CN112803493A - Marginal cost-based distributed energy storage and distributed photovoltaic cooperation method and system - Google Patents
Marginal cost-based distributed energy storage and distributed photovoltaic cooperation method and system Download PDFInfo
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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
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
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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
- 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
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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
- 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
- H02J2300/24—The renewable source being solar energy of photovoltaic origin
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
- Y02E10/56—Power conversion systems, e.g. maximum power point trackers
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E70/00—Other energy conversion or management systems reducing GHG emissions
- Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin
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- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The disclosure provides a marginal cost-based distributed energy storage and distributed photovoltaic cooperation method and system, which includes: obtaining centralized photovoltaic and distributed photovoltaic output; combining the load forecasting situation before the day to obtain the new energy consumption capacity of the next day, the new energy consumption difficulty time period and the total power of electricity abandonment; obtaining the distributed photovoltaic electricity abandoning power in the time period according to the distributed predicted output; calculating the electric quantity balance constraint of the electric automobile according to the trip plan of the next day in the past day, and obtaining the charging requirement and the charging window period of the next day by combining the electric quantity condition of the energy storage battery of the electric automobile in the past day; the charging arrangement of the electric automobile is determined according to the real-time new energy consumption condition of the power grid in the day, and in order to guarantee the requirement of the electric automobile on the capacity of the energy storage battery, even if electricity abandoning does not occur in the day, the charging is started for the electric automobile according to a plan.
Description
Technical Field
The disclosure belongs to the technical field of distributed power generation and energy storage, and particularly relates to a distributed energy storage and distributed photovoltaic cooperation method and system based on marginal cost.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
In recent years, distributed photovoltaic is rapidly developed, and a part of provincial distributed photovoltaic installation machines exceed a centralized photovoltaic installation machine, and the number of the provincial distributed photovoltaic installation machines is more than 1200 ten thousand kilowatts. Distributed photovoltaic often adopts spontaneous self-service, and surplus electricity internet mode has caused the noon load to become the load valley of whole day, causes new forms of energy to consume the difficulty in the period of sending out greatly simultaneously of scene, takes place to abandon the electricity even, has influenced distributed photovoltaic's economic benefits, needs to consider flexible load measure to promote distributed photovoltaic utilization efficiency and economic benefits urgently.
With the progress of energy storage technology and the obvious environmental problems, electric vehicles develop faster and faster. However, the current electric automobile has the problems of high energy storage cost and the like, and the development of the electric automobile is restricted. If the charging and discharging behaviors (traveling behaviors) of the electric automobile and the electricity abandoning of the distributed photovoltaic can be combined, the electric power with the marginal cost close to zero can be provided for the energy storage of the electric automobile, the power generation benefit of the distributed photovoltaic can be improved, and the maximum comprehensive benefit is achieved.
Currently, an electric vehicle charging and discharging behavior optimization research is available, but the research is limited to optimally guiding the charging and discharging behavior of the electric vehicle according to peak-valley time-of-use electricity price and trip behavior, but the possibility that electric energy with zero marginal cost brought by distributed photovoltaic electricity abandonment is used for charging the electric vehicle is not considered in the existing strategy. Actually, with the increase of the distributed installation machine, in a period of time when wind power and photovoltaic are simultaneously generated, the electricity abandoning behavior becomes a normal state, and the analysis of the marginal cost-based electric vehicle distributed energy storage and distributed photovoltaic cooperation strategy has an important meaning for providing comprehensive economic benefits.
Disclosure of Invention
In order to overcome the defects of the prior art, the distributed energy storage and distributed photovoltaic cooperation method based on marginal cost is provided, and the charging and discharging behaviors of distributed photovoltaic and electric vehicles are optimized from the overall perspective.
In order to achieve the above object, one or more embodiments of the present disclosure provide the following technical solutions:
in a first aspect, a marginal cost-based distributed energy storage and distributed photovoltaic cooperation method is disclosed, which includes:
obtaining centralized photovoltaic and distributed photovoltaic output, and predicting the day-ahead load based on the photovoltaic output;
combining the load forecasting situation before the day to obtain the new energy consumption capacity of the next day, the new energy consumption difficulty time period and the total power of electricity abandonment;
obtaining the distributed photovoltaic abandoned electricity power in the consumption difficulty time period according to the distributed predicted output;
calculating the electric quantity balance constraint of the electric automobile according to the trip plan of the next day in the past day, and obtaining the charging requirement and the charging window period of the next day by combining the electric quantity condition of the energy storage battery of the electric automobile in the past day;
the charging arrangement of the electric automobile is determined according to the real-time new energy consumption condition of the power grid in the day, electricity abandonment is determined based on the power grid in the day and the balance of supply and demand of users, and even if the electricity abandonment does not occur in the day, the charging is started for the electric automobile according to a plan in order to ensure the requirement of the electric automobile on the capacity of the energy storage battery.
