부산대학교 도서관

Solar Energy Storage Systems (SESS) are deployed to address the unstable and intermittent nature of photovoltaic (PV) systems by allowing swift adjustments to system power. In such scenarios, the freshness of collected status updates is critical for controlling system performance. This timeliness is quantified through the Age of Information (AoI) metric. LoRa is introduced to the SESS as a Low-Power Wide-Area Network technology (LPWAN) to ensure reliable and energy-efficient data transmission. We explore a load monitoring system employing LoRa devices with periodic Slotted ALOHA (SA) access, where data updates with unfixed priority since the load process can be modeled as the time-variant Ornstein-Uhlenbeck (OU) process. The long-term expectation of AoI under fixed priorities is derived while the monitored process is time-invariant. Then the estimation error minimization problem is formulated while monitoring the time-variant OU process, which jointly considers delivered samples, estimated parameters, and AoI. To efficiently solve the problem, a greedy maximum scheduling (GMS) algorithm is proposed. Simulation results validate the AoI analysis with time-invariant processes and demonstrate the effectiveness of our data updating and estimation strategy using both generated data and real industry park data.