CN109390949A - A kind of household photovoltaic, energy storage and with can control method - Google Patents

A kind of household photovoltaic, energy storage and with can control method Download PDF

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CN109390949A
CN109390949A CN201811333001.2A CN201811333001A CN109390949A CN 109390949 A CN109390949 A CN 109390949A CN 201811333001 A CN201811333001 A CN 201811333001A CN 109390949 A CN109390949 A CN 109390949A
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energy
power
day
family
value
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毕锐
卓敏仪
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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
    • 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
    • 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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • 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
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • 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
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/10—The network having a local or delimited stationary reach
    • H02J2310/12—The local stationary network supplying a household or a building
    • H02J2310/14—The load or loads being home appliances
    • 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
    • Y02B10/00—Integration of renewable energy sources in buildings
    • Y02B10/10—Photovoltaic [PV]
    • 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
    • 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
    • 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
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers
    • 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
    • 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
    • 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

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention discloses a kind of household photovoltaic, energy storage and the control methods with energy, are related to photovoltaic, energy storage and the control field with energy.The present invention includes the steps of determining that the electrical equipment day power sequence of firm demand；Calculate the day power sequence of all firm demand equipment of family；Using similar day algorithm, the prediction of family's photovoltaic power generation day power sequence is carried out；Calculate the net load day power sequence of home-use energy；The load power sequence of family's deferrable load is added, carries out charging energy-storing in the net load power paddy period；Establish the energy model based on energy-storage battery.The present invention is coordinated by carrying out energy based on energy storage, take into account family's energy consumption efficiency and the requirement of life comfort, reduce photovoltaic power generation and the mismatch problem of customer charge in time, with the balance of user and the comprehensive benefit of power grid for guiding in energy is coordinated, by establishing optimal coordination model, the target for reducing home-use energy cost and power purchase power swing is realized.

Description

A kind of household photovoltaic, energy storage and with can control method
Technical field
The invention belongs to photovoltaic, energy storage and the control field technical fields for using energy, more particularly to a kind of household photovoltaic, storage It can be with the control method with energy.
Background technique
The country has correlative study, achievement report in terms of family's energy management, family's photovoltaic, energy.Such as with Based on WIFI network technology, by embedded central controller, complete long-range to the radio operation and domestic environment of household electrical appliance The solution of monitoring；The patent (CN204595460U) of Xinan Nuclear Physics Research Academy discloses a kind of intelligence based on WIFI Can house system, including WIFI gateway, remotely check controlling terminal and at least one smart home monitoring device, realize family's middle ring The monitoring in border；The household energy management system optimal scheduling described based on Spot Price is write articles in ten thousand celebrations of North China University of Tech Research proposes a kind of home energy scheduling strategy of consideration household lines load factor constraint under Spot Price environment.Ningxia The Ma Yujuan of university has carried out the research of the home intelligent power policy optimization based on SAE, by dividing grid side to electric current price strategy It is organically combined with user power utilization behavioural habits, the starting time of household electricity equipment is selected always to use expense for decision variable, family It is at least optimization aim, establishes the Optimized model of electricity consumption strategy.
It is independent to household appliance by smart home system, in groups or timing controlled realization is set that studies in China achievement is common The functions such as standby control, security protection, home communications；Generated energy prediction, load electricity demand forecasting are studied, it is minimum to solve customer charge Change；Research improves household electricity efficiency to the full extent, reduces energy consumption to achieve the effect that save electric energy etc..
The research achievement delivered above, not for the following electrical equipment based on timing energy characteristic, generation of electricity by new energy function Rate characteristic and family's outgoing specificity analysis, are not based on family's green energy consumption system, household electricity sequential coupling, and design meets house The front yard strategy and method that can be coordinated and optimized does not propose deeply to improve distribution using family as unit, comprehensive utilization family's energy storage The method of the digestion capability of formula photovoltaic.
This invention address that invent a kind of household photovoltaic, energy storage and with can control method, for solving existing photovoltaic, storage Can, with can fail characteristic and to be not based on family's green energy consumption system, household electricity for timing future electrical equipment energy Sequential coupling leads to photovoltaic, energy storage, with can the low problem of utilization rate.
