CN110535143B - Energy management method and device facing intelligent residence dynamic demand response - Google Patents
Energy management method and device facing intelligent residence dynamic demand response Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
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- 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
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
- Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
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- 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
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
- Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
- Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
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- 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
- Y02E70/00—Other energy conversion or management systems reducing GHG emissions
- Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin
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- 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
- Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
- Y04S20/20—End-user application control systems
- Y04S20/222—Demand response systems, e.g. load shedding, peak shaving
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- 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
- Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
- Y04S20/20—End-user application control systems
- Y04S20/242—Home appliances
Abstract
The invention discloses an energy management method and device facing to dynamic demand response of an intelligent house. And the method also aims at managing new energy consumption of the battery energy storage, and provides an optimized resource capacity scheduling method to match the new energy and the battery capacity which need to be consumed, so that renewable resources are utilized more effectively. The invention can meet the maximum power demand of power consumers under various limiting conditions of controlling household load and renewable energy operation.
Description
Technical Field
The invention relates to the technical field of intelligent power grids, in particular to an energy management method and device for intelligent house dynamic demand response.
Background
In recent years, demand-side management is a very effective load regulation and control means adopted by power grid companies in smart power grid environments, and energy consumption of users is reduced to the maximum extent. Demand-side management means include energy conservation, electric energy substitution, and demand response, in which demand-side management encourages consumers to directly interact with the grid by actively participating in the electricity market, expecting energy consumers to change their energy consumption patterns in response to changes in electricity prices, thereby reducing electricity cost. Direct load control and real-time electricity prices are two common techniques in power demand side management proposed by different power suppliers. In direct load control, the service provider may directly control the switching of the customer load. Real-time electricity prices achieve peak clipping and valley filling purposes by implementing higher prices during periods of high demand.
With the progress of science and technology and the development of economy, more and more intelligent houses appear, and the automatic management of services such as information, security, home and the like is realized through an effective transmission network by utilizing the technologies such as modern communication network, computer, automatic control and the like. However, the development of the current intelligent residence generally focuses on the property management function, improves the living experience of people, and rarely considers the comprehensive energy management of the intelligent residence. The existing intelligent residence management system is usually limited to a common demand-side management means, and a strategy for how main household appliances of a family interact with a distributed power supply and a power grid under the condition of not considering the access of the distributed power supply of the family. Particularly, when a demand response event occurs in a power grid, how to dynamically and real-timely configure the distributed power supply and the energy storage capacity can meet the limitation condition of system operation, ensure that the maximum power demand of a user is met, and maximally reduce the power consumption overhead is a practical problem.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide an energy management method and device facing to dynamic demand response of an intelligent house, which can be used for scheduling transferable loads and energy storage and power supply on the basis of considering real-time electricity price, and achieving the purpose of meeting the maximum power demand of power users under various limiting conditions of controlling household loads and renewable energy operation.
The technical scheme is as follows: according to a first aspect of the present invention, there is provided a smart home dynamic demand response oriented energy management method, the method comprising the steps of:
(1) establishing an energy storage running state model and calculating the output power of energy storage;
(2) collecting historical data in a certain time interval of annual wind speed, solar radiation, environment temperature and load demand data;
(3) calculating available solar photovoltaic and wind energy in each time interval from the first time interval according to wind speed, solar radiation and ambient temperature, and taking the sum of the available solar photovoltaic and wind energy as the generated energy of the distributed power supply in the time interval;
(4) and determining the running time of different loads and stored energy in a scheduling period according to the collected load demand, the generated energy of the distributed power supply and the output power of the stored energy by taking the minimum electricity consumption overhead as a target.
Wherein the step 4 comprises:
(41) predicting expected power consumption of a user and expected power generation amount of the distributed power supply according to the collected user load, the collected energy storage output power and the collected power generation data of the distributed power supply, wherein the expected power consumption of the user comprises an uninterruptible transferable load and future power demand of the interruptible transferable load;
(42) solving the running time of the transferable load based on a genetic algorithm according to the real-time electricity price, judging whether the uninterruptable and non-transferable loads and the interruptable and non-transferable loads exceed the limit, and if the uninterruptable and non-transferable loads exceed the limit, sending out a warning; controlling the interruptible non-transferable load if the total amount of energy demand exceeds the maximum energy limit;
(43) judging the relation between the distributed power supply and the residential power demand, if the output of the distributed power supply is greater than the power demand, judging whether the power grid can be consumed, if so, transmitting the power grid to the power grid, otherwise, reducing the output of distributed energy in the residential;
(44) update the electricity rate information, wait for the next period to be entered, and resume execution from step 41.
