•   When: Friday, June 12, 2015 from 10:00 AM to 12:00 PM
•   Speakers: Hesham Altaleb
•   Location: Nguyen Engineering, Room 4801
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Abstract

Commercial & Industrial (C&I) organizations typically have diverse energy resource options which present a need for decisions that can streamline consumption and reduce costs. When alternative power resources and the need for limiting harmful emissions are also considered, the search space for optimal decisions becomes increasingly complex. Therefore, there is a need for a collaborative decision guidance system for electrical components power generation, storage, and consumption.

Proposed in this dissertation is an extensible framework to facilitate C&I entities forming a consortium to collaborate on their electric power supply and demand. The collaborative framework includes the structure of market setting, bids, and market resolution that produces a schedule of how power components are controlled as well as the resulting payment. The market resolution must satisfy a number of desirable properties (i.e., feasibility, Nash equilibrium, Pareto optimality, and equal collaboration profitability) which are formally defined in the dissertation. Additionally, peak-demand cost constitutes a significant portion of an organization�s energy budget. Units of an organization can separately run their services by choosing their peak-demand budget. Alternatively, they can collaborate to determine their peak-demand budget in lieu of monetary benefit.

Therefore, a primary market is proposed where units can reduce their peak-demand cost by collaboratively determining optimal peak-demand bounds and their respective payments. Moreover, to support the extensible framework components� library, power components such as utility contract, back-up power generator, renewable resource, and power consuming service are formally modeled. Finally, the validity of this framework is evaluated by a case study using simulated load scenarios to examine the ability of the framework to efficiently operate at the specified time intervals with minimal overhead cost.