Summary of the invention
The purpose of the present invention is to provide a kind of household photovoltaic, energy storage and the control methods for using energy, by being opened based on energy storage It opens up energy to coordinate, takes into account family's energy consumption efficiency and the requirement of life comfort, with user and power grid in energy coordination The balance of comprehensive benefit be guiding, by establishing optimal coordination model, realize reduce it is home-use can cost and power purchase power waves Dynamic target solves existing photovoltaic, energy storage, with can fail characteristic and to be not based on for timing future electrical equipment energy Family's green energy consumption system, household electricity sequential coupling lead to photovoltaic, energy storage, with can the low problem of utilization rate.
In order to solve the above technical problems, the present invention is achieved by the following technical solutions:
The present invention is a kind of household photovoltaic, energy storage and the control method with energy, is included the following steps:
S000: the electrical equipment day power sequence of firm demand is determined；
S001: the day power sequence of all firm demand equipment of family is calculated；
S002: using similar day algorithm, carries out the prediction of family's photovoltaic power generation day power sequence；
S003: the net load day power sequence of home-use energy is calculated；
S004: the load power sequence of family's deferrable load is added, obtains modified net load day power sequence；Using can Adjust load can energy storage characteristic, the net load power paddy period carry out charging energy-storing；
S005: the energy model based on energy-storage battery is established.
Preferably, determine that the electrical equipment day power sequence of firm demand comprises the following processes in S000:
A000: selection artificial mode or intelligent mode；If artificial mode then executes A001；If intelligent mode is then held Row A004；
A001: determine firm demand equipment in intraday each hour power: PL, i, 1......PL, i, 24；
A002: traversing each firm demand equipment, determines the day power sequence of all firm demand equipment of family: PL, i, t (PL, i, 1......PL, i, 24)；
A003: the fixed review equipment day power sequence of all firm demand equipment of family being set as under artificial mode Operating power；
A004: similar day algorithm is used, obtains firm demand equipment in intraday each hour power: PL, i, t (PL, i, 1......PL, i, 24)；
A005: using the method in A004, traversing each firm demand equipment, determines all firm demand equipment of family Day power sequence: PL, i, t(PL, i, 1......PL, i, 24)；
Wherein, PL, i, tThe operating power of time is corresponded to for corresponding equipment；I is equipment identity；T is the time；Phase in S004 It is identical as the similar day algorithm in S002 like day algorithm.
Preferably, the day power sequence formula that all firm demand equipment of family are calculated in S001 is as follows:
Preferably, similar day algorithm is used in S002, carrying out the prediction of family's photovoltaic power generation day power sequence includes:
Using similar day selection algorithm training sample；Using the BP for the BP learning algorithm and variable learning rate for increasing momentum term The learning algorithm that learning algorithm combines is trained；Initial data is normalized；
Wherein, included the following steps: using similar day selection algorithm training sample
B000: selecting and predicts the consistent n historical record of day weather pattern, season type, forms sample set D；
B001: the temperature Euclidean distance d of historical record in prediction day and sample set D is calculatedi,
Wherein, Y1, Y2, Y3 are respectively the highest temperature for predicting day, lowest temperature peace Equal temperature value；X1, X2, X3 are respectively the highest temperature, the lowest temperature and the temperature on average value of i-th record in sample set D；
B002: by temperature Euclidean distance collection { d1, d2......dnAccording to the size ascending sort of value, corresponding to minimum value Date be prediction day corresponding to similar day.
Preferably, using the BP learning algorithm of increase momentum term in conjunction with the BP learning algorithm of variable learning rate It practises algorithm and is trained and specifically comprise the following steps:
C000: simultaneously t is assigned a value of 1 to initialization weight；
C001: training sample p is assigned a value of 1；
C002: input training sample p simultaneously calculates each layer output valve；
C003: whether training of judgement sample p is greater than number of training P；If so, executing C004；If it is not, then executing C005；
C004: p+1 is assigned to p and executes C002；
C005: regularized learning algorithm rate η；
C006: the connection weight w of feature is adjusted；
C007: each output layer systematic error E (t) is calculated；
C008: judge whether E (t) < ε ∪ t > T；If so, training terminates；If it is not, executing C009；
C009: t+1 is assigned to t and executes C001；
Wherein, learning rate adjustment formula is as follows:
Wherein, connection weight w adjustment is as follows:
W (t)=Δ wbp(t)+σ[w(t-1)-w(t-2)]；
Wherein, P is training sample number, and training sample counts in p training process, and T is maximum frequency of training, and w is connection Weight, w (t) are the weight of the t times iteration, Δ wbp(t) for according to the weight knots modification of the t times iteration of traditional BP learning algorithm, E It (t) is the systematic error of the t times iteration, ε is system allowable error, and η is learning rate.
