Date of Award
5-2025
Document Type
Thesis
Degree Name
Master of Science (MS)
College/School
College of Science and Mathematics
Department/Program
Earth and Environmental Studies
Thesis Sponsor/Dissertation Chair/Project Chair
Pankaj Lal
Committee Member
Aparna Varde
Committee Member
Manveer Mann
Abstract
Reliable, affordable energy is vital to America’s economic prosperity and national security, many urban centers operate aging infrastructure that strains both budgets and the environment. This thesis turns a longstanding challenge into a strategic opportunity: repurposing under-utilized brownfield properties into decentralized clean-energy hubs that strengthen local grids, lower consumer costs, and create skilled jobs. The research develops a resilience-oriented framework that couples renewable resources with Modular Combined Cooling, Heating & Power (CCHP), compact systems capable of delivering electricity, heating, and cooling from a single fuel source. Focusing on Northern New Jersey as a high-demand testbed representative of dense U.S. metro areas. The analysis evaluates 442,955 candidate ModIV property parcels using Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA) to rank land suitability, interconnection potential, and redevelopment readiness. Artificial-intelligence techniques simulate and refine energy output, and operating efficiency using multi-year performance data from an active 5 MW trigeneration facility. The optimized plant configurations can then be modeled in MATLAB to forecast outputs, simulate systems, and provide economic analysis. Key feasibility metrics—capital vs operational projects (Capex vs OpEx), return on investment, total cost of ownership, and net-present-value payback—can be calculated within MATLAB, which would enable rapid scenario testing that includes remediation expenses, maintenance schedules, and adaptive dispatch strategies. The results show that redeveloping brownfields as distributed energy assets can (1) cut site-level greenhouse-gas emissions, (2) provide lifecycle energy savings versus conventional supply, and (3) deliver positive cash flow depending on fuel mix and renewable-credit structures. By integrating GIS-based siting analytics with high-fidelity MATLAB simulations, this thesis offers a replicable blueprint for modernizing critical infrastructure across U.S. cities. The workflow empowers planners, investors, and policymakers to unlock dormant real estate, enhance grid resilience, and advance domestic clean-energy leadership—benefiting households and businesses nationwide.
File Format
Recommended Citation
Ferdinand, Rolihlahla S., "Optimizing Urban Energy Landscapes: Integrating Micro Polygeneration Systems with Geographic Information Systems & Artificial Intelligence" (2025). Theses, Dissertations and Culminating Projects. 1555.
https://digitalcommons.montclair.edu/etd/1555