Optimization of a Power Line Communication System to Manage Electric Vehicle Charging Stations in a Smart Grid

Sara Carcangiu, Alessandra Fanni and Augusto Montisci
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Sara Carcangiu: Department of Electrical and Electronic Engineering, University of Cagliari, 09123 Cagliari, Italy
Alessandra Fanni: Department of Electrical and Electronic Engineering, University of Cagliari, 09123 Cagliari, Italy
Augusto Montisci: Department of Electrical and Electronic Engineering, University of Cagliari, 09123 Cagliari, Italy

Energies, 2019, vol. 12, issue 9, 1-13

Abstract: In this paper, a procedure is proposed to design a power line communication (PLC) system to perform the digital transmission in a distributed energy storage system consisting of fleets of electric cars. PLC uses existing power cables or wires as data communication multicarrier channels. For each vehicle, the information to be transmitted can be, for example: the models of the batteries, the level of the charge state, and the schedule of charging/discharging. Orthogonal frequency division multiplexing modulation (OFDM) is used for the bit loading, whose parameters are optimized to find the best compromise between the communication conflicting objectives of minimizing the signal power, maximizing the bit rate, and minimizing the bit error rate. The off-line design is modeled as a multi-objective optimization problem, whose solution supplies a set of Pareto optimal solutions. At the same time, as many charging stations share part of the transmission line, the optimization problem includes also the assignment of the sub-carriers to the single charging stations. Each connection between the control node and a charging station has its own frequency response and is affected by a noise spectrum. In this paper, a procedure is presented, called Chimera, which allows one to solve the multi-objective optimization problem with respect to a unique frequency response, representing the whole set of lines connecting each charging station with the central node. Among the provided Pareto solutions, the designer will make the final decision based on the control system requirements and/or the hardware constraints.

Keywords: power line communication (PLC); energy storage management; vehicle to grid (V2G); smart grid; multi-objective optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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