Preferably, the normalization formula that use is normalized to initial data is as follows:
Wherein, xn, xmax, xminRespectively original input data, the maximum value in original input data, original input data In minimum value；yn, ymax, yminRespectively original output data, the maximum value in original output data, in original output data Minimum value.
Preferably, the net load day power sequence formula that home-use energy is calculated in S003 is as follows:
PN, L, t=PL, t-PV, t。
Preferably, the load power sequence of family's deferrable load is added in S004, obtains modified net load day power sequence Column；Using deferrable load can energy storage characteristic, the net load power paddy period carry out charging energy-storing detailed process is as follows:
D000: the valley moment is found from Household purifying load power data:
PM=min { P1......Pm......Pt}；
D001: traversing each deferrable load equipment, determines that the charging start time of each deferrable load equipment is timely It is long: TI, t......TI, duration；
D002: amendment net load day power sequence: PN, L, to=PN, L, t-PL, t, i；
Wherein, charged recurrence relation is as follows under battery model:
0≤ω c+ ω d≤1, ω c, ω d ∈ { 0,1 }
Wherein, SOC (t) is the remaining state-of-charge of energy-accumulating medium t period Mo；When SOC (t-1) is energy-accumulating medium t-1 Between section end remaining state-of-charge；Pc (t), Pd (t) are respectively energy-accumulating medium t period charging and discharging power；ρ is energy-accumulating medium Self-discharge rate；Δ t is calculation window duration, and t differs t duration with the t-1 moment；ηcAnd ηdRespectively entire energy-storage system fills Electricity and discharging efficiency；EcapFor energy-storage system rated capacity；ωcWith ωdFor charge and discharge control mark, when charge or discharge: ωc+ ωd=1；When floating charge: ωc+ωd=0.
Preferably, the energy-storage system electricity and SOC relationship are as follows: E (t)=SOC (t) Ecap；
Wherein, the constraint condition of the battery model includes Constraint and power constraint；
The Constraint is characterized by state-of-charge, is constrained state-of-charge as follows:
SOCmin≤SOC(t)≤SOCmax；
Wherein SOCmin, SOCmaxThe respectively lower and upper limit of battery energy storage system Constraint；
Wherein, power constraints are as follows:
Maximum charge power permissible value:
Maximum discharge power permissible value:
Wherein, min { } is to be minimized function；PC, max (t)And PD, max (t)The respectively maximum charge and discharge of energy-storage system Power；PC, maxAnd PD, maxThe maximum that respectively energy-storage system allows continues charge and discharge power.
Preferably, detailed process is as follows for energy model of the foundation based on energy-storage battery in S005:
E000: the objective function for establishing model is as follows:
Wherein, t is the period for carrying out power optimization coordination；PL(t)For the net power value of t period family energy consumption system； PBESS(t)For the charge-discharge electric power value of t period energy storage, wherein positive value is charging, negative value is electric discharge；For Rate period when peak, the average value of home-use energy system net power；
E001: IBM CPLEX software modeling is used, determines the P of batteryBESS(t)Value.
The invention has the following advantages:
1, the present invention is coordinated by carrying out energy based on energy storage, takes into account family's energy consumption efficiency and life comfort is wanted It asks, reduces photovoltaic power generation and the mismatch problem of customer charge in time, with user and power grid in energy coordination The balance of comprehensive benefit is guiding, and by establishing optimal coordination model, realizing reduces home-use energy cost and power purchase power swing Target；
2, the present invention is based on energy storage power bi-directional characteristic, the home-use of customizable device electricity consumption plan can coordinate and optimize plan Slightly, algorithm can be coordinated and optimized by realizing the use based on sequential coupling, can be using the home-use system APP that can control as platform carrier reality Now to family green can coordinated control, project provides real solution to average family energy, improve photovoltaic, Energy storage and the efficient control for using energy, improve energy-saving efficiency.
Certainly, it implements any of the products of the present invention and does not necessarily require achieving all the advantages described above at the same time.
