Abstract
Decentralized renewable energy systems (DRES) integrate renewable energy sources with energy-efficient building technologies and represent an important instrument for a sustainable built environment. Given their technological complexity, DRES also include comprehensive monitoring systems that offer important opportunities to optimize energy flows and increase energy efficiency. For these reasons, research has developed a range of automated optimization models and algorithms, such as association rule mining or fault detection diagnosis. To date, however, it remains unclear under what conditions these advanced and automated technologies may best be integrated to optimize DRES. This paper provides a complementary industry perspective, drawing on an in-depth case study of the optimization activities within one of the most advanced DRES in Switzerland. Over the course of five years, some of the optimization measures helped reduce energy consumption by 55–60%. Yet, the optimization potential of other measures remained unclear. The case study shows that, while technical aspects have given rise to optimization potential, organizational aspects have prevented, or at least delayed, the application of scientific algorithms, and have thus obstructed the realization of this optimization potential. These findings call for researchers to better integrate the technical and operational aspects into the optimization of energy systems and also offer important recommendations for policymakers, investors, and energy planners.
Highlights
The built environment is one of the most energy intensive sectors in the world
To develop a better understanding of the extent to which and under what conditions advanced optimization techniques may best be integrated for optimizing Decentralized renewable energy systems (DRES), this paper provides a complementary industry perspective in the form of a case study of the optimization activities that took place in the Suurstoffi district in Switzerland
We explore the challenges and opportunities that arise from embedding energy optimization in the operations of a DRES
Summary
The built environment is one of the most energy intensive sectors in the world. In the EU, it consumes approximately 40% of the total energy consumption and is responsible for 36% of CO2 emissions [1]. DRES use multiple technologies, in particular renewable energy and storage technologies, to provide electricity, heat, and cooling at either the building, neighborhood, district, or city level. Our findings highlight the substantive challenges of applying the advanced mathematical energy model and optimization techniques that research currently offers. DRES are widely considered a key solution for countries and cities to swiftly transition to a more sustainable built environment Given their technical complexity, DRES involve comprehensive monitoring systems that facilitate operations DRES and offer opportunities to optimize energy usage in DRES. Realizing these opportunities usually requires the use of advanced optimization techniques involving sophisticated mathematical data-mining tools and building automation
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