and the type of the family load;
according to the method, the photovoltaic power consumption of the user is predicted through the historical data of the meteorological data and the household load power consumption, and the relation between the new energy power generation and the energy consumption demand of the user is combined; meanwhile, an economic optimization model and an environmental protection optimization model are obtained by combining real-time electricity price data, so that the aim of economic electricity utilization or environmental protection electricity utilization can be fulfilled to the maximum extent by the working states of all loads.
Optionally, the model obtaining module 12 further adopts an improved genetic algorithm to obtain an economic optimization model with the minimum cost and an environmental protection optimization model with the minimum carbon emission;
wherein, the improvement of the genetic algorithm is to move the chromosome to the right to generate a new chromosome.
Further, the apparatus further comprises:
and the model determining module is used for selecting the economic optimization model or the environmental protection optimization model to carry out family energy management according to a user selection instruction.
Further, the economic optimization model is as follows:
wherein,
C2(t)＝G(t)×PGformula three
Wherein Cost is the total Cost of a user in one day; c1(t) settling the total cost for buying and selling the electricity by the user and the power grid; c2(t) is the countrySubsidy of photovoltaic power generation; pb(t) the electricity purchase price in the real-time electricity prices; ps(t) a selling price among the real-time electricity prices; p (t) is the interaction electric quantity between the household and the power grid, P (t)>When 0, the family buys power from the power grid, P (t)<When 0, the family sells electricity to the power grid; t is the length of a time interval, T is 1h, the day is divided into 24 time intervals, and T is an integer from 1 to 24; g (t) is predicted photovoltaic power generation; pGThe price is subsidized for the state of the full electric quantity of the photovoltaic power generation.
Specifically, the environmental protection optimization model has the following formula four:
wherein F is the carbon emission generated by purchasing electricity in one day; carbon emission generated by first-degree electricity generation of firepower; p (t) is the purchase of electricity from the grid; a set of epochs with Ω of P (t) > 0; t is the length of one period, T ═ 1h, divides the day into 24 periods, and T is an integer from 1 to 24.
The optimization device of the home energy management system described in this embodiment may be used to implement the above method embodiments, and the principle and technical effect are similar, which are not described herein again.
In the description of the present invention, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Claims (10)
1. A method for optimizing a home energy management system, comprising:
classifying the family loads according to the using modes of the family loads to obtain the type of each family load; predicting the photovoltaic power consumption according to the meteorological data and the historical data of the household load power consumption;
according to the photovoltaic power consumption, preset constraint conditions, electricity price data and the type of family load, obtaining an economic optimization model with the minimum cost and an environmental protection optimization model with the minimum carbon emission;
wherein the optimization model is the working state of each family load in each period.
2. The method according to claim 1, wherein the obtaining of the economic optimization model with the minimum cost and the environmental optimization model with the minimum carbon emission according to the photovoltaic power consumption, the preset constraint condition, the electricity price data and the type of the household load further comprises:
an improved genetic algorithm is adopted to obtain an economic optimization model with the minimum cost and an environmental protection optimization model with the minimum carbon emission;
wherein, the improvement of the genetic algorithm is to move the chromosome to the right to generate a new chromosome.
3. The method according to claim 1, wherein after obtaining the economic optimization model with the minimum cost and the environmental optimization model with the minimum carbon emission according to the photovoltaic power consumption, the preset constraint conditions, the electricity price data and the type of the household load, the method further comprises:
and selecting the economic optimization model or the environmental protection optimization model to carry out household energy management according to a user selection instruction.
4. The method of claim 1, wherein the economic optimization model is given by the following equation one:
wherein,
C2(t)＝G(t)×PGthe third formula is that Cost is the total Cost of the user in one day; c1(t) settling the total cost for buying and selling the electricity by the user and the power grid; c2(t) is a national pairPatching for photovoltaic power generation; pb(t) the electricity purchase price in the real-time electricity prices; ps(t) a selling price among the real-time electricity prices; p (t) is the interaction electric quantity between the household and the power grid, P (t)>When 0, the family buys power from the power grid, P (t)<When 0, the family sells electricity to the power grid; t is the length of a time interval, T is 1h, the day is divided into 24 time intervals, and T is an integer from 1 to 24; g (t) is predicted photovoltaic power generation; pGThe price is subsidized for the state of the full electric quantity of the photovoltaic power generation.