Detailed description of the invention
In order to illustrate the technical solution of the embodiments of the present invention more clearly, will be described below to embodiment required Attached drawing is briefly described, it should be apparent that, drawings in the following description are only some embodiments of the invention, for ability For the those of ordinary skill of domain, without creative efforts, it can also be obtained according to these attached drawings other attached Figure.
Fig. 1 is the flow chart of a kind of household photovoltaic of the invention, energy storage and the control method with energy；
Fig. 2 be S000 of the invention in determine firm demand electrical equipment day power sequence flow chart；
Fig. 3 is the BP learning algorithm using increase momentum term of the invention in conjunction with the BP learning algorithm of variable learning rate The flow chart that learning algorithm is trained.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts all other Embodiment shall fall within the protection scope of the present invention.
Refering to Figure 1, the present invention is a kind of household photovoltaic, energy storage and the control method with energy, include the following steps:
S000: the electrical equipment day power sequence of firm demand is determined；
S001: the day power sequence of all firm demand equipment of family is calculated；
S002: using similar day algorithm, carries out the prediction of family's photovoltaic power generation day power sequence；
S003: the net load day power sequence of home-use energy is calculated；
S004: the load power sequence of family's deferrable load is added, obtains modified net load day power sequence；Using can Adjust load can energy storage characteristic, the net load power paddy period carry out charging energy-storing；
S005: the energy model based on energy-storage battery is established.
Wherein, determine that the electrical equipment day power sequence of firm demand comprises the following processes in S000:
A000: selection artificial mode or intelligent mode；If artificial mode then executes A001；If intelligent mode is then held Row A004；
A001: determine firm demand equipment in intraday each hour power: PL, i, 1......PL, i, 24；
A002: traversing each firm demand equipment, determines the day power sequence of all firm demand equipment of family: PL, i, t (PL, i, 1......PL, i, 24)；
A003: by the day power sequence P of all firm demand equipment of familyL, i, t(PL, i, 1......PL, i, 24) set as people Fixed review equipment operating power under work mode；
A004: similar day algorithm is used, obtains firm demand equipment in intraday each hour power: PL, i, 1......PL, i, 24；
A005: using the method in A004, traversing each firm demand equipment, determines all firm demand equipment of family Day power sequence: PL, i, t(PL, i, 1......PL, i, 24)；
Wherein, PL, i, tThe operating power of time is corresponded to for corresponding equipment；I is equipment identity；T is the time；Phase in S004 It is identical as the similar day algorithm in S002 like day algorithm.
Wherein, the day power sequence formula that all firm demand equipment of family are calculated in S001 is as follows:
Wherein, similar day algorithm is used in S002, carrying out the prediction of family's photovoltaic power generation day power sequence includes:
Using similar day selection algorithm training sample；Using the BP for the BP learning algorithm and variable learning rate for increasing momentum term The learning algorithm that learning algorithm combines is trained；Initial data is normalized；
Wherein, included the following steps: using similar day selection algorithm training sample
B000: selecting and predicts the consistent n historical record of day weather pattern, season type, forms sample set D；
B001: the temperature Euclidean distance d of historical record in prediction day and sample set D is calculatedi,
Wherein, Y1, Y2, Y3 are respectively the highest temperature for predicting day, lowest temperature peace Equal temperature value；X1, X2, X3 are respectively the highest temperature, the lowest temperature and the temperature on average value of i-th record in sample set D；
B002: by temperature Euclidean distance collection { d1, d2......dnAccording to the size ascending sort of value, corresponding to minimum value Date be prediction day corresponding to similar day.
Wherein, the learning algorithm using the BP learning algorithm of increase momentum term in conjunction with the BP learning algorithm of variable learning rate It is trained and specifically comprises the following steps:
C000: simultaneously t is assigned a value of 1 to initialization weight w；
C001: training sample p is assigned a value of 1；
C002: input training sample p simultaneously calculates each layer output valve；
C003: whether training of judgement sample p is greater than number of training P；If so, executing C004；If it is not, then executing C005；
C004: p+1 is assigned to p and executes C002；
C005: regularized learning algorithm rate η；
C006: the connection weight w of feature is adjusted；
C007: each output layer systematic error E (t) is calculated；
C008: judge whether E (t) < ε ∪ t > T；If so, training terminates；If it is not, executing C009；
C009: t+1 is assigned to t and executes C001；
Wherein, learning rate adjustment formula is as follows:
Wherein, connection weight w adjustment is as follows:
W (t)=Δ wbp(t)+σ[w(t-1)-w(t-2)]；
Wherein, P is training sample number, and training sample counts in p training process, and T is maximum frequency of training, and w is connection Weight, w (t) are the weight of the t times iteration, Δ wbp(t) for according to the weight knots modification of the t times iteration of traditional BP learning algorithm, E It (t) is the systematic error of the t times iteration, ε is system allowable error, and η is learning rate.
