CN110415016A - A kind of charging pricing practice strategy based on optimization charge and discharge strategy - Google Patents
A kind of charging pricing practice strategy based on optimization charge and discharge strategy Download PDFInfo
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Abstract
The present invention relates to a kind of based on the charging pricing practice strategy for optimizing charge and discharge strategy, the defect of high randomness and charging station power load fluctuation mainly for charging behavior, based on by electric car history stop data and optimizing charge and discharge peak load shifting strategy, according to time-of-use tariffs and local power supply structure, charging price is formulated, reduces and optimizes power grid peak load and user's charging cost.Since the demand response of charging price is difficult to seek, electric car charging moment it is difficult to predict, the present invention will not calculate the demand response of user, data are stopped using history, formulate the optimization charge and discharge strategy under electric car charging constraint condition, local power grid peak load and user cost are reduced, attracts user's charging by formulating more preferably charge and discharge price to reach the benign cycle of user and operation of power networks.
Description
Technical field
The present invention relates to a kind of based on the charging pricing practice strategy for optimizing charge and discharge strategy, fills for electric car
The charging price in power station designs, and with equalization region load and reduces user's charging cost.
Background technique
Electric car quantity rapid development, charge requirement increase therewith.Since the randomness of electric car is strong, scale
Afterload fluctuation is larger, brings huge challenge to traditional power grid.
Scale electric car is that traditional power grid brings load fluctuation simultaneously, and the battery of electric car can also be used as shifting
A part of dynamic energy storage participates in power grid interaction.There are many researches show that participate in region load using batteries of electric automobile
Peak load shifting, load transfer and the consumption advantages such as new energy fluctuation, how to formulate the charge and discharge strategy of electric car with
And charging price demand is most important.
The correlative study of electric car is numerous at present, relates to the Site Selection constant volume problem of electric automobile charging station, electricity
Electrical automobile orderly charges research, using batteries of electric automobile as energy storage participate in power grid interaction V2G research and electronic vapour
Vehicle charging price strategy research etc..It is interacted for electric car with power grid and its price fixing strategy, the mainly convex optimization of utilization is managed
By, game theory, data load prediction etc. modes realize, controlled mainly for the cost of user side, maximize operator
Interests.But research at present is not concerned about the power response energy that local load and local power supply are considered in charging price strategy
Power, so that there are still irrationalities for price fixing.For this algorithm still using user side cost as main target function, fusion is local
The peak load shifting strategy of load, and in view of local power supply determines charging price for power response ability integration.
Summary of the invention
Present invention is generally directed to the defects of the high randomness for the behavior of charging and charging station power load fluctuation, with electronic
Based on automobile history stops data and optimizes charge and discharge peak load shifting strategy, according to time-of-use tariffs and local power supply knot
Structure formulates charging price, reduces and optimize power grid peak load and user's charging cost.This method process includes the following steps,
As shown in Figure 1:
1, it is a kind of based on optimize charge and discharge strategy charging pricing practice strategy, which is characterized in that this method include with
Lower step:
Step 1 stops charge data, battery capacity data and region load data according to charging station history electric car,
The region load data of period need to be optimized using the methods of ARIMA, SVR or neural network prediction, electric car stops number
According to；
Step 2, in step 1 based on prediction data, with load charge power balance, battery capacity and charge and discharge
Power is constraint condition, and multinomial charges electricity price as variable, and peak load and user cost are objective function, build optimization
Charging and recharging model, selection parameter, carries out optimizing charge and discharge Optimization Solution in region according to the actual situation；
Step 3, according to charge and discharge user cost after local electricity price or time-of-use tariffs calculation optimization, obtain multinomial charging
Electric price parameter relationship；
Step 4 predicts local load capacity, determines charging valence according to local power supply structure ratio and power supply capacity are comprehensive
Lattice；
Process supplementary explanation:
It includes the charging time started that history electric car, which stops charge data, in the step 1, and charge dwell time, is filled
Electric particular capacity size and Rechargeable vehicle battery maximum capacity.
Region load data should be including the load data including the charging station in the step 1.
Multinomial charging Spot Price Model in affiliated step 2 are as follows:
Wherein, fcIndicate paying price, p indicates active power load, kiIndicate load coefficient.
Optimized model in the step 2 are as follows:
Wherein, N indicates to gather at the time of emulation, and i indicates moment value,Indicate the i-th moment charging total amount, M expression is filled
Electric car set, m indicate the m Rechargeable vehicle,Whether indicate the m Rechargeable vehicle of the i-th moment is in charged state,
EimIndicate the m Rechargeable vehicle charging primary power, EcmIndicate the m Rechargeable vehicle battery capacity,Indicate that charging terminates
When battery capacity be at least not less than the accounting of battery capacity,Indicate batteries of electric automobile residual capacity lower limit in charging process
Accounting, PmaxIndicate charging pile charge power.Objective function meaning is in multinomial charging electricity price condition and to consider local load
Under conditions of, minimum user's charging cost.
