Yongpei Guan

Robust Unit Commitment With Wind Power and Pumped Storage Hydro

As renewable energy increasingly penetrates into power grid systems, new challenges arise for system operators to keep the systems reliable under uncertain circumstances, while ensuring high utilization of renewable energy. With the naturally intermittent renewable energy, such as wind energy, playing more important roles, system robustness becomes a must. In this paper, we propose a robust optimization approach to accommodate wind output uncertainty, with the objective of providing a robust unit commitment schedule for the thermal generators in the day-ahead market that minimizes the total cost under the worst wind power output scenario. Robust optimization models the randomness using an uncertainty set which includes the worst-case scenario, and protects this scenario under the minimal increment of costs. In our approach, the power system will be more reliable because the worst-case scenario has been considered. In addition, we introduce a variable to control the conservatism of our model, by which we can avoid over-protection. By considering pumped-storage units, the total cost is reduced significantly.

A Chance-Constrained Two-Stage Stochastic Program for Unit Commitment With Uncertain Wind Power Output

In this paper, we present a unit commitment problem with uncertain wind power output. The problem is formulated as a chance-constrained two-stage (CCTS) stochastic program. Our model ensures that, with high probability, a large portion of the wind power output at each operating hour will be utilized. The proposed model includes both the two-stage stochastic program and the chance-constrained stochastic program features. These types of problems are challenging and have never been studied together before, even though the algorithms for the two-stage stochastic program and the chance-constrained stochastic program have been recently developed separately. In this paper, a combined sample average approximation (SAA) algorithm is developed to solve the model effectively. The convergence property and the solution validation process of our proposed combined SAA algorithm is discussed and presented in the paper. Finally, computational results indicate that increasing the utilization of wind power output might increase the total power generation cost, and our experiments also verify that the proposed algorithm can solve large-scale power grid optimization problems.

The berth allocation problem: models and solution methods

In this paper, we consider the problem of allocating space at berth for vessels with the objective of minimizing total weighted flow time. Two mathematical formulations are considered where one is used to develop a tree search procedure while the other is used to develop a lower bound that can speed up the tree search procedure. Furthermore, a composite heuristic combining the tree search procedure and pair-wise exchange heuristic is proposed for large size problems. Finally, computational experiments are reported to evaluate the efficiency of the methods.

A multiprocessor task scheduling model for berth allocation: heuristic and worst-case analysis

We consider a scheduling problem in which the processors are arranged along a straight line, and each job requires simultaneous processing by multiple consecutive processors. We assume that the job sizes and processing times are agreeable. Our objective is to minimize the total weighted completion time of the jobs. This problem is motivated by the operation of berth allocation, which is to allocate vessels (jobs) to a berth with multiple quay cranes (processors), where a vessel may be processed by multiple consecutive cranes simultaneously. We develop a heuristic for the problem and perform worst-case analysis.

Data-driven chance constrained stochastic program

In this paper, we study data-driven chance constrained stochastic programs, or more specifically, stochastic programs with distributionally robust chance constraints (DCCs) in a data-driven setting to provide robust solutions for the classical chance constrained stochastic program facing ambiguous probability distributions of random parameters. We consider a family of density-based confidence sets based on a general

Stochastic Unit Commitment With Uncertain Demand Response

\phi

Two-stage network constrained robust unit commitment problem

ϕ-divergence measure, and formulate DCC from the perspective of robust feasibility by allowing the ambiguous distribution to run adversely within its confidence set. We derive an equivalent reformulation for DCC and show that it is equivalent to a classical chance constraint with a perturbed risk level. We also show how to evaluate the perturbed risk level by using a bisection line search algorithm for general