CN112134300B - 基于预约的电动汽车光储充电站滚动优化运行方法及*** - Google Patents
基于预约的电动汽车光储充电站滚动优化运行方法及*** Download PDFInfo
- Publication number
- CN112134300B CN112134300B CN202011071370.6A CN202011071370A CN112134300B CN 112134300 B CN112134300 B CN 112134300B CN 202011071370 A CN202011071370 A CN 202011071370A CN 112134300 B CN112134300 B CN 112134300B
- Authority
- CN
- China
- Prior art keywords
- charging
- charging station
- power
- reservation
- energy storage
- 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.)
- Active
Links
- 238000005457 optimization Methods 0.000 title claims abstract description 58
- 238000000034 method Methods 0.000 title claims abstract description 50
- 238000005096 rolling process Methods 0.000 title claims abstract description 50
- 238000003860 storage Methods 0.000 title claims abstract description 31
- 238000004146 energy storage Methods 0.000 claims abstract description 106
- 238000007599 discharging Methods 0.000 claims abstract description 45
- 230000005611 electricity Effects 0.000 claims abstract description 40
- 238000010248 power generation Methods 0.000 claims abstract description 33
- 230000003287 optical effect Effects 0.000 claims abstract description 18
- 238000003062 neural network model Methods 0.000 claims description 19
- 238000004364 calculation method Methods 0.000 claims description 16
- 230000004044 response Effects 0.000 claims description 15
- 238000004422 calculation algorithm Methods 0.000 claims description 13
- 238000012549 training Methods 0.000 claims description 12
- 230000006870 function Effects 0.000 claims description 11
- 230000007246 mechanism Effects 0.000 claims description 10
- 238000012360 testing method Methods 0.000 claims description 6
- 238000013528 artificial neural network Methods 0.000 claims description 4
- 238000012937 correction Methods 0.000 claims description 4
- 230000000694 effects Effects 0.000 claims description 4
- 239000000284 extract Substances 0.000 claims description 3
- 230000010354 integration Effects 0.000 claims description 3
- 238000012795 verification Methods 0.000 claims description 3
- 230000007787 long-term memory Effects 0.000 claims description 2
- 239000002245 particle Substances 0.000 claims description 2
- 238000012545 processing Methods 0.000 claims description 2
- 230000006403 short-term memory Effects 0.000 claims description 2
- 230000006399 behavior Effects 0.000 description 3
- 230000008901 benefit Effects 0.000 description 3
- 230000002354 daily effect Effects 0.000 description 3
- 238000011161 development Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000009826 distribution Methods 0.000 description 2
