A stochastic optimization approach to the design and operation planning of a hybrid renewable energy system. (1st August 2019)

• Record Type: Journal Article
• Authors: Yu, Jiah; Ryu, Jun-Hyung; Lee, In-beum
• Is Part Of: Applied energy. Volume 247(2019)
• Page Start: 212
• Page End: 220
• Publication Date: 2019-08-01
• Subjects: Hybrid renewable energy system -- Energy storage system -- Two-stage stochastic optimization -- Multi-scenario approach; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2019.03.207
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Design and operation planning of hybrid renewable energy systems is established. A stochastic approach is proposed to consider the uncertainty in energy profiles. The model is formulated with mixed-integer linear programming. Five scenario generation methods for stochastic model are conducted and compared. A hypothetical system is analyzed for deterministic and stochastic models. Abstract: Hybrid renewable energy systems (HRESs) have been introduced globally with the increasing emphasis on sustainable energy and the environment. It is very challenging to manage HRESs due to the inherent uncertainty in energy supply and demand. Recently, Energy Storage Systems (ESSs) have been drawing increasing attention as a promising alternative to minimize the difference between varying supply and demand. The ESS should be designed and operated based on the explicit consideration of uncertainty because a deterministic approach only captures a fixed snapshot of the varying system. The resulting scheduling problem for ESS operation was formulated as a two-stage stochastic programming model in this study. The model was then transformed into a mixed integer linear programming problem based on multiple equivalent scenarios. Five different scenario-generation methodologies were employed to illustrate the applicability of the approach. A numerical example illustrates that the HRES design and operation cost according to a stochastic model (US$ 6981/day) was at least 9.1% more economical than deterministic model (US$ 7680/day). From the results, it is shown that the proposed approach results in intelligent ESS operation that can increase the applicability of the HRES.