CN107039975A - A kind of distributed energy resource system energy management method - Google Patents
A kind of distributed energy resource system energy management method Download PDFInfo
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- 238000005457 optimization Methods 0.000 claims abstract description 41
- 238000005096 rolling process Methods 0.000 claims abstract description 25
- 230000001172 regenerating effect Effects 0.000 claims abstract description 23
- 230000005611 electricity Effects 0.000 claims description 24
- 238000013528 artificial neural network Methods 0.000 claims description 18
- 238000004146 energy storage Methods 0.000 claims description 11
- 238000007599 discharging Methods 0.000 claims description 10
- 238000004422 calculation algorithm Methods 0.000 claims description 8
- 238000000034 method Methods 0.000 claims description 7
- 230000002068 genetic effect Effects 0.000 claims description 6
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- 239000002028 Biomass Substances 0.000 description 1
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Classifications
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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
- H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
-
- 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
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- H02J3/383—
-
- H02J3/386—
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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
- 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
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/76—Power conversion electric or electronic aspects
-
- 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
- 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
- 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
Abstract
The present invention provides a kind of distributed energy resource system energy management method, comprises the following steps：(1) according to same day situation, the historical data of selection correspondence classification；(2) management module carries out power prediction a few days ago according to the historical data of classification；(3) based on power prediction a few days ago, using the lowest coursing cost as optimization aim, dispatch command a few days ago is provided；(4) using the lowest coursing cost as optimization aim, rolling optimization is carried out；(5) to be limited to control targe above and below Power Exchange, regulation control in real time is carried out.The distributed energy resource system energy management method that the present invention is provided, historical data is classified, predicted a few days ago, by the way that a few days ago, the setting cycle becomes more meticulous step by step, rolling optimization, it is ensured that the accuracy and reliability of optimum results；Based on optimum results, regulation control in real time is carried out using threshold value of surfing the Net as control targe, regenerative resource intermittence, the influence of fluctuation is reduced, has ensured the safety and reliability of distributed energy resource system and bulk power grid.
Description
Technical field
The present invention relates to distributed energy resource system field of energy management, and in particular to a kind of distributed energy resource system energy pipe
Reason method.
Background technology
For traditional centralized large-scale power station, distributed energy be miniaturization, modularization close to load side confession
Can system, have the advantages that cleaning, it is environmentally friendly, flexible, efficient.
In recent years, advocated under " energy-saving and emission-reduction ", the epoch overall background of " walking sustainable development path ", be distributed energetically in China
The formula energy is in the stage of fast development.Both comprising the cool and thermal power three using clean energy resource natural gas as fuel in distributed energy
Co-feeding system, also including regenerative resources such as wind energy, solar energy, tide energy, biomass energies, in addition to battery, heat storage can etc.
Energy storage device.Wherein, the influence of the external factor such as regenerative resource climate, geography, environment, with intermittence, fluctuation, no
Deterministic feature.If effective management, efficiency, environmental protection, the economic benefit of system can not be carried out to this kind of regenerative resource
Often it is difficult to, or even the safe operation of bulk power grid can be threatened.Therefore, the distributed energy resource system of regenerative resource is entered
The energy-optimised management of row just seems necessary.
The existing method that energy-optimised management is carried out for distributed energy resource system, is often absorbed in miniature combustion engine, internal combustion
The controllable power source such as machine, is optimized, such as Chinese patent application by optimizing the energy management for system of realizing of exerting oneself of power-equipment
CN201510925463.3 (publication number CN105375479, a kind of distributed energy energy management side based on Model Predictive Control
Method), it is mainly used in controlled distribution formula power supply, such as miniature combustion engine, waste heat boiler, heat pump, Chinese patent application
CN201610119089.2 (publication number CN105676824A, a kind of energy-optimised scheduling system of regenerative resource supply of cooling, heating and electrical powers
With method), mainly predicted by short-term forecast, to for cold heat, storage cold heat, power supply, storing up electricity and schedulable functional unit
Running status and power carry out Short-term Optimal and/or ultra-short term optimization；And for the distributed energy resource system of regenerative resource
The function of energy management, algorithm relative simplicity, such as Chinese patent application CN201510859522.1 (publication numbers
CN105576825A, a kind of intelligent micro-grid EMS and method containing multiple renewable energy sources), mainly consider number
According to prediction, decision optimization, predicated error is not modified, do not account for yet regenerate distributed energy resource system with
The influence of machine and fluctuation to bulk power grid.
