School of
Information Technology and Electrical Engineering

Speaker: Juliana Barbosa Nunes
Seminar Date: Tue, 05/12/2017 - 11:00
Venue: 49-502; AEB Seminar Room
Host: Prof Tapan Saha

Seminar Type:  PhD Thesis Review


Power system planning refers to a comprehensive analysis to establish the timing, location, size, and type of new facilities of generation, transmission, and distribution to adequately meet the expected future demand. However, the traditional power system planning must evolve to cope with challenges arising from climate change and lack of perfect information in the long-term. Climate change has encouraged renewable energy sources and, to some extent, provided incentive to accelerate gas-to-power projects worldwide. Although renewable energy sources are clean and provide energy at almost negligible operation costs, they impose high capital cost requirements and have significant variability and dependence on weather conditions. In particular, their highly uncertain production, as well as the pace of their technological development may affect both short-term operation and long-term investment decisions. Increasing gas-fired generation results in a strong interdependency between electricity and natural gas systems that might affect the natural gas supply adequacy in both short-term operation and long-term investment decisions. This is further intensified by the lack of perfect information in the long-term and the difficulty in predicting how market uncertainties will unfold in the future.

This research aims to cope with these challenges introduced to the traditional power system planning. A comprehensive multi-stage (dynamic) model is developed to co-optimize power and gas systems while dealing with variable renewable energy resources (VRE). This framework is designed to capture accurately the unfolding of both short-term and long-term uncertainties faced by power and gas systems at different stages of the decision horizon. While long-term uncertainties are represented by load growth, renewable technology development, and gas prices, the hourly profile of demand, as well as wind and solar capacity factors, are considered as short-term uncertainties. The proposed multistage model adopts a decision sequence and system constraints using an intertemporal optimization that enables a “look ahead” to accurately reflect how uncertainties might affect decisions over the entire period. In other words, the model allows optimizing the original global window instead of optimizing successively each sub-problem separately. The proposed multiple-stage model provides a more faithful representation of real-world decision-making as it allows adapting investment and operating decisions as uncertainties unfold over time. The effects of high renewable energy penetration, as well as renewable energy uncertainty on power and gas systems, are assessed by considering different strategies of renewable penetration. To prove the benefits of the proposed multi-stage co-planning approach, we compare the results of our framework with other methods used in the literature, i.e. static and rolling-window static framework. The effectiveness of the framework is validated on a realistic case of Queensland, Australia, in which both networks are driven to capture the link between these systems and to accommodate the state’s unique features of renewable availability and its gas network.


Juliana Barbosa Nunes received her B.Sc. and M.Sc. in Economics and Industrial Engineering from Federal University of Rio de Janeiro (UFRJ), Brazil in 2008 and 2012, respectively. She served as a faculty member of Economics School and has 3 years of industrial experience as an energy consultant. Currently, she is a final-year PhD Student in the Power & Energy Systems Group at the School of Information Technology and Electrical Engineering, The University of Queensland, Australia. Her research interests include mathematical optimization, renewable energy integration, and energy economics, stochastic modelling of power and natural gas systems along with planning and operation.