A review of optimal control methods for energy storage systems - energy trading, energy balancing and electric vehicles

R. Machlev; N. Zargari; N. R. Chowdhury; J. Belikov; Yoash Levron

doi:10.1016/j.est.2020.101787

A review of optimal control methods for energy storage systems - energy trading, energy balancing and electric vehicles

R. Machlev, N. Zargari, N. R. Chowdhury, J. Belikov, Yoash Levron

Electrical and Computer Engineering

Research output: Contribution to journal › Review article › peer-review

Abstract

This paper reviews recent works related to optimal control of energy storage systems. Based on a contextual analysis of more than 250 recent papers we attempt to better understand why certain optimization methods are suitable for different applications, what are the currently open theoretical and numerical challenges in each of the leading applications, and which control strategies will rise in the following years. The reviewed research works are divided to “classic” methods and “advanced” methods, in order to highlight the current developments and trends within each of these two groups. The classic methods include linear programming, dynamic programming, stochastic control methods, and Pontryagin's minimum principle, and the advanced methods are further divided into metaheuristic and machine learning techniques.

Original language	English
Article number	101787
Journal	Journal of Energy Storage
Volume	32
DOIs	https://doi.org/10.1016/j.est.2020.101787
State	Published - Dec 2020

Keywords

Dynamic programming
Energy storage
Linear programming
Machine learning
Pontryagin's minimum principle
Stochastic optimization

ASJC Scopus subject areas

Renewable Energy, Sustainability and the Environment
Energy Engineering and Power Technology
Electrical and Electronic Engineering

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10.1016/j.est.2020.101787

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title = "A review of optimal control methods for energy storage systems - energy trading, energy balancing and electric vehicles",

abstract = "This paper reviews recent works related to optimal control of energy storage systems. Based on a contextual analysis of more than 250 recent papers we attempt to better understand why certain optimization methods are suitable for different applications, what are the currently open theoretical and numerical challenges in each of the leading applications, and which control strategies will rise in the following years. The reviewed research works are divided to “classic” methods and “advanced” methods, in order to highlight the current developments and trends within each of these two groups. The classic methods include linear programming, dynamic programming, stochastic control methods, and Pontryagin's minimum principle, and the advanced methods are further divided into metaheuristic and machine learning techniques.",

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A review of optimal control methods for energy storage systems - energy trading, energy balancing and electric vehicles

Abstract

Keywords

ASJC Scopus subject areas

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