CN106655243B - The automatic demand response method of electric car for the purpose of maintaining the micro-capacitance sensor equilibrium of supply and demand - Google Patents
The automatic demand response method of electric car for the purpose of maintaining the micro-capacitance sensor equilibrium of supply and demand Download PDFInfo
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- H02J3/383—
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
-
- 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
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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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- H02J3/386—
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- 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
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- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/76—Power conversion electric or electronic aspects
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
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- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
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- Y02T90/16—Information or communication technologies improving the operation of electric vehicles
- Y02T90/167—Systems integrating technologies related to power network operation and communication or information technologies for supporting the interoperability of electric or hybrid vehicles, i.e. smartgrids as interface for battery charging of electric vehicles [EV] or hybrid vehicles [HEV]
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- 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
- Y04S30/00—Systems supporting specific end-user applications in the sector of transportation
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- Y04S30/00—Systems supporting specific end-user applications in the sector of transportation
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Abstract
A kind of automatic demand response method of electric car for the purpose of maintaining the micro-capacitance sensor equilibrium of supply and demand, comprising the following steps: establish containing the ADR optimization architecture including automatic demand response ADR server, demand response DR operational management unit, ADR client and web response body Web；Sliding-model control was carried out to one day time, is divided into multiple periods；In any one period, supply side force information and Demand-side power information are collected in DR operational management, are calculated compensation demand, are issued ADR server；ADR server collects the urgent horizontal parameters of web response body Web, calculates virtual electricity price and need state signal according to compensation supply and compensation demand, is sent to all web response body Webs；Each web response body Web is after receiving virtual price signal and need state signal, adjust automatically itself charge-discharge electric power size；Until optimization section terminates.Architectural framework of the present invention is simple and reliable, interactivity is good, grid connection efficiency is higher and is suitable for extensive PEV dispatches.
Description
Technical field
The invention belongs to electric car charge and discharge control technique fields, and in particular to one kind is to maintain the micro-capacitance sensor equilibrium of supply and demand
For the purpose of the automatic demand response method of electric car.
Background technique
Due to global primary energy consumption totally, environmental problem get worse, using photoelectricity, wind-powered electricity generation as the new energy of representative
Strategic position starts to be promoted, and entire New Energy Industry is just experiencing the development of high speed.But new energy interval, the power output of fluctuation
Characteristic becomes one of the restraining factors that its high permeability utilizes to a certain extent.
On the other hand, the electric car that networks (PEV) industry of rapid development be increasingly becoming smart grid field can not or
Scarce important component, especially electric car access the proposition of power grid (Vehicle to Grid, V2G) concept, so that greatly
Scale PEV, which networks, to generate profound impact to the operation of the planning of electric system, operation and electricity market.
From the point of view of current development situation, realize by adjusting the approach of grid side primary energy structure to extensive new energy
Consumption will increase dramatically electric network reconstruction cost.Meanwhile the battery of PEV cannot generate apparent shadow to power grid due to too small
It rings, and the implementation of extensive V2G is also faced with many social technology obstacles.Realize PEV to new energy by platform of micro-capacitance sensor
Integrated utilization will become grid-connected one of the major way of the following extensive renewable energy.
Usually exist in the microgrid of renewable energy containing high permeability, between supply side generation of electricity by new energy and workload demand serious
Imbalance, therefore, it is a kind of it is effective realize equilibrium of supply and demand degree mode be maximize utilization of new energy resources rate, improve system stability
Necessary means.Carry out demand response (DR) project based on information technology and supply and demand two sides bidirectional real-time technology, guidance needs
Side PEV cluster charge and discharge behavior is asked, can effectively realize the equilibrium of supply and demand inside microgrid, improves new-energy grid-connected efficiency, reduces electricity
Online shopping electricity and the configuration needs of energy storage achieve the purpose that promote microgrid whole economic efficiency.
It is existing many as the research of the booster action of means performance PEV cluster using DR, according to electricity consumption mode tuning decision
The DR project carried out to PEV can be divided into two classes: supply side DR optimization and Demand-side DR optimization by the difference of main body.Supply side DR
Optimization carries out centerized fusion to region within the jurisdiction often through central control entity.But it is concentrated simultaneously with the increase of PEV scale
Formula optimization difficulty will constantly increase.Secondly, centerized fusion is mostly the scheduling a few days ago obtained on the basis of certain statistical probability, it is difficult
To accomplish the real-time DR management to single PEV.Some drawbacks that Demand-side DR optimization can overcome supply side to optimize, but optimize
In the process there is still a need for contributing to predict to renewable energy, showed in terms of uncertain ability is contributed in reply undesirable.With
The development and perfection of energy internet and electricity market, without manpower intervention, according to the real time information of price or excitation dynamic
The ADR (Automated demand response) of Load adjustment is the newest way of realization of DR.How by ADR form by
Virtual Price Mechanisms reflect system balance demand and the PEV cluster charge and discharge behavior of guide demand side is renewable containing high permeability
Energy microgrid effectively realizes the key of equilibrium of supply and demand degree.
Summary of the invention
In order to more effectively utilize renewable energy in micro-capacitance sensor, meets the networking demand of extensive PEV, realize micro- electricity
The autonomous equilibrium of supply and demand in net system, and guarantee the stable operation of micro-grid system, the present invention provides one kind to maintain micro-capacitance sensor to supply
The automatic demand response method of electric car for the purpose of need to balancing, is gone out by the renewable energy in ADR system real-time monitoring system
Power and load level obtain supply and demand power difference and need state (FG) and formulate virtual Spot Price (vRTP) and FG signal,
Each controll plant automatically determines responding power according to current own situation and vRTP, FG signal received.
