An integrated algorithm for evaluating plug-in electric vehicle’s impact on the state of power grid assets

Abstract

Plug-in Electric Vehicles (PEV) exert an increasingly disruptive influence on power delivery systems with penetration surge in the past decade. Therefore, accurately assessing their impact plays a crucial role in managing grid assets and maintaining power grids' reliability. However, PEV loads are stochastic and impulsive, which means they are of high power density and vary in a fast and discrete manner. These load characteristics make conventional assessment methods unsuitable. This paper proposes an algorithm, which captures the intertemporal response of grid assets and allows fast assessment through an integrated interface. To realize these advantageous features, we establish analytical models for two generic classes of grid assets (continuous and discrete operating assets) and recast their cost functions in the statistical settings of PEV charging. Distinct from simulation-based methods, the proposed method is analytical, and thus greatly reduce the computation resources and data required for accurate assessment. The effectiveness of the proposed algorithm has been demonstrated on a set of power distribution networks in Columbus metropolitan area, in comparison with the conventional assessment methods.

Location Planning of Fast Charging Station Considering its Impact on the Power Grid Assets

“…where the definition of parameters and modification of TCO method in utility practice have been detailed in Ref. [11].…”

Electric Vehicle Charging Warning and Path Planning Method Based on Spark

“…Mehta [16]. Mao D., Gao Z, Wang J. propose an algorithm, which capture the inter-temporal response of grid assets and allows fast assessment through an integrated interface, to accurately assessing their impact plays a crucial role in managing grid assets and maintaining power grids reliability [17]. Meanwhile, some researchers are hoping to increase the load level of distribution system while reducing the travel costs of electric vehicle users [18].…”

Distribution grid response monitor

“…driving habits, charging stations, and PEV types) and their interactions. An asset depreciation model coupled with time‐series power flow (TSPF) is derived for dynamic impact assessment by extending our prior work [8] which captures inter‐temporal grid asset response to impulsive loads. DGROM provides a range of predictions of infrastructure upgrading requirements in the distribution grid with increasing PEV penetration. The prediction range is generated by deploying an optimistic algorithm, which mimics the available policies in future mitigating PEV's negative impacts. Finally, DGROM provides a visualisation interface for fast dissemination and interpretation of analysis results. Comparing to software packages available on the market, DGROM is different in three aspects.…”

“…driving habits, charging stations, and PEV types) and their interactions. An asset depreciation model coupled with time‐series power flow (TSPF) is derived for dynamic impact assessment by extending our prior work [8] which captures inter‐temporal grid asset response to impulsive loads.…”