Integration of Open-Source URBANopt and Dragonfly Energy Modeling Capabilities into Practitioner Workflows for District-Scale Planning and Design
1.2. Urban-Scale Energy Modeling
1.3. Challenges in Urban Energy Modeling
1.4. Dragonfly and URBANopt™ Urban Energy Modeling Toolset
2. Materials and Methods
2.1. URBANopt and Ladybug Tools Software Integration Approach
2.1.1. Overview of the URBANopt SDK
2.1.2. Dragonfly Plugin Overview
2.1.3. Dragonfly Visualization and Analysis of URBANopt Results
2.2. Case Study Approach
2.2.1. Site Description
2.2.2. SOM Design Workflow
- Program and massing analysis;
- HVAC system selection;
- Energy conservation measures;
- PV system sizing;
- Outdoor thermal comfort.
- Building load flexibility;
- Battery sizing and dispatching;
- EV load analysis;
- Electrical distribution system modeling;
- District-scale geometry processing.
2.2.3. Baseline Scenario
- How does the overall district energy consumption change depending on the mix of programs as well as the location and orientation of the buildings?
- What is the projected baseline or “business-as-usual” (BAU) energy performance of the buildings and district considering the default starting point for the program design—for example, aligning with minimum, code-specified energy performance?
2.2.4. High-Efficiency Scenario
- How does energy performance change when individual ECMs and packages of ECMs are applied to the buildings in the district?
- Annual energy costs, consumption and emissions;
- Peak energy consumption.
2.2.5. District-Scale Systems Scenario
- To what degree is there overlap in heating and cooling loads, and is there potential for a district thermal system that allows rejected heat from cooling in certain buildings to be used for space or water heating in other buildings?
- What are the aggregated electricity load profiles of the district? To what degree do different program types contribute to different peaks?
- How do outdoor environmental conditions, and specifically the urban heat island effect, impact energy performance and comfort?
- Given the load profiles, utility rates, and potential rooftop/canopy/ground mount areas available for solar PV, what are the life cycle cost-optimal solar system sizes and locations? What are the life cycle cost-optimal electric battery sizes and dispatching strategies?
- What is the impact on the electric energy consumption and aggregate load profile of the district for different degrees of EV adoption and associated charging? How do the district program types affect the type of charging that is anticipated (e.g., home, workplace, public) and the timing of peak electric loads for charging?
- Given the building net electric load profiles (considering solar and batteries), is the planned design for the electric distribution system within the district (network topology, transformer/wire types, etc.) sufficient?
3. Results and Discussion
3.1. Scenario Analysis
3.1.1. Baseline Scenario
3.1.2. High-Efficiency Scenario
3.1.3. District-Scale Systems Scenario
- Potential for Shared District Thermal Systems
- Impacts of Microclimate and Urban Heat Island Effect
- Optimal PV/Battery Sizing
- Impacts of Electrification on Aggregate Loads and Peak Timing
- Impacts of Electrification on Electric Infrastructure
- Supporting the creation of urban models from the wide range of geometry sources that designers typically use at different stages of the design and planning process (e.g., 2D building footprints, 3D building massing, 2D zoned floor plans, and detailed 3D room volumes).
- Providing an integrated means of sending data between various simulation engines for community scale energy systems (e.g., PV, batteries, EVs, electric distribution systems), thereby unifying assumptions across these engines.
- Making OpenStudio and EnergyPlus more accessible for UBEM, especially the libraries of standards and templates, which can help provide reasonable assumptions to fill information gaps in urban data sets.
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|ECM||Energy Savings||EUI (kWh/m2)|
|Upgrading HVAC system||18%||137|
|Adding daylight controls||4%||131|
|Adding natural ventilation||5%||124|
|Cost-Optimal Rooftop Solar PV Capacity||3503 kW|
|25 Year Operational Electricity Cost (BAU)||$41,092,275|
|25 Year Operational Electricity Cost with PV||$37,852,778|
|25 Year PV + Electricity Life Cycle Cost||$40,912,481|
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Charan, T.; Mackey, C.; Irani, A.; Polly, B.; Ray, S.; Fleming, K.; El Kontar, R.; Moore, N.; Elgindy, T.; Cutler, D.; Roudsari, M.S.; Goldwasser, D. Integration of Open-Source URBANopt and Dragonfly Energy Modeling Capabilities into Practitioner Workflows for District-Scale Planning and Design. Energies 2021, 14, 5931. https://doi.org/10.3390/en14185931
Charan T, Mackey C, Irani A, Polly B, Ray S, Fleming K, El Kontar R, Moore N, Elgindy T, Cutler D, Roudsari MS, Goldwasser D. Integration of Open-Source URBANopt and Dragonfly Energy Modeling Capabilities into Practitioner Workflows for District-Scale Planning and Design. Energies. 2021; 14(18):5931. https://doi.org/10.3390/en14185931Chicago/Turabian Style
Charan, Tanushree, Christopher Mackey, Ali Irani, Ben Polly, Stephen Ray, Katherine Fleming, Rawad El Kontar, Nathan Moore, Tarek Elgindy, Dylan Cutler, Mostapha Sadeghipour Roudsari, and David Goldwasser. 2021. "Integration of Open-Source URBANopt and Dragonfly Energy Modeling Capabilities into Practitioner Workflows for District-Scale Planning and Design" Energies 14, no. 18: 5931. https://doi.org/10.3390/en14185931