Khezr Sanjani

Khezr (Bestun) Sanjani

Thesis title: Adaptive Robust Self-Scheduling of a Compressed Air Energy Storage Coupled with a Wind-Farm to Participate in the Competitive Electricity Market

Period: 2017-2019

Supervisors: Dr. Behnam Mohammadi-Ivatloo and Dr. Mousa Marzband

Advisor: Dr. Masood Parvania

Renewable energy sources (RESs) are the most useful options to come over to climate change challenges. To do so, the power system structure has been enjoyed practicing the RESs on its main grid so far. Hence, the RESs have some huge opportunities in declining the climate change effects, but on the other hand, using such facilities has its relative issues. While the RESs' penetration is going to increase significantly, at the same time, practicing the RESs has been creating comparatively specific problems in the power system operation. Because of the highly unstable nature of the renewables, the RESs are the most uncertain participant of the grid. Moreover, employing energy storage systems (ESSs) is a pleasant approach to control these uncertainties. The more challenging problem is when these facilities are going to contribute to the electricity market. In fact, the market problem is so complex, while, by adding the above-mentioned commodities, it will be much more complex to solve. In this regard, this paper considers a coupled wind-farm and compressed air energy storage (WF-CAES) system to contribute to the competitive electricity market (CEM). Modeling a contributor to the CEM is entirely non-linear work, which makes it so hard to solve. A sophisticated tri-level decomposition approach is acknowledged to solve the problem and schedule the WF-CAES system optimally. An adaptive robust optimization (ARO) method is engaged on the uncertain parameters of the problem, too. Locational marginal price (LMP) and the WF production are the volatile parameters on the ARO approach.

Publications in journals and conference papers may be found at Publications Page or Google Scholar.