To achieve the goals above, technical solution of the present invention includes are as follows:
A kind of automatic demand response method of electric car for the purpose of maintaining the micro-capacitance sensor equilibrium of supply and demand, including following step
It is rapid:
S1. comprehensively consider supply side renewable energy power producing characteristics and Demand-side PEV charge-discharge characteristic information, foundation contain
ADR framework including ADR server, demand response DR operational management unit, ADR client and web response body Web；
S2. one day continuous time for 24 hours was subjected to sliding-model control, is divided into J period, for any kth time period, has
K ∈ { 1,2 ..., J }, and the when a length of Δ t of kth time period；
S3. for any kth time period, supply side renewable energy force information and Demand-side electricity consumption are collected in DR operational management
Information calculates compensation demand；
S4. when compensation demand is not zero, compensation demand is issued ADR server by DR operational management；ADR server is collected
The urgent horizontal parameters of web response body Web calculate the demand of virtual electricity price vRTP and system according to compensation supply and compensation demand
State FG signal, and all web response body Webs are sent to by ADR client；
S5. for each web response body Web, after receiving virtual price signal and FG signal, comprehensively consider own situation and
Itself charge-discharge electric power size of the signal adjust automatically received；
S6. step S1~S5 is repeated, until optimization section terminates.
Further, in the step S1, ADR framework includes:
ADR server, for based on the conventional load in system, renewable energy power output is horizontal and PEV cluster, energy storage system
Power information of uniting formulates virtual Spot Price, publication DR event notice；
DR operational management unit forms DR demand for the renewable energy power output and load level in monitoring system, and
Demand is sent to ADR server；Monitoring system operation, monitoring DR implementation result；
ADR client, for connecting all kinds of web response body Webs of DR user downwards, the charge and discharge for collecting each PEV and energy storage are compeled
It cuts horizontal parameters, and virtual price that ADR server is formulated, FG signal is distributed to all web response body Webs in system；
Web response body Web, including PEV cluster and energy-storage system, the web response body Web are ADR project for object, will be responded
Host complex is set as N+。
Further, in the step S3, for any kth time period, supply side renewable energy is collected in DR operational management
Force information and Demand-side power information calculate compensation demand, the photovoltaic array output power including the k periodBlower
Output power summationMicro-capacitance sensor conventional load power and the PEV charge power being not under responsive state and PEV enter
Net information；
For the record of PEV inbound information: the vehicle collection for setting micro-capacitance sensor access is combined into N, then vehicle scale is n=| N |,
For any vehicle l ∈ N, relevant parameter are as follows:
In formula:It respectively indicates the time of vehicle l access micro-capacitance sensor and is expected time departure；Respectively
Expectation SOC, SOC when indicating the starting state-of-charge SOC of power cell of vehicle and leaving micro-capacitance sensor indicate battery remaining power
With the ratio of battery capacity, therefore have Indicate PEV battery capacity；Respectively indicate specified charge and discharge power；
For compensating the calculating of demand: under the targeted micro-capacitance sensor environment of renewable energy containing high permeability, electric energy
Power difference between supply and Demand-side usage amount is compensation demand needed for system, and web response body Web is needed to go out jointly
Force compensating；Note the k period compensation demand beIt is defined as follows:
In formula:Indicate micro-grid system in the conventional load power of k period；It is positive and negative respectively indicate it is current
Power compensation status requirements are to send from grid charging (Grid to Vehicle, G2V) or by electric energy is counter to power grid V2G；Indicate the PEV charge power being not under responsive state.
In the step S4, if there is unbalanced supply-demand phenomenon, i.e., when compensation demand is not zero, then DR event is touched
Hair, DR operational management forms DR demand, and demand is sent to ADR server；The urgent water of ADR server collection web response body Web
Flat parameter formulates virtual real time price signal and FG signal according to compensation supply and compensation demand, and notifies microgrid system
All web response body Webs in system；
To the record of the urgent horizontal parameters of PEV: under electricity market, the need of power difference compensation in electric car participation system
It asks the cell condition for directly depending on its own and expected off-network time；When system needs to carry out
When, forIts urgent level in the k period may be defined as:
In formula:Indicate that web response body Web i is urgently horizontal in the electric discharge of k period；Indicate that web response body Web i's is specified
Charge power；Indicate that web response body Web i leaves the period of power grid；TkIndicate the current k period；Ei,kIndicate web response body Web i in k
The rechargeable energy that section needs；Indicate web response body Web i in the power interactive efficiency of k period；Indicate the electricity of web response body Web i
Tankage；
From the point of view of logically, if the V2G of certain web response body Web is urgent horizontal very high, it can be more likely to not in turn
It is ready to receive G2V charge power；Thus, when needing G2V power, i.e.,Urgent level of i-th PEV in the k period
Is defined as:
In formula:Indicate that web response body Web i is urgently horizontal in the charging of k period；
To the record of the urgent horizontal parameters of energy storage: in view of the energy storage in system will always be in networking state, without pre-
The concept of off-network time phase, therefore, the urgent level of energy storage charge and discharge are only determined by its current residual electricity；Redefine energy storage
It is urgent horizontal as follows:
In formula: Smin,ESS、Smax,ESSThe respectively upper and lower limit of energy-storage system (ESS) state-of-charge；Si,kIndicate response master
State-of-charge of the body i in the k period；
The formulation of virtual real time price signal: using for reference the congestion Price Algorithm of internet traffic control field, and definition is virtual
Real time price (vRTP) mechanism；Under micro-capacitance sensor environment, vRTP signal be used to instruct web response body Web, can timely respond to be
Power shortage or surplus in system；Accordingly, in microgrid the sum of all web response body Web power in responsive state should in system
The power that need to be compensated is identical, i.e.,
In formula: Pi,kIndicate web response body Web i in the compensation supply of k period；
When reaching the equilibrium of supply and demand under guidance of the system in virtual electricity price, forHave
In formula: γ indicates virtual electricity price coefficient, is constant；
Therefore, for all web response body Webs, total charge-discharge electric power is
VRTP reflects the supply and demand difference for needing to compensate in system as a nondimensional public price signal, essence
Power and electric car power in responsive state and between size mismatch degree；In actual application, due to PEV's
Traveling rule and power demand characteristic, above formula are generally difficult to strictly meet；Therefore, it is necessary to introduce a positive multiplication factor εk, right
The formulating method of vRTP is adjusted, so that the sum of web response body Web power in responsive state completes supply and demand power as much as possible
The compensation of difference, εkCalculation formula it is as follows:
Virtual electricity price depends on compensation supply and required compensation demand in micro-capacitance sensor:
Wherein: vRTPkIndicate the virtual electricity price signal of k period；
The formulation of FG signal: FG is the id signal that user is informed together with vRTP signal:
FG signal is that G2V (FG=1) or V2G (FG=-1), the signal are used for for marking current system need state
Guarantee that the direction of web response body Web power needs the power symbol compensated to be consistent with system automatically.