Wherein, the normalization formula for use being normalized to initial data is as follows:
Wherein, xn, xmax, xminRespectively original input data, the maximum value in original input data, original input data In minimum value；yn, ymax, yminRespectively original output data, the maximum value in original output data, in original output data Minimum value.
Wherein, the net load day power sequence formula that home-use energy is calculated in S003 is as follows:
PN, L, t=PL, t-PV, t。
Wherein, the load power sequence of family's deferrable load is added in S004, obtains modified net load day power sequence； Using deferrable load can energy storage characteristic, the net load power paddy period carry out charging energy-storing detailed process is as follows:
D000: the valley moment is found from Household purifying load power data:
PM=min { P1......Pm......Pt}；
D001: traversing each deferrable load equipment, determines that the charging start time of each deferrable load equipment is timely It is long: TI, t......TI, duration；
D002: amendment net load day power sequence: PN, L, to=PN, L, t-PL, t, i；
Wherein, charged recurrence relation is as follows under battery model:
0≤ω c+ ω d≤1, ω c, ω d ∈ { 0,1 }
Wherein, SOC (t) is the remaining state-of-charge of energy-accumulating medium t period Mo；When SOC (t-1) is energy-accumulating medium t-1 Between section end remaining state-of-charge；Pc (t), Pd (t) are respectively energy-accumulating medium t period charging and discharging power；ρ is energy-accumulating medium Self-discharge rate；Δ t is calculation window duration, and t differs t duration with the t-1 moment；ηcAnd ηdRespectively entire energy-storage system fills Electricity and discharging efficiency；EcapFor energy-storage system rated capacity；ωcWith ωdFor charge and discharge control mark, when charge or discharge: ωc+ ωd=1；When floating charge: ωc+ωd=0.
Wherein, energy-storage system electricity and SOC relationship are as follows: E (t)=SOC (t) Ecap。
Wherein, the constraint condition of battery model includes Constraint and power constraint；
Constraint is characterized by state-of-charge, is constrained state-of-charge as follows:
SOCmin≤SOC(t)≤SOCmax；Wherein SOCmin, SOCmaxThe respectively lower limit of battery energy storage system Constraint And the upper limit；
Wherein, power constraints are as follows:
Maximum charge power permissible value:
Maximum discharge power permissible value:
Wherein, min { } is to be minimized function；PC, max (t)And PD, max (t)The respectively maximum charge and discharge of energy-storage system Power；PC, maxAnd PD, maxThe maximum that respectively energy-storage system allows continues charge and discharge power.
Wherein, detailed process is as follows for energy model of the foundation based on energy-storage battery in S005:
E000: the objective function for establishing model is as follows:
Wherein, t is the period for carrying out power optimization coordination；PL(t)For the net power value of t period family energy consumption system； PBESS(t)For the charge-discharge electric power value of t period energy storage, wherein positive value is charging, negative value is electric discharge；For peak When rate period, it is home-use can system net power average value；
E001: IBM CPLEX software modeling is used, determines the P of batteryBESS(t)Value.
It is worth noting that, included each unit is only drawn according to function logic in the above system embodiment Point, but be not limited to the above division, as long as corresponding functions can be realized；In addition, each functional unit is specific Title is also only for convenience of distinguishing each other, the protection scope being not intended to restrict the invention.
In addition, those of ordinary skill in the art will appreciate that realizing all or part of the steps in the various embodiments described above method It is that relevant hardware can be instructed to complete by program.
