Prioritization based control strategy for Grid Ancillary support using Plug In Electric Vehicles Charging and Discharging
Publication Year: 2020
Author(s): Ramakrishna Reddy K
This work mainly focuses at DA level where load flattening using PEV’s storage is the main objective. Zones of energy need for load flattening are identified where charging and discharging of PEVs is required in order to achieve load flattening. In each zone, the total available energy from PEVs is distributed optimally among all the intervals using Water Filling Algorithm (WFA). During Optimal Energy Distribution (OED) using WFA, uncertainty of PEV availability for grid support has been taken into account for estimating the available PEVs energy capacity in each zone (charging and discharging). Multi-Objective Genetic Algorithm (MOGA) is used for setting optimal power transaction (OPT) between the grid and PEVs with two objectives: load flattening and voltage regulation. Pareto-front obtained from MOGA is used to decide OPT to achieve flat load profile by ensuring bus voltage limits. Further, an Adaptive Neuro-Fuzzy Inference System (ANFIS) based vehicle prioritization is implemented in order to maximize storage usage and to minimize the Cost-ofCharging (CoC). The results show the impact of OED and OPT on load flattening and voltage regulation have been studied. Different cases are studied in ANFIS prioritization and the resultant effect on total PEV power availability and CoC are also studied.
Rights: School of Electrical Engineering, VIT University
Theme: Vehicle Technology | Subtheme: Electric vehicles
Tags: Hybrid electric vehicles (HEVs), Plug in hybrid electric vehicles, Future of e-mobility, Grid connectivity, Vehicle to grid (V2G), Cost of Charging (COC), Multi-agent-based energy management system (MAEMS), Central Agent(CA), Aggregator Agent (AA), Distributor Agent (DA).
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