Further, the genetic algorithm in the step (42) takes the collected user load, the calculated energy storage output power and the power generation data of the distributed power supply, the predicted power consumption of the user, the predicted power generation amount of the distributed power supply and the real-time electricity price information as parents, takes different loads and energy storage running times as genes of chromosomes in the genetic algorithm, and takes the minimum power consumption overhead as a target.
Preferably, the step 4 further comprises: setting an energy storage current charging and discharging strategy according to the relation between the generated energy of the distributed power supply and the load demand of the current time period, wherein if the generated energy of the distributed power supply is greater than the demand, the storage battery is charged according to the energy storage charge state and the charging current; and if the generated energy of the distributed power supply is smaller than the required amount, the stored energy supplies power to the user according to the energy storage charge state and the discharge current.
According to a second aspect of the present invention, there is provided a computer apparatus, the apparatus comprising:
one or more processors;
a memory; and
one or more programs, wherein the one or more programs are stored in the memory and configured for execution by the one or more processors, which when executed by the processors perform the steps of the first aspect of the invention.
Has the advantages that: the invention firstly proposes a strategy of how the main household appliances of the family interact with the distributed power supply and the power grid under the condition of considering the access of the family distributed power supply, and realizes the aim of minimizing the power consumption expense by scheduling transferable loads on the basis of real-time power price while ensuring the operation dynamic state of the non-transferable loads and the availability of renewable resources. Meanwhile, aiming at the management of new energy consumption of battery energy storage, an optimized resource capacity scheduling method is provided to match new energy and battery capacity which need to be consumed. The invention meets the maximum power demand of power consumers under various limiting conditions of controlling household load and renewable energy operation; renewable resources are utilized more effectively through optimized scheduling of resource capacity.
Drawings
FIG. 1 is a flow chart of an implementation of the energy management method for intelligent home dynamic demand response according to the present invention;
FIG. 2 is a schematic illustration of electricity prices at various times of the day as employed in one embodiment;
fig. 3 is a graph of a distribution of intelligent residential transferable loads scheduled according to the electricity rates of fig. 2.
Detailed Description
The solution of the invention will now be further described with reference to the accompanying drawings.
The smart home includes, but is not limited to, all electrical devices that can participate in demand response load transfer to reduce the overall electricity price overhead of the smart home, such as smart home loads with communication functions, distributed power systems, battery storage, smart meters interacting with the power grid, and the like. The load of the intelligent residence is divided into an uninterruptible non-transferable load, an interruptible non-transferable load and a transferable load, the uninterruptible non-transferable load is a load which runs completely according to the preference of a user, and if the load is controlled, the user experience is reduced, such as a television, a personal computer and the like; interruptible non-transferable load is temperature control load, and the temperature is controlled in a user set interval; the transferable load is a user load which can be transferred according to the requirements of the power grid. The invention schedules transferable loads and energy storage and power supply on the basis of real-time electricity prices, and achieves the purpose of meeting the maximum power requirement of power consumers under various limiting conditions of controlling the operation of household loads and renewable energy sources. Fig. 1 shows a flow chart of an intelligent residential energy management method according to an embodiment of the present invention, which includes the following steps:
step 1: firstly, modeling the running state of the stored energy, considering the state of the distributed power supply, the requirement of a user and the maximum requirement limit, when the battery is charged, regarding the battery as a transferable load, distinguishing the battery state at different time points by using K, wherein a variable represents the state of the battery at each moment,
representing the stored energy as a state of charge,
representing a state in which the stored energy is not used by people,
is in the state of energy storage and discharge, beta is the battery state in K intervals, DBThe time interval at which the battery state is sampled, in minutes, is
The energy storage running state model is as follows:
Bkis a storage state matrix, is a 3 xk matrix, if the storage is at k1The moment charging state, then the matrix k1Column as (1, 0, 0)T，
Representing a certain column in the matrix.
Calculating energy storage output power:
for the charging power of the battery to be charged,
is the discharge power of the battery.
Step 2: historical data of annual wind speed, solar radiation, ambient temperature and load demand data in a certain time interval are collected.
The specific interval is set according to actual conditions, wind power is taken as an example according to the existing research, in order to predict the distributed wind power generation capacity, the power of wind power generation can be predicted by selecting data of nearly 8 days, and the error can be controlled within 5%.
