Evaluation of the Load Tunable Potential of Electric Vehicles Based on CNN-BiLSTM

Electric vehicles (EVs) are increasingly recognized as a key player in demand response (DR), significantly contributing to the flexibility and sustainability of the electrical grid. Assessing the response capabilities of these vehicles is crucial for effective network planning and identifying suitable user demographics for participation in demand response initiatives. However, the erratic nature and fluctuations in electricity usage among consumers create hurdles in accurately predicting the response potential of EVs. This paper introduces a structured approach to evaluate the achievable response potential for individual EVs as well as for groups of EVs on a day-ahead basis. To begin, we determine the range of response potential for electric vehicles by analyzing aggregated real-time charging data. Subsequently, a prediction model grounded in deep learning is constructed to gauge the response potential of electric vehicles (EVs) for the following day. This model incorporates the unpredictable aspects of user electricity consumption patterns, enhancing the accuracy of its forecasts regarding EV responsiveness. Ultimately, experimental analyses are performed to assess the effectiveness and precision of the proposed approach in evaluating response capabilities. This analysis employs actual load data from a specific region as a case study, thereby demonstrating the practical applicability of the methodology.