Energy management system of micro-grid topological diagram is as shown in figure 1, economy in order to improve system operation, generally considers collection
Weather meteorological data, carries out ultra-short term regenerative resource and exerts oneself prediction, this aspect improves the scheduling of rolling optimization in the cycle
Accuracy, while being the complexity for the program that also increases, increases the difficulty and workload of algorithm development.On the other hand, it is existing
Some energy management system of micro-grid, which do not account for impact of the renewable power supply on power network, yet to be influenceed, and thus causes energy management not
Actual requirement can be met, the economic benefit of system is difficult to embody.
The content of the invention
In view of problems of the prior art, it is an object of the invention to provide a kind of distributed energy resource system energy management
Method, the historical data in database is classified, and the regenerative resource under the conditions of different weather is exerted oneself and is predicted, and is protected
The accuracy and reliability of the optimum results of EMS are demonstrate,proved；By the way that a few days ago, hour rank is step by step in time scale
Become more meticulous, rolling optimization further ensures the accuracy and reliability of the optimum results of EMS；It is excellent based on rolling
Change result, to be limited to control targe above and below Power Exchange, regulation in real time is carried out to accumulator cell charging and discharging power, honourable breaker and controlled
System, reduces the influence that regenerative resource is intermittent, fluctuation optimize to economic load dispatching, ensured distributed energy resource system with greatly
The safety and reliability of power network.
The present invention provides a kind of distributed energy resource system energy management method, and distributed energy resource system includes regenerative resource
Subsystem, energy storage subsystem and management module, the described method comprises the following steps：
(1) according to same day situation, the historical data of selection correspondence classification, input to management module；
(2) management module carries out power prediction a few days ago according to the historical data of classification using BP neural network；
(3) based on power prediction a few days ago, using the lowest coursing cost as optimization aim, management module is according to selling (purchase) electricity price
One or more in the maximum charge-discharge electric power of lattice, battery SOC bounds or battery, provide dispatch command a few days ago；
(4) using the lowest coursing cost as optimization aim, management module carries out currently setting the cycle extremely to dispatch command a few days ago
The rolling optimization in last setting cycle on the same day；
(5) management module carries out regulation in real time to distributed energy resource system and controlled to be limited to control targe above and below Power Exchange
System；
(6) if currently setting end cycle, step (4) is performed, step (5) is otherwise performed.
Further, in step (1) in the historical data of classification, demand history data are divided into two kinds of working day, day off
Type.
Further, regenerative resource subsystem is included in photovoltaic and/or blower fan, step (1) in the historical data of classification,
Photovoltaic, which is exerted oneself, and/or blower fan is exerted oneself is divided into fine day, cloudy day, the cloudy and type of rainy day four.
Further, step (2) management module carries out power a few days ago according to the historical data of classification using BP neural network
Prediction, comprises the following steps：
(21) by inputoutput data normalization between [0,1], the connection weight between initialization input layer and hidden layer
Connection weight between value, hidden layer and output layer, initialization hidden layer threshold value and output layer threshold value；
(22) hidden layer output is calculated to export with output layer；
(23) according to output layer output and desired output, BP neural network predicated error is calculated；
(24) according to BP neural network predicated error, update connection weight between input layer and hidden layer, hidden layer and
Connection weight, hidden layer threshold value and output layer threshold value between output layer；
(25) judge whether BP neural network predicated error meets termination condition, if be unsatisfactory for, perform step 22；
(26) power prediction a few days ago is carried out with the BP neural network trained.
Further, management module also includes being used to control the breaker of regenerative resource subsystem, for controlling energy storage
The PCS of subsystem, step (3) is based on power prediction a few days ago, and management module is according to selling (purchase) electricity price lattice, surf the Net on battery SOC
One or more in lower limit or the maximum charge-discharge electric power of battery, dispatch command a few days ago is provided, is comprised the following steps：
(31) founding mathematical models, wherein control targe are the lowest coursing cost：
(32) use genetic algorithm, obtain each following setting cycle accumulator cell charging and discharging of the same day, photovoltaic breaker and/or
Blower break device switching state, provides dispatch command.