According to a further technical scheme, a numerical weather forecast form is adopted to predict the day-ahead load.
According to the further technical scheme, the whole network electricity abandonment is totally decomposed into each distributed electricity abandonment.
According to the further technical scheme, the electric quantity balance constraint of the electric automobile is used as a boundary condition, so that the requirement of the electric automobile of a user on the residual electric energy of the electric automobile can be met.
According to the further technical scheme, the next day charging requirement and the charging window period are boundary conditions, the requirement for the residual electric quantity of the electric automobile is met, and meanwhile zero-cost electricity abandonment is utilized as far as possible.
According to the further technical scheme, based on the next day irradiance condition forecasted in the day ahead, the distributed photovoltaic and concentrated photovoltaic output is calculated according to the earth rotation and revolution rules.
According to the further technical scheme, electric vehicle electric quantity balance constraints are calculated according to a trip plan of the next day in the day, and the next day charging requirement and the charging window period are obtained by combining the electric quantity condition of the electric vehicle energy storage battery in the day ahead; if the chargeable amount in the window period meets the charging requirement of the electric automobile, the electric automobile is charged with zero cost, otherwise, the electricity charge expense corresponding to the charging electric amount is calculated according to the charging electricity price.
The above one or more technical solutions have the following beneficial effects:
based on a day-ahead distributed photovoltaic digestion situation prediction method, a day-ahead and day-interior electric vehicle charging and discharging behavior optimization method and a day-interior distributed photovoltaic and electric vehicle cooperation strategy, the distributed photovoltaic and electric vehicle charging and discharging behaviors are optimized from the overall perspective. And predicting to provide a boundary for follow-up, and optimizing and cooperating charge and discharge to be a specific electric vehicle charging strategy.
By combining the current distributed power supply digestion situation and the distributed energy storage development situation of the electric automobile and the like, a distributed energy storage and distributed photovoltaic cooperation strategy considering the electricity abandonment cost of the distributed power supply is provided. According to the distributed energy storage photovoltaic power generation system, the charging and discharging strategies of the distributed energy storage and the distributed photovoltaic absorption situation are coordinated, the situations that the charging and discharging cost of the distributed energy storage represented by electric vehicle energy storage is high, and the absorption of the distributed photovoltaic power in a low-load period is difficult at present are solved, and an economic basis is provided for the development of the distributed energy storage represented by the electric vehicle.
The distributed electricity abandonment for charging the electric automobile is a new scene brought along with the great development of distributed photovoltaics and electric automobiles, and technically, the distributed electricity abandonment is used for charging the electric automobile, so that the uncertainty of the distributed photovoltaic electricity abandonment is solved, and the charging requirement of the electric automobile is met.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
Fig. 1 is a flow chart of a method of an embodiment of the disclosure.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
Example one
The embodiment discloses a marginal cost-based distributed energy storage and distributed photovoltaic cooperation method, which comprises the following steps:
the first stage, evaluating the distributed photovoltaic consumption form from the day ahead, and evaluating the distributed photovoltaic power abandon amount and time of the next day;
in the second stage, the next day charging and discharging behaviors of the electric automobile are evaluated from the day ahead, and an available charging time window is determined;
determining a distributed photovoltaic and electric vehicle cooperation strategy from the day; the day before belongs to the plan, and the day within belongs to the specific execution.
