Modeling Hybrid Renewable Energy Systems for Optimal Investment and Operational Decisions

• Published in: SN computer science Vol. 3; no. 3; p. 246
• Main Authors: Levy, Roberto, Brodsky, Alexander
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.05.2022; Springer Nature B.V
• Subjects: Alternative energy sources; Case studies; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Data Structures and Information Theory; Electric utilities; Electricity distribution; Energy consumption; Environmental impact; Guidance systems; Hybrid systems; Information Systems and Communication Service; Libraries; Linear programming; Mathematical programming; Network management systems; Operations Research and Enterprise Systems; Optimization; Original Research; Ownership; Pattern Recognition and Graphics; Power; Programming languages; Renewable energy; Renewable resources; Simulation; Software Engineering/Programming and Operating Systems; Supply & demand; Variables; Vision; Power networks; Mixed integer linear programming; Optimization; Investment decision guidance; Hybrid renewable energy system
• ISSN: 2662-995X; 2661-8907
• DOI: 10.1007/s42979-022-01125-w

This paper focuses on making optimal investment and operational recommendations for a Hybrid Renewable Energy System (HRES). For this purpose, we develop a modular composite analytic performance model for HRES investment, which is based on an extensible library of atomic component models, including power storage, generators, renewable sources, third-party contracts, and household consumption. The performance model is able to formally express feasibility constraints and different key performance indicators, including total cost of ownership, environment impact, and infrastructure resilience, as a function of investment and operational decision variables. Based on the performance model, we design and develop a decision guidance system to enable actionable investment recommendations that optimize total cost of ownership, subject to the operational constraints associated with the network. We demonstrate the model in a case study for a municipal electric utility.