Terrence W. K. Mak

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This paper considers the restoration of a transmission system after a significant disruption such as a natural disaster. It considers the Restoration Order Problem (ROP) that jointly considers generator dispatch, load pickups, and restoration prioritization to minimize the size of the blackout while satisfying the network operational constraints. The paper(More)
Soft constraints are functions returning costs, and are essential in modeling over-constrained and optimization problems. We are interested in tackling soft constrained problems with adversarial conditions. Aiming at generalizing the weighted and quantified constraint satisfaction frameworks, a Quantified Weighted Constraint Satisfaction Problem (QWCSP)(More)
We address the problem of power system restoration after a significant blackout. Prior work focus on optimization methods for finding high-quality restoration plans. Optimal solutions consist in a sequence of grid repairs and corresponding steady states. However, such approaches lack formal guarantees on the transient stability of restoration actions, a key(More)
Minimax Weighted Constraint Satisfaction Problems (formerly called QWCSPs) are a framework for modeling soft constrained problems with adversarial conditions. In this paper, we describe novel definitions and implementations of node, arc and full directional arc consistency notions to help reduce search space on top of the basic tree search with alpha-beta(More)
Minimax Weighted Constraint Satisfaction Problems (formerly called Quantified Weighted CSPs) are a framework for modeling soft constrained problems with adversarial conditions. In this paper, we study the effects of a value ordering heuristic in solving ultra-weak solutions on top of the alpha beta tree search with constraint propagation. The value ordering(More)
The growing reliance of electric power systems on gas-fired generation to balance intermittent sources of renewable energy has increased the variation and volume of flows through natural gas transmission pipelines. Adapting pipeline operations to maintain efficiency and security under these new conditions requires optimization methods that account for(More)
Distributed constraint solving are useful in tackling constrained problems when agents are not allowed to share his/her private information to others and/or gathering all necessary information to solve the problem in a centralized manner is infeasible. With these two limitations, distributed algorithms solve the problem by coordinating agents to negotiate(More)
The task at hand is that of a soft constraint problem with adversarial conditions. By amalgamating the weighted and quantified constraint satisfaction frameworks, a Minimax Weighted Constraint Satisfaction Problem (formerly Quantified Weighted Constraint Satisfaction Problem) consists of a set of finite domain variables, a set of soft constraints, and a min(More)
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