In the step S5, the process of adjust automatically is as follows:
S51. for any web response body Web, i.e.,ADR client is by calculating vehicle i in the battery threshold of k period
AmountVRTP signal whether is able to respond to measure electric car i in the k period:
From the above equation, we can see thatIndicate that charging is completed in vehicle i；If indicating, vehicle i continues before off-network
If charging, it can still complete to charge；
S52. when following formula is set up, step S53 is gone to, otherwise web response body Web no longer responds vRTP signal, then with maximum
Power charges:
In formula:For the battery threshold of setting；
S53.ADR client judges whether vRTP signal is greater than given threshold vRTPTH；Response lag inequality defines such as
Under:
vRTPk≤vRTPTH (14)
If S54. monitoring, vRTP signal is more than given threshold vRTPTH, web response body Web will by meet 0 < β < 1 scaling
Factor-beta reduces its V2G/G2V power until otherwise vRTP signal value goes to step S55 lower than upper limit value；Power adjustment formula
It is as follows:
Pi,k+1=β Pi,k (15)
In formula: Pi,k+1Indicate web response body Web i in the compensation supply of (k+1) period；
S55. each main body responds vRTP the and FG signal received, and combines own situation from main modulation itself charge and discharge electric work
Rate, it is as follows that power automated tos respond to mode:
In formula: φiIt is the parameter of an influence algorithm the convergence speed.
Networking of the present invention refers to accessing the micro-capacitance sensor.
Microgrid conventional load refers to: other loads in microgrid other than electric car.
The beneficial effects of the present invention are:
1, architectural framework is simple and reliable, does not need to contribute to new energy and PEV trip rule is predicted, do not need center
Controlled entity.
2, electric car householder oneself can choose whether to charge, and improve such system and the interactivity of user, more conducively
The realization and popularization of system.
3, the vehicle for being more suitable for participating in demand response can be selected automatically, meeting user's charge requirement and network transformer
Under the premise of capacity limit, the equilibrium of supply and demand inside microgrid can be effectively realized, improve new-energy grid-connected efficiency, reduce power grid power purchase
Amount and the configuration needs of energy storage achieve the purpose that promote microgrid whole economic efficiency.
4, when large-scale PEV networks, without investigating, its trip is regular, and with the increase of PEV scale, the increasing of calculation amount
Long speed is slower, therefore this method is more suitable for large-scale PEV scheduling.
Detailed description of the invention
Fig. 1 is automatic demand response model framework of the invention；
Fig. 2 is exemplary operation day base load horizontal and photovoltaic, blower power curve figure, wherein (a) is photovoltaic, blower
Power curve；It (b) is base load power curve；
Fig. 3 is the daily power profile of amount to be compensated and practical compensation magnitude, non-compensation rate, wherein (a) is demand benefit
The daily power distribution curve of the amount of repaying and practical compensation rate；It (b) is the daily power distribution curve of non-compensation rate；
Fig. 4 is PEV and the daily power profile of energy storage difference compensation rate in practical compensation electricity；
Fig. 5 be charging during PEV1 and PEV2 SOC variation diagram, wherein (a) be PEV1 in 17:48 off-network, PEV2 in
The off-grid SOC variation diagram of 19:00；(b) it is exchanged for the two vehicles estimated off-network time in 15:00, i.e. PEV1 is in 19:00 off-network, PEV2
In the off-grid SOC variation diagram of 17:48；
Fig. 6 is the load level curve under 4 kinds of Different Optimization modes；
Fig. 7 is the Demand-side cost and the comparison of energy loss amount of 4 kinds of control models；
Fig. 8 is the net load curve under case 3, case 4；
Fig. 9 is the influence that the lower penalty coefficient of case 2, case 4 runs totle drilling cost and the equal cost of PEV vehicle to microgrid, that is, is mended
Repay influence of the coefficient to two sides economy, wherein (a) is shadow of the lower penalty coefficient of case 2, case 4 to the equal cost of PEV vehicle
It rings；(b) influence of totle drilling cost is run to microgrid for the lower penalty coefficient of case 2, case 4.