Referring to fig. 1, the first stage comprises the following specific steps:
s1: according to the weather forecast next day irradiance condition, obtaining centralized photovoltaic output and distributed photovoltaic output by combining a centralized installation machine and a distributed photovoltaic installation machine, wherein the centralized photovoltaic output and the distributed photovoltaic output are used for determining the operation arrangement and electricity abandonment condition of the power grid in the day;
s2: combining the load forecasting situation before the day to obtain the new energy consumption capacity, the new energy consumption difficulty time period and the total power of electricity abandoning on the next day, so as to meet the requirement of the user on the electric quantity of the electric automobile when going out; obtaining the distributed photovoltaic electricity abandoning power in the time period according to the distributed predicted output; predicting the day-ahead load in a numerical weather forecast mode; the total sum of the power abandonment of the whole network is decomposed into each distributed power abandonment;
s3: calculating the electric quantity balance constraint of the electric automobile according to the trip plan of the next day in the past day, and obtaining the charging requirement and the charging window period of the next day by combining the electric quantity condition of the energy storage battery of the electric automobile in the past day;
s4: the charging arrangement of the electric automobile is determined according to the real-time new energy consumption condition of the power grid in the day, and in order to guarantee the requirement of the electric automobile on the capacity of the energy storage battery, even if electricity abandoning does not occur in the day, the charging is started for the electric automobile according to a plan.
The next day charging requirement and the charging window period are boundary conditions, and in order to meet the requirement of the residual electric quantity of the electric automobile and to use zero-cost power abandonment as far as possible, the power abandonment is determined based on the daily power grid and the balance of supply and demand of users.
The specific implementation steps are as follows:
based on the next day irradiance condition forecasted in the day, the distributed photovoltaic and centralized photovoltaic output and the irradiance variable I are calculated according to the earth rotation and revolution ruletAnd photovoltaic output ppv(I) The sampling result of (2):
I＝It·[Isc+Ki(Tc-25)] (4)
V＝Voc-Kv·Tc (5)
ppv(I)＝N·FF·V·I (7)
Tais ambient temperature, NOTIs the nominal operating temperature of the photovoltaic panel; t iscIs the battery temperature; i istSampling value of illumination intensity at t moment; i isscIs the short circuit current of the photovoltaic cell panel; kiIs the temperature coefficient of the photovoltaic panel; vocIs the open circuit voltage of the photovoltaic panel. KvIs the voltage temperature coefficient of the battery; vmpIs the maximum power operating point voltage of the photovoltaic panel; i ismpIs the maximum power operating point current of the photovoltaic panel.
Combining the load forecasting situation before the day to obtain the new energy consumption capacity of the next day, the new energy consumption difficulty time period and the total power of electricity abandonment; obtaining the distributed photovoltaic electricity abandoning power in the time period according to the distributed predicted output;
calculating the electric quantity balance constraint of the electric automobile according to the trip plan of the next day in the past day, and obtaining the charging requirement and the charging window period of the next day by combining the electric quantity condition of the energy storage battery of the electric automobile in the past day; if the chargeable amount in the window period meets the charging requirement of the electric automobile, namely as shown in (8), the electric automobile can be charged at zero cost, otherwise, the electricity fee expenditure of the corresponding charging electric amount is calculated according to the charging electricity price.
The charging arrangement of the electric automobile is determined according to the real-time new energy consumption condition of the power grid in the day, and in order to guarantee the requirement of the electric automobile on the capacity of the energy storage battery, even if electricity abandoning does not occur in the day, the charging is started for the electric automobile according to a plan.
Example two
It is an object of this embodiment to provide a computing device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the above method when executing the program.
EXAMPLE III
An object of the present embodiment is to provide a computer-readable storage medium.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method.
Example four
The embodiment aims at providing a marginal cost-based distributed energy storage and distributed photovoltaic cooperation system, which comprises:
the day-ahead load prediction module is used for obtaining centralized photovoltaic and distributed photovoltaic output and predicting the day-ahead load based on the photovoltaic output;
the distributed photovoltaic electricity abandoning power module is combined with the load prediction situation before the day to obtain the new energy consumption capacity of the next day, the new energy consumption difficulty time period and the total electricity abandoning power;
obtaining the distributed photovoltaic abandoned electricity power in the consumption difficulty time period according to the distributed predicted output;
the electric vehicle charging module is used for calculating electric vehicle electric quantity balance constraint according to a trip plan of the next day in the past day and obtaining the next day charging demand and a charging window period by combining the electric quantity condition of an electric vehicle energy storage battery in the past day;
the charging arrangement of the electric automobile is determined according to the real-time new energy consumption condition of the power grid in the day, electricity abandonment is determined based on the power grid in the day and the balance of supply and demand of users, and even if the electricity abandonment does not occur in the day, the charging is started for the electric automobile according to a plan in order to ensure the requirement of the electric automobile on the capacity of the energy storage battery.
