Energy Dispatch Optimization with Combined Heat and Power: Opportunities in Controlled Environment Agriculture and Data Centers
Restricted (Penn State Only)
- Author:
- Seiler, Jacob
- Graduate Program:
- Architectural Engineering
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- February 28, 2025
- Committee Members:
- James Freihaut, Program Head/Chair
Seth Blumsack, Outside Unit & Field Member
Greg Pavlak, Major Field Member
James Freihaut, Chair & Dissertation Advisor
Julian Wang, Major Field Member - Keywords:
- Energy Dispatch Optimization
Controlled Environment Agriculture
Data Center
Energy Management
Combined Heat and Power
Energy Storage
Carbon Capture
Carbon Utilization - Abstract:
- Controlled Environment Agriculture (CEA) provides sustainable solutions for food production but faces significant financial and environmental challenges due to high energy consumption. Carbon dioxide is a valuable resource in the CEA industry, utilized to promote crop growth but otherwise minimized to avoid unnecessary emissions. Combined Heat and Power (CHP) systems are well-suited to meet CEA’s demands by simultaneously generating electricity, heat, and CO2. By integrating CHP, CEA facilities can enhance efficiency, reduce costs, and improve resilience. However, the potential of CHP as a dual provider of energy and CO2 in CEA remains underexplored, particularly with respect to optimization efforts that explicitly consider CO2 generation, management, and utilization. Research has not yet sufficiently addressed the gap of optimizing energy systems with multiple storage and utilization technologies to best meet CEA's energy demands under dynamic pricing and fluctuating conditions. This research introduces a multi-objective mixed-integer linear programming model that optimizes energy procurement and management for CEA facilities, minimizing both costs and emissions. The optimization framework incorporates a quantitative energy distribution model, constrained to meet heating, cooling, electricity, and CO2 demands of a CEA site. A case study, based on modeled energy requirements for a 25-acre tomato greenhouse in State College, Pennsylvania, demonstrates the model’s capabilities and evaluates its effectiveness. Simulation results from the optimization are compared to conventional load-following control strategies for CHP. Additionally, the multi-objective functionality is explored through a Pareto front analysis, illustrating financial and environmental trade-offs by varying the weighting of cost and emissions objectives. Beyond CHP, several auxiliary technologies, including thermal and battery storage, absorption cooling, and CO2 capture and storage, can further improve system efficiency. This research investigates the impact of these technologies by extending the scope of the model. The integration of absorption cooling facilitates “quad-generation,” allowing CHP systems to supply four essential resources: heating, cooling, electricity, and CO2. Furthermore, the model considers electricity sales as an additional revenue stream. A second case study evaluates the effectiveness of different technology configurations in reducing operational expenses and emissions, also investigating how manipulating market price factors that affect electricity sale prices influences overall system outcomes. Finally, this research explores the advantages of co-locating CEA facilities and data centers to maximize energy utilization with an integrated energy system and waste heat recovery. The model incorporates both electricity and cooling demands of the data center, and includes a waste heat recovery process that can transfer excess heat from the data center to the greenhouse. Another case study evaluates the operational benefits attributed to co-location, quantifying cost savings and emission reductions across various technology configurations. Results indicate that an integrated energy system can significantly enhance the economic and environmental performance of combined CEA-data center facilities. The developed optimization tool is highly adaptable, capable of evaluating and comparing a wide range of scenarios involving diverse technology portfolios, facility types, operational objectives, and grid-connectivity conditions. This tool aims to support the CEA industry by addressing energy-related challenges during both design and operation stages. While case study results are not intended as industry-wide benchmarks, they provide a methodology for on-site energy assessments and can support broader life-cycle cost analyses. This optimization tool also establishes a framework to optimize energy procurement and management strategies across various technology combinations to meet CEA demands and operational goals.