TITLE: Advances in Electric Power Systems: Robustness, Adaptability, and Fairness

SPEAKER: Dr. Andy Sun

ABSTRACT:

The electricity industry has been experiencing fundamental changes over the past decade. Two of the arguably most significant driving forces are the integration of renewable energy resources into the electric power system and the creation of deregulated electricity markets. Many new challenges arise. In this talk, we present some new results on two important issues: How to reliably operate a power system under high penetration of uncertain supply and demand; and how to design an electricity market that balances efficiency and fairness.

In the first part of the talk, we address the first issue in the context of the so-called unit commitment (UC) problem, one of the most critical operations of an electric power system facing with new challenges of increasing uncertainty from both generation and load. We propose a fully adaptive robust model for the security constrained UC problem in the presence of nodal net load uncertainty. We develop a practical solution methodology and present an extensive numerical study on the real-world large scale power system operated by the ISO New England (ISO-NE). Computational results demonstrate the advantages of the robust model over the traditional reserve adjustment approach in terms of economic efficiency, operational reliability, and robustness to uncertain distributions.

As motivated by the above application, we study a more general notion of finite adaptability in a rather general setting of multistage stochastic and adaptive optimization. We show that geometric properties of uncertainty sets, such as symmetry, play a significant role in determining the power of robust and finitely adaptable solutions, and these solutions are good approximation for multistage stochastic as well as adaptive optimization problems. To the best of our knowledge, these are the first approximation results for the multistage problem in such generality. Moreover, the results and proof techniques are quite general and extend to include important constraints such as integrality and linear conic constraints.

In the final part of the talk, we present a new perspective on electricity market design. We propose and investigate the notion of $\beta$-fairness that addresses the tradeoff between social welfare and fairness. The case $\beta=0$ corresponds to current practice, whereas $\beta=1$ corresponds to a solution that maximizes the minimum utility among market participants, the so-called max-min fairness. Such a max-min fair solution eliminates side payments, thus provides a solution to a long standing problem in the current practice. We investigate the tradeoff curve, and show that the current practice $(\beta=0)$ is not Pareto efficient. Our scheme also gives a solution to another well-known problem, namely achieving fairness and integrity of the auction in choosing from multiple (near) optimal solutions.