- 230000003203 everyday effect Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000005295 random walk Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- YHXISWVBGDMDLQ-UHFFFAOYSA-N moclobemide Chemical compound C1=CC(Cl)=CC=C1C(=O)NCCN1CCOCC1 YHXISWVBGDMDLQ-UHFFFAOYSA-N 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000013486 operation strategy Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 210000000352 storage cell Anatomy 0.000 description 1
- 239000000758 substrate Substances 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
- 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
- H02J3/322—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging
-
- 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
-
- 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
- B60L53/64—Optimising energy costs, e.g. responding to electricity rates
-
- 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
- B60L55/00—Arrangements for supplying energy stored within a vehicle to a power network, i.e. vehicle-to-grid [V2G] arrangements
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- 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
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
-
- 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
- H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
-
- 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
- H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
- H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
-
- 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
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/22—The renewable source being solar energy
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
- H02J2310/10—The network having a local or delimited stationary reach
- H02J2310/18—The network being internal to a power source or plant
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
- H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
- H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
- H02J2310/58—The condition being electrical
- H02J2310/60—Limiting power consumption in the network or in one section of the network, e.g. load shedding or peak shaving
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
- H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
- H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
- H02J2310/62—The condition being non-electrical, e.g. temperature
- H02J2310/64—The condition being economic, e.g. tariff based load management
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
- Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
- Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E70/00—Other energy conversion or management systems reducing GHG emissions
- Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- 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
-
- 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
- Y02T90/16—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]
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
- Y04S20/20—End-user application control systems
