Reinforcement learning based adaptive power pinch analysis for energy management of stand-alone hybrid energy storage systems considering uncertainty
| Autor: | Shady Gadoue, Spyros Voutetakis, Bassey Etim Nyong-Bassey, Charalampos Patsios, Athanasios I. Papadopoulos, Panos Seferlis, Simira Papadopoulou, Sara Walker, Damian Giaouris |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Mathematical optimization
Computer science Energy management business.industry 020209 energy Mechanical Engineering 02 engineering and technology Building and Construction Kalman filter Pollution Industrial and Manufacturing Engineering Energy storage Renewable energy Model predictive control General Energy 020401 chemical engineering 0202 electrical engineering electronic engineering information engineering Pinch analysis Reinforcement learning Microgrid 0204 chemical engineering Electrical and Electronic Engineering business Civil and Structural Engineering |
| ISSN: | 0360-5442 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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