The optimal charging electric price parameter relationship of multinomial in the step 3 are as follows:
fpv=kpv·Pc
Wherein, fpvIt indicates to rush electric car charge cost, k when time-of-use tariffspvFor time-of-use tariffs, PcFor charging capacity.Root
When according to optimizing charge and discharge strategy charging cost and fair time-of-use tariffs, multinomial electricity price coefficient relationship can be obtained are as follows:
Local power supply structure ratio and power supply capacity determine strategy in the step 4 are as follows:
Wherein, α is coefficient, σΣ% is the equivalent difference coefficient of local power grid.
Invention effect
Using based on the charging pricing practice strategy for optimizing charging strategy, it is ensured that price fixing is lower than peak-trough electricity
Charging cost under electricity price can attract more users to participate in using electric car to electricity to maintain present user's charging quantity
In net peak load shifting strategy.Simultaneously for local power grid, local power grid peak load can be reduced, reduces operation of power networks cost,
Improve social benefit.
Accompanying drawing content
Fig. 1 is based on the charging price fixing algorithm flow for optimizing charge and discharge strategy.
Fig. 2 is need to carry out charge and discharge electrically optimized load data (region base using historical load data prediction certain time period
Plinth load prediction figure).
Fig. 3,4 be to utilize the region load optimal figure under different charge and discharge strategies.
Fig. 5 is to utilize region charging load diagram (the region charge and discharge under different charge and discharge strategies under different charge and discharge strategies
Electrical power figure).
Fig. 6 is charging price fixing strategy schematic diagram.
Specific embodiment
Illustrate algorithm calculation process combined with specific embodiments below:
Step 1 stops charge data, battery capacity data and region load data according to charging station history electric car,
Region load data, the electric car for predicting next day using the methods of ARIMA, SVR or neural network stop data；
Step 2, in step 1 based on prediction data, with load charge power balance, battery capacity and charge and discharge
Power is constraint condition, and multinomial charges electricity price as variable, and peak load and user cost are objective function, build optimization
Charging and recharging model, selection parameter, carries out optimizing charge and discharge Optimization Solution in region according to the actual situation；
Step 3, according to charge and discharge user cost after local electricity price or time-of-use tariffs calculation optimization, obtain multinomial charging
Electric price parameter relationship；
Step 4 predicts local load capacity, is charged according to local power supply structure ratio and the multinomial of power supply capacity solution
Electric price parameter relationship, it is comprehensive to determine charging price with parameter simultaneous solution in step 3；
Process supplementary explanation:
It includes the charging time started that history electric car, which stops charge data, in the step 1, and charge dwell time, is filled
Electric particular capacity size and Rechargeable vehicle battery maximum capacity.
Region load data should be including the load data including the charging station in the step 1.
Multinomial charging Spot Price Model in affiliated step 2 are as follows:
Wherein, fcIndicate paying price, p indicates active power load, kiIndicate load coefficient.
Optimized model in the step 2 are as follows:
Wherein, N indicates to gather at the time of emulation, and i indicates moment value,Indicate the i-th moment charging total amount, M indicates charging
Automobile set, m indicate the m Rechargeable vehicle,Whether indicate the m Rechargeable vehicle of the i-th moment is in charged state, Eim
Indicate the m Rechargeable vehicle charging primary power, EcmIndicate the m Rechargeable vehicle battery capacity,At the end of indicating charging
Battery capacity is at least not less than the accounting of battery capacity,Indicate that batteries of electric automobile residual capacity lower limit accounts in charging process
Than PmaxIndicate charging pile charge power.Objective function meaning is in multinomial charging electricity price condition and to consider local load
Under the conditions of, minimum user's charging cost.Embodiment generally usesLower limit 10% withThe charge and discharge strategy of the upper limit 90%.Pmax
It is general to be limited using local charging pile fast charge power maximum.
The optimal charging electric price parameter relationship of multinomial in the step 3 are as follows:
fpv=kpv·Pc
Wherein, fpvIt indicates to rush electric car charge cost, k when time-of-use tariffspvFor time-of-use tariffs, PcIt is real for charging capacity
Apply the local time-of-use tariffs of middle application, electricity price when using normal if without time-of-use tariffs.According to optimization charge and discharge strategy charging cost
When maintaining an equal level with time-of-use tariffs, multinomial electricity price coefficient relationship can be obtained are as follows:
Quasi- once linear relationship of multinomial charging price in implementation, solution is away from mainly for basic load, Monomial coefficient
Mainly for the charging load on basic load, relational expression are as follows:
Local power supply structure ratio and power supply capacity determine strategy in the step 4 are as follows:
Wherein, α is coefficient, σΣ% is the equivalent difference coefficient of local power grid.
