Supervised by Dr. Christopher Swartz
Economic Model Predictive Control | Optimal Planning and Scheduling
MACC Researcher since September 2015
Department of Chemical Engineering
B.Tech., Chemical Engineering, National Institute of Technology Surat, India (2008-2012).
M.Tech., Chemical Engineering, Indian Institute of Technology Delhi, India (2012-2014).
Research Associate, Indian Institute of Technology Delhi, India (2014-2015).
Novel Modeling & Solution Frameworks for Optimal Scheduling of Process Systems under Uncertainty
The successful operation of modern process systems across industrial and service sectors around the world relies greatly on the effective allocation and efficient utilization of different types of available resources for the various constituent operations involving their transformation to useful products and services and their timely deliveries for use by a variety of customers across market segments. The major challenges faced by these modern process systems include providing a variety of products and services to different customers at the minimum possible costs in view of the increased local and global competition, tackling their rapidly varying demands and prices across application sectors and market segments, handling greater regulatory pressures from various apposite agencies, and managing the high level of uncertainty and risk resulting from these ever changing conditions. Scheduling deals with the effective allocation and efficient utilization of a variety of available resources over time to a set of constituent operations to ensure the timely supply of products and services to customers in an efficient manner in terms of various economic performance metrics. This motivates the need for development of novel modeling and solution frameworks for optimal scheduling with the systematic and explicit consideration of a variety of different sources of both continuous and discrete realizations of uncertainty. The primary goal of this research work is to develop and implement novel modeling and solution frameworks for optimal scheduling in such uncertain environments with the consideration of continuous and discrete manifestations of uncertainties arising from a variety of different sources. The application domain under consideration in this research work is the renewable energy sector with focus on hydroelectric power generation. The problem considered here is that of optimal short-term scheduling for cascaded hydroelectric power systems considering uncertainties in different operational and market parameters such as water inflows to the reservoirs, availabilities of generating units, electrical loads and electricity prices.