The impact of clustering strategies to site integrated community energy and harvesting systems on electrical demand and regional GHG reductions

Abstract

The current work examines the Integrated Community Energy and Harvesting system (ICE-Harvest), an integration of thermal and electrical distributed energy resources. The system prioritizes the harvesting of community waste energy resources—for example, residual heat rejected from cooling processes and peak electricity fossil-fuel fired generators, as well as energy from curtailed clean grid electricity resources—to help in satisfying the heating demands of commercial and residential buildings. As such, ICE-Harvest systems provide a solution that can minimize greenhouse gas emissions from high-energy-consumption buildings in cold-climate regions such as North America and Northern Europe. The current work focuses on where to locate these systems and introduces different clustering methods for integrated energy systems that focus on thermal load diversity among buildings in each cluster. In the first technique, buildings with the highest amount of rejected heat from their cooling systems (anchor buildings) are clustered with nearby high heating demands buildings to create a large opportunity for harvesting wasted energy however, buildings not located close to anchor buildings are clustered using density-based clustering methods. The energy harvesting capabilities of this technique are compared to those provided by full DB clustering without specifying the anchor buildings, as well as to full density-based clustering with a post-processing step in which the nearest anchor building is added to each cluster. The selected clustering method also demonstrates the grid level potential of the ICE-Harvest systems to impact greenhouse gas emissions, heating, and electricity consumption by presenting a reduced model for the ICE-Harvest system that can be applied to any number of clusters on a municipal or provincial/state scale. Specifically, the model is applied to a database of 14,832 high-energy-consumption buildings with the potential for forming 1,139 clusters in the province of Ontario, Canada. The results of this case study reveal that density-based clustering with post processing resulted in the largest emission reduction per unit piping network length of 360 t CO2eq /km/year. In addition, the use of ICE-Harvest systems can displace the energy required from the gas-fired heating resources by 11 TWh, accounting for over 70% of the clusters’ total heating requirements. This results in a 1.9 Mt CO2eq reduction in overall sites’ emissions, which represents around 60% of the clusters’ emissions.