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PAPER PRESENTATION

Real-Time Coordination of Plug-In Electric Vehicle Charging in Smart Grids to Minimize Power Losses and Improve Voltage Profile

IEEE TRANSACTIONS OF SMART GRID, VOL. 2, NO. 3, SEPTEMBER 2011

Xiangying Qian xq2120@columbia.edu

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STEP1: FIND THE OBJECTIVE

It proposes a real-time smart load management (RT-SLM) strategy for coordinating multiple plug-in electric vehicles (PEV) charging activities in a smart grid based residential network.

The sensitivities-based RT-SLM enables PEVs to begin charging as soon as possible within the priority time zones specified by the consumer, while complying with network operation criteria to maintain the reliability and security of smart grids.

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STEP1: FIND THE RESULTS

RT-SLM is capable of real-time coordination of PEVs, which significantly improves the efficiency and economy of smart grids.

RT-SLM respects PEV owner designated charging priority as long as system constraints are not violated.

RT-SLM helps reduce overall system overloads and power peaks.

MSS based RT-SLM offers fast and adaptable coordination strategy to cope with frequent and random nature of PEV charging activities.

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STEP2: OUTLINE THE PROCEDURES

1. Importance of Research on Coordinated Charging - growing popularity of PEV - problem of uncoordinated charging activities - development of smart grid technologies - recent related work

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STEP2: OUTLINE THE PROCEDURES (contd)

2. Formulation of PEV Charging Problem - system constraints - objective function

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STEP2: OUTLINE THE PROCEDURES (contd)3. Development of RT-SLM Algorithm - overview of RT-SLM - charging zone and priority scheme - PEV coordination based on maximum sensitivities selection (MSS) optimization - the proposed RT-SLM algorithm

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STEP2: OUTLINE THE PROCEDURES (contd)4. Topology of Smart Grid Distribution System - system topology - PEV energy requirements - PEV battery chargers - assumed load profiles - PEV penetration levels - designated PEV priorities

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STEP2: OUTLINE THE PROCEDURES (contd)5. Discussion of Simulation Results - 4 PEV charging scenarios - 4 different penetration levels: 16%, 32%, 47%, 63% - 3 designated time zones: 6pm—10pm, 10pm—1am, 1am—8am - 4 types of measurement: system power consumption, voltage deviation at worst node, total system losses, algorithm computing time

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STEP3: UNDERSTAND SECTION 2 IN DETAIL

System constraints:

Objective function:

where

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STEP3: UNDERSTAND SECTION 3 IN DETAIL

RT-SLM will decide which PEVs charge at what time by minimizing objective cost function (3) given system constraints (1) and (2).

Load variations and energy pricing over a 24h cycle as well as PEV owner preferences for charging time zone are taken into account.

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STEP3: UNDERSTAND SECTION 3 IN DETAIL

MSS optimization approach is computationally efficient.MSS quantifies the objective function sensitivities

(system losses) to PEV charger loads in the smart grid at a given time step.

MSS vector is sorted such that PEVs resulting in highest sensitivities in each priority group are selected last.

MSS sorting process is repeated for descending priority groups; a sorted PEV queue table corresponds with MSS values.

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STEP3: UNDERSTAND SECTION 3 IN DETAIL

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STEP3: UNDERSTAND SECTION 5 IN DETAIL

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Thank you very much!

Any Question?