Abstract
Distribution network analyses have been traditionally carried out by sequentially processing computational tasks, i.e., without taking advantage of parallel processing now available in multi-core machines. However, future distribution networks require studies that cater for the uncertainties due to the location and behavior of loads and low carbon technologies, resulting in a much more computationally demanding environment. This paper investigates the adoption of high performance computing (HPC) to accelerate probabilistic impact and control analyses carried out on residential low voltage (LV) networks with electric vehicles (EVs). First, the impacts of uncontrolled charging of EVs are quantified using a Monte Carlo-based approach using 1000 time-series daily simulations per penetration level (i.e., 0%–100%). Then, to mitigate these impacts, the coordinated management of the on-load tap changer and EVs is proposed considering a preventive control approach that caters for the uncertainties ahead (1000 scenarios). Two real residential, underground U.K. LV networks considering realistic demand and EV load profiles (1-min resolution) are analyzed. Results show that the processing time for the impact analysis is reduced almost proportionally to the number of cores. From the control perspective, it is demonstrated that HPC can be a feasible and implementable alternative in the management of future smart grids.
Highlights
T HE EXPECTED rapid adoption of domestic-scale low carbon technologies (LCTs), such as electric vehicles (EVs) or photovoltaic systems, in future distribution networks raises significant challenges due to their behavioral and locational uncertainties [1]
This work has investigated the adoption of high performance computing (HPC) to accelerate probabilistic impact and control analyses carried out on low voltage (LV) networks with EVs that would otherwise result in significant lead times given the number of analyses required to take into account the uncertainties in future distribution networks
A Monte Carlo-based methodology is presented to investigate the impacts of uncontrolled charging of EVs using 1,000 time-series simulations per penetration level (0-100%)
Summary
T HE EXPECTED rapid adoption of domestic-scale low carbon technologies (LCTs), such as electric vehicles (EVs) or photovoltaic systems, in future distribution networks raises significant challenges due to their behavioral and locational uncertainties [1]. LCTs will connect to, at low voltage (LV, < 1 kV), as well as potential solutions, probabilistic approaches must be adopted Such studies, create a much more computationally demanding environment which is likely to result in significant lead times when considering that distribution network analyses have been traditionally carried out by sequentially processing computational tasks (i.e., computation of single or multiple tasks, one after the other). This work investigates the adoption of HPC to accelerate probabilistic impact and control analyses carried out on residential LV networks with EVs. The modeling required to accommodate uncertainties related to EVs is one of the most challenging among LCTs given their user-dependent charging behavior (i.e., different start times and duration).
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