5. The method of claim 1, wherein the environmental friendliness optimization model is given by the following formula four:
wherein F is the carbon emission generated by purchasing electricity in one day; carbon emission generated by first-degree electricity generation of firepower; p (t) is the purchase of electricity from the grid; a set of epochs with Ω of P (t) > 0; t is the length of one period, T ═ 1h, divides the day into 24 periods, and T is an integer from 1 to 24.
6. An optimization device for a home energy management system, comprising:
the load classification and photovoltaic prediction module is used for classifying the family loads according to the using modes of the family loads to obtain the type of each family load; predicting the photovoltaic power consumption according to the meteorological data and the historical data of the household load power consumption;
the model acquisition module is used for acquiring an economic optimization model with the minimum cost and an environmental protection optimization model with the minimum carbon emission according to the photovoltaic power consumption, preset constraint conditions, electricity price data and the type of the family load;
wherein the optimization model is the working state of each family load in each period.
7. The apparatus of claim 6, wherein the model acquisition module further employs a modified genetic algorithm to obtain a least expensive economic optimization model and a least carbon-emitting environmental optimization model;
wherein, the improvement of the genetic algorithm is to move the chromosome to the right to generate a new chromosome.
8. The apparatus of claim 7, further comprising:
and the model determining module is used for selecting the economic optimization model or the environmental protection optimization model to carry out family energy management according to a user selection instruction.
9. The apparatus of claim 8, wherein the economic optimization model is given by the following equation one:
wherein,
C2(t)＝G(t)×PGformula three
Wherein Cost is the total Cost of a user in one day; c1(t) settling the total cost for buying and selling the electricity by the user and the power grid; c2(t) is a subsidy of the state for photovoltaic power generation; pb(t) the electricity purchase price in the real-time electricity prices; ps(t) a selling price among the real-time electricity prices; p (t) is the interaction electric quantity between the household and the power grid, P (t)>When 0, the family buys power from the power grid, P (t)<When 0, the family sells electricity to the power grid; t is the length of a time interval, T is 1h, the day is divided into 24 time intervals, and T is an integer from 1 to 24; g (t) is predicted photovoltaic power generation; pGThe price is subsidized for the state of the full electric quantity of the photovoltaic power generation.
10. The apparatus of claim 9, wherein the environmental protection optimization model is given by the following formula four:
wherein F is the carbon emission generated by purchasing electricity in one day; carbon emission generated by first-degree electricity generation of firepower; p (t) is the purchase of electricity from the grid; a set of epochs with Ω of P (t) > 0; t is the length of one period, T ═ 1h, divides the day into 24 periods, and T is an integer from 1 to 24.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610217646.4A CN105844365A (en) | 2016-04-08 | 2016-04-08 | Optimization method and device of household energy management system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610217646.4A CN105844365A (en) | 2016-04-08 | 2016-04-08 | Optimization method and device of household energy management system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN105844365A true CN105844365A (en) | 2016-08-10 |
Family
ID=56597172
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610217646.4A Pending CN105844365A (en) | 2016-04-08 | 2016-04-08 | Optimization method and device of household energy management system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105844365A (en) |
