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

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.

Publication DOI: https://doi.org/10.1016/j.energy.2019.116622
Divisions: Engineering & Applied Sciences > Electrical, Electronic & Power Engineering
Engineering & Applied Sciences > Power Electronics, Machines and Power System (PEMPS)
Engineering & Applied Sciences
Additional Information: © 2019, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ Funding: Petroleum Technology Development Fund.
Uncontrolled Keywords: Energy management strategies,Hybrid energy storage systems,Kalman filter,Model predictive control,Reinforcement learning,Civil and Structural Engineering,Building and Construction,Pollution,Mechanical Engineering,Industrial and Manufacturing Engineering,Electrical and Electronic Engineering
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Related URLs: https://linking ... 360544219323175 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2020-02-15
Published Online Date: 2019-12-02
Accepted Date: 2019-11-23
Authors: Etim, Nyong-bassey Bassey
Giaouris, Damian
Patsios, Charalampos
Papadopoulou, Simira
Papadopoulos, Athanasios I.
Walker, Sara
Voutetakis, Spyros
Seferlis, Panos
Gadoue, Shady

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Version: Accepted Version

Access Restriction: Restricted to Repository staff only until 2 December 2020.

License: Creative Commons Attribution Non-commercial No Derivatives


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