The load demand is calculated according to the family, the family is taken as a unit, the energy storage and the distributed power supply are combined, and a proper control strategy is adopted, so that the household power consumption expense can be reduced. However, since the genetic algorithm requires a large amount of data, the data acquisition may be in units of a certain region.
And step 3: and calculating the solar photovoltaic energy and the wind energy available in each time interval from the first time interval, and taking the sum of the solar photovoltaic energy and the wind energy as the power generation amount of the distributed power source in the time interval. At present, an algorithm for calculating the output of the distributed power supply according to factors such as wind speed, historical power generation curve, temperature, illumination radiation and the like can be calculated by referring to the prior art, and details are not repeated here.
And 4, step 4: and determining the running time of different loads and stored energy in a scheduling period according to the collected load requirements, the output data of the distributed power supply and the output of the stored energy, and with the aim of minimizing the electricity consumption overhead.
The scheduling period is divided into a plurality of time intervals, the scheduling period is usually measured by days, the scheduling time interval is measured by 15 minutes, 24 hours a day is divided into 96 time intervals, and the power utilization time of different electrical appliances is controlled according to a genetic algorithm.
Step 4-1: and predicting the expected power generation amount of the distributed energy according to the meteorological conditions, and predicting the expected power consumption of the user according to the statistical historical load demand, wherein the expected power consumption comprises the uninterruptible and non-transferable load of the user and the future power demand of the interruptible and non-transferable load.
Step 4-2: and solving the operation time of the transferable load based on a genetic algorithm.
In the embodiment, one year is taken as a time length, according to the user load demand collected in the step 2, the energy storage output calculated in the step 1, the historical output data of the distributed power supply calculated in the step 3 and the expected power consumption and the expected power generation amount obtained in the step 4-1, current and future power price information is obtained from a power grid side, the current and future power price information is taken as a parent generation of a genetic algorithm, different load and energy storage running times are taken as genes of chromosomes in the genetic algorithm, a next generation group is established from the parent generation by utilizing operators such as intersection, mutation and the like in the genetic algorithm, and the optimal capacity and the operation control strategy of the residential distributed power supply and the energy storage are obtained by taking the minimum electric quantity overhead as a target.
The genetic algorithm targets are:
for energy consumption of smart homes in time interval h, ChThe electricity consumption cost of the time interval is minimum, namely the electricity consumption cost in the total scheduling period H;
wherein the content of the first and second substances,
in the formula DsIs the interval time, in hours,
for a time period h the power consumption of the non-interruptible load is not interrupted,
to interrupt the power consumption of the non-transferable load for a period of time h,
for the electrical energy consumption of the transferable load during the time period h,
the consumption of electrical energy of the stored energy (which may be negative, indicating a discharge of the stored energy) over a period of time h,
the generated output of the distributed power supply in the time period h; the value is the electric energy consumption of various loads, and the electric energy consumption of various loads in different time periods can be obtained according to different use combinations in the genetic algorithm because various loads are already clear.
The constraints of the genetic algorithm are: when the load is a transferable load (SL), the load is operated by strictly referring to user instructions, namely the power of the transferable load
Is 0 during its off-time ([ beta ])l,ηl]As operating time):
calculating an interval constraint:
the transferable loads are scheduled only within a set time interval,
for the time that the load is working,
the total time required for the load to complete the job.
And (4) preferential operation constraint:
in a time period [ beta ]l,ηl]The internal requirements ensure that the electrical energy usage of the non-interruptible, non-transferable load is uninterrupted. OmegalFor uninterruptible non-transferable loads in time periods [ beta ]l,ηl]The time of the internal operation is the time of the internal operation,
is the operating power of the load, plρ is the total power usage for the load power usage. For an uninterruptible, non-transferable load l, v ═ ηl-βl-ωl+2, θ is the current operating time.
The energy demand limitation is to ensure that the electric energy supply of the user is sufficient at any moment, and the specific limitations are as follows:
wherein the content of the first and second substances,
representing that the power consumption of the non-transferable load cannot be interrupted during the time period t,
to interrupt the power consumption of the non-transferable load for a time period t,
for the electrical energy consumption of the transferable load during the time period t,
for the consumption of the stored energy during the time period t,
is the generated output of the distributed power supply in the time period t,
the maximum power supply power which can be provided by the power grid to the user in the time period t.