Present invention disclosed above preferred embodiment is only intended to help to illustrate the present invention.There is no detailed for preferred embodiment All details are described, are not limited the invention to the specific embodiments described.Obviously, according to the content of this specification, It can make many modifications and variations.These embodiments are chosen and specifically described to this specification, is in order to better explain the present invention Principle and practical application, so that skilled artisan be enable to better understand and utilize the present invention.The present invention is only It is limited by claims and its full scope and equivalent.

Claims (10)

1. a kind of household photovoltaic, energy storage and the control method with energy, which comprises the steps of:
S000: the electrical equipment day power sequence of firm demand is determined；
S001: the day power sequence of all firm demand equipment of family is calculated；
S002: using similar day algorithm, carries out the prediction of family's photovoltaic power generation day power sequence；
S003: the net load day power sequence of home-use energy is calculated；
S004: the load power sequence of family's deferrable load is added, obtains modified net load day power sequence；Utilize adjustable negative Lotus can energy storage characteristic, the net load power paddy period carry out charging energy-storing；
S005: the energy model based on energy-storage battery is established.
2. a kind of household photovoltaic according to claim 1, energy storage and the control method with energy, which is characterized in that in S000 Determine that the electrical equipment day power sequence of firm demand comprises the following processes:
A000: selection artificial mode or intelligent mode；If artificial mode then executes A001；If intelligent mode then executes A004；
A001: determine firm demand equipment in intraday each hour power: PL, i, 1......PL, i, 24；
A002: traversing each firm demand equipment, determines the day power sequence of all firm demand equipment of family: PL, i, t (PL, i, 1......PL, i, 24)；
A003: by the day power sequence P of all firm demand equipment of familyL, i, t(PL, i, 1......PL, i, 24) it is set as artificial mould Fixed review equipment operating power under formula；
A004: similar day algorithm is used, obtains firm demand equipment in intraday each hour power: PL, i, 1......PL, i, 24；
A005: using the method in A004, each firm demand equipment is traversed, determines the day of all firm demand equipment of family Power sequence: PL, i, t(PL, i, 1......PL, i, 24)；
Wherein, PL, i, tThe operating power of time is corresponded to for corresponding equipment；I is equipment identity；T is the time；Similar day in S004 Algorithm is identical as the similar day algorithm in S002.
3. a kind of household photovoltaic according to claim 1, energy storage and the control method with energy, which is characterized in that in S001 The day power sequence formula for calculating all firm demand equipment of family is as follows:
4. a kind of household photovoltaic according to claim 1, energy storage and the control method with energy, which is characterized in that in S002 Using similar day algorithm, carrying out the prediction of family's photovoltaic power generation day power sequence includes:
Using similar day selection algorithm training sample；Learnt using the BP of the BP learning algorithm and variable learning rate that increase momentum term The learning algorithm that algorithm combines is trained；Initial data is normalized；
Wherein, included the following steps: using similar day selection algorithm training sample
B000: selecting and predicts the consistent n historical record of day weather pattern, season type, forms sample set D；
B001: the temperature Euclidean distance d of historical record in prediction day and sample set D is calculatedi,
Wherein, Y1, Y2, Y3 are respectively the highest temperature, the lowest temperature and the temperature on average value for predicting day；X1, X2, X3 are respectively sample The highest temperature, the lowest temperature and the temperature on average value of i-th record in this collection D；
B002: by temperature Euclidean distance collection { d1, d2......dnAccording to the size ascending sort of value, day corresponding to minimum value Phase is similar day corresponding to prediction day.
5. a kind of household photovoltaic according to claim 4, energy storage and the control method with energy, which is characterized in that described to adopt Specific packet is trained with learning algorithm of the BP learning algorithm of momentum term in conjunction with the BP learning algorithm of variable learning rate is increased Include following steps:
C000: simultaneously t is assigned a value of 1 to initialization weight w；
C001: training sample p is assigned a value of 1；
C002: input training sample p simultaneously calculates each layer output valve；
C003: whether training of judgement sample p is greater than number of training P；If so, executing C004；If it is not, then executing C005；
C004: p+1 is assigned to p and executes C002；
C005: regularized learning algorithm rate η；
C006: the connection weight w of feature is adjusted；
C007: each output layer systematic error E (t) is calculated；
C008: judge whether E (t) < ε ∪ t > T；If so, training terminates；If it is not, executing C009；
C009: t+1 is assigned to t and executes C001；
Wherein, learning rate adjustment formula is as follows:
Wherein, connection weight w adjustment is as follows:
W (t)=Δ wbp(t)+σ[w(t-1)-w(t-2)]；
Wherein, P is training sample number, and training sample counts in p training process, and T is maximum frequency of training, and w is connection weight, W (t) is the weight of the t times iteration, Δ wbp(t) for according to the weight knots modification of the t times iteration of traditional BP learning algorithm, E (t) is The systematic error of the t times iteration, ε are system allowable error, and η is learning rate.
