During the spring 2023 semester, we partnered with the City of Indianapolis and Purdue University’s Energy Engineering program to create a capstone design project for students to help the City plan for its goal of achieving net carbon neutrality by 2050. The students were given access and trained to use both NEO and B3 Benchmarking software tools, to organize and analyze data from 12 city-owned buildings.
The project contained three stages: benchmark the energy consumption; evaluate energy efficiency improvements; and assess renewable energy integration.
Step 1: Energy Benchmarking
The first step in the process was to benchmark the buildings, creating a baseline of energy consumption and carbon emissions. Billed data from the city-owned buildings was imported into B3 Benchmarking to identify trends, compare to peers, and rank/prioritize which buildings have the highest energy and carbon emission reduction potential. Nine of the 12 buildings were identified to have substantial energy savings potential with cost-effective options.
Step 2: Energy Efficiency
Next, the students created calibrated energy models in NEO for nine of the buildings to identify specific energy conservation measures that are the most cost-effective at reducing energy consumption and carbon emissions. Switching to LED lighting and installing occupancy and daylight dimming sensors were identified as low-hanging fruit options. Cumulatively, those improvements could provide over $538,000 of annual energy cost savings. Taking the grid CO2e emission rates into account, those energy efficiency improvements could reduce carbon emissions by 30,000 metric tons of CO2e by the year 2050. That’s equivalent to the carbon sequestered by 3,500 acres of forest annually for 27 years!
As the electric grid gets cleaner, the next substantial improvement building owners can take to reduce carbon emissions is to install electric heating systems, which is more expensive but ultimately helps achieve net carbon neutrality in the long run. However, it’s important to focus on the low-hanging fruit options first and make other improvements over time when the capital becomes available.
Step 3: Distributed Energy Resources
The last step was looking at optimal locations for solar panels. The building load curves generated from NEO can be used for sizing solar arrays and battery storage. However, is throwing solar on every rooftop the solution? In this case, looking at an entire city, we found it more productive to consider as part of a system rather than individual buildings. In a dense urban area, finding ideal locations can be challenging. However, in cases like Indianapolis, some of the densest areas present ideal opportunities for microgrids that are connected to the grid. In two areas downtown, 60 parking lots and parking garages were identified as optimal locations for two microgrids due to their large areas, proximity, and avoidance of interferences that rooftops present. The sites together total 115 MW of solar and could generate 153 million kWh of electricity annually. That would offset 1% of the total carbon emissions from transportation and building energy consumption for the entire city (compared to 2016 emissions).
There is no ‘one size fits all’ solution. Many barriers and challenges need to be overcome with creative solutions, and every building and city needs to be viewed as a system within a system when making decisions. Buildings in cold climates present a particularly tough challenge to electrify due to energy costs and peak heating loads when the solar panels aren’t producing much energy. Considerable financial investment is required to fund both energy efficiency and renewable energy projects. And collaboration between building owners, utility providers, and city/state governments is necessary to effectively solve these issues. Finally, software tools that process mass amounts of data are needed to properly track incremental improvements over time across a vast system of sub-systems.