Dynamic power management for long-term energy neutral operation of solar energy harvesting systems

Abstract

In this work we consider a real-world environmental monitoring scenario that requires uninterrupted system operation over time periods on the order of multiple years. To achieve this goal, we propose a novel approach to dynamically adjust the system's performance level such that energy neutral operation, and thus long-term uninterrupted operation can be achieved. We first consider the annual dynamics of the energy source to design an appropriate power subsystem (i.e., solar panel size and energy store capacity), and then dynamically compute the long-term sustainable performance level at runtime. We show through trace-driven simulations using eleven years of real-world data that our approach outperforms existing predictive, e.g., EWMA, WCMA, and reactive, e.g., ENO-MAX, approaches in terms of average performance level by up to 177%, while reducing duty-cycle variance by up to three orders of magnitude. We further demonstrate the benefits of the dynamic power management scheme using a wireless sensor system deployed for environmental monitoring in a remote, high-alpine environment as a case study. A performance evaluation over two years reveals that the dynamic power management scheme achieves a two-fold improvement in system utility when compared to only applying appropriate capacity planning.

Optimal Power Management with Guaranteed Minimum Energy Utilization for Solar Energy Harvesting Systems

“…Experience has shown that these improvements stem from a combination of adequate power subsystem planning, e.g., [8], [17], and harvesting-aware dynamic power management, e.g., [7], [22], [25]. Despite significant efforts, particularly in the area of runtime power management, the proposed schemes generally aim at maximizing short-term energy efficiency and operate on a best-effort basis.…”

“…However, predicting future meteorological conditions is highly complex and may be computationally prohibitive [9]. Therefore, acceptable prediction accuracy with the limited computational resources available on contemporary WSN motes has so far only been possible for short prediction windows, i.e., minutes to hours [7]. Reactive approaches, on the other hand, schedule the service level in response to source variations, either by directly measuring the energy generation, or through monitoring the storage fill-level [19], [25].…”

Harvested Energy Prediction Schemes for Wireless Sensor Networks: Performance Evaluation and Enhancements

“…We describe the protocol in detail in subsequent sections. It must be noted that Pro-Energy and the schemes in [22,23] are improvements of the EWMA approach [15].…”

“…Energy harvesting enabled systems can be better managed when effective energy prediction models are readily available. For this reason, a number of energy prediction models have appeared in literature [15][16][17][18][19][20][21][22][23][24][25][26][27][28]. The principle aim of this work is to review recently proposed harvested energy prediction schemes and provide a comparative study against landmark solutions which appear in the literature in order to investigate the relative advantages of each policy.…”

GRAPMAN: Gradual power manager for consistent throughput of energy harvesting wireless sensor nodes

“…Therefore, a correction factor βrks, computed for each cycle k, similar to the adjustment factor defined in [9] is used. The idea is to use the recent observations of the harvested energy in order to adjust the predictions of the EWMA filter.…”