Further, step (4) is with the minimum optimization aim of cost, and management module is currently set to dispatch command a few days ago
Fixed cycle comprises the following steps to the rolling optimization in last setting cycle on the same day：
(41) founding mathematical models, wherein control targe are the lowest coursing cost：
(42) to each setting cycle in current setting last setting cycle on cycle to the same day, ultra-short term is carried out pre-
Survey, to correct the error of power prediction a few days ago；
(43) accumulator cell charging and discharging, photovoltaic breaker based on current last setting cycle on setting cycle to the same day
And/or blower break device switching state, using genetic algorithm, rolling optimization is carried out by optimization aim of the lowest coursing cost.
Further, step (5) management module is entered with being limited to control targe above and below Power Exchange to distributed energy resource system
Row regulation control in real time, comprises the following steps：
(51) the current online power of distributed energy resource system is detected；
(52) if currently online power is less than Power Exchange lower limit, increase online power；If currently online power is big
In or equal to Power Exchange lower limit and less than the Power Exchange upper limit, the dispatch command in the current setting cycle of Descend Prediction or holding
The control instruction of last moment；If currently online power is more than the Power Exchange upper limit, reduction online power.
Compared with prior art, the distributed energy resource system energy management method that the present invention is provided, with following beneficial effect
Really：
(1) historical data in database is classified, progress of being exerted oneself to the regenerative resource under the conditions of different weather
Prediction, it is ensured that the accuracy and reliability of the optimum results of EMS；
(2) by the way that a few days ago, hour rank becomes more meticulous step by step in time scale, rolling optimization further ensures energy
The accuracy and reliability of the optimum results of management system；
(3) rolling optimization result is based on, using threshold value of surfing the Net as control targe, to accumulator cell charging and discharging power, scene open circuit
Device carries out regulation control in real time, reduces the influence that regenerative resource is intermittent, fluctuation optimizes to economic load dispatching, has ensured point
The safety and reliability of cloth energy resource system and bulk power grid.
Brief description of the drawings
Fig. 1 is the structural representation of the distributed energy resource system of one embodiment of the present of invention；
Fig. 2 is the functional schematic of the management module of the distributed energy resource system shown in Fig. 1；
Fig. 3 is the schematic diagram of the distributed energy resource system energy management method of the distributed energy resource system shown in Fig. 1；
Fig. 4 is the photovoltaic power prediction schematic diagram a few days ago of the distributed energy resource system shown in Fig. 1；
Fig. 5 is the photovoltaic breaker optimizing scheduling instruction schematic diagram of the distributed energy resource system shown in Fig. 1；
Fig. 6 is the rolling optimization the simulation results schematic diagram of the distributed energy resource system shown in Fig. 1；
Fig. 7 is the real-time control schematic diagram of the distributed energy resource system shown in Fig. 1.
Embodiment
As shown in figure 1, the distributed energy resource system of one embodiment of the present of invention includes regenerative resource subsystem, energy storage
Subsystem and management module, management module pass through power supply system by each regenerative resource subsystem of breaker control
System (Power Control System, PCS) control energy storage subsystem.
Specifically, distributed energy resource system includes two regenerative resource subsystems, and wt1 is 8kW blower fans, and pv1 is 34kW
Photovoltaic, energy storage subsystem is 50kW energy-storage lithium batteries, is tested using 38kW fictitious loads.
As shown in Fig. 2 management module it is main according to historical data and sell (purchase) electricity price lattice, battery SOC bounds or
One or more in maximum charge-discharge electric power, prediction a few days ago power, determine a few days ago dispatch command, carry out in a few days rolling optimization with
And real time execution control.
The present invention provides a kind of distributed energy resource system energy management method, as shown in figure 3, comprising the following steps：
(1) according to same day situation, the historical data of selection correspondence classification, input to management module；
According to weather conditions, such as fine day, then the historical data for choosing fine day in database is predicted；Such as the cloudy day, then select
Historical data cloudy in database is taken to be predicted.It can be manually set, be used 4 days in the present embodiment on choosing number of days,
I.e. the data of selection 4 days, correspond to same day situation, same type in this 4 days.
(2) management module carries out power prediction a few days ago according to the historical data of the classification using BP neural network；
(3) based on the power prediction a few days ago, using the lowest coursing cost as optimization aim, management module is electric according to (purchase) is sold
One or more in the maximum charge-discharge electric power of price, battery SOC bounds or battery, provide a day day before yesterday dispatch command；
(4) using the lowest coursing cost as optimization aim, management module carries out currently setting the cycle extremely to dispatch command a few days ago
The rolling optimization in last setting cycle on the same day；
(5) management module carries out regulation in real time to distributed energy resource system and controlled to be limited to control targe above and below Power Exchange
System；
(6) if currently setting end cycle, step (4) is performed, step (5) is otherwise performed
In step (1) in the historical data of classification, demand history data are divided into working day, day off two types.
