Minimizing fuel use at remote sites - the roles of load and batteries

Abstract

As part of an energy-reduction study at remote sites, we explore a distributed generation system comprised of hybrid renewable energy technologies, specifically, photovoltaic cells, battery storage, and diesel generators. This dissertation examines the impact of load variability in the design and dispatch of a such a system, and presents novel ways to portray batteries within an optimization model aiming to minimize cost and fuel use. We investigate the characteristics of load that influence the optimization model’s behavior regarding the design and dispatch strategy and show that mean load has a more pronounced effect than its shape. In addition, the photovoltaic cells are often deployed to help the generators run more efficiently, especially under load variability. We then update the optimization model to a two-phase one that uses (i) a model with hourly time fidelity to make design decisions, namely, which type and sizes of energy producing technologies to procure and (ii) a minute-level model to provide a dispatch strategy, i.e., how those technologies are operated, in each time period, to optimally meet the load in every minute for a 24-hour horizon. We find that the hourly model is sufficient for providing a design strategy that is then fixed for the second phase; however, dispatch solutions from the minute-level model more closely match the load, better capture battery and generator behavior, and provide fuel savings from a few percent to 30% over that provided by the hour-level model for the tested scenarios. Lastly, we improve upon the existing hour-level model by incorporating battery capacity fade effects. We find that battery aging under harsh conditions, such as high temperatures, can lead to sizable capacity loss within a year-long time horizon.