CN112350304A - Distribution network energy optimization control method based on participation of electric vehicle aggregator in demand response service - Google Patents
Distribution network energy optimization control method based on participation of electric vehicle aggregator in demand response service Download PDFInfo
- Publication number
- CN112350304A CN112350304A CN202011057780.5A CN202011057780A CN112350304A CN 112350304 A CN112350304 A CN 112350304A CN 202011057780 A CN202011057780 A CN 202011057780A CN 112350304 A CN112350304 A CN 112350304A
- Authority
- CN
- China
- Prior art keywords
- distribution network
- electric vehicle
- power
- node
- demand response
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000004044 response Effects 0.000 title claims abstract description 44
- 238000000034 method Methods 0.000 title claims abstract description 31
- 238000005457 optimization Methods 0.000 title claims abstract description 29
- 238000004088 simulation Methods 0.000 claims abstract description 10
- 230000009466 transformation Effects 0.000 claims abstract description 7
- 238000012216 screening Methods 0.000 claims abstract description 4
- 238000000342 Monte Carlo simulation Methods 0.000 claims description 12
- 238000010586 diagram Methods 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 claims description 4
- 230000008569 process Effects 0.000 claims description 3
- 238000007599 discharging Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000004146 energy storage Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
Images
Classifications
-
- 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
-
- 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
- B60L53/60—Monitoring or controlling charging stations
- B60L53/63—Monitoring or controlling charging stations in response to network capacity
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- 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
- 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
-
- 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
- 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
- Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
-
- 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
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/10—Technologies relating to charging of electric vehicles
- Y02T90/12—Electric charging stations
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention relates to the technical field of optimized scheduling, in particular to a distribution network energy optimization control method based on participation of an electric vehicle aggregator in demand response service, which comprises the following steps: s10, building a net rack and collecting tide data at any moment of a load valley period; s20, establishing an electric automobile dispatching distribution network energy model; s30, recording the simulation times i as 1; s40, simulating and distributing power distribution schemes of all charging stations by Monte Carlo; s50, superposing power distributed to each electric vehicle charging station on the basis of the initial load power of each electric vehicle charging station, and judging whether the power exceeds distribution transformation capacity; s60, replacing the distribution scheme in a network quotient pile energy optimization model to solve, solving the distribution network loss, and screening an optimal scheme; and S70, distributing the required response power of each electric vehicle charging station by the electric vehicle load aggregator. The method can fully transfer the resources on the load side, ensure the safety of the operation of the power distribution network, and simultaneously reduce the network loss of the power distribution network and improve the economical efficiency of the operation of the power distribution network through load optimization by the electric vehicle load aggregators.
Description
Technical Field
The invention relates to the technical field of optimized scheduling, in particular to a distribution network energy optimization control method based on participation of an electric vehicle aggregator in demand response service.
Background
With the continuous expansion of the scale of electric automobiles, the operation risk of a power distribution network in a load peak period is continuously increased, the electric automobiles are used as special mobile energy storage devices, the operation risk of the power distribution network can be reduced through certain scheduling in time and space, an electric automobile load aggregator is used as a link between a power grid company and an electric automobile charging station, and when the load of the power distribution network is too low or overload, the electric automobile load aggregator adjusts the load of the electric automobiles by integrating the resources of the electric automobile charging station to achieve the purpose of adjusting the load of the power distribution network.
Chinese patent CN109823228A discloses a charging and discharging method and device for building load aggregator-oriented electric vehicle: step 1, acquiring parameters of a distributed power supply, a building load and an electric vehicle; step 2, acquiring data of distributed power supply output and each time interval of a building load in a typical day and electric vehicle travel data; step 3, carrying out peak clipping and valley filling on the distributed power supply and the building load curve from the maximum peak-valley difference according to a transversal line area method; and 4, optimizing by taking the minimum cost paid by the load aggregator as an objective function in the time period when the electric automobile participates in building interaction, and calling CPLEX to solve the specific charging and discharging vehicle configuration in the building in the time period when the electric automobile participates in building interaction. The scheme effectively integrates the dispersive resources of the electric automobile, but cannot fully reduce the network loss of the power distribution network, and the economic promotion of the operation of the power distribution network is limited.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides a distribution network energy optimization control method based on participation of an electric vehicle aggregator in a demand response service, which can fully mobilize load side resources and guarantee the operation safety of a distribution network, and meanwhile, the electric vehicle load aggregator reduces the network loss of the distribution network and improves the operation economy of the distribution network through load optimization.
