Global warming is among the key issues that scientists and policy makers worldwide are deliberating on. The U.S. Energy Information Administration reports that only 11-13% of the current U.S. energy portfolio corresponds to renewable sources, whereas 33% of the carbon emissions are produced in the electricity production sector. My research goal is to develop practical, cost effective and sustainable ways to implement and deploy renewable energy. In this context, I have worked in a number of projects related to assessment, development, and modeling of power markets using robust and state of the art methodologies involving game theory, bi-level and multi-objective optimization, and statistical techniques.
Clinical Trial Systems Project: MIT Collaborative
Over the past decade the clinical trial process has dramatically increased in complexity leading to significant increases in cost, risk and time to market. Clinical trials are seen as a problem from virtually every stakeholder viewpoint; medical, research, nano-tech, pharma, patients. The Clinical Trial Process project will apply systems based analysis as demonstrated in earlier projects to the pharmaceutical Clinical Trial process with the goal of improving the efficiency and effectiveness of the trial process to reduce cost and improve access for patients.
Design of Pareto Optimal CO2 Cap-and-Trade Policies for Deregulated Electricity Networks
Among the CO2 emission reduction programs, cap-and-trade (C&T) is one of the most popular policies. Economic studies have shown that C&T policies for electricity networks, while reducing emissions, will likely increase price and decrease consumption of electricity. In this research I have developed a two layer mathematical–statistical model to develop Pareto optimal designs for CO2 cap-and-trade policies. The bottom layer finds, for a given C&T policy, equilibrium bidding strategies of the competing generators while maximizing social welfare via a DC optimal power flow (DC-OPF) model. The top layer involves design of Pareto optimal C&T policies over a planning horizon considering the results of the bottom layer model. It was demonstrated that a Pareto envelope generated by our model can serve as a useful tool for policy makers to select alternative C&T policies while satisfying various interests of the electricity network constituents. Results presented in the paper also examine the sensitivity of important factors affecting electricity markets such as social cost of carbon and demand-price sensitivity.
Impact of Community Microgrids on Smartgrid under Carbon Emissions Control: A bilevel model and Pareto Analysis
The challenge of assuring operational reliability of smartgrids will continue to rise with the increase in the percentage of total electricity demand supplied by microgrids with renewable generating portfolios. This research aims to address this challenge by developing a model for obtaining optimal operational strategies for microgrids subject to optimal dispatch of electricity by the smartgrid. A bi-level mixed integer programming model is presented. The upper level model seeks to minimize the operational and emissions cost in the microgrids. The lower level model finds the optimal dispatch of electricity while accounting for interactions among the market participants: GENCOS, microgrids, and consumers. The lower level model is a DC optimal power flow (DC-OPF) formulation that is modified by incorporating the social cost of carbon, emissions cap, and considering microgrids as both consumers and suppliers. Nevertheless, we also develop a Pareto analysis to create policies for target levels of carbon emissions reduction and green penetration from microgrids without adversely affecting market demand for electricity and the associated economic growth.