Specific embodiment
The present invention will be further described with reference to the accompanying drawing, above-mentioned and/or additional aspect of the invention and advantage from
In conjunction with following accompanying drawings to will be apparent and be readily appreciated that in the description of embodiment.
Referring to Fig.1~Fig. 9, a kind of automatic demand response side of electric car for the purpose of maintaining the micro-capacitance sensor equilibrium of supply and demand
Method has built ADR optimization architecture, which does not need to contribute to new energy and PEV trip rule is predicted, do not need center
Controlled entity, the automatic demand response method will share to PEV collection in the reflection to virtual Price Mechanisms of system balance demand
Group, charge and discharge behavior fine by electricity price, guiding each PEV in real time；Method includes the following steps:
S1. comprehensively consider supply side renewable energy power producing characteristics and Demand-side PEV charge-discharge characteristic information, foundation contain
ADR framework including ADR server, DR operational management unit, ADR client and web response body Web；
S2. one day continuous time for 24 hours was subjected to sliding-model control, is divided into J period, for any kth time period, has
K ∈ { 1,2 ..., J }, and the when a length of Δ t of kth time period；
S3. for any kth time period, supply side renewable energy force information and Demand-side electricity consumption are collected in DR operational management
Information calculates compensation demand；
S4. when compensation demand is not zero, compensation demand is issued ADR server by DR operational management；ADR server is collected
The urgent horizontal parameters of web response body Web calculate virtual electricity price and FG signal according to compensation supply and compensation demand, and pass through
ADR client is sent to all web response body Webs；
S5. for each web response body Web, after receiving virtual price signal and FG signal, comprehensively consider own situation and
Itself charge-discharge electric power size of the signal adjust automatically received；
S6. step S1~S5 is repeated, until optimization section terminates.
Further, in the step S1, ADR framework includes:
ADR server, for based on the conventional load in system, renewable energy power output is horizontal and PEV cluster, energy storage system
Power information of uniting formulates virtual Spot Price, publication DR event notice；
DR operational management unit forms DR demand for the renewable energy power output and load level in monitoring system, and
Demand is sent to ADR server；Monitoring system operation, monitoring DR implementation result；
ADR client, for connecting all kinds of web response body Webs of DR user downwards, the charge and discharge for collecting each PEV and energy storage are compeled
It cuts horizontal parameters, and virtual price that ADR server is formulated, FG signal is distributed to all web response body Webs in system；
Web response body Web includes PEV cluster and energy-storage system, and the web response body Web is ADR project for object, will be responded
Host complex is set as N+；
Automatic demand response framework of the invention is aimed at when renewable energy power output is higher than load level, response master
Body can enter charge mode according to virtual price signal, dissolve excessive renewable energy in time；And when renewable energy goes out
Power can carry out a degree of electric discharge according to the guidance of virtual signal when lower to be reduced simultaneously to improve equilibrium of supply and demand degree
System operation cost.
In the step S3, for any kth time period, DR operational management collect supply side renewable energy force information and
Demand-side power information calculates compensation demand, the photovoltaic array output power including the k periodBlower output power is total
WithMicro-capacitance sensor conventional load power and the PEV charge power and PEV inbound information being not under responsive state；
For the record of PEV inbound information: the vehicle collection for setting micro-capacitance sensor access is combined into N, then vehicle scale is n=| N |,
For any vehicle l ∈ N, relevant parameter are as follows:
In formula:It respectively indicates the time of vehicle l access micro-capacitance sensor and is expected time departure；Point
Not Biao Shi power cell of vehicle starting SOC and expectation SOC when leaving micro-capacitance sensor, therefore have Indicate PEV battery capacity； Respectively indicate specified charge and discharge function
Rate；
For compensating the calculating of demand: under the targeted micro-capacitance sensor environment of renewable energy containing high permeability, electric energy
Power difference between supply and Demand-side usage amount is compensation demand needed for system, and web response body Web is needed to go out jointly
Force compensating；Note the k period compensation demand beIt is defined as follows:
In formula:Indicate micro-grid system in the conventional load power of k period；It is positive and negative respectively indicate it is current
Power compensation status requirements are to send from grid charging (Grid to Vehicle, G2V) or by electric energy is counter to power grid (V2G)；Indicate the PEV charge power being not under responsive state.