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106410967A (en) * | 2016-11-03 | 2017-02-15 | 国网江西省电力公司电力科学研究院 | Gateway modeling method for household load energy management |
| CN106786555A (en) * | 2017-01-20 | 2017-05-31 | 南京南瑞继保电气有限公司 | A kind of intelligent dispatching method of household energy management system |
| CN107657339A (en) * | 2017-09-19 | 2018-02-02 | 北京许继电气有限公司 | Family's energy efficiency managing method and system |
| CN107817700A (en) * | 2016-09-13 | 2018-03-20 | 北京易方通达科技有限公司 | Power consumption control device, power consumption control system and power consumption control method |
| CN107994574A (en) * | 2017-12-13 | 2018-05-04 | 国网辽宁省电力有限公司葫芦岛供电公司 | Towards the decision-making technique of the centralized temperature control load side demand response of new energy consumption |
| CN108347053A (en) * | 2018-03-28 | 2018-07-31 | 山东科技大学 | Family's load access control apparatus and method based on new energy and mains-supplied |
| CN108628201A (en) * | 2017-03-22 | 2018-10-09 | 鸿富锦精密工业（深圳）有限公司 | Item Management device and method |
| CN109831525A (en) * | 2019-03-12 | 2019-05-31 | 合肥阳光新能源科技有限公司 | Terminal equipment control method, apparatus and system based on cloud and mist calculating |
| CN110112784A (en) * | 2019-05-24 | 2019-08-09 | 科世达(上海)机电有限公司 | A kind of family intelligent energy management method, device, system and controller |
| CN110376897A (en) * | 2019-08-02 | 2019-10-25 | 西安建筑科技大学 | A kind of home energy Multipurpose Optimal Method based on GA-BFO |
| CN110543969A (en) * | 2019-07-31 | 2019-12-06 | 国网江苏省电力有限公司电力科学研究院 | household electricity consumption behavior optimization algorithm |
| WO2020242375A1 (en) * | 2019-05-30 | 2020-12-03 | Singapore University Of Technology And Design | A renewable energy-based system, an apparatus and a control method in association thereto |
| CN113673779A (en) * | 2021-08-30 | 2021-11-19 | 西安建筑科技大学 | Rural family new energy system optimal configuration method |
| CN113780660A (en) * | 2021-09-13 | 2021-12-10 | 广东电网有限责任公司 | Resident electricity consumption prediction method, system and storage medium |
-
2016
- 2016-04-08 CN CN201610217646.4A patent/CN105844365A/en active Pending
Cited By (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107817700A (en) * | 2016-09-13 | 2018-03-20 | 北京易方通达科技有限公司 | Power consumption control device, power consumption control system and power consumption control method |
| CN106410967B (en) * | 2016-11-03 | 2018-11-27 | 国网江西省电力公司电力科学研究院 | A kind of family's load and energy management gateway modeling method |
| CN106410967A (en) * | 2016-11-03 | 2017-02-15 | 国网江西省电力公司电力科学研究院 | Gateway modeling method for household load energy management |
| CN106786555A (en) * | 2017-01-20 | 2017-05-31 | 南京南瑞继保电气有限公司 | A kind of intelligent dispatching method of household energy management system |
| CN108628201A (en) * | 2017-03-22 | 2018-10-09 | 鸿富锦精密工业（深圳）有限公司 | Item Management device and method |
| CN107657339A (en) * | 2017-09-19 | 2018-02-02 | 北京许继电气有限公司 | Family's energy efficiency managing method and system |
| CN107994574A (en) * | 2017-12-13 | 2018-05-04 | 国网辽宁省电力有限公司葫芦岛供电公司 | Towards the decision-making technique of the centralized temperature control load side demand response of new energy consumption |
| CN107994574B (en) * | 2017-12-13 | 2021-07-16 | 国网辽宁省电力有限公司葫芦岛供电公司 | New energy consumption-oriented centralized temperature control load side demand response decision method |
| CN108347053A (en) * | 2018-03-28 | 2018-07-31 | 山东科技大学 | Family's load access control apparatus and method based on new energy and mains-supplied |
| CN108347053B (en) * | 2018-03-28 | 2022-02-15 | 山东科技大学 | Home load access control device and method based on new energy and mains supply |
| CN109831525A (en) * | 2019-03-12 | 2019-05-31 | 合肥阳光新能源科技有限公司 | Terminal equipment control method, apparatus and system based on cloud and mist calculating |
| CN110112784A (en) * | 2019-05-24 | 2019-08-09 | 科世达(上海)机电有限公司 | A kind of family intelligent energy management method, device, system and controller |
