فهرست نشریات علمی دسترسی آزاد ایران

The convergence of widespread renewable energy sources (RES) and Internet of Things (IoT) technologies has catalyzed the development of the Energy Internet (EI), enabling advanced energy management paradigms. The EI framework facilitates the integration of numerous distributed generation units and leverages digital intelligence to enhance energy sharing, optimize grid asset utilization, and bolster overall power system security. Concurrently, rapid socio-economic growth has intensified global energy demand, leading to periodic shortages that challenge grid reliability. These scarcity conditions are predominantly manifested during peak load periods of the system. Consequently, a significant body of research is dedicated to peak load shifting and shaving to mitigate this issue. Nevertheless, few studies have systematically exploited the full capabilities of the EI framework to achieve this critical objective. This research, therefore, aims to develop and propose an EI-based optimization problem specifically designed to solve the peak load shifting problem with the primary goal of minimizing total system cost. The proposed methodology achieves this by optimizing the scheduled charging and discharging cycles of end-user Energy Storage Systems (ESS). Within this formulated problem, each prosumer—an entity that is both a consumer and a potential supplier—participates in a localized energy market. The operational cost model must comprehensively account for the costs of power sourced from the conventional grid and local RES, the storage dynamics within the ESS, and the accurate application of Real-Time Pricing (RTP) signals to all generated and consumed energy.