In the step S4, if there is unbalanced supply-demand phenomenon, i.e., when compensation demand is not zero, then DR event is touched
Hair, DR operational management forms DR demand, and demand is sent to ADR server；The urgent water of ADR server collection web response body Web
Flat parameter formulates virtual real time price signal and FG signal according to compensation supply and compensation demand, and notifies microgrid system
All web response body Webs in system；
To the record of the urgent horizontal parameters of PEV: under electricity market, the need of power difference compensation in electric car participation system
It asks the cell condition for directly depending on its own and expected off-network time；When system needs to carry out
When, forIts urgent level in the k period may be defined as:
In formula:Indicate that web response body Web i is urgently horizontal in the electric discharge of k period；Indicate that web response body Web i's is specified
Charge power；Indicate that web response body Web i leaves the period of power grid；TkIndicate the current k period；Ei,kIndicate web response body Web i in k
The rechargeable energy that section needs；Indicate web response body Web i in the power interactive efficiency of k period；Indicate the electricity of web response body Web i
Tankage；
From the point of view of logically, if the V2G of certain web response body Web is urgent horizontal very high, it can be more likely to not in turn
It is ready to receive G2V charge power；Thus, when needing G2V powerWhen, urgent level of i-th PEV in the k period
It may be defined as:
In formula:Indicate that web response body Web i is urgently horizontal in the charging of k period；
To the record of the urgent horizontal parameters of energy storage: in view of the energy storage in system will always be in networking state, without pre-
The concept of off-network time phase, therefore, the urgent level of energy storage charge and discharge are only determined by its current residual electricity；Redefine energy storage
It is urgent horizontal as follows:
In formula: Smin,ESS、Smax,ESSThe respectively upper and lower limit of energy-storage system (ESS) state-of-charge；Si,kIndicate response master
State-of-charge of the body i in the k period；
The formulation of virtual real time price signal: using for reference the congestion Price Algorithm of internet traffic control field, and definition is virtual
Real time price (vRTP) mechanism；Under micro-capacitance sensor environment, vRTP signal be used to instruct web response body Web, can timely respond to be
Power shortage or surplus in system；Accordingly, in microgrid the sum of all web response body Web power in responsive state should in system
The power that need to be compensated is identical, i.e.,
In formula: Pi,kIndicate web response body Web i in the compensation supply of k period；
When reaching the equilibrium of supply and demand under guidance of the system in virtual electricity price, forHave
In formula: γ indicates virtual electricity price coefficient, is constant；
Therefore, for all web response body Webs, total charge-discharge electric power is
VRTP reflects the supply and demand difference for needing to compensate in system as a nondimensional public price signal, essence
Power and electric car power in responsive state and between size mismatch degree；In actual application, due to PEV's
Traveling rule and power demand characteristic, above formula are generally difficult to strictly meet；Therefore, it is necessary to introduce a positive multiplication factor εk, right
The formulating method of vRTP is adjusted, so that the sum of web response body Web power in responsive state completes supply and demand power as much as possible
The compensation of difference, εkCalculation formula it is as follows:
Virtual electricity price depends on compensation supply and required compensation demand in micro-capacitance sensor:
Wherein: vRTPkIndicate the virtual electricity price signal of k period；
The formulation of FG signal: FG is the id signal that user is informed together with vRTP signal:
FG signal is that G2V (FG=1) or V2G (FG=-1), the signal are used for for marking current system need state
Guarantee that the direction of web response body Web power needs the power symbol compensated to be consistent with system automatically.
In the step S5, self-regulating process is as follows:
S51. for any web response body Web, i.e.,ADR client is by calculating vehicle i in the battery threshold of k period
AmountVRTP signal whether is able to respond to measure electric car i in the k period:
From the above equation, we can see thatIndicate that charging is completed in vehicle i；If indicating, vehicle i continues before off-network
If charging, it can still complete to charge；
S52. when following formula is set up, step S53 is gone to, otherwise web response body Web no longer responds vRTP signal, then with maximum
Power charges:
In formula:For the battery threshold of setting；
S53.ADR client judges whether vRTP signal is greater than given threshold vRTPTH；Response lag inequality defines such as
Under:
vRTPk≤vRTPTH (30)
If S54. monitoring, vRTP signal is more than given threshold vRTPTH, web response body Web will by meet 0 < β < 1 scaling
Factor-beta reduces its V2G/G2V power until otherwise vRTP signal value goes to step S55 lower than upper limit value；Power adjustment formula
It is as follows:
Pi,k+1=β Pi,k (31)
In formula: Pi,k+1Indicate web response body Web i in the compensation supply of (k+1) period；
S55. each main body responds vRTP the and FG signal received, and combines own situation from main modulation itself charge and discharge electric work
Rate, it is as follows that power automated tos respond to mode:
In formula: φiIt is the parameter of an influence algorithm the convergence speed.
In order to enable those skilled in the art to better understand the present invention, applicant tests using certain Administrative Area micro-capacitance sensor as embodiment
Demonstrate,prove the validity of the proposed ADR optimization method of this project.
The photovoltaic installed capacity of the micro-capacitance sensor is 2 000kW, and blower installed capacity is 1400kW, and the PEV scale of service is
120.This area's exemplary operation day routine load level and photovoltaic, blower power curve are as shown in Fig. 2.For the sake of simplicity, examining
Consider and 120 electric cars be divided into 20 groups, every group of electric car initial SOC state having the same, it is contemplated that network entry time and
The off-network time.Initial SOC level is between 25% to 50%, it is contemplated that 5:00 is distributed network entry time between 10:07 in the morning, in advance
The meter off-network time distributes in 18:00 between 23:07.Other parameter settings are as shown in table 1.
Table 1
Optimized model and the above-mentioned parameter setting proposed according to the present invention, is modeled, simulation analysis, obtained ADR is excellent
Change result.Under ADR optimization of the invention, the PEV1 and PEV2 assumed changes expected off-network in expected off-network time and midway
SOC level change curve in the case of two kinds of time is as shown in Fig. 5.
Attached drawing 3 (a) shows that expected compensation electricity and practical compensation electricity are in intraday variation feelings in micro-grid system
Condition, under mentioned ADR strategy, the practical compensation electricity of web response body Web and expected compensation electric quantity curve in system coincide substantially, demonstrate,prove
ADR method is illustrated to the validity of the orderly charge and discharge guiding function of web response body Web.Attached drawing 3 (b) is not compensate electricity at one day
Interior distribution situation further demonstrates that curve 9:00 and 19:00 in afternoon or so in the morning, does not compensate electricity difference in system slightly
Obviously, this is because the permeability of electric car not enough, causes part electric car to enter/off-network shape in microgrid in present period
The transformation of state reduces the real-time compensation dispatching of system.