- Y04S20/222—Demand response systems, e.g. load shedding, peak shaving
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S30/00—Systems supporting specific end-user applications in the sector of transportation
- Y04S30/10—Systems supporting the interoperability of electric or hybrid vehicles
- Y04S30/14—Details associated with the interoperability, e.g. vehicle recognition, authentication, identification or billing
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Business, Economics & Management (AREA)
- Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- General Health & Medical Sciences (AREA)
- Evolutionary Computation (AREA)
- Software Systems (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Computational Linguistics (AREA)
- Mathematical Physics (AREA)
- Human Resources & Organizations (AREA)
- Biophysics (AREA)
- Biomedical Technology (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Artificial Intelligence (AREA)
- Data Mining & Analysis (AREA)
- Life Sciences & Earth Sciences (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Game Theory and Decision Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Development Economics (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
本发明涉及基于预约的电动汽车光储充电站滚动优化运行方法及***,电动汽车充电站通过互联网发布最新的预约奖励策略和分时充电电价相关信息、空闲充电桩个数、平均等待时间;通过客户端查看动态分时充电电价,自主选择充电时间进行预约,并提供预约信息,预约成功后锁定充电价格,根据预约信息结合历史数据进行充电负荷预测;充电站根据历史数据进行光伏发电预测、基础负荷预测;以充电站运行成本最低为目标进行双层模型滚动优化,上层模型根据充电站现有空闲充电资源稀缺程度对预约充电价格进行滚动调整;下层模型以充电站运行成本最低为目标进行优化,计算未来时段的储能充放电功率曲线,将储能充放电功率曲线的第一个数据作为储能的控制指令。
Description
技术领域
本发明属于智能电网控制技术领域,涉及基于预约的电动汽车光储充电站滚动优化运行方法及***。
背景技术
随着电动汽车数量的不断增大,大规模电动汽车接入电网后对电网影响不容忽视。电动汽车有序充电策略是指在满足电动汽车充电需求的前提下,运用实际有效地经济或技术措施引导、控制电动汽车进行充电,对电网负荷曲线进行削峰填谷,使负荷曲线方差较小,减少了发电装机容量建设,保证了电动汽车与电网的协调互动发展。
可再生能源发电以及大规模储能技术发展迅速,越来越多的电动汽车充电站安装光伏发电设备以及储能***,通过有效的控制策略控制储能充放电,合理调节充电站与电网的交换功率,可以在很大程度上减小其运营成本,提高充电站经济效益。
随着电力市场的发展,电动汽车充电站(Charging Stations,CS)作为独立的市场主体与配电网运营商(Distribution System Operator,DSO)签订购电合约,并通过向电动汽车(EV)用户收取充电服务费获得收益。电动汽车充电站参与电力市场需求侧响应,增加充电站收益,从而降低运营成本。电动汽车充电站要在满足EV的充电需求的同时最小化其运营成本。
总之,现有技术可归纳为以下若干技术不足:
(1)以往对电动汽车充电站及电动汽车充电行为的研究大多以电网运行效益为目标,通过分时电价机制引导电动汽车有序充电,但并未考虑车主充电意愿,或者将车主对电价的响应行为抽象为响应曲线来研究,并未将预约充电机制与充电站优化运行有机结合。
(2)以往的充电站有序充电与储能优化控制方法常采用日前优化的方式,日前24小时计算得出分时充点电价和储能控制曲线,该方式对于日前预测要求较高,对于预测偏差以及突发情况,往往缺乏应对方案,或导致实际控制效果较差。
(3)以往的电动汽车有序充电优化常常以配电网网损最低或者考虑实时电价下的运营成本最低为目标,并未考虑电力市场合约以及合约的履行。
发明内容
为了解决现有技术存在的问题,本发明的目的在于,为克服现有技术未考虑到充电站与电动汽车车主的互动,过多依赖日前预测数据,以及缺乏考虑电力市场运行机制的技术不足与技术问题,提供基于预约的电动汽车光储充电站滚动优化运行方法及***,通过预约充电机制,采用双层模型优化来实现电动汽车充电站高效低成本运营,上层模型通过计算站内剩余资源和购电成本的方法得到动态分时预约电价,有效的引导敏感用户转移充电时间,实现了削峰填谷,同时也有效减少电动汽车充电排队等待时间,动态调节预约充电价格,以达到电动汽车有序预约充电的目的;下层模型通过滚动优化的方法控制站内储能设备充放电,实现充电站实际运营成本最小化,以降低充电站运营成本。
本发明所采用的技术解决方案为:
基于预约的电动汽车光储充电站滚动优化运行方法,所述优化运行方法包括以下步骤:
步骤1:电动汽车光储充电站每间隔Δt作为一个时段,发布一次充电信息,所述充电信息包括未来连续的T个时段内每个时段的充电价格、空闲充电桩个数、平均等待时间,并在所述充电站的服务器中存储充电预约列表;
