As the adoption of electric vehicles (EVs) continues to rise, efficient scheduling methods that minimize operational costs are critical. This paper introduces a novel EV scheduling method utilizing a heuristic graph-search algorithm for cost minimization due to its admissible nature. The approach optimizes EV charging and discharging schedules by considering real-time energy prices and battery degradation costs. The method is tested on systems with solar generation, electric loads, and EVs featuring vehicle-to-grid (V2G) connections. Various charging rates, such as standard, fast, and supercharging, along with uncertainties in EV arrival and departure times, are factored into the analysis. Results from various case studies demonstrate that the proposed method outperforms popular heuristic optimization techniques, such as particle swarm optimization and genetic algorithms, by 3–5% for different real-time energy prices. Additionally, the method’s effectiveness in reducing operational costs for workplace EVs is confirmed through extensive case studies under varying uncertain conditions. Finally, the system is implemented on a digital real-time simulator with DNP3 communication, where real-time results align closely with offline simulations, confirming the algorithm’s efficacy for real-world applications.

The smart building energy management system (BEMS) has become essential for prosumers to manage their power consumption efficiently and conveniently, as building energy management evolves with the integration of distributed resources. This paper combines a reference point-based non-dominated sorting genetic algorithm (NSGA-III) with a Neural Prophet-based forecasting model to address the multi-objective BEMS problem and identify an optimal solution by considering the 24 h ahead real-time energy prices. The BEMS problem is designed as a multi-objective formulation that includes minimizing operating costs and alleviating user discomfort with their corresponding weights. To implement the proposed method, a building energy system is modeled, encompassing energy storage, electric vehicles, renewable energy resources, and various building loads. Multiple test cases conducted on residential and commercial buildings demonstrate significant cost savings in both types of buildings during both summer and winter days. A comparative cost analysis shows that relative to GA and NSGA-II, the proposed model reduced the reported operating cost by approximately 6.2% and 11.9%, and the execution time by approximately 30.3% and 14.4%, respectively. A real distribution feeder model with additional building energy system components is modeled on a digital real-time simulator using a reduced model approach to create a digital real-time system. The close agreement between the offline and digital real-time simulation results supports the feasibility of implementing the proposed framework under the operating conditions.