Data-Driven Risk Analysis of Joint Electric Vehicle and Solar Operation in Distribution Networks

Abstract

Increasing electric vehicle (EV) charging demand and residential solar photovoltaic (PV) generation greatly alter traditional distribution system operation and have the potential to overload and otherwise threaten the operating life of legacy infrastructure. The rate and location of adoption of these technologies on residential distribution systems introduce operational uncertainties for which traditional utilities may not be prepared. This paper proposes a user-defined, data-driven risk assessment method to quantify the severity and likelihood of transformer and secondary conductor overload conditions posed by high levels of EV charging demand coupled with rooftop solar PV. The stochasticity inherent in the operation of a distribution secondary system is captured by employing data-driven probability distribution functions for residential loading, EV charging, rooftop solar generation and ambient temperature. Samples then are repeatedly drawn from each function as inputs to a Monte-Carlo, multi-period power flow analysis to calculate secondary line currents, total loading and accelerated transformer aging. The proposed approach is utilized to study transformer and secondary conductor overload risk as well as transformer loss-of-life for multiple EV and PV penetration scenarios using historical EV charging profiles and residential Salt Lake City load profile data for a peak summer load day.

Cybersecurity in Power Grids: Challenges and Opportunities

“…With the increasing move towards smart grids as, e.g., evidenced by the advancing deployment of smart meters [ 16 ] and charging infrastructure for electronic vehicles [ 78 , 126 ], further security challenges, but also research trends and cyber innovations addressing these challenges, arise [ 26 ]. Currently, one of the most prominent research fields in smart grid security is the challenge of appropriately employing blockchain and distributed ledger technology, and the research community addresses this challenge from various angles.…”

“…A major challenge will be integrating (automatically) detected physical security violations [ 125 ] into an overall security solution involving IDS on the network side. With an increasing integration of novel, easily-accessible assets, such as smart meters [ 16 ] and charging infrastructure for electronic vehicles [ 78 , 126 ], into the communication infrastructure of smart grids, the challenge of physical security is further exaggerated.…”

Adapted Stochastic PV Hosting Capacity Approach for Electric Vehicle Charging Considering Undervoltage

“…The stochastic analysis is recommended to correctly quantify the likelihood and severity of EV charging in distribution networks [19]. The approach in [24,25] quantifies the risk of overloading by modelling the EV and customer loading with Poisson and Gaussian models. The presence of solar PV was considered in that study as well.…”

Ensemble Learning, Prediction and Li-Ion Cell Charging Cycle Divergence

“…In general model-based approaches such as Kalman filtering which make predictions based on previous localized state data, lose prediction accuracy when the monitored charging parameter states abruptly change due to noise or anomalous circumstances. Such abrupt state changes often result in future state prediction errors [15][16]. Data-driven approaches such as the ensemble method of this paper, on the other hand generalize predominant charging trends on larger non-localized spans of parameter state data.…”