In order to solve the technical problems, the invention adopts the technical scheme that:
the distribution network energy optimization control method based on participation of electric vehicle aggregators in demand response services is provided, and comprises the following steps:
s10, building a net rack and collecting tide data at any moment of a load valley period;
s20, establishing an electric automobile dispatching distribution network energy model;
s30, setting the total Monte Carlo simulation times K, and recording the simulation times i as 1;
s40, carrying out Monte Carlo simulation distribution on power distribution schemes of all charging stations, and recording the Monte Carlo simulation times i as i + 1;
s50, superposing the power distributed to each electric vehicle charging station in the step S40 on the basis of the initial load power of each electric vehicle charging station, and judging whether the power exceeds the distribution transformation capacity: if yes, returning to the step S40, otherwise, executing the step S60;
s60, solving in an energy optimization model instead of a network commercial pile according to a power distribution scheme of the electric vehicle charging station, solving the network loss of a distribution network, and screening an optimal scheme;
and S70, distributing the required response power of each electric vehicle charging station by the electric vehicle load aggregator.
The invention discloses a distribution network energy optimization control method based on participation of an electric vehicle aggregator in a demand response service, which comprises the following steps: the load side resources can be fully transferred, the running safety of the power distribution network is guaranteed, and meanwhile, the electric automobile load aggregator can reduce the network loss of the power distribution network and improve the running economy of the power distribution network through load optimization.
Preferably, in step S20, the objective function of the electric vehicle dispatching distribution network energy model is:
in formula (1), J is the active loss of the distribution network, PijFor the active power, Q, flowing from node i to node j of the distribution networkijFor reactive power, U, flowing from node i to node j of the distribution networkiFor nodes i of the distribution networkPoint voltage, RijIs the resistance between node i and node j.
Preferably, in step S20, the constraints of the electric vehicle dispatching distribution network energy model include distribution network node power balance constraints, node voltage constraints, electric vehicle charging station demand response constraints, and electric vehicle charging station capacity constraints.
Preferably, the configuration is represented by a mesh node power balance constraint as:
in the formula (2), Pi(t) is the active power of distribution network node i at time t, Qi(t) is the reactive power, V, of the distribution network node i at time ti(t) node voltage of distribution network node i at time t, GijFor the conductance between nodes i and j of the distribution network, BijFor susceptance, theta, between nodes i and j of the distribution networkij＝θi-θj，θijIs the voltage phase angle difference, sin theta, between nodes i and j of the power distribution networkij(t) is the sine value of the voltage phase angle difference between nodes i and j of the power distribution network at the time t, cos thetaijAnd (t) is a cosine value of a voltage phase angle difference between nodes i and j of the power distribution network at the moment t.
Preferably, the node voltage constraint is expressed as:
Vmin≤Vi(t)≤Vmax (3)
v in formula (3)minIs the lower limit of the node voltage amplitude, ViIs the voltage amplitude, V, of node imaxIs the upper limit of the node voltage amplitude.
Preferably, the electric vehicle charging station demand response constraint is expressed as:
p in formula (4)DRIs a demand response quantity, P, issued to a load aggregatorSTAiAs a load aggregator pairThe power distributed by the ith charging station.
Preferably, the electric vehicle charging station capacity constraint is expressed as:
in the formula (5), PSTAiCharging power for the ith electric vehicle charging station, SNiThe rated power of the ith electric vehicle charging station is distributed,
and taking 0.95 as the rated power factor of the transformer of the charging station.
Preferably, in step S10, the net rack is built up according to the following steps: in Matlab's matpower, each branch node and load node are numbered according to the distribution network single line diagram, and each branch impedance parameter, the active power and the reactive power of each load node, the transformer transformation ratio, the impedance parameter and the branch node connected with the impedance parameter are determined.
