Electric vehicles (EVs) are expected to play a critical role in future transportation systems. A number of countries have published roadmaps aiming to facilitate the adoption of EVs on the road. It is estimated that existing charging facilities will not be able to satisfy the tremendous charging demands of a dramatically increasing number of EVs. Following the rapid development of artificial intelligence and mobile communication technology, certain charging pricing mechanism is expected to influence the charging behavior of EV drivers. In order to maximize the working efficiency of highway charging facilities and the consumption of the renewable energy near charging facilities, this paper proposes a pricing methodology taking into account the charging facility service ratio, traffic flow and renewable energy generation. To support the adoption of the proposed pricing methodology, forecasts of hour-by-hour traffic flow and renewable generation as well as calculation of the shortest paths to different charging stations (CSs) are investigated. A road network testbed based on the Dublin traffic network is established to evaluate the proposed pricing methodology. It is discovered that for certain wind-rich CSs, the proposed pricing methodology can increase the consumption rate of wind energy by up to 82.97%, with an average improvement of 30.73%; for certain solar-rich CSs, it can improve the level of solar energy consumption by up to 59.50% and an average increase of 29.28% is achieved. The proposed pricing methodology can also reduce traffic jams to some extent at both peak and off-peak times.

High penetration of distributed photovoltaic (PV) systems has significantly increased microgrid net load volatility and uncertainty, posing challenges for conventional point forecasting methods that fail to provide sufficient operational risk information. To address this, this study proposes a multi-step net load forecasting framework that explicitly accounts for renewable energy fluctuations and system dynamics. A multi-quantile model generates 90% confidence prediction intervals for 1 and 4 h horizons at 15 min resolution. To mitigate under-coverage caused by cumulative errors, a stepwise conformal calibration strategy is applied to adjust each forecasting step independently, enhancing interval reliability and consistency. Net load volatility scenarios derived from PV ramping intensity are used to analyze uncertainty evolution under low, medium, and high fluctuation conditions. Case studies based on a high-PV microgrid dataset from eastern China demonstrate that calibrated intervals improve coverage, particularly in high-volatility scenarios, and, when integrated into rolling energy management, enhance battery state-of-charge safety margins and reduce peak grid import with minimal additional cost. The approach maintains point forecast accuracy while providing interpretable net load risk bounds, supporting informed scheduling and demand management in high-renewable microgrids.

This paper presents a detailed description of data predictive control (DPC) applied to a demand-side energy management system. Different from traditional model-based predictive control (MPC) algorithms, this approach introduces two model-free algorithms of artificial neural network (ANN) and random forest (RF) to make control strategy predictions on system operation, while avoiding the huge cost and effort associated with learning a grey/white box model of the physical system. The operating characteristics of electrical appliances, system energy consumption, and users’ comfort zones are also considered in the selected energy management system based on a real-time electricity pricing system. Case studies consisting of two scenarios (0% and 15% electricity price fluctuation) are delivered to demonstrate the effectiveness of the proposed approach. Simulation results demonstrate that the DPC controller based on ANN pays only 0.18% additional bill cost to maintain users’ comfort zones and system economy standardization while using only 0.096% optimization time cost compared with the MPC controller.