The steps involved in the apparatuses of the above second, third and fourth embodiments correspond to the first embodiment of the method, and the detailed description thereof can be found in the relevant description of the first embodiment. The term "computer-readable storage medium" should be taken to include a single medium or multiple media containing one or more sets of instructions; it should also be understood to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by a processor and that cause the processor to perform any of the methods of the present disclosure.
Those skilled in the art will appreciate that the modules or steps of the present disclosure described above can be implemented using general purpose computer means, or alternatively, they can be implemented using program code executable by computing means, whereby the modules or steps may be stored in memory means for execution by the computing means, or separately fabricated into individual integrated circuit modules, or multiple modules or steps thereof may be fabricated into a single integrated circuit module. The present disclosure is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Although the present disclosure has been described with reference to specific embodiments, it should be understood that the scope of the present disclosure is not limited thereto, and those skilled in the art will appreciate that various modifications and changes can be made without departing from the spirit and scope of the present disclosure.
Claims (10)
1. Distributed energy storage and distributed photovoltaic cooperation method based on marginal cost is characterized by comprising the following steps:
obtaining centralized photovoltaic and distributed photovoltaic output, and predicting the day-ahead load based on the photovoltaic output;
combining the load forecasting situation before the day to obtain the new energy consumption capacity of the next day, the new energy consumption difficulty time period and the total power of electricity abandonment;
obtaining the distributed photovoltaic abandoned electricity power in the consumption difficulty time period according to the distributed predicted output;
calculating the electric quantity balance constraint of the electric automobile according to the trip plan of the next day in the past day, and obtaining the charging requirement and the charging window period of the next day by combining the electric quantity condition of the energy storage battery of the electric automobile in the past day;
the charging arrangement of the electric automobile is determined according to the real-time new energy consumption condition of the power grid in the day, electricity abandonment is determined based on the power grid in the day and the balance of supply and demand of users, and even if the electricity abandonment does not occur in the day, the charging is started for the electric automobile according to a plan in order to ensure the requirement of the electric automobile on the capacity of the energy storage battery.
2. The marginal cost-based distributed energy storage and distributed photovoltaic cooperative method as claimed in claim 1, wherein a numerical weather forecast form is adopted to predict the day-ahead load.
3. The marginal cost-based distributed energy storage and distributed photovoltaic cooperative method as claimed in claim 1, wherein the total electric power abandonment from the whole network is decomposed into each distributed electric power abandonment.
4. The marginal cost-based distributed energy storage and distributed photovoltaic cooperation method as claimed in claim 1, wherein electric vehicle electric quantity balance constraint is used as a boundary condition to ensure that the requirement of the user electric vehicle on the residual electric energy of the electric vehicle can be met.
5. The marginal cost-based distributed energy storage and distributed photovoltaic cooperation method as claimed in claim 1, wherein the next day charging requirement and the charging window period are boundary conditions for satisfying the requirement of the electric vehicle on the residual electric quantity and simultaneously utilizing the zero-cost electricity abandonment as far as possible.
6. The marginal cost-based distributed energy storage and distributed photovoltaic cooperation method according to claim 1, wherein distributed photovoltaic and concentrated photovoltaic output is calculated according to earth rotation and revolution rules based on the next-day irradiance condition forecasted in the day-ahead.
7. The distributed energy storage and distributed photovoltaic cooperation method based on marginal cost as claimed in claim 1, wherein the electric vehicle electric quantity balance constraint is calculated according to a trip plan of the next day in the past day, and the next day charging demand and the charging window period are obtained by combining the electric quantity condition of the electric vehicle energy storage battery in the past day; if the chargeable amount in the window period meets the charging requirement of the electric automobile, the electric automobile is charged with zero cost, otherwise, the electricity charge expense corresponding to the charging electric amount is calculated according to the charging electricity price.
8. A computing device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps of the method of any one of claims 1 to 7.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, is adapted to carry out the steps of the method according to any one of the preceding claims 1 to 7.
