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Article

A Comparative Study of Optimal Energy Management Strategies for Energy Storage with Stochastic Loads

1
Department of Electrical Engineering, Hashemite University, Zarqa 13113, Jordan
2
Mathematical Institute, University of Oxford, Andrew Wiles Building, Oxford OX2 6GG, UK
3
Department of Electrical Engineering, Mutah University, Karak 61710, Jordan
4
Mechanical Engineering and Design, Aston Institute of Materials Research, Aston University, Birmingham B4 7ET, UK
*
Authors to whom correspondence should be addressed.
Energies 2020, 13(10), 2596; https://doi.org/10.3390/en13102596
Received: 1 April 2020 / Revised: 14 May 2020 / Accepted: 19 May 2020 / Published: 20 May 2020
(This article belongs to the Special Issue Optimal Control and Nonlinear Dynamics in Electrical Power Systems)
This paper aims to present the significance of predicting stochastic loads to improve the performance of a low voltage (LV) network with an energy storage system (ESS) by employing several optimal energy controllers. Considering the highly stochastic behaviour that rubber tyre gantry (RTG) cranes demand, this study develops and compares optimal energy controllers based on a model predictive controller (MPC) with a rolling point forecast model and a stochastic model predictive controller (SMPC) based on a stochastic prediction demand model as potentially suitable approaches to minimise the impact of the demand uncertainty. The proposed MPC and SMPC control models are compared to an optimal energy controller with perfect and fixed load forecast profiles and a standard set-point controller. The results show that the optimal controllers, which utilise a load forecast, improve peak reduction and cost savings of the storage device compared to the traditional control algorithm. Further improvements are presented for the receding horizon controllers, MPC and SMPC, which better handle the volatility of the crane demand. Furthermore, a computational cost analysis for optimal controllers is presented to evaluate the complexity for a practical implementation of the predictive optimal control systems. View Full-Text
Keywords: energy storage system; stochastic loads; load forecasting; model predictive controller energy storage system; stochastic loads; load forecasting; model predictive controller
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MDPI and ACS Style

Alasali, F.; Haben, S.; Foudeh, H.; Holderbaum, W. A Comparative Study of Optimal Energy Management Strategies for Energy Storage with Stochastic Loads. Energies 2020, 13, 2596. https://doi.org/10.3390/en13102596

AMA Style

Alasali F, Haben S, Foudeh H, Holderbaum W. A Comparative Study of Optimal Energy Management Strategies for Energy Storage with Stochastic Loads. Energies. 2020; 13(10):2596. https://doi.org/10.3390/en13102596

Chicago/Turabian Style

Alasali, Feras, Stephen Haben, Husam Foudeh, and William Holderbaum. 2020. "A Comparative Study of Optimal Energy Management Strategies for Energy Storage with Stochastic Loads" Energies 13, no. 10: 2596. https://doi.org/10.3390/en13102596

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