步骤2:查看步骤1中充电站所发布的充电信息,根据每个时段的充电价格及平均等待时间,选择特定时段进行预约并提供预约信息,预约成功后锁定所述预约时段对应的价格作为电动汽车的充电价格,所述充电站的服务器将预约信息存入所述充电预约列表,所述预约信息包括预约充电量、预约充电功率、起始充电时间、充电时长;
步骤3:所述充电站根据步骤2中未来T个时段内每个时段的预约信息,利用LSTM神经网络模型预测未来T个时段内每个时段所述充电站的预测总充电功率;
步骤4:充电站根据光伏发电历史数据和基础负荷历史数据,采用时间序列预测方法,进行光伏发电预测和基础负荷预测,从而得到T个时段的光伏发电功率预测和基础负荷功率预测;
步骤5:对每个所划分的时段Δt,通过双层模型滚动优化未来T个时段内所述充电站的运行成本。
LSTM神经网络模型包括五层结构,
输入层,包括历史充电数据、历史预约数据、节假日信息、天气信息;
LSTM层,利用LSTM神经网络模型获取历史时间序列;
注意力层,利用注意力机制提取不同时间特征信息,得到特征的注意力权重,特征包括历史充电数据、历史预约数据、节假日信息、天气信息;
全连接层,进行局部特征整合;
输出层,输出预测结果数据,
其中,LSTM为长短期记忆人工神经网络。
所述LSTM神经网络模型训练方法为:
步骤3.1,将历史充电数据分为训练集和测试集;
步骤3.2,采用均方误差和平均绝对误差作为误差指标,确定LSTM层及注意力层的隐含节点个数为2n,n为输入节点个数,调整模型参数,
其中,模型参数包括:输入层维数、隐藏层维数和堆叠的层数;
步骤3.3,采用训练集作为输入,训练LSTM神经网络模型;
步骤3.4,采用测试集对LSTM神经网络模型进行验证,当验证效果不满足误差指标时,需要回到步骤3.2,重新调整模型参数和LSTM层及注意力层的隐含节点个数,直到满足误差指标,停止调整,得到训练好的模型。
所述误差指标采用相对误差,对于基础负荷预测,相对误差在7%以内,对于光伏发电功率预测,相对误差在15%以内。
所述步骤4中,所述时间序列预测方法选择LSTM神经网络模型。
所述步骤5中,所述双层模型包括上层模型与下层模型;
所述上层模型,根据所述充电站现有尚未预约的充电桩个数所占比例对未来T个时段内每个时段的充电价格进行滚动优化计算,得出未来T个时段内每个时段的优化充电价格;
所述下层模型以未来T个时段内所述充电站的运行成本最低为目标进行优化,在不同约束下,采用优化算法,对所述充电站的储能充放电功率进行滚动优化计算,得到未来T个时段内每个时段的储能***的优化充放电功率,并将储能***的优化充放电功率作为储能充放电功率控制指令,通过控制未来T个时段内每个时段储能充放电功率来降低所述充电站的运行成本。
根据所述充电站现有剩余资源所占比例对未来T个时段内每个时段的充电价格进行滚动优化计算;
针对第t个时段,第t个时刻所述充电站现有剩余资源所占比例为第t个时刻所述充电站尚未预约的充电桩个数与总可用的充电桩个数之比,
所述优化充电价格:
所述下层模型以未来T个时段内所述充电站的运行成本最低为目标进行优化的目标函数为,
min CTotal=CG+CESd-REV-Cp,
其中,CTotal为所述充电站的运行成本,CG为所述充电站的购电成本,CESd为所述储能***的折旧成本,REV为所述充电站的充电收入,Cp为需求响应奖励;
所述充电站购电成本CG计算方法为:
其中,未来T个时段内,为充电站参与电力市场时,第t个时段所述充电站的购电合约中的购电电价,为第t个时段所述充电站的购电功率,为第t个时段所述充电站的预测总充电功率,为第t个时段所述充电站的基础负荷功率预测,为第t个时段所述充电站的储能充放电功率,为第t个时段所述充电站的光伏发电功率预测,
其中,储能***充放电功率的函数为
所述充电站充电收入REV的计算方法为:
所述不同约束包括储能充放电功率约束、储能容量约束、储能SOC值约束和储能放电总量约束;
所述储能充放电功率约束:
所述储能容量约束:
其中,ηES为所述充电站的储能自放电率,ηch、ηdis分别为所属充电站的储能充电效率和放电效率,Δt为时段间隔,EES,Max为所述充电站的储能总容量;
所述储能SOC值约束:
所述储能充放电总量约束
所述储能SOC值约束中,设定阈值ε=5%。
所述滚动优化计算包括粒子群优化、混合整数规划或序列二次规划算法。
一种基于预约的电动汽车光储充电站滚动优化运行方法的***,所述优化运行方法的***包括充电信息发布模块、自主选择预约模块、总充电功率预测模块、光伏发电功率预测和基础负荷功率预测模块和双层模型滚动优化模块,
所述充电信息发布模块对于电动汽车光储充电站,每间隔Δt作为一个时段,发布一次充电信息,所述充电信息包括未来连续的T个时段内每个时段的充电价格、空闲充电桩个数、平均等待时间,并在所述充电站的服务器中存储充电预约列表;
所述自主选择预约模块,电动汽车车主查看充电站所发布的充电信息,根据每个时段的充电价格及平均等待时间,自主选择特定时段进行预约并提供预约信息,预约成功后锁定所述预约时段对应的价格作为电动汽车的充电价格,所述充电站的服务器将预约信息存入所述充电预约列表,所述预约信息包括预约充电量、预约充电功率、起始充电时间、充电时长;
所述总充电功率预测模块,充电站根据未来T个时段内每个时段的预约信息,利用LSTM神经网络模型预测未来T个时段内每个时段所述充电站的预测总充电功率;
所述光伏发电功率预测和基础负荷功率预测模块,充电站根据光伏发电历史数据和基础负荷历史数据,采用时间序列预测方法,进行光伏发电预测和基础负荷预测,从而得到T个时段的光伏发电功率预测和基础负荷功率预测;
所述双层模型滚动优化模块对每个所划分的时段Δt,通过双层模型滚动优化未来T个时段内所述充电站的运行成本。
与现有技术相比,本发明的有益效果在于:
(1)提出的优化方法将预约充电机制与充电站优化运行相结合,在上层模型中考虑价格机制,并利用供求关系来引导用户充电行为,同时可以缓解充电站充电高峰期排队等待现象;
(2)提出的优化方法采用双层滚动优化模型,日内每时间段进行一次滚动优化,可以有效修正日前预测误差,同时可以根据电动汽车充电实时预约情况滚动修正,所以效果更加趋近理论最优结果;
(3)提出的优化方法在计算目标函数时,考虑了充电站与电网运营商的购电合约以及充电站参与电力市场需求响应时获得的奖励,该方法可以有效应用于电力市场环境下电动汽车充电站运营优化。
附图说明
图1为基于预约的电动汽车光储充电站运行策略流程图;
图2为电动汽车光储充电站接线框图;
图3为下层模型的优化算法在t=0时刻的输入数据曲线;其中a是电动汽车充电功率预测曲线,b是充电站与电网运营商的合约电价曲线,c是光伏功率预测曲线,d是基础负荷功率预测曲线;
图4为下层模型的滚动优化输出,即在t=0时刻优化后的储能充放电功率和储能SOC曲线;
图5为基于预约的电动汽车光储充电站滚动优化运行方法的***的示意图。