Preferably, the monte carlo simulation distribution is performed as follows: the method comprises the following steps: firstly, setting a total simulation time K; step two, in the simulation process of the ith time, i is more than or equal to 1 and less than or equal to N, and a group of demand response quantities of all electric vehicle charging stations are generated randomly; step three: judging whether the constraint is met or not according to the demand response demand constraint and the charging capacity constraint of each charging station; step four: if the constraint is met, substituting the demand response quantity of each electric vehicle charging station into the model for calculation; step five: and if the constraint is not satisfied, returning to the step two.
Compared with the prior art, the invention has the beneficial effects that:
the distribution network energy optimization control method based on participation of the electric vehicle aggregator in the demand response service can fully mobilize load side resources and guarantee the operation safety of the distribution network, and meanwhile, the electric vehicle load aggregator reduces the distribution network loss and improves the operation economy of the distribution network through load optimization.
Drawings
Fig. 1 is a flowchart of a distribution network energy optimization control method based on participation of an electric vehicle aggregator in a demand response service;
FIG. 2 is a schematic diagram of power distribution of electric vehicle charging stations according to a first embodiment;
fig. 3 is a schematic diagram illustrating calculation results of active loss of the power distribution network in the first to fifth schemes in the first embodiment;
Detailed Description
The present invention will be further described with reference to the following embodiments. Wherein the showings are for the purpose of illustration only and are shown by way of illustration only and not in actual form, and are not to be construed as limiting the present patent; to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there is an orientation or positional relationship indicated by the terms "upper", "lower", "left", "right", etc. based on the orientation or positional relationship shown in the drawings, it is only for convenience of describing the present invention and simplifying the description, but it is not intended to indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and are not to be construed as limiting the present patent, and the specific meaning of the terms may be understood by those skilled in the art according to specific circumstances.
Examples
Fig. 1 shows an embodiment of a distribution network energy optimization control method based on participation of an electric vehicle aggregator in a demand response service, which includes the following steps:
s10, building a net rack and collecting tide data at any moment of a load valley period;
s20, establishing an electric automobile dispatching distribution network energy model;
s30, setting the total Monte Carlo simulation times K, and recording the simulation times i as 1;
s40, carrying out Monte Carlo simulation distribution on power distribution schemes of all charging stations, and recording the Monte Carlo simulation times i as i + 1;
s50, superposing the power distributed to each electric vehicle charging station in the step S40 on the basis of the initial load power of each electric vehicle charging station, and judging whether the power exceeds the distribution transformation capacity: if yes, returning to the step S40, otherwise, executing the step S60;
s60, solving in an energy optimization model instead of a network commercial pile according to a power distribution scheme of the electric vehicle charging station, solving the network loss of a distribution network, and screening an optimal scheme;
and S70, distributing the required response power of each electric vehicle charging station by the electric vehicle load aggregator.
In step S20, the objective function of the electric vehicle dispatching distribution network energy model is:
in formula (1), J is the active loss of the distribution network, PijFor the active power, Q, flowing from node i to node j of the distribution networkijFor reactive power, U, flowing from node i to node j of the distribution networkiNode voltage, R, for node i of the distribution networkijIs the resistance between node i and node j.
In step S20, the constraint conditions of the electric vehicle dispatching distribution network energy model include a distribution network node power balance constraint, a node voltage constraint, an electric vehicle charging station demand response constraint, and an electric vehicle charging station capacity constraint.
The power distribution network node power balance constraint is expressed as:
in the formula (2), Pi(t) is the active power of distribution network node i at time t, Qi(t) is the reactive power, V, of the distribution network node i at time ti(t) is node of power distribution network node i at time tPoint voltage, GijFor the conductance between nodes i and j of the distribution network, BijFor susceptance, theta, between nodes i and j of the distribution networkij＝θi-θj，θijIs the voltage phase angle difference, sin theta, between nodes i and j of the power distribution networkij(t) is the sine value of the voltage phase angle difference between nodes i and j of the power distribution network at the time t, cos thetaijAnd (t) is a cosine value of a voltage phase angle difference between nodes i and j of the power distribution network at the moment t.
