Like all companies, ESD faces challenges it must overcome every day. I wanted to share one instance where I saw our team react quickly to a challenge to meet our clients’ needs.
During a routine site visit to a major research campus client, a question arose about scrapping an aging distributed campus steam loop in favor of point-of-use generation. The steam requirements for the research campus had decreased, and it was necessary to determine the future of cost-effective energy generation on the site, which already featured the infrastructure to generate electricity with a natural gas turbine that produced excess steam for heating or cooling. What was missing was the knowledge of how to make the best use of the resources at hand, given the natural fluctuation in gas and electricity prices, as well as in system loads.
ESD faced the challenge of determining quickly whether it was cost-effective to scrap the cogeneration system in favor of point-of-use generation or to conduct a major overhaul of the system. We responded by developing a fast, Excel-based modeling application that performed a yearly hour-by-hour analysis of weather and plant-load data to determine operating costs/benefits. Rather than being limited by constraints of third- party modeling software, such as eQUEST, or spending time on acquiring, learning, and customizing more sophisticated “black-box” packages, we developed our own, site-specific parametric spreadsheet model. It gave us precise control over each part of the modelling process, and allowed the client to see the necessary component details to understand the Why behind the final numbers.
The main advantage of this analysis tool over standard industry software is its ability to rapidly and systematically perform energy-use and cost simulations and to visualize the multi-dimensional results. The complexity of accurately modelling a real co-generation plant in existing software was efficiently handled by the Excel algorithm. Excel also allowed customization specific to the project, with VB macro programming to capture and re-produce specific parametric variations.
In the end, the research campus was able to effectively operate its cogeneration equipment and sell back excess electricity to the local utility. Peak demand and normal operational loads were fully covered, while campus operational costs were reduced. The modeling app was deployed multiple times, generating thousands of parametric runs, to evaluate performance with changing operational constraints in a more cost-effective manner than standard industry software. The program gave the design team flexibility to adjust rapidly to the client’s needs and to deliver accurate, actionable results.