Attached drawing 4 indicates the difference compensation rate of PEV and energy storage, and simulation result is shown, main body in response is electronic in system
Automobile and energy storage can act in agreement in intraday power output trend with the holding of system balance demand.From the point of view of specific, electric car
Cluster is much higher than energy storage in the power output to network to response system current demand in the off-grid period；And as electric car is under
Class's period gradually off-network, response power output obviously decline to a great extent, based on hereafter system amount to be compensated is then contributed with energy storage.
During meeting system adjusting purpose, the charge requirement of automobile user should also be as being met.
In attached drawing 5 (a), the PEV1 plan time of departure is to plan the time of departure in 19:00 in 17:48, PEV2；Attached drawing 5 (b)
In, the expection off-network time of two electric cars, 15:00 was exchanged in the afternoon.As can be seen that two kinds in the case of, PEV1 and
It is expected PEV2 can charge to SOC before in the expected off-network time, complete charging plan, SOC value whole process in [0.2,
0.9] between, meet battery behavior requirement.
To sum up, the response policy under ADR framework can be good at the available energy storage resource in coordination system, accurate guidance rule
The charge and discharge behavior of modelling PEV, energy-storage system give full play to the effect of PEV auxiliary energy storage, improve Demand-side part throttle characteristics, make it
Dynamic Matching supply side power output, to improve the system equilibrium of supply and demand.The ADR optimization method that this project proposes is had the ability to meet electronic
The charge requirement of user vehicle, and guidance PEV auxiliary meets system requirements with this condition.
In order to more preferably, more intuitively illustrate that the control effect of mentioned electric car ADR Optimizing Mode, the present invention emulate simultaneously
Following 4 kinds of control models are calculated, and carry out the comparison of simulation result, analysis:
Case 1: unordered charge mode, electrically-charging equipment provide lasting invariable power charging service for the electric car of access,
Until user leaves, if batteries of electric automobile fills with before this, stops charging, only consider energy storage；
Case 2: the only ADR Optimizing Mode under charging optimization does not consider the V2G function of PEV cluster, at this point, having
Case 3: a kind of real-time control mode does not need central control entity, and considers the V2G function of PEV cluster
Energy.
Case 4: consider the ADR Optimizing Mode of the V2G function of PEV cluster, i.e., mode of the present invention.
Load level curve, Demand-side cost under 4 kinds of control models and the comparison of energy loss amount, net load curve and
Influence of the penalty coefficient to two sides economy is respectively as shown in attached drawing 6~9.Further statistical information is listed in Table 2 below.(dividing
In analysis, the interaction electricity price of micro-capacitance sensor and bulk power grid: the fixed electricity price of electricity is bought: 1 yuan/kWh, sell electricity using renewable energy online electricity
Valence；The interaction electricity price of PEV and micro-capacitance sensor: fixed 1 yuan/kWh of electricity price), table 2 is the analog result under different control programs.
Table 2
In conjunction with 6~attached drawing of attached drawing 9 and table 2, it can be deduced that following phenomenon and conclusion:
In Case1, load is 7:00 in morning to 10:00 much larger than the period of new energy power output, and load is significantly less than newly
The period of energy power output is evening 19:00 to 24:00, leads to its day total load curve tendency and new energy power curve tendency
Differ greatly, interaction power and cost greatly rise, while to its net load bring great peak-valley difference, stability bandwidth and compared with
Small RPR does not also optimize the equal charge and discharge cost of vehicle.
Relative to Case1, Case 2 considers ADR optimization, and web response body Web is contributed in new energy compared with Gao Shi great
Amount improves load level by G2V charging, thus compared to the day total load curve under Case 1, a day total load curve is walked
Gesture is more matched with new energy power curve tendency, improves the equilibrium of supply and demand degree of system, as shown in Fig. 6.Although it is net negative
The peak-valley difference and stability bandwidth of lotus are still larger, but it makes the interaction cost of power grid and the equal charge and discharge cost of vehicle decline to a great extent, and PEV is mended
Repay also less, economy is greatly improved.
Compared to case 2, web response body Web passes through V2G for electric energy when new energy power output is lower as far as possible at case 4
Power grid is sent back to reduce load level, so that Demand-side load follow new energy power output under Case 4 in figure 6
Ability is outstanding, declines net load peak-valley difference, the stability bandwidth of Case 4 further, RPR is further up.Further analysis two
The cost calculation result of Demand-side is it is found that PEV participates in V2G process bring electric discharge cost and energy loss takes under kind different situations
With so that the Demand-side cost under 4 mode of Case is higher than Case 2, and this point depends greatly on penalty coefficient
It is horizontal.In attached drawing 9 it can be seen that at Case 2, the variation of penalty coefficient either runs the equal cost of vehicle or micro-capacitance sensor total
Cost will not all impact；When at Case 4, with the increase of penalty coefficient, the equal cost sheet of the vehicle of PEV downgrade it is low, and it is same
When microgrid operating cost be continuously improved.Therefore in application in fact, need to comprehensively consider the practical compensation demand of system and user response
Degree rationally manages it.It should also be noted that by reduce unit net load stability bandwidth by system energy bring
Income is that our present conditions can not accurately calculate, thus comparison knot of the Case 4 and Case 2 in terms of microgrid runs income
Still there is discussion in fruit.
