ePrints

Reinforcement learning based adaptive power pinch analysis for energy management of stand-alone hybrid energy storage systems considering uncertainty

Lookup NU author(s): Bassey Nyong-Bassey, Professor Damian Giaouris, Professor Haris Patsios, Professor Sara Walker, Dr Shady Gadoue

Downloads

Full text for this publication is not currently held within this repository. Alternative links are provided below where available.

Abstract

© 2019 Elsevier Ltd. 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.

Publication metadata

Author(s): Nyong-Bassey BE, Giaouris D, Patsios C, Papadopoulou S, Papadopoulos AI, Walker S, Voutetakis S, Seferlis P, Gadoue S

Publication type: Article

Publication status: Published

Journal: Energy

Year: 2020

Volume: 193

Print publication date: 15/02/2020

Online publication date: 02/12/2019

Acceptance date: 23/11/2019

Date deposited: 05/02/2020

ISSN (print): 0360-5442

ISSN (electronic): 1873-6785

Publisher: Elsevier Ltd

URL: https://doi.org/10.1016/j.energy.2019.116622

DOI: 10.1016/j.energy.2019.116622

Altmetrics

Share