Efficient Modeling of Distributed Energy Resources’ Impact on Electric Grid Technical Losses: A Dynamic Regression Approach

Alain Aoun (), Mehdi Adda, Adrian Ilinca (), Mazen Ghandour, Hussein Ibrahim and Saba Salloum
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Alain Aoun: Département de Mathématiques, Informatique et Génie, Université du Québec à Rimouski (UQAR), Rimouski, QC G5L 3A1, Canada
Mehdi Adda: Département de Mathématiques, Informatique et Génie, Université du Québec à Rimouski (UQAR), Rimouski, QC G5L 3A1, Canada
Adrian Ilinca: Ecole de Technologie Supérieure (ETS), Montréal, QC H3C 1K3, Canada
Mazen Ghandour: Faculty of Engineering, Lebanese University, Beirut 1003, Lebanon
Hussein Ibrahim: Ecole de Technologie Supérieure (ETS), Montréal, QC H3C 1K3, Canada
Saba Salloum: Faculty of Engineering, Lebanese University, Beirut 1003, Lebanon

Energies, 2024, vol. 17, issue 9, 1-28

Abstract: Technical losses in electrical grids are inherent inefficiencies induced by the transmission and distribution of electricity, resulting in energy losses that can reach up to 40% of the generated energy. These losses pose significant challenges to grid operators regarding energy sustainability, reliability, and economic viability. Distributed Energy Resources (DERs) offer promising solutions to lower technical losses by decentralizing energy generation and consumption, reducing the need for long-distance transmission and optimizing grid operation. Hence, estimating the impact of DERs on grid technical losses becomes paramount for grid operators and planners. In response, this article proposes the application of regression modeling and nonlinear curve fitting algorithms to provide a more nuanced understanding and better characterize the intricate interplay between DER deployment and technical losses. Through a comprehensive case study based on more than 1080 computer simulations, we demonstrate the effectiveness of our proposed dynamic polynomial varying coefficient regression model in estimating the impact of DERs on technical losses within electrical grids. The proposed model offers a simple and effective methodology that allows grid operators to gain insights into the nonlinear dynamics of DER integration and make quicker and more informed decisions regarding grid management strategies, infrastructure investments, and policy interventions. Also, this research contributes to advancing the field of grid optimization by offering a simple equation that enhances our ability and haste to assess and mitigate technical losses in the context of an evolving energy landscape characterized by increasing DER adoption.

Keywords: technical losses; distributed energy resources; regression model; nonlinear curve fitting; electric grid optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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