Increased demand for energy, rising energy costs, and heightened environmental concerns are driving forces that continually press for the improvement and development of new technologies to promote energy savings and emissions reduction. Combined heating and power (CHP), combined cooling, heating, and power (CCHP), and organic Rankine cycles (ORC) are a few of the technologies that promise to reduce primary energy consumption (PEC), cost, and emissions. CHP systems generate electricity at or near the place of consumption using a prime mover, e.g. a combustion engine or a turbine, and utilize the accompanying exhaust heat that would otherwise be wasted to satisfy the building’s thermal demand. In the case of CCHP systems, exhaust heat also goes to satisfy a cooling load. An organic Rankine cycle (ORC) combined with a CHP or CCHP system can generate electricity from any surplus low-grade heat, thereby reducing the total primary energy, cost, and emissions. This research first presents a review of the economical, energetic, and environmental benefits of CHP and CHP-ORC systems for a small office in various climates. Operating the systems 24 hours a day is compared to operating the system during typical office hours and benefits of the CHP system in terms of the EnergyStar and LEED programs are presented. Another objective of this dissertation is to study the critical role of the prime mover on the performance of CHP, CCHP, CHP-ORC, and CCHP-ORC systems under different pricing structures. Three different size natural gas engines are simulated for a small office under different operational strategies such as: follow the facility's electric demand, follow the facility's thermal demand, and follow a constant load. Simple optimizations were carried out to improve the system's performance. Using real prices for electricity and fuel to compute operational costs was compared to using the building's average prices without a CCHP system. Finally, a CCHP system using a load-share turbine for a large office building was examined while considering the source of carbon dioxide emissions, carbon offsetting through purchasing carbon credits, and available capital costs.

This study explores the application of micro-CCHP systems that utilize a 30 kW gas microturbine and an absorption chiller. Engineering Equation Solver (EES) is used to model a novel single-effect and double-effect water-lithium bromide absorption chiller that integrates the heat recovery unit and cooling tower of a conventional CCHP system into the chiller’s design, reducing the cost and footprint of the system. The results of the EES model are used to perform heat and material balances for the micro-CCHP systems employing the novel integrated chillers, and energy budgets for these systems are developed. While the thermal performance of existing CCHP systems range from 50-70%, the resulting thermal performance of the new systems in this study can double those previously documented. The size of the new system can be significantly reduced to less than one third the size of the existing system.

This research focuses on developing strategies for the optimal control of large-scale Combined Cooling, Heating and Power (CCHP) systems to meet electricity, heating, and cooling demands, and evaluating the cost savings potential associated with it. Optimal control of CCHP systems involves the determination of the mode of operation and set points to satisfy the specific energy requirements for each time period. It is very complex to effectively design optimal control strategies because of the stochastic behavior of energy loads and fuel prices, varying component designs and operational limitations, startup and shutdown events and many more. Also, for large-scale systems, the problem involves a large number of decision variables, both discrete and continuous, and numerous constraints along with the nonlinear performance characteristic curves of equipment. In general, the CCHP energy dispatch problem is intrinsically difficult to solve because of the non-convex, non-differentiable, multimodal and discontinuous nature of the optimization problem along with strong coupling to multiple energy components. <div><br></div><div>This work presents a solution methodology for optimizing the operation of a campus CCHP system using a detailed network energy flow model solved by a hybrid approach combining mixed-integer linear programming (MILP) and nonlinear programming (NLP) optimization techniques. In the first step, MILP optimization is applied to a plant model that includes linear models for all components and a penalty for turning on or off the boilers and steam chillers. The MILP step determines which components need to be turned on and their respective load needed to meet the campus energy demand for the chosen time period (short, medium or long term) with one-hour resolution. Based on the solution from MILP solver as a starting point, the NLP optimization determines the actual hourly state of operation of selected components based on their nonlinear performance characteristics. The optimal energy dispatch algorithm provides operational signals associated with resource allocation ensuring that the systems meet campus electricity, heating, and cooling demands. The chief benefits of this formulation are its ability to determine the optimal mix of equipment with on/off capabilities and penalties for startup and shutdown, consideration of cost from all auxiliary equipment and its applicability to large-scale energy systems with multiple heating, cooling and power generation units resulting in improved performance. </div><div><br></div><div>The case-study considered in this research work is the Wade Power Plant and the Northwest Chiller Plant (NWCP) located at the main campus of Purdue University in West Lafayette, Indiana, USA. The electricity, steam, and chilled water are produced through a CCHP system to meet the campus electricity, heating and cooling demands. The hybrid approach is validated with the plant measurements and then used with the assumption of perfect load forecasts to evaluate the economic benefits of optimal control subjected to different operational conditions and fuel prices. Example cost optimizations were performed for a 24-hour period with known cooling, heating, and electricity demand of Purdue’s main campus, and based on actual real-time prices (RTP) for purchasing electricity from utility. Three optimization cases were considered for analysis: MILP [no on/off switch penalty (SP)]; MILP [including on/off switch penalty (SP)] and NLP optimization. Around 3.5% cost savings is achievable with both MILP optimization cases while almost 10.7% cost savings is achieved using the hybrid MILP-NLP approach compared to the current plant operation. For the selected components from MILP optimization, NLP balances the equipment performance to operate at the state point where its efficiency is maximum while still meeting the demand. Using this hybrid approach, a high-quality global solution is determined when the linear model is feasible while still taking into account the nonlinear nature of the problem. </div><div><br></div><div>Simulations were extended for different seasons to examine the sensitivity of the optimization results to differences in electric, heating and cooling demand. All the optimization results suggest there are opportunities for potential cost savings across all seasons compared to the current operation of the power plant. For a large CCHP plant, this could mean significant savings for a year. The impact of choosing different time range is studied for MILP optimization because any changes in MILP outputs impact the solutions of NLP optimization. Sensitivity analysis of the optimized results to the cost of purchased electricity and natural gas were performed to illustrate the operational switch between steam and electric driven components, generation and purchasing of electricity, and usage of coal and natural gas boilers that occurs for optimal operation. Finally, a modular, generalizable, easy-to-configure optimization framework for the cost-optimal control of large-scale combined cooling, heating and power systems is developed and evaluated.</div>

<p>This research was a pilot study in a larger project that focused on how to retrieve knowledge from retiring long-term employees of a small university utility plant, incorporate that material into their existing training program, and during the process reduce the training time for current and future employees. Wade utility plant faced the retirement of eight employees with nearly 200 years of corporate knowledge within three years, but their current training program required seven to nine years to complete. The study utilized interviews, first-hand observation and partnership with current employees to explore how best to obtain the corporate knowledge that would be lost when the proletarian workers retired. The study revealed that the training program needed to be updated, and communication, trust and training evaluation continuity needed to be addressed. Due to these issues, trust was built through transparency by the researcher, and suggestions were made to management for moving forward. This study adds to the body of knowledge by utilizing knowledge capture techniques in a utility plant, highlighting effective knowledge capture techniques for proletarian workers, the importance of corporate planning for the effect of group retirements, and how incorporating proletarian workers into training creation can make a positive impact on company relationships.</p>