Analysis of Energy Transition Impact on the Low-Voltage Network Using Stochastic Load and Generation Models

Abstract

The energy transition poses a challenge for the electricity distribution network design as new energy technologies cause increasing and uncertain network loads. Traditional static load models cannot cope with the stochastic nature of this new technology adoption. Furthermore, traditional nonlinear power methods have difficulty evaluating very large networks with millions of cables, because they are computationally expensive. This paper proposes a method which uses copulas for modeling the uncertainty of technology adoption and load profiles, and combines it with a fast linear load flow model. The copulas are able to accurately model the stochastic behavior of solar irradiance, load measurements, and mobility data, converting them into electricity load profiles. The linear load flow model has better scalability and stability compared to traditional load flow models. The models are applied to a case study which uses a real-world dataset consisting of a realistic technology adoption scenario and a low-voltage network with millions of cables, which considers both voltage and current problems. Results show that risk profiles can be generated for all cables in the network, resulting in a valuable map for the district network operator as to where to focus their efforts.

Electric Vehicles Charging Management System for Optimal Exploitation of Photovoltaic Energy Sources Considering Vehicle-to-Vehicle Mode

“…Based on the statistical analysis carried out in (Lo Franco et al, 2020), a Weibull function having K 1.7 and λ 37.5 is used for modeling the daily traveled distance. A similar statistical approach has been discussed in (Ul-Haq et al, 2018;Bernards et al, 2020;Branco and Affonso, 2020). Figure 3A shows the distribution of the distance D(d k ) considering a population of 100-vehicles, estimated on seven different days.…”

Scenario generation of residential electricity consumption through sampling of historical data

“…Unfortunately, the full dataset used in our experiments cannot be released publicly; but a sample of 100 yearlong time series is made available 12 . The publicly available dataset contains consumption data of 100 daylong time series and all consumer attributes (except for connectionCapacity) associated with these time series.…”

“…Given the limitations of the state-of-the-art, existing studies that model the LVG still sample randomly from a set of known load time series [8,9,10,11,12]. These studies do not leverage any known consumer attributes to sample more accurate load time series and often do not adapt the sampled time series to account for calendar and weather information.…”

Systematic Evaluation of Possible Maximum Loads Caused by Electric Vehicle Charging and Heat Pumps and Their Effects on Common Structures of German Low-Voltage Grids

“…Several scenarios were defined that included both the significant and moderate integration of PVs, EVs, HPs, and BSS in the grids; however, no studies have been conducted on SF, voltage stability, or line/transformer overloadings. Bernards et al [11] suggested an approach that incorporated copulas and fast linear load flow models in a real Dutch LV network in order to simulate the uncertainty of adopting new technologies, generate load profiles (EV, HP, and PV), and analyze their effects on the voltage and line loading; but, the loading of the transformer and SF have not been addressed. To effectively and economically accommodate the adoption of loads, such as EVs and HPs, in the urban grids, Wintzek et al [12] proposed novel strategies for grid planning and dimensioning under the consideration of SFs.…”