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Method and Apparatus for Scheduling Large Capacity Charging Devices to Balance Grid Load

IP.com Disclosure Number: IPCOM000240969D
Publication Date: 2015-Mar-16
Document File: 6 page(s) / 189K

Publishing Venue

The IP.com Prior Art Database

Abstract

Electricity is a major clean energy that is widely used to replace gas in automobile industry. As a perishable energy, it cannot be restored effectively, and most excessive electricity will be wasted. Under this condition, how to improve energy efficiency and reduce waste is a very significant problem. Nowadays, large-capacity charging equipment such as electric vehicles has been widely used as a green transportation method. This kind of equipment consumes a large amount of electricity and increases the burden of electricity grid during charging. How to reduce the energy consumption and reduce the burden of the grid under the condition that consumer demand is fulfilled is an important issue. To reduce the waste of electricity, the grid appreciates that the fluctuation of electricity usage is eliminated. Traditional methods set differential prices for peak hours and trough hours respectively to reduce fluctuation, but the status of the grid changes so fast that the predefined peak/trough hours are likely to be different from the actual ones. Inspired by smart grid initiative, we propose a method and apparatus that automatically collects personalized requirement through the charging spots, and schedules the charging process with multiusers over a specific region. Reasonable charging schedule will be generated to improve energy efficiency as well as meet charging demand in time over the holistic network.

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Page 01 of 6

Method and Apparatus for Scheduling Large Capacity Charging Devices to Balance Grid Load


1. Background

  Electricity is a major clean energy that is widely used to replace gas in automobile industry. As a perishable energy, it cannot be restored effectively, and most excessive electricity will be wasted. Under this condition, how to improve energy efficiency and reduce waste is a very significant problem.

  To reduce the waste of electricity, the grid appreciates that the fluctuation of electricity usage is eliminated, i.e., the load of peak hours and trough hours can be more balanced. Currently, the government sets differential prices for peak hours and trough hours respectively to reduce fluctuation.

  Nowadays, large-capacity charging equipment such as electric vehicles has been widely used as a green transportation method. This kind of equipment consumes a large amount of electricity and increases the burden of electricity grid during charging. For example, afterthe evening peak, drivers come back home and start charging the battery, which results in a load peak for grid.

  The following example roughly estimates the load peak caused by electric vehicles. The charging power of Tesla, a famous electric vehicle, is 10-20 kW, and the number of automobiles in Beijing is about 5.6 million, assume 2 million of those are electric vehicles in the future and half of them charges at the same time after evening peak, the total power will be

2.0 * 0.5 * 15 = 15 million kW


It's quite large burden for the grid, since the highest grid burden in Beijing is about 18 million kW at this moment.

  Differential prices and timer cannot solve the problem. For example, if the low price starts from 11:00 PM, most of electric vehicle users will set the charging beginning time to 11:00 PM, which also leads to a load peak.

2. Main Idea


It can be shown that each personalized charging demand contains the following three elements:

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Power, the suitable charging power for the specific vehicle, e.g., p = 10kW.


Duration, the time needed to charge the battery to the desired level, e.g., t = 6h.

Deadline, the latest finish time for the charging, usually the time when the driver plans to drive this vehicle away, e.g., d = 8:00 AM.

Other elements can be converted to the previous ones. For example, charging mileage can be converted to power and duration automatically by charging station according to vehicle type.

  Currently, the grid is clear about its running status but it has no way to know each personalized charging demand. If the grid can get information about each personalized demand and control the charging process, an optimized schedule will be generated and the load will be more balanced. An illustrating result is shown in the following figure.

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  Since the number of charging devices is too large, it is quite difficult that the grid controls the devices directly, thus hierarchical control structure is needed. A possible hierarchical structure...