And limiting the energy storage operation state:
and energy storage boundary condition limitation:
SOCmin≤SOCt≤SOCmax
therein, SOCminFor minimum value of stored energy chargeability, SOCtFor the energy storage chargeability over time t, SOCmaxFor maximum value of stored energy chargeability, PBCCharging power for energy storage, PBDFor the discharge power of the stored energy, the indices min and max represent the minimum and maximum values of power, respectively.
Energy output limitation:
The steps aim to determine the time period in which the transferable load should operate by taking the minimum electricity consumption as a target, the integral input of the algorithm is user load historical information, data of stored energy and distributed power sources and electricity price information of the power grid side, and the integral input of the algorithm is an operation time scheme of different loads, so that different loads are used according to the scheme, and the minimum electricity consumption is realized. Meanwhile, the capacity optimization of the distributed power supply and the energy storage in the intelligent residence is realized, and the dependence of a family on a power grid can be reduced.
Step 4-3: and judging whether the uninterruptible un-transferable load exceeds the limit, and if the uninterruptible un-transferable load exceeds the limit, giving out a warning.
Step 4-4: and judging whether the total energy demand exceeds the maximum energy limit, and controlling the interruptible load if the total energy demand exceeds the maximum energy limit, such as reducing the running power of an air conditioner or shutting down the temperature control equipment. The load limit is the demand of the power grid, the power grid can generate larger load when the load is larger in a certain period, the load can also be set by a user, and the user can control the load at home to be smaller than a certain value.
And 4-5: and judging the relation between the distributed power supply and the residential power demand, if the output of the distributed power supply is greater than the residential power demand, judging whether the power grid can be consumed, if so, transmitting the power grid to the power grid, and otherwise, reducing the output of the distributed power supply in the residential.
Alternatively, the energy storage current charge and discharge strategy can be set according to the relation between the generated energy of the distributed power supply and the load demand of the current time period: if the generated energy of the distributed power supply is larger than the required amount, charging the storage battery according to the energy storage charge state and the charging current; if the generated energy of the distributed power supply is smaller than the required amount, the stored energy supplies power to the user according to the energy storage charge state and the discharge current; under the condition of energy storage and power supply, when the power consumption of a user is calculated by a power grid, the output power of the stored energy needs to be subtracted firstly, and if the energy storage power is smaller than the power required by the user, the user needs the power grid to supply power.
And 4-6: updating the electricity rate information, waiting for the next time period to be entered, and starting again from step 4-1.
In one embodiment, according to the power rate information in one day shown in fig. 2, the distribution of transferable loads of an intelligent residence scheduled by the method is shown in fig. 3, and it can be seen that the power of the transferable loads is significantly improved by adopting the method of the present invention, and the more transferable loads, the greater the scheduling potential is, i.e. for the grid side, the more load of load of load clipping or load filling can be borne by the power consumer, thereby realizing more accurate and flexible scheduling. Meanwhile, the overall power consumption cost in unit time is reduced by 8%, and the calculated power consumption cost comprises construction cost, daily electric energy use overhead and equipment maintenance cost.
Based on the same technical concept as the method embodiment, according to another embodiment of the present invention, there is provided a computer apparatus including: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, which when executed by the processors implement the steps in the method embodiments.
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.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (9)
1. An energy management method facing intelligent housing dynamic demand response, characterized in that the method comprises the following steps:
(1) establishing an energy storage running state model and calculating the output power of energy storage;
(2) collecting historical data of annual wind speed, solar radiation, environment temperature and load demand data within a certain time interval;
(3) calculating the available solar photovoltaic and wind energy of each time interval from the first time interval according to the wind speed, the solar radiation and the ambient temperature, and taking the sum as the generated energy of the distributed power supply in the time interval;
(4) according to the collected load demand, the generated energy of the distributed power supply and the output power of the stored energy, with the minimum electricity consumption overhead as a target, determining the running time of different loads and stored energy in a scheduling period, specifically comprising:
(41) predicting expected power consumption of a user and expected power generation amount of the distributed power supply according to the collected user load, the output power of the stored energy and the power generation data of the distributed power supply, wherein the expected power consumption of the user comprises an uninterruptible transferable load and future power demand of the interruptible transferable load;
(42) solving the running time of the transferable load based on a genetic algorithm according to the real-time electricity price, judging whether the uninterruptable and non-transferable loads and the interruptable and non-transferable loads exceed the limit, and if the uninterruptable and non-transferable loads exceed the limit, sending out a warning; controlling the interruptible non-transferable load if the total amount of energy demand exceeds the maximum energy limit;
(43) judging the relation between the distributed power supply and the residential power demand, if the output of the distributed power supply is greater than the power demand, judging whether the power grid can be consumed, if so, transmitting the power grid to the power grid, otherwise, reducing the output of distributed energy in the residential;
(44) update the electricity rate information, wait for the next period to be entered, and resume execution from step 41.