The node voltage constraint is expressed as:
Vmin≤Vi(t)≤Vmax (3)
v in formula (3)minIs the lower limit of the node voltage amplitude, Vi is the voltage amplitude of the node i, VmaxIs the upper limit of the node voltage amplitude.
The electric vehicle charging station demand response constraint is expressed as:
p in formula (4)DRIs a demand response quantity, P, issued to a load aggregatorSTAiThe power to the ith charging station is the load aggregator.
The electric vehicle charging station capacity constraint is expressed as:
in the formula (5), PSTAiCharging power for the ith electric vehicle charging station, SNiThe rated power of the ith electric vehicle charging station is distributed,
and taking 0.95 as the rated power factor of the transformer of the charging station.
In step S10, a net rack is built up according to the following steps: in Matlab's matpower, each branch node and load node are numbered according to the distribution network single line diagram, and each branch impedance parameter, the active power and the reactive power of each load node, the transformer transformation ratio, the impedance parameter and the branch node connected with the impedance parameter are determined.
In step S10, the monte carlo simulation distribution is performed as follows: the method comprises the following steps: firstly, setting a total simulation time K; step two, in the simulation process of the ith time, i is more than or equal to 1 and less than or equal to N, and a group of demand response quantities of all electric vehicle charging stations are generated randomly; step three: judging whether the constraint is met or not according to the demand response demand constraint and the charging capacity constraint of each charging station; step four: if the constraint is met, substituting the demand response quantity of each electric vehicle charging station into the model for calculation; step five: and if the constraint is not satisfied, returning to the step two.
In this embodiment, when a 500kW load needs to be added to the demand response center, fig. 2 provides five power schemes for electric vehicle charging stations, where the first scheme is to uniformly distribute the corresponding amount of demand, the second, third, and fourth schemes randomly distribute charging stations in the area, and the fifth scheme is to distribute power of each charging station in the area according to the strategy of this embodiment. The active loss of the power distribution network with the five schemes is shown in fig. 3, and as can be seen from fig. 3, the average loss of the four schemes in the first scheme to the fourth scheme is about 14.57%, and the maximum loss can reach 17.97%. Therefore, the method provided by the embodiment effectively reduces the network loss of the distribution network through the power distribution scheme of the electric vehicle charging station, and improves the operation safety of the distribution network to a certain extent.
Therefore, the distribution network energy optimization control method based on the participation of the electric vehicle aggregator in the demand response service can fully mobilize the resources on the load side, ensure the operation safety of the distribution network, and simultaneously reduce the distribution network loss and improve the operation economy of the distribution network through load optimization by the electric vehicle load aggregator.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (10)
1. A distribution network energy optimization control method based on participation of an electric vehicle aggregator in demand response services is characterized by comprising the following steps:
s10, building a net rack and collecting tide data at any moment of a load valley period;
s20, establishing an electric automobile dispatching distribution network energy model;
s30, setting the total Monte Carlo simulation times K, and recording the simulation times i as 1;
s40, carrying out Monte Carlo simulation distribution on power distribution schemes of all charging stations, and recording the Monte Carlo simulation times i as i + 1;
s50, superposing the power distributed to each electric vehicle charging station in the step S40 on the basis of the initial load power of each electric vehicle charging station, and judging whether the power exceeds the distribution transformation capacity: if yes, returning to the step S40, otherwise, executing the step S60;
s60, substituting the power distribution scheme of the electric vehicle charging station into the electric vehicle dispatching distribution network energy model in the step S20 to solve, solving the distribution network loss, and screening an optimal scheme;
and S70, distributing the required response power of each electric vehicle charging station by the electric vehicle load aggregator.
2. The distribution network energy optimization control method based on participation of electric vehicle aggregators in demand response services according to claim 1, wherein in step S20, the objective function of the electric vehicle dispatching distribution network energy model is as follows:
in formula (1), J is the active loss of the distribution network, PijFor the active power, Q, flowing from node i to node j of the distribution networkijFor reactive power, U, flowing from node i to node j of the distribution networkiNode voltage, R, for node i of the distribution networkijIs the resistance between node i and node j.
