Paper Info
Title
Chance-Constrained Energy Management System for Power Grids With High Proliferation of Renewables and Electric Vehicles
Abstract
This paper proposes a two-stage energy management system (EMS) for power grids with massive integration of electric vehicles (EVs) and renewable energy resources. The first stage economic dispatch determines the optimal operating points of charging stations and battery swapping stations (BSS) for EVs under plug-in and battery swapping modes, respectively. The proposed stochastic model predictive control (SMPC) problem in this stage is characterized through a chance-constrained optimization formulation that can effectively capture the system and the forecast uncertainties. A distributed algorithm, the alternating direction method of multipliers (ADMM), is applied to accelerate the optimization computation through parallel computing. The second stage is aimed in coordinating the EV charging mechanisms to continuously follow the first-stage solutions, i.e., the target operating points, and meeting the EV customers’ charging demands captured via the Advanced Metering Infrastructure (AMI). The proposed solution offers a holistic control strategy for large-scale centralized power grids in which the aggregated individual parameters are predictable and the system dynamics do not vary sharply within a short time-interval.
Year
DOI
Venue
2020
10.1109/TSG.2019.2951797
IEEE Transactions on Smart Grid
Keywords
DocType
Volume
Energy management,Batteries,Optimization,Electric vehicle charging,Stochastic processes,Power grids,Indexes
Journal
11
Issue
ISSN
Citations 
3
1949-3053
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Bo Wang100.34
Payman Dehghanian231.78
Zhao, D.3366.52