Abstract-For efficient network investments, insight in the expected spatial spread of new load and generation units is of prime importance. This paper presents and applies a method to determine key factors for adoption of photovoltaics, electric vehicles and heat pumps. Using a logistic regression analysis the impact of geographical and socio-economic factors on adoption probabilities of these new energy technologies is quantified. Income level, average age and household composition are shown to be important factors. Additionally for photovoltaics, peer effects were also shown to significantly influence the likelihood of adoption. The implementation of the developed models and the achievable improvement in prediction accuracy is demonstrated by application to a scenario study based on historical data. The models can be incorporated in future energy scenarios to provide a probabilistic spatial forecast of future penetration levels of the mentioned technologies and identify key areas of interest.
This study aims to quantify the impact of uncontrolled charging of Electric Vehicles (EVs) on the low voltage distribution networks with increasing EV penetration levels. For this objective, key indicators are developed to show the magnitude, scale and duration of the impact on the distribution network. The disseminated results are based on the case study with actual data from the existing distribution networks. The findings of this paper can serve as a benchmark for determining the potential of smart EV charging algorithms and/or the extent of necessary infrastructural reinforcement that the grid operators must incorporate.
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.
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