Optimizing Electric Vehicle Charging Schedules Based on Probabilistic Forecast of Individual Mobility

Abstract

Abstract. The number of electric vehicles (EVs) has been rapidly increasing over the last decade, motivated by the effort to decrease greenhouse gas emissions and the fast development of battery technology. This trend challenges distribution grids since EVs will bring significant stress if the charging of many EVs is not coordinated. Among the many strategies to cope with this challenge, next-day EV energy demand forecasting plays a key role. Existing studies have focused on predicting the next-day energy demand of EVs on the aggregated and individual levels. However, these studies have not yet extensively considered individual user mobility behaviors, which exhibit a high level of predictability. In this study, we consider several mobility features of individual users when forecasting the next-day energy demand of individual EVs. Three types of quantile regression models are used to generate probabilistic forecasts of energy demand, particularly the next-day energy consumption and parking duration. Based on the prediction results, two time-shifting smart charging strategies are designed: unidirectional and bidirectional smart charging. These two strategies are compared with an uncontrolled charging baseline to evaluate their financial benefits and peak-shaving effects. Our results show that human mobility features can partially improve the prediction of next-day individual EV energy demand. Additionally, users and distribution grids can benefit from smart charging strategies both financially and technically.

Review of Building Integrated Photovoltaics System for Electric Vehicle Charging

“…Rafique et al explored energy management difficulties that emerged in high-density residential apartment complexes and presented a method to improve grid stability by introducing energy management systems (EMSs) that employ artificial neural networks to anticipate home demand and PV generation. [213] Authors in [214] addressed a major challenge related to next-day charging. They offered solution using quantile regression models that have been utilized to create probabilistic estimates of energy demand for next-day charging, benefitting customers and the distribution grid with smart charging techniques.…”

A Comprehensive Review on Smart Electromobility Charging Infrastructure

“…Intelligent charging apparatuses for EVs have several technical possibilities for optimising charging schedules based on individual mobility patterns [65] and grid conditions [66]. Intelligent charging systems enable benefits like financial savings [65] for electromobility users and improved grid stability [66] for grid operators.…”

“…Intelligent charging apparatuses for EVs have several technical possibilities for optimising charging schedules based on individual mobility patterns [65] and grid conditions [66]. Intelligent charging systems enable benefits like financial savings [65] for electromobility users and improved grid stability [66] for grid operators. The study by [67] demonstrates the substantial improvement while incorporating renewable energy sources into the transportation industry through smart electromobility charging.…”