Online coordinated charging decision algorithm for electric vehicles without future information

Tang, Wanrong; Bi, Suzhi; Zhang, Ying Jun

doi:10.1109/tsg.2014.2346925

Cited by 135 publications

(88 citation statements)

References 23 publications

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“…For the application of EV charging, the state-of-the-art research on DSM (see, e.g., [13][14][15][16][17]) requires EV owners to share information (e.g., EV arrival) with a centralized controller. The approach presented in this article does not require such sharing of privacy sensitive information and has a low communication footprint.…”

Section: Groups Of Electric Vehiclesmentioning

confidence: 99%

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Robust Peak-Shaving for a Neighborhood with Electric Vehicles

Gerards¹,

Hurink²

2016

Energies

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Demand Side Management (DSM) is a popular approach for grid-aware peak-shaving. The most commonly used DSM methods either have no look ahead feature and risk deploying flexibility too early, or they plan ahead using predictions, which are in general not very reliable. To counter this, a DSM approach is presented that does not rely on detailed power predictions, but only uses a few easy to predict characteristics. By using these characteristics alone, near optimal results can be achieved for electric vehicle (EV) charging, and a bound on the maximal relative deviation is given. This result is extended to an algorithm that controls a group of EVs such that a transformer peak is avoided, while simultaneously keeping the individual house profiles as flat as possible to avoid cable overloading and for improved power quality. This approach is evaluated using different data sets to compare the results with the state-of-the-art research. The evaluation shows that the presented approach is capable of peak-shaving at the transformer level, while keeping the voltages well within legal bounds, keeping the cable load low and obtaining low losses. Further advantages of the methodology are a low communication overhead, low computational requirements and ease of implementation.

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Section: Groups Of Electric Vehiclesmentioning

confidence: 99%

“…The approach ORCHARD presented in [14] also flattens the load profile for a group of EVs without requiring knowledge of future EV arrivals. It has a time complexity of O(N 5 ) for N EVs and is similar to our work in the sense that we also do not know about EV arrivals beforehand.…”

Section: Groups Of Electric Vehiclesmentioning

confidence: 99%

Robust Peak-Shaving for a Neighborhood with Electric Vehicles

Gerards¹,

Hurink²

2016

Energies

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“…The first term in the objective function F nch represents the active power on a certain bus in 48 time slots to make sure that the electric energy generated by DGs can be consumed temporally by PEVs as much as possible; the second term represents the total charging cost in each time slot, as shown in (6). As the time slot is 30 min long, T equals 0.5.…”

Section: Temporal Scheduling Strategy Of Normal Chargingmentioning

confidence: 99%

“…[5][6][7][8][9][10][11][12][13]), but only a few works considered the interaction with DGs (e.g. [3,[14][15][16][17]).…”

Section: Introductionmentioning

confidence: 99%

Optimal temporal-spatial PEV charging scheduling in active power distribution networks

Sun

Yang

Yan

2017

Prot Control Mod Power Syst

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Background: The increasing penetration of a massive number of plug-in electric vehicles (PEVs) and distributed generators (DGs) into current power distribution networks imposes obvious challenges on power distribution network operation. Methods: This paper presents an optimal temporal-spatial scheduling strategy of PEV charging demand in the presence of DGs. The solution is designed to ensure the reliable and secure operation of the active power distribution networks, the randomness introduced by PEVs and DGs can be managed through the appropriate scheduling of the PEV charging demand, as the PEVs can be considered as mobile energy storage units.

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“…Within this technique of dynamic voltage and frequency scaling (DVFS) (see, e.g. Yao et al 1995;Li et al 2006), problems similar to those we encounter in decentralized energy management are commonly solved (Huang and Wang 2009;Tang et al 2014). Another area of application is logistics (see, e.g.…”

Section: Introductionmentioning

confidence: 99%

Resource allocation problems in decentralized energy management

2017

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Changes in our electricity supply chain are causing a paradigm shift from centralized control towards decentralized energy management. Within the framework of decentralized energy management, devices that offer flexibility in their load profile play an important role. These devices schedule their flexible load profile based on steering signals received from centralized controllers. The problem of finding optimal device schedules based on the received steering signals falls into the framework of resource allocation problems. We study an extension of the traditional problems studied within resource allocation and prove that a divide-and-conquer strategy gives an optimal solution for the considered extension. This leads to an efficient recursive algorithm, with quadratic complexity in the practically relevant case of quadratic objective functions. Furthermore, we study discrete variants of two problems common in decentralized energy management. We show that these problems are NP-hard and formulate natural relaxations of both considered discrete problems that we solve efficiently. Finally, we show that the solutions to the natural relaxations closely resemble solutions to the original, hard problems.This research is conducted within the EASI project (12700) supported by STW and Alliander and the EU FP7 project e-balance (609132).

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Online coordinated charging decision algorithm for electric vehicles without future information

Cited by 135 publications

References 23 publications

Robust Peak-Shaving for a Neighborhood with Electric Vehicles

Robust Peak-Shaving for a Neighborhood with Electric Vehicles

Optimal temporal-spatial PEV charging scheduling in active power distribution networks

Resource allocation problems in decentralized energy management

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