Case 3 and Case 4 similar performance in terms of Demand-side economy, part throttle characteristics, but in supply side economy side
Face, ADR method used in Case 4 are substantially better than real-time control method used in Case 3, main reason is that, in real time
Control method is designed towards more small-scale micro-capacitance sensor, and operating mechanism is relatively simple, in the urgent horizontal characterization of charge and discharge, power point
It is inconsiderate complete with aspect, in example under the biggish microgrid situation of PEV cluster, it is difficult to cope with environment complicated and changeable, cause new
Energy utilization rate is relatively low, purchase of electricity is larger.Mentioned ADR method considers that the uneven degree of two sides and main body responding ability are adjusted in real time
Whole vRTP is simultaneously responded, power allocation rules by given threshold setting, and PEV can be guided to carry out reasonable charge and discharge, effectively reduced
System unbalanced supply-demand degree, increases the stand-alone capability of micro-capacitance sensor.
In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ", " specifically show
The description of example " or " some examples " etc. means specific features, structure, material or spy described in conjunction with this embodiment or example
Point is included at least one embodiment or example of the invention.In the present specification, schematic expression of the above terms are not
It must be directed to identical embodiment or example.Moreover, particular features, structures, materials, or characteristics described can be in office
It can be combined in any suitable manner in one or more embodiment or examples.In addition, those skilled in the art can be by this specification
Described in different embodiments or examples be combined.
Although the embodiments of the present invention has been shown and described above, it is to be understood that above-described embodiment is example
Property, it is not considered as limiting the invention, those skilled in the art within the scope of the invention can be to above-mentioned
Embodiment is changed, modifies, replacement and variant.
Claims (5)
1. a kind of automatic demand response method of electric car for the purpose of maintaining the micro-capacitance sensor equilibrium of supply and demand, which is characterized in that should
Method the following steps are included:
S1. comprehensively consider supply side renewable energy power producing characteristics and Demand-side PEV charge-discharge characteristic information, establish and taken containing ADR
ADR framework including business device, demand response DR operational management unit, ADR client and web response body Web；
S2. one day continuous time for 24 hours was subjected to sliding-model control, is divided into J period, for any kth time period, there is k ∈
{ 1,2 ..., J }, and the when a length of Δ t of kth time period；
S3. for any kth time period, DR operational management unit collects supply side renewable energy force information and Demand-side electricity consumption
Information calculates compensation demand；
S4. when compensation demand is not zero, compensation demand is issued ADR server by DR operational management unit；ADR server is collected
The urgent horizontal parameters of web response body Web according to compensation supply and compensate demand and calculate virtual real time price vRTP and system
Need state FG signal, and all web response body Webs are sent to by ADR client；
S5. for each web response body Web, after receiving virtual real time price signal and FG signal, comprehensively consider own situation and
Itself charge-discharge electric power size of the signal adjust automatically received；
S6. step S1~S5 is repeated, until optimization section terminates.
2. the automatic demand response side of the electric car according to claim 1 for the purpose of maintaining the micro-capacitance sensor equilibrium of supply and demand
Method, which is characterized in that in the step S1, the ADR framework includes:
ADR server, for based on the conventional load in system, renewable energy power output is horizontal and PEV cluster, energy-storage system function
Rate information formulates virtual Spot Price, publication DR event notice；
DR operational management unit forms DR demand, and need to for the renewable energy power output and load level in monitoring system
It asks and is sent to ADR server；Monitoring system operation, monitoring DR implementation result；
ADR client collects the urgent water of charge and discharge of each PEV and energy storage for connecting all kinds of web response body Webs of DR user downwards
Flat parameter, and virtual real time price that ADR server is formulated, FG signal are distributed to all web response body Webs in system；
Web response body Web includes PEV cluster and energy-storage system, and the web response body Web is ADR project for object, by web response body Web
Set is set as N+。
3. the automatic demand response of the electric car according to claim 1 or 2 for the purpose of maintaining the micro-capacitance sensor equilibrium of supply and demand
Method, which is characterized in that in the step S3, for any kth time period, DR operational management unit collects supply side renewable energy
Source force information and Demand-side power information calculate compensation demand, specifically include the photovoltaic array output power of k periodBlower output power summationMicro-capacitance sensor conventional load power and the PEV charge power being not under responsive state with
And PEV inbound information；
For the record of PEV inbound information: the vehicle collection for setting micro-capacitance sensor access is combined into N, then vehicle scale is n=| N |, for
Any vehicle l ∈ N, relevant parameter are as follows:
In formula: Tl in、Tl leftIt respectively indicates the time of vehicle l access micro-capacitance sensor and is expected time departure；It respectively indicates
The starting state-of-charge (SOC) of power cell of vehicle and expectation SOC when leaving micro-capacitance sensor, SOC indicate battery remaining power with
The ratio of battery capacity, therefore have Indicate PEV battery capacity；Pl maxc、Pl maxdTable respectively
Show specified charge and discharge power；
For compensating the calculating of demand: under the targeted micro-capacitance sensor environment of renewable energy containing high permeability, electric energy supply
Power difference between amount and Demand-side usage amount is compensation demand needed for system, and web response body Web muck in is needed to mend
It repays；Note the k period compensation demand beIt is defined as follows:
In formula:Indicate micro-grid system in the conventional load power of k period；It is positive and negative respectively indicate current power benefit
Repaying status requirements is to send from grid charging or by electric energy is counter to power grid；Indicate that the PEV being not under responsive state fills
Electrical power.
