Energy Harvesting Potential of a Micro-Thermal Network Using a Nodal Approach to Reduce GHG Emissions in Mixed Electrical Grids

Integrating the electrical and thermal community buildings' energy systems can play an important role in harvesting wasted energy resources and reduction of carbon emissions from buildings and electricity generation sectors. It also increases demand management flexibility by minimizing the curtailed electricity on the grid through electrified heating without increasing the electricity peak demand. The current work examines Integrated Community Energy and Harvesting systems (ICE-Harvest), a new generation of distributed energy resources systems (DERs). They prioritize the harvesting of community waste energy resources—for example, heat rejected from cooling processes and distributed 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. In the current research, a thermal energy sharing model was developed to provide a dynamic characterization of the potential benefits of integrating and harvesting energy within a community of any number of buildings. The proposed model estimates the amount of rejected heat from cooling and refrigeration systems that can be simultaneously collected and used to heat other nearby buildings connected with a low temperature microthermal network (MTN). It also determines the proper timing and quantity of electricity used by the heat pumps in low-temperature MTNs as well as the reduction of both GHG emissions and the energy required from the EMC relative to conventional stand-alone systems. For an energy-balanced community cluster, the model showed that, over the course of a year, the energy harvesting would reduce this node’s GHG emissions by 74% and cover approximately 82% of the heating requirements compared to the BAU system. The results also revealed that the diversity in thermal demand between the connected buildings increases the harvesting potential. This research develops two clustering methods for the ICE-Harvest system. The proposed methods are clustering around anchor building and density-based (DB) clustering with post-processing by adding the closest anchor building to each cluster that focuses on the diversity of the buildings in each cluster. The energy sharing model is used to examine these techniques in comparison with the density-based clustering technique, the commonly used technique in the literature on a large database of 14000 high energy consumption buildings collected in Ontario, Canada. The results of this case study reveal that DB clustering with post-processing resulted in the largest emission reduction per unit piping network length of 360 t CO2eq /km/year. In addition, this research identified seven different cluster categories based on the total and simultaneous cooling-to-heating ratios of each cluster. The ICE harvest system integrates the thermal and electrical networks to add more flexibility to the electricity grid and schedule the electrification of heating (EoH). Current research provides a reduced model for the ICE-Harvest system to study its impact for over 1100 clusters of different categories on a provincial scale on the GHG emission and electricity demand from the grid. The use of ICE-Harvest systems at this scale 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 total GHG emissions, which represents around 60% of the clusters’ emissions. Operating conditions of the thermal network (TN) in the integrated community energy systems affect the ability to harvest waste energy and the reduction of GHG emissions as well as the electricity peak demand and consumption. In the current research, modeling of different thermal distribution network operating scenarios was performed for the different community energy profile clusters. These operation scenarios include low-temperature (fourth generation), ultra-low (fifth generation), a binary range-controlled temperature modulating thermal network operating between Low and Ultra-low temperatures (ICE-Harvest), and a new proposed scenario wherein a continuous range-controlled temperature modulating micro-thermal network. The continuous range-controlled temperature scenario shows the most benefits with the large implementation on the identified clusters. It adds more flexibility to balance the electricity grid as well as results in large GHG emission savings while controlling the increase in site electricity peak demand. The load profile of the cluster affects the selection of the most beneficial energy integrated system. This research shows that, for most of the heating-dominated clusters, it is better to employ the continuous range-controlled temperature TN with peak control and CHP on sites to serve the high heating demands along with short term and seasonal thermal storage. For the majority of balanced and /or cooling-dominated clusters, it is better to implement more carbon-free resources to the electricity grid or on-site that produce electricity but are not associated with heat such as wind, hydro, and solar PV panels. Parametric studies were performed in this research including changing the CHP size, the CHP utilization efficiency, and the grid gas-fired generators usage conditions to show their impact on the GHG emissions reduction from the clustered buildings. The analysis was implemented on a fleet of 1139 sites in Ontario and the results showed that the CHP size and operating hours have a measurable impact on GHG emission saving. The system can reach up to 58% and 66.5% emission savings of the total sites’ emissions with 93% and 39% operating hours respectively following the Ontario grid natural gas peaking power plants for the years of 2016 and 2017 with larger CHP sizes. The largest share of GHG emission saving in 2016 is by the CHP (61%) as opposed to 30% in 2017. The reduced models introduced in this research for the thermal energy sharing, the ICE-harvest system operation and sizing, and the MTN operation aid the investigation of the impact of the large implementation of the ICE-Harvest systems on the GHG emissions and electricity grid.

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