Uncertainty handling for Electric Vehicle aggregator using IGDT
| Autor: | Vinit Khemka, Pranjal Pragya Verma, V.E Anoop, S T P Srinivas, K.S. Swarup, Jitendra Kumar Pradhan |
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| Rok vydání: | 2018 |
| Předmět: |
Mathematical optimization
business.product_category Job shop scheduling Uncertainty handling Computer science 020209 energy Node (networking) Retail market 02 engineering and technology computer.software_genre News aggregator Smart grid Electric vehicle 0202 electrical engineering electronic engineering information engineering business computer Integer programming |
| Zdroj: | 2018 20th National Power Systems Conference (NPSC). |
| DOI: | 10.1109/npsc.2018.8771745 |
| Popis: | Electric vehicles are an integral part of futuristic smart grids. Electric vehicles give rise to a new player in the retail market called as aggregator. This paper proposes an intelligent charging scheduling problem for an Electric Vehicle (EV) aggregator considering vehicle-to-grid (V2G) and grid-to-vehicle (G2V) capabilities with an objective to minimize the total charging cost. Since electricity price at the charging node may be subject to uncertainties, Information Gap Decision Theory (IGDT) is proposed in this paper to handle uncertainties in the price. The original intelligent charging scheduling problem is non-linear. The paper proposes a modified Mixed Integer Linear Programming (MILP) based reformulation and solves with CPLEX using GAMS as an aggregator. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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