4. the automatic demand response side of the electric car according to claim 3 for the purpose of maintaining the micro-capacitance sensor equilibrium of supply and demand
Method, which is characterized in that in shown step S4, if there is unbalanced supply-demand phenomenon, i.e., when compensation demand is not zero, then DR thing
Part is triggered, and DR operational management unit forms DR demand, and demand is sent to ADR server；ADR server collects response master
The urgent horizontal parameters of body formulate virtual real time price signal and FG signal according to compensation supply and compensation demand, and
Notify all web response body Webs in micro-grid system；
To the record of the urgent horizontal parameters of PEV: under electricity market, the demand that power difference compensates in electric car participation system will
Directly depend on its own cell condition and the expected off-network time；When system needs to carry out V2G,ForIts urgent level in the k period is defined as:
In formula:Indicate that web response body Web i is urgently horizontal in the electric discharge of k period；Pi maxcIndicate the specified charging function of web response body Web i
Rate；Ti leftIndicate that web response body Web i leaves the period of power grid；TkIndicate the current k period；Ei,kIndicate that web response body Web i is needed in the k period
The rechargeable energy wanted；Indicate web response body Web i in the power interactive efficiency of k period；Indicate that the battery of web response body Web i holds
Amount；
From the point of view of logically, if the V2G of certain web response body Web is urgent horizontal very high, it can be more likely to be unwilling in turn
Receive G2V charge power；Thus, when needing G2V power,Urgent level of i-th PEV in the k period is defined as:
In formula:Indicate that web response body Web i is urgently horizontal in the charging of k period；
To the record of the urgent horizontal parameters of energy storage: in view of the energy storage in system will always be in networking state, it is not anticipated that from
The concept of time is netted, therefore, the urgent level of energy storage charge and discharge is only determined by its current residual electricity；Redefine the urgent of energy storage
It is horizontal as follows:
In formula: Smin,ESS、Smax,ESSThe respectively upper and lower limit of energy-storage system (ESS) state-of-charge；Si,kIndicate web response body Web i in k
The state-of-charge of period；
The formulation of virtual real time price signal: using for reference the congestion Price Algorithm of internet traffic control field, and definition is virtual real-time
Price (vRTP) mechanism；Under micro-capacitance sensor environment, vRTP signal be used to instruct web response body Web, can timely respond in system
Power shortage or surplus；Accordingly, in microgrid the sum of all web response body Web power in responsive state should with need to mend in system
The power repaid is identical, i.e.,
In formula: Pi,kIndicate web response body Web i in the compensation supply of k period；
When reaching the equilibrium of supply and demand under guidance of the system in virtual real time price, forHave
In formula: γ indicates virtual real time price coefficient, is constant；
Therefore, for all web response body Webs, total charge-discharge electric power is
Introduce a positive multiplication factor εk, the formulating method of vRTP is adjusted, so that being in the web response body Web function of responsive state
The sum of rate completes the compensation of supply and demand power difference, ε as much as possiblekCalculation formula it is as follows:
Virtual real time price depends on compensation supply and required compensation demand in micro-capacitance sensor:
Wherein: vRTPkIndicate the virtual real time price signal of k period；
The formulation of FG signal: FG is the id signal that user is informed together with vRTP signal:
FG signal is for marking current system need state, when current system need state is G2V, FG=1；Current system demand
When state is V2G, FG=-1, the power which is used to guarantee that the direction of web response body Web power to need to compensate with system automatically is accorded with
It number is consistent.
5. the automatic demand response of the electric car according to claim 1 or 2 for the purpose of maintaining the micro-capacitance sensor equilibrium of supply and demand
Method, which is characterized in that in the step S5, self-regulating process is as follows:
S51. for any web response body Web, i.e.,ADR client is by calculating vehicle i in the battery threshold doseag of k period
VRTP signal whether is able to respond to measure electric car i in the k period:
From the above equation, we can see thatIndicate that charging is completed in vehicle i；If indicating what vehicle i persistently charged before off-network
Words, can still complete to charge；
S52. when following formula is set up, step S53 is gone to, otherwise web response body Web no longer responds vRTP signal, then with maximum power
It charges:
In formula:For the battery threshold of setting；
S53.ADR client judges whether vRTP signal is greater than given threshold vRTPTH；Response lag inequality is defined as follows:
vRTPk≤vRTPTH (14)
If S54. monitoring, vRTP signal is more than given threshold vRTPTH, web response body Web will by meet 0 < β < 1 zoom factor
β reduces its V2G/G2V power until otherwise vRTP signal value goes to step S55 lower than upper limit value；Power adjustment formula is as follows
It is shown:
Pi,k+1=β Pi,k (15)
In formula: Pi,k+1Indicate web response body Web i in the compensation supply of (k+1) period；
S55. each main body responds vRTP the and FG signal received, and combines own situation from main modulation itself charge-discharge electric power,
It is as follows that power automated tos respond to mode:
In formula: φiIt is the parameter of an influence algorithm the convergence speed.
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| CN109636056B (en) * | 2018-12-24 | 2021-08-03 | 浙江工业大学 | Multi-energy microgrid decentralized optimization scheduling method based on multi-agent technology |
| CN109823228B (en) * | 2018-12-29 | 2023-05-23 | 国网天津市电力公司电力科学研究院 | Electric automobile charging and discharging method and device for building load aggregator |
| CN110472785B (en) * | 2019-08-08 | 2022-12-09 | 西安交通大学 | Electric automobile group scheduling method based on load classification |
| JP7461769B2 (en) * | 2020-03-25 | 2024-04-04 | 本田技研工業株式会社 | Energy supply system and energy supply method |
| CN111959330B (en) * | 2020-08-28 | 2021-08-20 | 华北电力大学（保定） | User DR scheme customization method based on user charging and traveling habits |
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