Claims (1)
1. a kind of based on the charging pricing practice strategy for optimizing charge and discharge strategy, which is characterized in that this method includes following step
It is rapid:
Step 1 stops charge data, battery capacity data and region load data according to charging station history electric car, utilizes
Region load data, the electric car of next day of prediction of the methods of ARIMA, SVR or neural network stop data；
Step 2 based on prediction data, is balanced, battery capacity and charge-discharge electric power are in step 1 with load charge power
Constraint condition, multinomial charge electricity price as variable, and peak load and user cost are objective function, build and optimize charge and discharge mould
Type, selection parameter, carries out optimizing charge and discharge Optimization Solution in region according to the actual situation；
Step 3, according to charge and discharge user cost after local electricity price or time-of-use tariffs calculation optimization, obtain multinomial charging electricity price ginseng
Number relationship；
Step 4 predicts local load capacity, according to local power supply structure ratio and the multinomial of power supply capacity solution charging electricity price ginseng
Number relationship, it is comprehensive to determine charging price with parameter simultaneous solution in step 3；
Process supplementary explanation:
It includes the charging time started that history electric car, which stops charge data, in the step 1, and charge dwell time, and charging is specific
Amount of capacity and Rechargeable vehicle battery maximum capacity.
Region load data should be including the load data including the charging station in the step 1.
Multinomial charging Spot Price Model in affiliated step 2 are as follows:
Wherein, fcIndicate paying price, p indicates active power load, kiIndicate load coefficient.
Optimized model in the step 2 are as follows:
Wherein, N indicates to gather at the time of emulation, and i indicates moment value,Indicate the i-th moment charging total amount, M indicates Rechargeable vehicle
Set, m indicate the m Rechargeable vehicle,Whether indicate the m Rechargeable vehicle of the i-th moment is in charged state, EimIt indicates
The m Rechargeable vehicle charging primary power, EcmIndicate the m Rechargeable vehicle battery capacity,Battery is electric at the end of indicating charging
Amount is at least not less than the accounting of battery capacity,Indicate batteries of electric automobile residual capacity lower limit accounting in charging process, PmaxTable
Show charging pile charge power.Objective function meaning is under conditions of multinomial charges electricity price condition and consideration local load, most
Low user's charging cost.
The optimal charging electric price parameter relationship of multinomial in the step 3 are as follows:
fpv=kpv·Pc
Wherein, fpvIt indicates to rush electric car charge cost, k when time-of-use tariffspvFor time-of-use tariffs, PcFor charging capacity.According to most
When optimizing charge and discharge strategy charging cost and fair time-of-use tariffs, multinomial electricity price coefficient relationship can be obtained are as follows:
Local power supply structure ratio and power supply capacity determine strategy in the step 4 are as follows:
Wherein, α is coefficient, σΣ% is the equivalent difference coefficient of local power grid.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110829473A (en) * | 2019-11-08 | 2020-02-21 | 山东大学 | Power distribution network energy storage optimization configuration method and system considering power four-quadrant output |
| CN110826801A (en) * | 2019-11-06 | 2020-02-21 | 江苏万帮德和新能源科技股份有限公司 | Distributed electric energy management method for electric vehicle charging station |
| CN112101624A (en) * | 2020-08-13 | 2020-12-18 | 国网辽宁省电力有限公司电力科学研究院 | ArIMA-based electric vehicle random charging demand prediction and scheduling method |
| CN114519487A (en) * | 2020-11-20 | 2022-05-20 | 准时达国际供应链管理有限公司 | Engineering vehicle management method, electronic device and storage medium |
| CN116757760A (en) * | 2023-08-22 | 2023-09-15 | 国网山东省电力公司聊城供电公司 | Method, system, terminal and storage medium for checking electric charge of business user |
| CN116911696A (en) * | 2023-09-12 | 2023-10-20 | 湖北华中电力科技开发有限责任公司 | Evaluation method for interaction correspondence capacity of electric automobile participating in power grid |
-
2019
- 2019-06-19 CN CN201910534157.5A patent/CN110415016A/en not_active Withdrawn
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110826801A (en) * | 2019-11-06 | 2020-02-21 | 江苏万帮德和新能源科技股份有限公司 | Distributed electric energy management method for electric vehicle charging station |
| CN110826801B (en) * | 2019-11-06 | 2023-04-18 | 万帮数字能源股份有限公司 | Distributed electric energy management method for electric vehicle charging station |
| CN110829473A (en) * | 2019-11-08 | 2020-02-21 | 山东大学 | Power distribution network energy storage optimization configuration method and system considering power four-quadrant output |
| CN112101624A (en) * | 2020-08-13 | 2020-12-18 | 国网辽宁省电力有限公司电力科学研究院 | ArIMA-based electric vehicle random charging demand prediction and scheduling method |
| CN114519487A (en) * | 2020-11-20 | 2022-05-20 | 准时达国际供应链管理有限公司 | Engineering vehicle management method, electronic device and storage medium |
| CN116757760A (en) * | 2023-08-22 | 2023-09-15 | 国网山东省电力公司聊城供电公司 | Method, system, terminal and storage medium for checking electric charge of business user |
| CN116757760B (en) * | 2023-08-22 | 2023-11-24 | 国网山东省电力公司聊城供电公司 | Method, system, terminal and storage medium for checking electric charge of business user |
| CN116911696A (en) * | 2023-09-12 | 2023-10-20 | 湖北华中电力科技开发有限责任公司 | Evaluation method for interaction correspondence capacity of electric automobile participating in power grid |
| CN116911696B (en) * | 2023-09-12 | 2023-12-08 | 湖北华中电力科技开发有限责任公司 | Evaluation method for interaction correspondence capacity of electric automobile participating in power grid |
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