Data reconciliation frameworks for dynamic operation of hybrid renewable energy systems

Hybrid renewable energy systems (HRES) are a nexus of various renewable energy sources that have been proposed as a solution to circumvent various issues of renewable energy systems when installed in isolation. During the operation of an HRES, efficient demand-side management of energy and real-time power trading requires accurate estimation of real-time variables for each sub-component. Generally, these variables are corrupted by measurement errors. To address this issue, in this study, we present various frameworks for reconciliation strategies that can be used to rectify the inconsistencies in the sensor measurements of HRES. Specifically, in this study, we evaluate the efficacy of various static and dynamic reconciliation strategies such as Regularized Particle filter (RPF), Ensemble Kalman filter (EnKF), and Extended Kalman filter (EKF) of a candidate HRES system with a solar panel, a fuel cell, and an electrolyzer. Proposed frameworks are evaluated using various simulation-based validation studies. To this end, we have considered different operational scenarios, namely, (i) single-rate sampling, (ii) multi-rate sampling, and (iii) sensor outage, to make the study comprehensive. Simulation results indicate that RPF yields the best estimation accuracy for all three operational scenarios with a performance improvement of 75% from EKF and by 50% from EnKF, with only a fractional increment in computational time.