10. Distributed energy storage and distributed photovoltaic cooperative system based on marginal cost is characterized by comprising:
the day-ahead load prediction module is used for obtaining centralized photovoltaic and distributed photovoltaic output and predicting the day-ahead load based on the photovoltaic output;
the distributed photovoltaic electricity abandoning power module is combined with the load prediction situation before the day to obtain the new energy consumption capacity of the next day, the new energy consumption difficulty time period and the total electricity abandoning power;
obtaining the distributed photovoltaic abandoned electricity power in the consumption difficulty time period according to the distributed predicted output;
the electric vehicle charging module is used for calculating electric vehicle electric quantity balance constraint according to a trip plan of the next day in the past day and obtaining the next day charging demand and a charging window period by combining the electric quantity condition of an electric vehicle energy storage battery in the past day;
the charging arrangement of the electric automobile is determined according to the real-time new energy consumption condition of the power grid in the day, electricity abandonment is determined based on the power grid in the day and the balance of supply and demand of users, and even if the electricity abandonment does not occur in the day, the charging is started for the electric automobile according to a plan in order to ensure the requirement of the electric automobile on the capacity of the energy storage battery.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113128790A (en) * | 2021-05-18 | 2021-07-16 | 国网河北省电力有限公司电力科学研究院 | Absorption optimization method and device of distributed photovoltaic system and terminal equipment |
| CN113468722A (en) * | 2021-06-08 | 2021-10-01 | 国网浙江省电力有限公司经济技术研究院 | Distributed photovoltaic planning method for thermoelectric integrated energy system |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102509176A (en) * | 2011-11-10 | 2012-06-20 | 山东电力集团公司德州供电公司 | Decision method for rolling optimization of wind and light storage cooperative scheduling |
| JP2015095983A (en) * | 2013-11-13 | 2015-05-18 | パナソニックＩｐマネジメント株式会社 | Charge/discharge management system |
| CN106160091A (en) * | 2016-07-25 | 2016-11-23 | 东南大学 | Promote the electric automobile charging station discharge and recharge dispatching method that regenerative resource is dissolved |
| CN107054145A (en) * | 2017-04-28 | 2017-08-18 | 北京新能源汽车股份有限公司 | Charging control method and device, vehicle control unit and electric vehicle |
| CN110288271A (en) * | 2019-07-11 | 2019-09-27 | 北京全来电科技有限公司 | A kind of platform area grade charging load control strategy and method based on Model Predictive Control |
| CN111489009A (en) * | 2019-06-06 | 2020-08-04 | 国网辽宁省电力有限公司 | Optimal calculation method and device for operation mode of electric vehicle charging station |
| CN111626527A (en) * | 2020-06-10 | 2020-09-04 | 太原理工大学 | Intelligent power grid deep learning scheduling method considering fast/slow charging/discharging form of schedulable electric vehicle |
-
2021
- 2021-02-09 CN CN202110173322.6A patent/CN112803493B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102509176A (en) * | 2011-11-10 | 2012-06-20 | 山东电力集团公司德州供电公司 | Decision method for rolling optimization of wind and light storage cooperative scheduling |
| JP2015095983A (en) * | 2013-11-13 | 2015-05-18 | パナソニックＩｐマネジメント株式会社 | Charge/discharge management system |
| CN106160091A (en) * | 2016-07-25 | 2016-11-23 | 东南大学 | Promote the electric automobile charging station discharge and recharge dispatching method that regenerative resource is dissolved |
| CN107054145A (en) * | 2017-04-28 | 2017-08-18 | 北京新能源汽车股份有限公司 | Charging control method and device, vehicle control unit and electric vehicle |
| CN111489009A (en) * | 2019-06-06 | 2020-08-04 | 国网辽宁省电力有限公司 | Optimal calculation method and device for operation mode of electric vehicle charging station |
| CN110288271A (en) * | 2019-07-11 | 2019-09-27 | 北京全来电科技有限公司 | A kind of platform area grade charging load control strategy and method based on Model Predictive Control |
| CN111626527A (en) * | 2020-06-10 | 2020-09-04 | 太原理工大学 | Intelligent power grid deep learning scheduling method considering fast/slow charging/discharging form of schedulable electric vehicle |
Non-Patent Citations (1)
| Title |
|---|
| 朱海鹏等: "风光储协同调度的多时间尺度决策方法", 《山东电力技术》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113128790A (en) * | 2021-05-18 | 2021-07-16 | 国网河北省电力有限公司电力科学研究院 | Absorption optimization method and device of distributed photovoltaic system and terminal equipment |
| CN113468722A (en) * | 2021-06-08 | 2021-10-01 | 国网浙江省电力有限公司经济技术研究院 | Distributed photovoltaic planning method for thermoelectric integrated energy system |
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