3. The distribution network energy optimization control method based on participation of electric vehicle aggregators in demand response services, according to claim 2, wherein in step S20, the constraints of the electric vehicle dispatching distribution network energy model include distribution network node power balance constraints, node voltage constraints, electric vehicle charging station demand response constraints, and electric vehicle charging station capacity constraints.
4. The distribution network energy optimization control method based on participation of electric vehicle aggregators in demand response services according to claim 3, wherein the distribution network node power balance constraint is expressed as:
in the formula (2), Pi(t) is the active power of distribution network node i at time t, Qi(t) is the reactive power, V, of the distribution network node i at time ti(t) node voltage of distribution network node i at time t, GijFor the conductance between nodes i and j of the distribution network, BijFor susceptance, theta, between nodes i and j of the distribution networkij＝θi-θj，θijIs the voltage phase angle difference, sin theta, between nodes i and j of the power distribution networkij(t) is the sine value of the voltage phase angle difference between nodes i and j of the power distribution network at the time t, cos thetaijAnd (t) is a cosine value of a voltage phase angle difference between nodes i and j of the power distribution network at the moment t.
5. The distribution network energy optimization control method based on participation of electric vehicle aggregators in demand response services according to claim 3, wherein the node voltage constraint is expressed as:
Vmin≤Vi(t)≤Vmax (3)
v in formula (3)minIs the lower limit of the node voltage amplitude, ViIs the voltage amplitude, V, of node imaxIs the upper limit of the node voltage amplitude.
6. The distribution network energy optimization control method based on participation of electric vehicle aggregators in demand response services according to claim 3, wherein the demand response constraint of the electric vehicle charging station is expressed as:
p in formula (4)DRIs a demand response quantity, P, issued to a load aggregatorSTAiThe power to the ith charging station is the load aggregator.
7. The distribution network energy optimization control method based on participation of electric vehicle aggregators in demand response services according to claim 3, wherein the electric vehicle charging station capacity constraint is expressed as:
in the formula (5), PSTAiCharging power for the ith electric vehicle charging station, SNiThe rated power of the ith electric vehicle charging station is distributed,
the transformer is rated for a charging station power factor.
8. The distribution network energy optimization control method based on participation of electric vehicle aggregators in demand response services according to claim 7,
take 0.95.
9. The distribution network energy optimization control method based on participation of electric vehicle aggregators in demand response services according to any one of claims 1 to 8, wherein in step S10, the net rack is built according to the following steps: in Matlab's matpower, each branch node and load node are numbered according to the distribution network single line diagram, and each branch impedance parameter, the active power and the reactive power of each load node, the transformer transformation ratio, the impedance parameter and the branch node connected with the impedance parameter are determined.
10. The distribution network energy optimization control method based on participation of electric vehicle aggregators in demand response services according to any one of claims 1 to 8, wherein Monte Carlo simulation distribution is performed according to the following steps: the method comprises the following steps: firstly, setting a total simulation time K; step two, in the simulation process of the ith time, i is more than or equal to 1 and less than or equal to N, and a group of demand response quantities of all electric vehicle charging stations are generated randomly; step three: judging whether the constraint is met or not according to the demand response demand constraint and the charging capacity constraint of each charging station; step four: if the constraint is met, substituting the demand response quantity of each electric vehicle charging station into the model for calculation; step five: and if the constraint is not satisfied, returning to the step two.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011057780.5A CN112350304A (en) | 2020-09-30 | 2020-09-30 | Distribution network energy optimization control method based on participation of electric vehicle aggregator in demand response service |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011057780.5A CN112350304A (en) | 2020-09-30 | 2020-09-30 | Distribution network energy optimization control method based on participation of electric vehicle aggregator in demand response service |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112350304A true CN112350304A (en) | 2021-02-09 |
Family
ID=74361588