| WO2020242375A1 (en) * | 2019-05-30 | 2020-12-03 | Singapore University Of Technology And Design | A renewable energy-based system, an apparatus and a control method in association thereto |
| CN110543969A (en) * | 2019-07-31 | 2019-12-06 | 国网江苏省电力有限公司电力科学研究院 | household electricity consumption behavior optimization algorithm |
| CN110376897A (en) * | 2019-08-02 | 2019-10-25 | 西安建筑科技大学 | A kind of home energy Multipurpose Optimal Method based on GA-BFO |
| CN110376897B (en) * | 2019-08-02 | 2022-04-19 | 西安建筑科技大学 | GA-BFO-based family energy multi-objective optimization method |
| CN113673779A (en) * | 2021-08-30 | 2021-11-19 | 西安建筑科技大学 | Rural family new energy system optimal configuration method |
| CN113673779B (en) * | 2021-08-30 | 2024-04-30 | 西安建筑科技大学 | Rural family new energy system optimal configuration method |
| CN113780660A (en) * | 2021-09-13 | 2021-12-10 | 广东电网有限责任公司 | Resident electricity consumption prediction method, system and storage medium |
| CN113780660B (en) * | 2021-09-13 | 2024-02-13 | 广东电网有限责任公司 | Residential electricity consumption prediction method, system and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105844365A (en) | Optimization method and device of household energy management system | |
| Sedhom et al. | IoT-based optimal demand side management and control scheme for smart microgrid | |
| Shafie-Khah et al. | A stochastic home energy management system considering satisfaction cost and response fatigue | |
| Ma et al. | Multi-party energy management for smart building cluster with PV systems using automatic demand response | |
| CA2809011C (en) | Adaptive energy management system | |
| Vivekananthan et al. | Demand response for residential appliances via customer reward scheme | |
| Xie et al. | Use of demand response for voltage regulation in power distribution systems with flexible resources | |
| JP6495510B2 (en) | Power management system, power management method, and power management apparatus | |
| Ogunjuyigbe et al. | An intelligent load manager for PV powered off-grid residential houses | |
| CN111027747A (en) | Household energy control method considering user comfort risk preference | |
| Rahman et al. | An intelligent approach of achieving demand response by fuzzy logic based domestic load management | |
| Ullah et al. | A survey of home energy management for residential customers | |
| CN106292286B (en) | A kind of home electrical user energy management method | |
| Kalair et al. | DSM in hybrid AC/DC rooftop PV integrated smart nano grid | |
| Dongbaare et al. | A smart energy management system for residential use | |
| Tsioumas et al. | Enhanced effectiveness in the appliance scheduling and energy storage control by utilizing the particle swarm technique for a nearly zero energy building | |
| Ali et al. | Day ahead appliance scheduling with renewable energy integration for smart homes | |
| Firoozi et al. | Optimal day-ahead scheduling of distributed generations and controllable appliances in microgrid | |
| Ajao et al. | Operating cost optimization of interconnected nanogrids considering bidirectional effect of V2G and V2H | |
| JP6128999B2 (en) | Power accommodation system and control procedure determination apparatus for the power accommodation system | |
| Saxena et al. | Demand Response Management of Residential Loads with Integrated Temperature Dependent Appliances | |
| Guo et al. | The optimization of household and apartment energy management system | |
| Peash et al. | Optimizing Residential Energy Consumption: Exploring Demand Side Management Strategies | |
| Liang et al. | Adaptive robust energy management strategy for campus-based commercial buildings considering comprehensive comfort levels | |
| Darbandsari et al. | Optimal Energy Management of a Smart Building Considering Rooftop Photovoltaic, Electric Vehicles and Schedulable Loads |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20160810 |
|
| RJ01 | Rejection of invention patent application after publication |