具体实施方式
为使本发明的目的、技术方案和优点更加清楚,下面将结合本发明实施例中的附图,对本发明的技术方案进行清楚、完整地描述。本申请所描述的实施例仅仅是本发明一部分的实施例,而不是全部实施例。基于本发明精神,本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其它实施例,都属于本发明的保护范围。
根据图1所示的流程,本发明提供的基于预约的电动汽车光储充电站滚动优化运行方法包括以下步骤:
步骤1:电动汽车充电站CS通过互联网,每间隔Δt作为一个时段,发布一次充电信息,包括未来T个时段内每个时段的充电价格、空闲充电桩个数、平均等待时间;优化运行的周期可以根据实际情况调整;
步骤2:电动汽车车主通过客户端查看动态分时充电电价(包括服务费),自主选择充电时间进行预约,并提供预约信息,预约成功后锁定该车主本次预约的充电价格,并将预约信息保存入充电预约列表中,所述预约信息包括充电量、充电功率、起始充电时间、充电时长;
步骤3:充电站根据预约信息,预测充电站内电动汽车实际充电功率,其方法为:未来T个时段内,第t个时段所述充电站的预测总充电功率为第t个时段的预约到站的充电功率与未预约到站的充电功率之和: 其中,为t时段的预约到站的充电功率, 其中β为预约到站率,K为充电站内的充电桩个数,为充电预约列表中第t个时段第k个在站充电的充电桩输出功率,为t时段未预约到站的充电功率;根据历史数据,采用LSTM神经网络加注意力机制进行充电负荷预测得出;预测模型包括五层结构,(1)输入层,包括历史充电数据、历史预约数据、节假日信息、天气信息;(2)LSTM层,利用LSTM模型获取历史时间序列;(3)注意力层,利用注意力机制提取不同时间特征信息,得到各特征的注意力权重,各特征包括但不限于如历史充电数据、历史预约数据、节假日信息、天气信息;(4)全连接层,进行局部特征整合;(5)输出层,
输出预测结果数据;训练方法为:(1)将已有历史数据分为训练数据和测试数据;(2)采用均方误差和平均绝对误差作为误差指标,确定LSTM层及注意力层的隐含节点个数为2n(n为输入节点个数),调整模型参数;(3)采用训练数据作为输入训练模型;(4)采用测试数据对模型进行验证,如果验证效果不理想,需要回到步骤2重新调整模型参数和LSTM层及注意力层的隐含节点个数,直到满足误差指标,停止调整,得到训练好的模型;(5)采用训练好的模型进行预测;
步骤4:充电站根据光伏发电历史数据、基础负荷历史数据,采用时间序列预测方法,进行光伏发电预测、基础负荷预测,从而得到T个时段的光伏发电预测曲线和基础负荷预测曲线,预测方法可以采用滑动平均自回归模型(ARIMA)或者步骤3中采用的LSTM神经网络加注意力机制进行预测;
步骤5:以充电站运行成本最低为目标进行双层模型滚动优化。
上层模型为,即根据所述充电站现有剩余资源稀缺程度对预约充电价格进行调整,计算剩余资源所占比例,并对未来T个时段内每个时段的充电价格进行滚动优化计算,得出未来T个时段内每个时段的优化充电价格的具体实现为:针对第t个时段,第t个时刻所述充电站现有剩余资源所占比例为第t个时刻所述充电站尚未预约的充电桩个数与总可用的充电桩个数之比,所述优化充电价格 其中,为充电站参与电力市场时,第t个时段所述充电站的购电合约中的购电电价,a与b为设定变动系数,为设定修正价格,可由运营人员根据节假日以及政策等灵活调整,即价格变动范围为上层模型通常每30分钟计算一次,用以更新充电站发布的实时电价信息。
下层模型为,以未来T个时段内所述充电站的运行成本最低为目标进行优化,计算未来时段的储能充放电功率曲线,通常每15分钟计算一次,并将储能充放电功率曲线的第一个数据作为储能的控制指令;
目标函数为:minCTotal=CG+CESd-REV-Cp
其中:CTotal为充电站运行成本,CG为充电站购电成本,CESd为储能折旧成本,REV为充电站充电收入,Cp为需求响应奖励,即当天24小时内充电站实际总用电量与充电站购电合约中的日用电量(或称为日计划用电量)偏差计算得到的需求响应奖励;
其中:未来T个时段内,为第t个时段所述充电站的购电功率,为第t个时段所述充电站的预测总充电功率,为第t个时段所述充电站的预测基础负荷功率,为第t个时段所述充电站的储能充放电功率(为储能充电,为储能放电),为第t个时段所述充电站的预测光伏发电功率;
以蓄电池作为储能为例,Δt时段储能充放电成本表示为:时,在时,蓄电池在充电过程中其损耗成本可以近似为零,其中:deff为储能单元的有效放电安培-小时数,CEs,T为储能单元的总投资成本,DR为额定放电深度,LR为额定放电深度和额定放电电流下的额定循环放电次数,EEs,R为储能单元额定容量;
约束条件为:
其中,ηES为所述充电站的储能自放电率,ηch、ηdis分别为所属充电站的储能充电效率和放电效率,Δt为时段间隔,EES,Max为所述充电站的储能总容量;
通过动态步长随机游走算法对上述模型进行求解并发布电价及控制储能***充放电,等待时间间隔Δt后进入下一个循环滚动优化。
动态步长随机游走算法的步长根据迭代次数和局部最优值的变化大小动态更新,并随机变异以便兼顾寻优的全局性。
如图2所示,为电动汽车光储充电站接线框图,充电站内设有若干充电桩,用于给电动汽车充电;同时站内设有储能***,通过对储能***的优化控制,可以削峰填谷,并保证电力市场合约的履行,提高充电站运营收益;随着新能源的发展,通常会在充电站内装设一定数量的光伏发电装置,进一步节约运营成本;充电站内日常用电损耗包括厂用电、控制用电等,可以等效视为站内基础负荷。
图3中为电动汽车充电功率预测曲线、充电站与电网运营商的合约电价曲线、光伏功率预测曲线和基础负荷功率预测曲线,将这些数据作为优化算法的输入,以每一时段储能充放电功率为优化变量,采用动态规划的方法对优化问题进行求解,即可得到以充电站运行成本最小为目标,优化后的储能充放电曲线。
如图4中的实线所示,即为t=1时段的优化结果。将所得曲线中的第一个点的数据作为当前时段的储能充放电运行指令,下一时段获取最新的预约数据和滚动预测数据重新进行优化计算。通过仿真实验可见,在合约电价相对较低且用电负荷小的时候,储能处于充电状态,电价高且用电负荷较大的时候,储能处于放电状态,且满足整个T个时间段整体最优,储能充放电及SOC均满足约束条件;通过本发明的优化模型及算法,具有储能***的充电站可以参与电力市场日前和日内市场,并通过电价差获利。
本申请还同时公开了一种基于预约的电动汽车光储充电站滚动优化运行方法的***,具体工作流程如图5所示。
优化运行方法的***包括充电信息发布模块、自主选择预约模块、总充电功率预测模块、光伏发电功率预测和基础负荷功率预测模块和双层模型滚动优化模块,