6. a kind of household photovoltaic according to claim 4, energy storage and the control method with energy, which is characterized in that described right The normalization formula that use is normalized in initial data is as follows:
Wherein, xn, xmax, xminRespectively original input data, the maximum value in original input data, in original input data Minimum value；yn, ymax, yminRespectively original output data, the maximum value in original output data, in original output data most Small value.
7. a kind of household photovoltaic according to claim 1, energy storage and the control method with energy, which is characterized in that in S003 The net load day power sequence formula for calculating home-use energy is as follows:
PN, L, t=PL, t-PV, t。
8. a kind of household photovoltaic according to claim 1, energy storage and the control method with energy, which is characterized in that in S004 The load power sequence of family's deferrable load is added, obtains modified net load day power sequence；Utilize storing up for deferrable load Energy characteristic, carrying out charging energy-storing in the net load power paddy period, detailed process is as follows:
D000: the valley moment is found from Household purifying load power data:
PM=min { P1......Pm......Pt}；
D001: traversing each deferrable load equipment, determines the charging start time and duration of each deferrable load equipment: TI, t......TI, duration；
D002: amendment net load day power sequence: PN, L, to=PN, L, t-PL, t, i；
Wherein, charged recurrence relation is as follows under battery model:
0≤ω c+ ω d≤1, ω c, ω d ∈ { 0,1 }
Wherein, SOC (t) is the remaining state-of-charge of energy-accumulating medium t period Mo；SOC (t-1) is the energy-accumulating medium t-1 period The remaining state-of-charge at end；Pc (t), Pd (t) are respectively energy-accumulating medium t period charging and discharging power；ρ is oneself of energy-accumulating medium Discharge rate；Δ t is calculation window duration, and t differs t duration with the t-1 moment；ηcAnd ηdThe charging of respectively entire energy-storage system and Discharging efficiency；EcapFor energy-storage system rated capacity；ωcWith ωdFor charge and discharge control mark, when charge or discharge: ωc+ωd= 1；When floating charge: ωc+ωd=0.
9. a kind of household photovoltaic according to claim 8, energy storage and the control method with energy, which is characterized in that the storage Energy system charge and SOC relationship are as follows: E (t)=SOC (t) Ecap；
Wherein, the constraint condition of the battery model includes Constraint and power constraint；
The Constraint is characterized by state-of-charge, is constrained state-of-charge as follows:
SOCmin≤SOC(t)≤SOCmax；Wherein SOCmin, SOCmaxThe respectively lower limit of battery energy storage system Constraint and upper Limit；
Wherein, power constraints are as follows:
Maximum charge power permissible value:
Maximum discharge power permissible value:
Wherein, min { } is to be minimized function；PC, max (t)And PD, max (t)The respectively maximum charge and discharge power of energy-storage system； PC, maxAnd PD, maxThe maximum that respectively energy-storage system allows continues charge and discharge power.
10. a kind of household photovoltaic according to claim 1, energy storage and the control method with energy, which is characterized in that in S005 Establishing the energy model based on energy-storage battery, detailed process is as follows:
E000: the objective function for establishing model is as follows:
Wherein, t is the period for carrying out power optimization coordination；PL(t)For the net power value of t period family energy consumption system；PBESS(t)For t The charge-discharge electric power value of period energy storage, wherein positive value is charging, negative value is electric discharge；When for peak when electricity price Section, the average value of home-use energy system net power；
E001: IBM CPLEX software modeling is used, determines the P of batteryBESS(t)Value.
CN201811333001.2A 2018-11-09 2018-11-09 A kind of household photovoltaic, energy storage and with can control method Pending CN109390949A (en)

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CN110112783A (en) * 2019-05-23 2019-08-09 深圳市建筑科学研究院股份有限公司 Photovoltaic storage battery micro-capacitance sensor dispatch control method
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