Reinforcement learning based adaptive power pinch analysis for energy management of stand-alone hybrid energy storage systems considering uncertainty. (15th February 2020)

• Record Type: Journal Article
• Authors: Nyong-Bassey, Bassey Etim; Giaouris, Damian; Patsios, Charalampos; Papadopoulou, Simira; Papadopoulos, Athanasios I.; Walker, Sara; Voutetakis, Spyros; Seferlis, Panos; Gadoue, Shady
• Is Part Of: Energy. Volume 193(2020)
• Publication Date: 2020-02-15
• Subjects: Hybrid energy storage systems -- Energy management strategies -- Model predictive control -- Kalman filter -- Reinforcement learning; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2019.116622
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Hybrid energy storage systems (HESS) involve synergies between multiple energy storage technologies with complementary operating features aimed at enhancing the reliability of intermittent renewable energy sources (RES). Nevertheless, coordinating HESS through optimized energy management strategies (EMS) introduces complexity. The latter has been previously addressed by the authors through a systems-level graphical EMS via Power Pinch Analysis (PoPA). Although of proven efficiency, accounting for uncertainty with PoPA has been an issue, due to the assumption of a perfect day ahead (DA) generation and load profiles forecast. This paper proposes three adaptive PoPA-based EMS, aimed at negating load demand and RES stochastic variability. Each method has its own merits such as; reduced computational complexity and improved accuracy depending on the probability density function of uncertainty. The first and simplest adaptive scheme is based on a receding horizon model predictive control framework. The second employs a Kalman filter, whereas the third is based on a machine learning algorithm. The three methods are assessed on a real isolated HESS microgrid built in Greece. In validating the proposed methods against the DA PoPA, the proposed methods all performed better with regards to violation of the energy storage operating constraints and plummeting carbon emission footprint. Highlights: Adaptive Power Pinch achieved enhanced results over the Day-ahead Power Pinch. Adaptive based Power Pinch achieved 6% reduction in fossil fuel usage. Kalman based Adaptive Power Pinch was optimal only under Gaussian uncertainty. Reinforcement learning based Adaptive Power Pinch analysis had the best performance.