充电信息发布模块对于电动汽车光储充电站,每间隔Δt作为一个时段,发布一次充电信息,所述充电信息包括未来连续的T个时段内每个时段的充电价格、空闲充电桩个数、平均等待时间,并在所述充电站的服务器中存储充电预约列表;
自主选择预约模块,电动汽车车主查看充电站所发布的充电信息,根据每个时段的充电价格及平均等待时间,自主选择特定时段进行预约并提供预约信息,预约成功后锁定所述预约时段对应的价格作为电动汽车的充电价格,所述充电站的服务器将预约信息存入所述充电预约列表,所述预约信息包括预约充电量、预约充电功率、起始充电时间、充电时长;
总充电功率预测模块,充电站根据未来T个时段内每个时段的预约信息,利用LSTM神经网络模型预测未来T个时段内每个时段所述充电站的预测总充电功率;
光伏发电功率预测和基础负荷功率预测模块,充电站根据光伏发电历史数据和基础负荷历史数据,采用时间序列预测方法,进行光伏发电预测和基础负荷预测,从而得到T个时段的光伏发电功率预测和基础负荷功率预测;
双层模型滚动优化模块对每个所划分的时段Δt,通过双层模型滚动优化未来T个时段内所述充电站的运行成本。
最后应当说明的是:以上实施例仅用以说明本发明的技术方案而非对其限制,尽管参照上述实施例对本发明进行了详细的说明,所属领域的普通技术人员应当理解:依然可以对本发明的具体实施方式进行修改或者等同替换,而未脱离本发明精神和范围的任何修改或者等同替换,其均应涵盖在本发明的权利要求保护范围之内。
Claims (10)
1.基于预约的电动汽车光储充电站滚动优化运行方法,其特征在于,所述优化运行方法包括以下步骤:
步骤1:电动汽车光储充电站每间隔Δt作为一个时段,发布一次充电信息,所述充电信息包括未来连续的T个时段内每个时段的充电价格、空闲充电桩个数、平均等待时间,并在所述充电站的服务器中存储充电预约列表;
步骤2:查看步骤1中充电站所发布的充电信息,根据每个时段的充电价格及平均等待时间,选择特定时段进行预约并提供预约信息,预约成功后锁定所述预约时段对应的价格作为电动汽车的充电价格,所述充电站的服务器将预约信息存入所述充电预约列表,所述预约信息包括预约充电量、预约充电功率、起始充电时间、充电时长;
步骤3:所述充电站根据步骤2中未来T个时段内每个时段的预约信息,利用LSTM神经网络模型预测未来T个时段内每个时段所述充电站的预测总充电功率;
步骤4:充电站根据光伏发电历史数据和基础负荷历史数据,采用时间序列预测方法,进行光伏发电预测和基础负荷预测,从而得到T个时段的光伏发电功率预测和基础负荷功率预测;
步骤5:对每个所划分的时段Δt,通过双层模型滚动优化未来T个时段内所述充电站的运行成本;
所述双层模型包括上层模型与下层模型;
所述上层模型,根据所述充电站现有尚未预约的充电桩个数所占比例对未来T个时段内每个时段的充电价格进行滚动优化计算,得出未来T个时段内每个时段的优化充电价格,具体的:
根据所述充电站现有剩余资源所占比例对未来T个时段内每个时段的充电价格进行滚动优化计算;
针对第t个时段,第t个时刻所述充电站现有剩余资源所占比例为第t个时刻所述充电站尚未预约的充电桩个数与总可用的充电桩个数之比,
所述优化充电价格:
所述下层模型以未来T个时段内所述充电站的运行成本最低为目标进行优化,在不同约束下,采用优化算法,对所述充电站的储能充放电功率进行滚动优化计算,得到未来T个时段内每个时段的储能***的优化充放电功率,并将储能***的优化充放电功率作为储能充放电功率控制指令,通过控制未来T个时段内每个时段储能充放电功率来降低所述充电站的运行成本;
所述下层模型以未来T个时段内所述充电站的运行成本最低为目标进行优化的目标函数为,
min CTotal=CG+CESd-REV-Cp,
其中,CTotal为所述充电站的运行成本,CG为所述充电站的购电成本,CESd为所述储能***的折旧成本,REV为所述充电站的充电收入,Cp为需求响应奖励;
所述充电站购电成本CG计算方法为:
其中,未来T个时段内,为充电站参与电力市场时,第t个时段所述充电站的购电合约中的购电电价,为第t个时段所述充电站的购电功率,为第t个时段所述充电站的预测总充电功率,为第t个时段所述充电站的基础负荷功率预测,为第t个时段所述充电站的储能充放电功率,为第t个时段所述充电站的光伏发电功率预测,
其中,储能***充放电功率的函数为
所述充电站充电收入REV的计算方法为:
4.根据权利要求3所述的基于预约的电动汽车光储充电站滚动优化运行方法,其特征在于:
所述LSTM神经网络模型训练方法为:
步骤3.1,将历史充电数据分为训练集和测试集;
步骤3.2,采用均方误差和平均绝对误差作为误差指标,确定LSTM层及注意力层的隐含节点个数为2n,n为输入节点个数,调整模型参数,
其中,模型参数包括:输入层维数、隐藏层维数和堆叠的层数;
步骤3.3,采用训练集作为输入,训练LSTM神经网络模型;
步骤3.4,采用测试集对LSTM神经网络模型进行验证,当验证效果不满足误差指标时,需要回到步骤3.2,重新调整模型参数和LSTM层及注意力层的隐含节点个数,直到满足误差指标,停止调整,得到训练好的模型。
5.根据权利要求4所述的基于预约的电动汽车光储充电站滚动优化运行方法,其特征在于:
所述误差指标采用相对误差,对于基础负荷预测,相对误差在7%以内,对于光伏发电功率预测,相对误差在15%以内。
6.根据权利要求1所述的基于预约的电动汽车光储充电站滚动优化运行方法,其特征在于:
所述步骤4中,所述时间序列预测方法选择LSTM神经网络模型。
7.根据权利要求1所述的基于预约的电动汽车光储充电站滚动优化运行方法,其特征在于:
所述不同约束包括储能充放电功率约束、储能容量约束、储能SOC值约束和储能放电总量约束;
所述储能充放电功率约束:
所述储能容量约束:
其中,ηES为所述充电站的储能自放电率,ηch、ηdis分别为所属充电站的储能充电效率和放电效率,Δt为时段间隔,EES,Max为所述充电站的储能总容量;
所述储能SOC值约束:
所述储能充放电总量约束
8.根据权利要求7所述的基于预约的电动汽车光储充电站滚动优化运行方法,其特征在于:
所述储能SOC值约束中,设定阈值ε=5%。
9.根据权利要求1所述的基于预约的电动汽车光储充电站滚动优化运行方法,其特征在于:
所述滚动优化计算包括粒子群优化、混合整数规划或序列二次规划算法。
10.一种利用权利要求1-9中任一权利要求所述基于预约的电动汽车光储充电站滚动优化运行方法的***,所述优化运行方法的***包括充电信息发布模块、自主选择预约模块、总充电功率预测模块、光伏发电功率预测和基础负荷功率预测模块和双层模型滚动优化模块,其特征在于:
所述充电信息发布模块对于电动汽车光储充电站,每间隔Δt作为一个时段,发布一次充电信息,所述充电信息包括未来连续的T个时段内每个时段的充电价格、空闲充电桩个数、平均等待时间,并在所述充电站的服务器中存储充电预约列表;
所述自主选择预约模块,电动汽车车主查看充电站所发布的充电信息,根据每个时段的充电价格及平均等待时间,自主选择特定时段进行预约并提供预约信息,预约成功后锁定所述预约时段对应的价格作为电动汽车的充电价格,所述充电站的服务器将预约信息存入所述充电预约列表,所述预约信息包括预约充电量、预约充电功率、起始充电时间、充电时长;
所述总充电功率预测模块,充电站根据未来T个时段内每个时段的预约信息,利用LSTM神经网络模型预测未来T个时段内每个时段所述充电站的预测总充电功率;
所述光伏发电功率预测和基础负荷功率预测模块,充电站根据光伏发电历史数据和基础负荷历史数据,采用时间序列预测方法,进行光伏发电预测和基础负荷预测,从而得到T个时段的光伏发电功率预测和基础负荷功率预测;
所述双层模型滚动优化模块对每个所划分的时段Δt,通过双层模型滚动优化未来T个时段内所述充电站的运行成本。
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202011071370.6A CN112134300B (zh) | 2020-10-09 | 2020-10-09 | 基于预约的电动汽车光储充电站滚动优化运行方法及*** |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202011071370.6A CN112134300B (zh) | 2020-10-09 | 2020-10-09 | 基于预约的电动汽车光储充电站滚动优化运行方法及*** |
Publications (2)
Publication Number | Publication Date |
---|---|
CN112134300A CN112134300A (zh) | 2020-12-25 |
CN112134300B true CN112134300B (zh) | 2022-03-08 |
Family
ID=73843906
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202011071370.6A Active CN112134300B (zh) | 2020-10-09 | 2020-10-09 | 基于预约的电动汽车光储充电站滚动优化运行方法及*** |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN112134300B (zh) |
Families Citing this family (24)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112757954B (zh) * | 2020-12-30 | 2022-07-26 | 华南理工大学 | 联合专变共享模式下的电动汽车有序充电联合调节方法 |
CN112700069B (zh) * | 2021-01-15 | 2024-04-19 | 国网湖北省电力有限公司经济技术研究院 | 一种含储能的区域配电网短期负荷预测方法 |
CN112874380B (zh) * | 2021-01-18 | 2022-09-16 | 浙江零跑科技股份有限公司 | 一种新能源汽车有序充电方法及计算机可读存储介质 |
CN112784491B (zh) * | 2021-01-26 | 2024-04-16 | 浙江中新电力工程建设有限公司 | 一种基于lstm与iqpso面向高弹性电网的城市充电网点规划方法 |
CN112990719B (zh) * | 2021-03-23 | 2023-02-24 | 广东电网有限责任公司电力调度控制中心 | 一种智能供电管理方法及***、计算机可读存储介质 |
CN113285464B (zh) * | 2021-05-12 | 2022-03-22 | 国网江苏省电力有限公司南通供电分公司 | 一种电动公交车与分布式光伏协调控制*** |
CN113128790B (zh) * | 2021-05-18 | 2022-09-13 | 国网河北省电力有限公司电力科学研究院 | 一种分布式光伏***的消纳优化方法、装置及终端设备 |
CN113224758A (zh) * | 2021-05-25 | 2021-08-06 | 上海玫克生储能科技有限公司 | 光储充电站的储能充放电的控制方法、***、设备及介质 |
CN113283807B (zh) * | 2021-06-23 | 2024-04-05 | 阳光慧碳科技有限公司 | 光储充电站的运营调度方法和装置 |
CN113592553B (zh) * | 2021-08-02 | 2023-10-17 | 广西大学 | 一种竞争条件生成式对抗网络的云储能双层优化控制方法 |
CN113627791A (zh) * | 2021-08-11 | 2021-11-09 | 上海交通大学 | 一种一体化电动汽车管理方法 |
CN113799640B (zh) * | 2021-08-17 | 2023-10-13 | 浙江大学 | 适用于含电动汽车充电桩微电网的能量管理方法 |
CN113610429B (zh) * | 2021-08-20 | 2023-06-30 | 青岛鼎信通讯股份有限公司 | 一种应用于光-储-充一体化电站的能量管理算法 |
CN113746122B (zh) * | 2021-08-24 | 2024-03-01 | 国网江苏省电力有限公司苏州供电分公司 | 考虑用户选择的电动汽车优化调度方法及优化调度*** |
CN113807589B (zh) * | 2021-09-18 | 2024-01-26 | 广东电网有限责任公司 | 基于模型预测的运营商储能的滚动优化方法及装置 |
CN113902183B (zh) * | 2021-09-28 | 2022-11-08 | 浙江大学 | 基于bert的非侵入式台区充电桩状态监测与电价调整方法 |
CN114039348B (zh) * | 2021-11-16 | 2023-07-14 | 国网湖南省电力有限公司 | 考虑光储充多主体互动的微电网能量调度方法 |
CN114118787A (zh) * | 2021-11-19 | 2022-03-01 | 国网安徽省电力有限公司合肥供电公司 | 一种基于lstm算法的城市分布式源网荷储的调度优化方法 |
CN114498634B (zh) * | 2022-02-17 | 2023-08-29 | 四川大学 | 一种基于电表数据的电动汽车充电负荷预测方法 |