Regenerative resource subsystem is included in photovoltaic and/or blower fan, step (1) in the historical data of classification, and photovoltaic is exerted oneself
And/or blower fan is exerted oneself and is divided into fine day, cloudy day, the cloudy and type of rainy day four.
Step (2) management module carries out power prediction a few days ago according to the historical data of the classification using BP neural network,
Comprise the following steps：
(21) by inputoutput data normalization between [0,1], the connection weight between initialization input layer and hidden layer
Connection weight between value, hidden layer and output layer, initialization hidden layer threshold value and output layer threshold value；
(22) hidden layer output is calculated to export with output layer；
(23) according to output layer output and desired output, BP neural network predicated error is calculated；
(24) according to BP neural network predicated error, update connection weight between input layer and hidden layer, hidden layer and
Connection weight, hidden layer threshold value and output layer threshold value between output layer；
(25) judge whether BP neural network predicated error meets termination condition, if be unsatisfactory for, perform step 22；
(26) power prediction a few days ago is carried out with the BP neural network trained.
Power prediction is as shown in figure 4, the period at morning to noon a few days ago for photovoltaic, and photovoltaic pv1, which exerts oneself, is presented becoming of gradually increasing
Gesture, at noon to afternoon hours, photovoltaic pv1, which exerts oneself, is presented the trend that is gradually reduced, and blower fan wt1 exerts oneself then is presented night on high, daytime
Low trend.
Management module also includes being used to control the breaker of regenerative resource subsystem, for controlling energy storage subsystem
PCS, step (3) is based on power prediction a few days ago, using the lowest coursing cost as optimization aim, and management module is according to selling (purchase) electricity price
One or more in the maximum charge-discharge electric power of lattice, battery SOC bounds or battery, dispatch command a few days ago is provided, including
Following steps：
(31) founding mathematical models, wherein control targe are operating cost CTotalIt is minimum：
In the present embodiment, the setting cycle uses 1 hour, there is within one day 24 setting cycles, it would however also be possible to employ other setting weeks
Phase, such as 0.5 hour, 2 hours.
Variable is：
Pbat=[Pbat,1,Pbat,2,...,Pbat,24]；
δpv=[δpv,1,δpv,2,...,δpv,24]；
δwt=[δwt,1,δwt,2,...,δwt,24]；
Wherein PbatIt is the accumulator cell charging and discharging power in each setting cycle：δpvIt is the photovoltaic breaker in each setting cycle
Opening and closing state；δwtIt is the blower break device opening and closing state in each setting cycle；
Constraints is：
Wherein：Pbat,t：T-th of setting cycle battery charge and discharge amount, it is negative to discharge, and is charged as just；
Pgrid,t：T-th of setting cycle microgrid is sold to bulk power grid, purchase of electricity, and sale of electricity is negative, and power purchase is just；
Pload,t：T-th of setting cyclic load；
δPV,t：T-th of setting cycle photovoltaic circuit-breaker status；
δWT,t：T-th of setting cycle blower fan circuit-breaker status；
SOCbat,t,T-th of setting cycle battery SOC state, SOC lower limits, the SOC upper limits；
Accumulator cell charging and discharging power limit.
(32) use genetic algorithm, obtain each following setting cycle accumulator cell charging and discharging of the same day, photovoltaic breaker and/or
Blower break device switching state, provides the correlation behavior that 24 hours futures of the same day are obtained in dispatch command, the present embodiment.
Optimized Operation instruction a few days ago is cut in 1-5,20-24 period as shown in figure 5, photovoltaic breaker is put into the 6-19 periods
Go out, blower break device is all put into the 1-24 periods.
Step (4) is with the minimum optimization aim of cost, and management module carries out currently setting the cycle extremely to dispatch command a few days ago
The rolling optimization in last setting cycle on the same day.
In the present embodiment, the rolling optimization of integral point is carried out.Such as 15 points, then 15~24 points of rolling optimization is carried out, and by 15
The result of calculation of point is issued to dispatch command of the control module as the 15th period as dispatch command.For another example 16 points, then carry out
16~24 points of rolling optimization, and control module is issued to as the 16th period using 16 points of result of calculation as dispatch command
Dispatch command.By that analogy.
