Three papers on inverse optimization algorithms, PEV sales forecasting, and PEVs' potential impact on power systems

Duan, Zhaoyang
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This thesis consists of three journal papers that I have been worked on during my PhD program of study.

The first paper presents heuristic algorithms that are designed to be implemented and executed in parallel with an existing algorithm in order to overcome its two limitations. Computational experiments show that implementing the heuristic algorithm on one auxiliary processor in parallel with the existing algorithm on the main processor significantly improves its computational efficiency, in addition to providing a series of improving feasible upper bound solutions.

In the second paper, we present two interactive models to jointly forecast PEV sales and the diurnal recharging load curve in the U.S. between 2012 and 2020. A case study is conducted for the Midwest ISO region. Compared to the sales forecasts from the literature, our results turn out to be less optimistic. Our recharging load forecast results also suggest that, if appropriately managed, the impact of PEVs on electricity load would not be overwhelming in the next decade.

The third paper focuses on assessing and mitigating the potential impact of PEVs recharging load on power systems, Case study show that electricity rates with higher flexibility induces PEVs to have less impact on the power systems in terms of both generation cost and uncertainties. Also, PEV users higher recharging behavior responsiveness will improve the effectiveness of price incentives and the control ability of the power utilities under all types of TOU rates mechanisms.

Inverse optimization, optimize electricity rate, Parallel computing, PEV sales forecasting, Risk measures