CN114612160B (zh) * | 2022-03-23 | 2024-05-28 | 广东电网有限责任公司 | 电动车充电定价方法及装置 |
CN115001054B (zh) * | 2022-07-29 | 2022-11-01 | 东南大学溧阳研究院 | 一种基于模型预测含电动汽车的电力***频率控制策略 |
CN115395619B (zh) * | 2022-09-23 | 2024-04-05 | 青岛鼎信通讯股份有限公司 | 一种基于能量路由器的电动汽车有序充电方法 |
CN116353402B (zh) * | 2023-05-19 | 2023-10-10 | 贵州天任电力科技自动化有限公司 | 一种电动汽车充电桩时段控制***及充电桩 |
CN116757877B (zh) * | 2023-08-22 | 2023-11-28 | 国网山西省电力公司运城供电公司 | 新能源接入配电网的电网线损降损优化方法及*** |
Family Cites Families (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104077635B (zh) * | 2014-07-09 | 2017-12-26 | 北京交通大学 | 一种基于光伏发电***的电动汽车充电站充电优化方法 |
CN109409939B (zh) * | 2018-10-08 | 2022-02-22 | 国网天津市电力公司电力科学研究院 | 用于配网电压控制的双层电动汽车快速充电电价修正方法 |
CN110288271B (zh) * | 2019-07-11 | 2022-10-11 | 北京全来电科技有限公司 | 一种基于模型预测控制的台区级充电负荷调控策略及方法 |
-
2020
- 2020-10-09 CN CN202011071370.6A patent/CN112134300B/zh active Active
Also Published As
Publication number | Publication date |
---|---|
CN112134300A (zh) | 2020-12-25 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN112134300B (zh) | 基于预约的电动汽车光储充电站滚动优化运行方法及*** | |
CN110119886B (zh) | 一种主动配网动态规划方法 | |
Raoofat et al. | Wind power smoothing using demand response of electric vehicles | |
Erol-Kantarci et al. | Prediction-based charging of PHEVs from the smart grid with dynamic pricing | |
CN109599856B (zh) | 一种微网多楼宇中电动汽车充放电管理优化方法及装置 | |
CN107565607B (zh) | 一种基于实时电价机制的微电网多时间尺度能量调度方法 | |
El-Zonkoly | Intelligent energy management of optimally located renewable energy systems incorporating PHEV | |
CN111934360B (zh) | 基于模型预测控制的虚拟电厂-储能***能量协同优化调控方法 | |
Wang et al. | A hierarchical control algorithm for managing electrical energy storage systems in homes equipped with PV power generation | |
CN112277711A (zh) | 一种考虑匹配电动汽车的多充电模式充电桩控制方法 | |
Zheng et al. | Optimal dispatch for reversible solid oxide cell-based hydrogen/electric vehicle aggregator via stimuli-responsive charging decision estimation | |
CN109950900B (zh) | 基于电动汽车负荷最小峰值模型的微网负荷削减控制方法 | |
Feng et al. | Flexible optimal scheduling of power system based on renewable energy and electric vehicles | |
Hajidavalloo et al. | Energy cost minimization in an electric vehicle solar charging station via dynamic programming | |
CN110826801A (zh) | 电动车充电站分布式电能管理方法 | |
CN117543581A (zh) | 考虑电动汽车需求响应的虚拟电厂优化调度方法及其应用 | |
TWI725606B (zh) | 電動車充電站分散式電能管理方法 | |
CN116090675B (zh) | 基于区块链与神经网络联合的短时充电调度方法 | |
CN114491997B (zh) | 考虑需求响应及电动汽车的虚拟电厂运行优化方法及*** | |
CN110826210A (zh) | 基于功率互联的多区域楼宇虚拟电厂建模及优化协调方法 | |
CN113705914B (zh) | 利用区块链的电动车充电站管理方法 | |
CN115626072A (zh) | 基于用户间博弈的网联电动汽车协同充放电调控方法 | |
CN116404629A (zh) | 一种高比例分布式光伏配电网中的电动汽车协调充电方法 | |
CN114362169A (zh) | 一种计及光储型虚拟电厂边际成本的分层协调调控方法 | |
Zheng et al. | A Cooperative Game-based Negotiation Strategy of Electric Vehicle Discharging Price |
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 | ||
GR01 | Patent grant | ||
GR01 | Patent grant |