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011057780.5A Pending CN112350304A (en) | 2020-09-30 | 2020-09-30 | Distribution network energy optimization control method based on participation of electric vehicle aggregator in demand response service |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112350304A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114493329A (en) * | 2022-02-14 | 2022-05-13 | 国网河北省电力有限公司营销服务中心 | Demand response regulation and control method and device for electric automobile |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120253567A1 (en) * | 2011-03-28 | 2012-10-04 | Levy Paul S | Method and process for acquiring and delivering electric vehicle owner-operator preference data which is used to schedule and regulate the charging of multiple electric vehicle batteries within a shared local power distribution network |
| CN107133415A (en) * | 2017-05-22 | 2017-09-05 | 河海大学 | A kind of electric automobile charge and discharge Electric optimization for considering user's satisfaction and distribution safety |
-
2020
- 2020-09-30 CN CN202011057780.5A patent/CN112350304A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120253567A1 (en) * | 2011-03-28 | 2012-10-04 | Levy Paul S | Method and process for acquiring and delivering electric vehicle owner-operator preference data which is used to schedule and regulate the charging of multiple electric vehicle batteries within a shared local power distribution network |
| CN107133415A (en) * | 2017-05-22 | 2017-09-05 | 河海大学 | A kind of electric automobile charge and discharge Electric optimization for considering user's satisfaction and distribution safety |
Non-Patent Citations (1)
| Title |
|---|
| 张家献: "含分布式电源及电动汽车的配电网重构研究", 《中国优秀硕士学位论文全文数据库工程科技II辑》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114493329A (en) * | 2022-02-14 | 2022-05-13 | 国网河北省电力有限公司营销服务中心 | Demand response regulation and control method and device for electric automobile |
| CN114493329B (en) * | 2022-02-14 | 2024-05-10 | 国网河北省电力有限公司营销服务中心 | Demand response regulation and control method and device for electric automobile |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11267361B2 (en) | System and method for ordered charging management of charging station | |
| CN104917173B (en) | Adapt to the distribution optimization method of power distribution network Large Copacity load transfer | |
| CN104966127B (en) | A kind of electric vehicle economic load dispatching method based on demand response | |
| CN106004503B (en) | Power distribution method between a kind of more electric automobile charging stations for frequency regulation | |
| CN109492824B (en) | Distributed wind storage system optimization method considering source-network-load multi-party benefits | |
| CN104269896A (en) | Control method for large-scale orderly electric vehicle charging and discharging | |
| CN110110941A (en) | The orderly charging management system of alternating-current charging pile and orderly charging method | |
| CN106960279A (en) | Consider the electric automobile energy efficiency power plant characteristic parameter appraisal procedure of user's participation | |
| CN103457326B (en) | Distributed uniting coordination control method of large-scale electric automobile charging load | |
| CN101764404B (en) | Method for judging coordination running state between provincial and local regions in coordination voltage control between provincial and local regions | |
| CN110065410A (en) | A kind of electric car charge and discharge rate control method based on fuzzy control | |
| CN113263938B (en) | Intelligent ordered charging system and method based on edge calculation | |
| Wang et al. | Unified strategy for electric vehicles participate in voltage and frequency regulation with active power in city grid | |
| Wu et al. | PEV-based reactive power compensation for wind DG units: A stackelberg game approach | |
| Faddel et al. | An automated charger for large-scale adoption of electric vehicles | |
| CN115360804A (en) | Ordered charging system and ordered charging method | |
| Sheng et al. | A potential security threat and its solution in coupled urban power-traffic networks with high penetration of electric vehicles | |
| CN104283288B (en) | Charging method and device of charging station | |
| CN112350304A (en) | Distribution network energy optimization control method based on participation of electric vehicle aggregator in demand response service | |
| CN108808655A (en) | A kind of economic evaluation method and system for participating in microgrid frequency modulation to electric vehicle | |
| CN106712042A (en) | Power grid energy conservation and loss reduction control method considering reactive response capability of charging pile | |
| CN104009482B (en) | Method for dynamically setting gateway reactive power control range under single-line and single-variable wiring mode | |
| CN111987717B (en) | Internet of vehicles charging and discharging management method and system, electric power system and storage medium | |
| Wang et al. | Distribution grid voltage support with four quadrant control of electric vehicle chargers | |
| CN111463781B (en) | Elastically-guided power distribution network recovery method and system considering electric buses |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210209 |