Real-Time Algorithm Based Intelligent EV Parking Lot Charging Management Strategy Providing PLL Type Demand Response Program

Thanks to the demand response (DR), the system operators have a substantial opportunity to manipulate the demand side of the power system. Moreover, the system operators can easily incorporate trending technologies such as electric vehicles (EV) due to the idea of introducing improved level of contr...

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Bibliographic Details
Published in:IEEE transactions on sustainable energy 2021-04, Vol.12 (2), p.1256-1264
Main Authors: Sengor, Ibrahim, Guner, Sitki, Erdinc, Ozan
Format: Article
Language:English
Subjects:
Algorithms
Demand response
Electric power demand
Electric power systems
Electric vehicles
Energy levels
Energy management
Historical account
Linear programming
Load modeling
Mathematical model
online scheduling
Operators
Optimal scheduling
Optimization
Parking facilities
parking lots
Peak load
Phase locked loops
Real time
Real-time systems
Uncertainty
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Summary:Thanks to the demand response (DR), the system operators have a substantial opportunity to manipulate the demand side of the power system. Moreover, the system operators can easily incorporate trending technologies such as electric vehicles (EV) due to the idea of introducing improved level of controllable loads. In this study, a real-time optimization-based energy management model for an EV parking lot (EVPL) is propounded by using linear programming. The proposed algorithm offers a peak load limitation oriented DR program providing operational flexibility from the load-serving entity point of view together with the objective of maximizing the load factor of the EVPL in daily operation. Because of the mobility of the EVs, uncertain arrival/departure times along with the state-of-energy levels upon their arrival are generated by considering historical data to ensure a more realistic approach. In order to prove the validness of the suggested optimization model, a bunch of case studies is performed. Besides, credible results and useful findings are found out by activating the propounded model.
ISSN:1949-3029
1949-3037
DOI:10.1109/TSTE.2020.3040818