2. The energy management method oriented to intelligent home dynamic demand response according to claim 1, wherein the energy storage operation state model in step 1 is as follows:
Bkis a storage state matrix, is a 3 x k matrix,
representing a certain column in the matrix;
the energy storage output power calculation formula is as follows:
for the charging power of the battery to be charged,
is the discharge power of the battery.
3. The energy management method oriented to intelligent home dynamic demand response according to claim 2, wherein the genetic algorithm in the step (42) takes collected user load, calculated energy storage output power and data of the distributed power supply, predicted expected power consumption of the user, expected power generation amount of the distributed power supply, and real-time electricity price information as a parent, takes different load and energy storage operation time as genes of a chromosome in the genetic algorithm, and aims at minimum electricity consumption cost.
4. The intelligent home dynamic demand response oriented energy management method according to claim 3, wherein the genetic algorithm targets:
for energy consumption of smart homes in time interval h, ChFor the electricity consumption of the period, i.e.The electricity consumption cost in the total scheduling period H is minimum;
wherein the content of the first and second substances,
in the formula DsIs the interval time, in hours,
for a time period h the power consumption of the non-interruptible load is not interrupted,
to interrupt the power consumption of the non-transferable load for a period of time h,
for the electrical energy consumption of the transferable load during the time period h,
for the consumption of electrical energy stored for a period of time h,
the generated output of the distributed power supply in the time period h.
5. The intelligent home dynamic demand response-oriented energy management method according to claim 4, wherein the constraint of the genetic algorithm is that the transferable loads are only in working hours [ β [ ]l,ηl]Internal operation:
calculating an interval constraint:
the transferable loads are scheduled only during a set time interval,
in order to be able to shift the time at which the load works,
the total length of time required to complete the job for the transferable load;
and (4) preferential operation constraint:
in a time period [ beta ]l,ηl]Internal guarantee of uninterruptible power usage, omega, of an undeliverable loadlFor uninterruptible non-transferable loads in time periods [ beta ]l,ηl]The time of the internal operation is the time of the internal operation,
is the operating power of the load, plFor the electric energy usage of the load, ρ is the total electric energy usage, and v ═ η for the load ll-βl-ωl+2, θ is the current operating time.
6. The intelligent home dynamic demand response-oriented energy management method according to claim 4, wherein the genetic algorithm has an energy demand limit of:
wherein
Representing that the power consumption of the non-transferable load cannot be interrupted during the time period t,
to interrupt the power consumption of the non-transferable load for a time period t,
for the electrical energy consumption of the transferable load during the time period t,
for the consumption of the stored energy during the time period t,
is the generated output of the distributed power supply in the time period t,
the maximum power supply power which can be provided for the user by the power grid in the time period t;
energy output limitation:
7. The intelligent home dynamic demand response oriented energy management method according to claim 4, wherein the genetic algorithm has energy storage operating state limits of:
for the elements in the energy storage operation state matrix,
representing the stored energy as a state of charge,
representing a state in which the stored energy is not used by people,
is in the state of energy storage discharge;
and energy storage boundary condition limitation:
SOCmin≤SOCt≤SOCmax
therein, SOCminFor minimum value of stored energy chargeability, SOCtFor the storage charge rate in time t，SOCmaxFor maximum value of stored energy chargeability, PBCCharging power for energy storage, PBDFor the discharge power of the stored energy, the indices min and max represent the minimum and maximum values of power, respectively.
8. The intelligent home dynamic demand response oriented energy management method according to claim 1, wherein the step 4 further comprises: setting an energy storage charging and discharging strategy according to the relation between the generated energy of the distributed power supply and the load demand of the current time period, wherein if the generated energy of the distributed power supply is greater than the load demand, the storage battery is charged according to the energy storage charge state and the charging current; and if the generated energy of the distributed power supply is smaller than the load demand, the stored energy supplies power to the user according to the energy storage charge state and the discharge current.
9. A computer apparatus, the apparatus comprising:
one or more processors;
a memory; and
one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, which when executed by the processors implement the steps of any of claims 1-8.
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