Rolling optimization comprises the following steps：
(41) founding mathematical models, wherein control targe are the lowest coursing cost：
Variable is：
Pbat=[Pbat,time,Pbat,time+1,...,Pbat,24]；
δpv=[δpv,time,δpv,time+1,...,δpv,24]；
δwt=[δwt,time,δwt,time+1,...,δwt,24]；
Constraints is：
Wherein：Time is the current setting cycle；
Pbat,t：T-th of setting cycle battery charge and discharge amount, it is negative to discharge, and is charged as just；
Pgrid,t：T-th of setting cycle microgrid is sold to bulk power grid, purchase of electricity, and sale of electricity is negative, and power purchase is just；
PPV,time：Current setting cycle photovoltaic is exerted oneself ultra-short term prediction；
PWT,time：Current setting cycle blower fan is exerted oneself ultra-short term prediction；
Pload,time：Current setting cycle user load ultra-short term prediction；
Pload,t：T-th of setting cyclic load；
δPV,t：T-th of setting cycle photovoltaic circuit-breaker status；
δWT,t：T-th of setting cycle blower fan circuit-breaker status；
SOCbat,t,T-th of setting cycle battery SOC state, SOC lower limits, the SOC upper limits；
Accumulator cell charging and discharging power limit.
Wherein Pbat,time、δPV,time、δWT,timePbatRef is assigned to respectively, and pvs0Ref, pvs1Ref is used as real-time control
Input；
(42) to each setting cycle in current setting last setting cycle on cycle to the same day, ultra-short term is carried out pre-
Survey, to correct the error of power prediction a few days ago；
(43) (i.e. the integral point in future on the same day) battery based on current last setting cycle on setting cycle to the same day
Discharge and recharge, photovoltaic breaker and/or blower break device switching state, using genetic algorithm, using the lowest coursing cost as optimization mesh
Mark carries out rolling optimization.
Rolling optimization the simulation results are as shown in Figure 6 (test proceeds at 17 points since 14 points)：14 period loads increase
Plus, crest segment electricity price, battery discharge；15-17 periods flat section electricity price, battery charging.
Step (5) management module is adjusted in real time with being limited to control targe above and below Power Exchange to distributed energy resource system
Section control, comprises the following steps：
(51) the current online power of distributed energy resource system is detected；
(52) if currently online power is less than Power Exchange lower limit, increase online power；If currently online power is big
In or equal to Power Exchange lower limit and less than the Power Exchange upper limit, the dispatch command in the current setting cycle of Descend Prediction or holding
The control instruction of last moment；If currently online power is more than the Power Exchange upper limit, reduction online power.
In the present embodiment, power, i.e. purchase of electricity are exchanged with power network, lower limit is 0kw, and the upper limit is 10kw.
When purchase of electricity is between 0-10kw, the dispatch command after rolling optimization is kept to issue；When purchase of electricity is less than 0kw
When, battery reduces discharge capacity, reduction online power；When purchase of electricity is more than 10, battery increases discharge capacity, increase online work(
Rate.
14：00-14：59 controls in real time are as shown in Figure 7：Now rolling scheduling instruction is pBat2=20kW, pvs0Ref=
1, wts0Ref=1, it is 0 that the lower limit of the power is exchanged with power network, and the upper limit is 10kW；When purchase of electricity is between 0-10, scheduling is kept to refer to
Order is issued；When purchase of electricity is less than 0, battery reduces discharge capacity, increases power purchase volume and electricity；When purchase of electricity is more than 10, electric power storage
Pond increases discharge capacity, reduces purchase of electricity.
Circular dot graticule elec1 represents not set the distributed energy resource system net and bulk power grid for exchanging power bound in Fig. 7
Power curve is exchanged, square points graticule elec2, which represents to set, exchanges distributed energy resource system net and big electricity after power bound
Net exchanges power curve；Diamond spot graticule pBat2 represents the charge-discharge electric power of energy-storage battery.
Measuring and calculating shows 220 yuan/day of increasing economic efficiency.
Preferred embodiment of the invention described in detail above.It should be appreciated that one of ordinary skill in the art without
Need creative work just can make many modifications and variations according to the design of the present invention.Therefore, all technologies in the art
Personnel are available by logical analysis, reasoning, or a limited experiment on the basis of existing technology under this invention's idea
Technical scheme, all should be in the protection domain being defined in the patent claims.
