Urban Energy Master Planning Methodology for District Energy Systems
Open Access DepositedUrban areas consume seventy-eight percent of all global power and produce sixty percent of all global CO2 emissions. These numbers are only expected to increase as the global population in urban areas will double by 2050. Urban areas typically have the most ambitious emission and energy reduction goals. However, urban areas experience the most challenges when planning these goals with renewable energy initiatives. Urban areas can face more extreme seasonal energy load swings, environmental and noise regulations, space and utility restrictions, and an increased cost burden. District energy systems are one method that can allow urban areas to share resources and make the most of energy technologies while also decreasing the cost and vulnerability. This study will look at how we can plan a district energy system for an urban community such that it can reach its energy and emission reduction goals. Currently, urban communities generally use one building energy modeling tool to create a master plan of how a singular building can reach its energy and emission reduction goals. However, no tools can do this for a district energy system. The current tools cannot model multiple forms of energy, new energy generation and storage technologies, nor the energy connections between buildings. Building energy modeling tools designed specifically for district energy systems are currently in development but are not where they need to be to help communities today. These urban communities have energy and emission reduction goals that must be reached in the next five to ten years, and thus they are already in the planning and project development phase and cannot wait for tools to help them. This study identifies ten key attributes that are necessary to model a district energy system and evaluates five building energy modeling tools on their ability to meet these attributes. The five tools evaluated were: URBANopt™, OpenBlue Plant Simulator™, MATLAB® with Simulink® and Simscape™ libraries, OpenModelica® with Modelica® buildings library, and HOMER Pro®. The study establishes key limitations of building energy modeling tools, that will need to be overcome before building energy modeling tools can successfully be used to model district energy systems. This study then uses a combination of building energy modeling tools to create a method for planning district energy systems in urban communities. This method aims to let those communities answer tough planning questions, such as whether to include renewable energy and storage technologies in their community. The tools used in this study include EnergyPlus™, MATLAB® & Simulink®, OpenModelica®, OpenStudio®, DOE Steam System Modeler Tool, and the Solar Turbines Engine Performance Program©. The method was then applied to five case studies: a hospital, a hospital with solar panels, a hospital with solar panels and a battery, a hospital with community solar panels, and a hospital with community solar panels and a battery. The study finds that solar panels in urban areas have a significantly varied ability to support a building’s electrical load. Additionally, district/community solar can allow an urban community to support critical buildings, such as hospitals, in emergencies where standalone solar cannot. The findings indicate that using energy storage technologies in an urban environment only becomes useful when there is a large enough energy load to support and when there is an on/off-peak demand schedule, unless the goal is to increase resiliency. Finally, traditional renewable energy initiatives may never allow urban communities to reach emission reduction goals; therefore, there will need to be more support from utility, federal, and state programs to give urban communities a fair chance to meet their ambitious but needed goals.
- All rights reserved
Notice to Authors
If you are the author of this work and you have any questions about the information on this page, please use the Contact form to get in touch with us.
| Thumbnail | Title | Date Uploaded | Visibility | Actions |
|---|---|---|---|---|
|
|
Gray_gwu_0075M_16708.pdf | 2024-10-02 | Open Access |
|
|
|
Synthetic_DES_Data.xlsx | 2024-10-02 | Open Access |
|