Infomorphism: an urban planning framework for local renewable energy integration

Authors
Li, Fengqi
ORCID
https://orcid.org/0000-0002-3887-9968
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Other Contributors
Shelden, Dennis, DS
Krueger, Ted, TK
Schell, Kristen R., KS
Papanikolaou, Dimitris, DP
Tsamis, Alexandros, AT
Issue Date
2022-08
Keywords
Architecture
Degree
PhD
Terms of Use
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute (RPI), Troy, NY. Copyright of original work retained by author.
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Abstract
What a future city could be and why has always created discourse within the discipline of architecture. The form of a city constantly evolves through policy and regulation changes, which are affected by social, economic, technological, and environmental forces. The negotiation of these forces has always provoked explorations in envisioning the future state of a city. Today, energy consumption is becoming of primary importance when considering city planning processes. Because of rapid population growth and global climate catastrophes, improving urban energy efficiency has forced computational designers and planners to address the broader impacts that renewable energy systems can have on the form and function of cities. At the same time, Centralized energy grids are no longer the only base for a city to grow. They have become an object of design. Urban planners and engineers are designing new tools that address how renewable energy integration can inform the evolution of cities. This thesis introduces a computational planning framework for addressing renewable energy integration problems using system optimization approaches and reinforcement learning algorithms that generate urban collective forms with optimal local energy exchange networks. More specifically, the concept of Zero Energy Buildings (ZEBs) is a response to the question of how to improve renewable energy integration at the building level. ZEBs are energy-optimized buildings. They capture, store, and exchange locally available renewable energy through the use of integrated, energy metabolizing building technologies. When considering ZEB buildings as an urban network, a fundamental ``right to renewable energy access" must be introduced into planning processes. Just as urban planning today ensures that buildings have access to fresh air, sunlight, or water through policies, regulations, and building codes, ZEBs - as renewable energy-dependent buildings - now need access to local solar, wind, or geothermal energy equitably. It is my conjecture for the development of this body of work that we need to acknowledge that the sun, wind, or heat from the earth belongs to everyone and, as such, develop urban planning processes that grant equitable access to these newly accessible, basic, and free resources. Many different modeling efforts and software platforms have been designed to address energy efficiency issues related to ZEBs. For example, generative design frameworks and Urban Building Energy Modeling (UBEM) tools integrated with joint simulations have been developed to evaluate the energy performance of ZEBs through multi-objective optimizations and data analytics. However, these tools do not have renewable energy rights as drivers for a city’s form, function, and infrastructure. The developed computational planning framework, titled Infomorphism, augments a generative planning process with Artificial Intelligence (AI)-based energy-sharing network optimization models to explore potential planning policies associated with renewable energy rights. Taking renewable energy accessibility as a driver for optimizing planning envelopes, Infomorphism as an AI-based framework helps optimize energy absorption for a city as a whole and balances energy exchange between areas of supply and areas of demand. Several case studies for Manhattan have been conducted to provide alternative planning environments for validating the effectiveness of the proposed workflow. The case studies show how a city can be developed as an energy network that ensures equitable access to renewable energy (in the form of heat and electricity) absorbed from the planning envelopes with minimum levelized energy costs. The results from the case study show how geothermal and solar drive a future city's collective form and infrastructure to achieve up to 79% local renewable energy integration (37.9% from solar energy and 41.1% from geothermal heat pumps at designated locations) with a total levelized energy cost of $3.55 * 10^7. Establishing new policies and regulations according to equitable energy rights associated with renewable energy integration can collectively drive a city's form, function, and infrastructure and discuss energy policies emerging from this research. It is anticipated that the development of Infomorphism will support the decision-making process related to architectural design, urban planning, energy infrastructure design, and renewable energy integration at both building and urban scales.
Description
August2022
School of Architecture
Department
School of Architecture
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection
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