Claims (7)
1. a kind of distributed energy resource system energy management method, distributed energy resource system includes regenerative resource subsystem, energy storage
Subsystem and management module, it is characterised in that the described method comprises the following steps：
(1) according to same day situation, the historical data of selection correspondence classification, input to management module；
(2) management module carries out power prediction a few days ago according to the historical data of the classification using BP neural network；
(3) based on the power prediction a few days ago, using the lowest coursing cost as optimization aim, management module is according to selling (purchase) electricity price
One or more in the maximum charge-discharge electric power of lattice, battery SOC bounds or battery, provide dispatch command a few days ago；
(4) using the lowest coursing cost as optimization aim, management module carries out currently setting cycle to the same day to dispatch command a few days ago
The rolling optimization in last setting cycle；
(5) management module carries out regulation in real time to distributed energy resource system and controlled to be limited to control targe above and below Power Exchange；
(6) if currently setting end cycle, step (4) is performed, step (5) is otherwise performed.
2. distributed energy resource system energy management method as claimed in claim 1, it is characterised in that classification in step (1)
In historical data, demand history data are divided into working day, day off two types.
3. distributed energy resource system energy management method as claimed in claim 1, it is characterised in that regenerative resource subsystem
Including photovoltaic and/or blower fan, in the historical data classified in step (1), photovoltaic, which is exerted oneself, and/or blower fan is exerted oneself is divided into fine day, the moon
My god, the cloudy and type of rainy day four.
4. distributed energy resource system energy management method as claimed in claim 1, it is characterised in that step (2) management module
According to the historical data of the classification, power prediction a few days ago is carried out using BP neural network, comprised the following steps：
(21) by inputoutput data normalization between [0,1], the connection weight, hidden between initialization input layer and hidden layer
Containing the connection weight between layer and output layer, initialization hidden layer threshold value and output layer threshold value；
(22) hidden layer output is calculated to export with output layer；
(23) according to output layer output and desired output, BP neural network predicated error is calculated；
(24) according to BP neural network predicated error, connection weight, hidden layer and the output between input layer and hidden layer are updated
Connection weight, hidden layer threshold value and output layer threshold value between layer；
(25) judge whether BP neural network predicated error meets termination condition, if be unsatisfactory for, perform step 22；
(26) power prediction a few days ago is carried out with the BP neural network trained.
5. distributed energy resource system energy management method as claimed in claim 1, it is characterised in that management module also includes using
In breaker, the PCS for controlling energy storage subsystem of control regenerative resource subsystem, step (3) is based on the work(a few days ago
Rate is predicted, using the lowest coursing cost as optimization aim, and management module is according to selling (purchase) electricity price lattice, sell (purchase) electricity price lattice, battery
One or more in SOC bounds or the maximum charge-discharge electric power of battery, dispatch command a few days ago is provided, is comprised the following steps：
(31) founding mathematical models, wherein control targe are the lowest coursing cost：
(32) genetic algorithm is used, the same day following each setting cycle accumulator cell charging and discharging, photovoltaic breaker and/or blower fan is obtained
Circuit Breaker Switching state, provides dispatch command.
6. distributed energy resource system energy management method as claimed in claim 5, it is characterised in that step (4) with cost most
Low is optimization aim, and management module carries out the rolling in currently last setting cycle on setting cycle to the same day to dispatch command a few days ago
Dynamic optimization, comprises the following steps：
(41) founding mathematical models, wherein control targe are the lowest coursing cost：
(42) to each setting cycle in current setting last setting cycle on cycle to the same day, ultra-short term prediction is carried out,
To correct the error of power prediction a few days ago；
(43) accumulator cell charging and discharging, photovoltaic breaker based on current last setting cycle on setting cycle to the same day and/or
Blower break device switching state, using genetic algorithm, rolling optimization is carried out by optimization aim of the lowest coursing cost.
7. distributed energy resource system energy management method as claimed in claim 6, it is characterised in that step (5) management module
To be limited to control targe above and below Power Exchange, regulation in real time is carried out to distributed energy resource system and is controlled, is comprised the following steps：
(51) the current online power of distributed energy resource system is detected；
(52) if currently online power is less than Power Exchange lower limit, increase online power；If currently online power be more than or
Equal to Power Exchange lower limit and less than the Power Exchange upper limit, the dispatch command in the current setting cycle of Descend Prediction or holding upper one
The control instruction at moment；If currently online power is more than the Power Exchange upper limit, reduction online power.
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