Abstract-Unit commitment, one of the most critical tasks in electric power system operations, faces new challenges as the supply and demand uncertainty increases dramatically due to the integration of variable generation resources such as wind power and price responsive demand. To meet these challenges, we propose a two-stage adaptive robust unit commitment model for the security constrained unit commitment problem in the presence of nodal net injection uncertainty. Compared to the conventional stochastic programming approach, the proposed model is more practical in that it only requires a deterministic uncertainty set, rather than a hard-to-obtain probability distribution on the uncertain data. The unit commitment solutions of the proposed model are robust against all possible realizations of the modeled uncertainty. We develop a practical solution methodology based on a combination of Benders decomposition type algorithm and the outer approximation technique. We present an extensive numerical study on the real-world large scale power system operated by the ISO New England. Computational results demonstrate the economic and operational advantages of our model over the traditional reserve adjustment approach.Index Terms-Bilevel mixed-integer optimization, power system control and reliability, robust and adaptive optimization, security constrained unit commitment.

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This paper proposes three strong second order cone programming (SOCP) relaxations for the AC optimal power flow (OPF) problem. These three relaxations are incomparable to each other and two of them are incomparable to the standard SDP relaxation of OPF. Extensive computational experiments show that these relaxations have numerous advantages over existing convex relaxations in the literature: (i) their solution quality is extremely close to that of the SDP relaxations (the best one is within 99.96% of the SDP relaxation on average for all the IEEE test cases) and consistently outperforms previously proposed convex quadratic relaxations of the OPF problem, (ii) the solutions from the strong SOCP relaxations can be directly used as a warm start in a local solver such as IPOPT to obtain a high quality feasible OPF solution, and (iii) in terms of computation times, the strong SOCP relaxations can be solved an order of magnitude faster than standard SDP relaxations. For example, one of the proposed SOCP relaxations together with IPOPT produces a feasible solution for the largest instance in the IEEE test cases (the 3375-bus system) and also certifies that this solution is within 0.13% of global optimality, all this computed within 157.20 seconds on a modest personal computer. Overall, the proposed strong SOCP relaxations provide a practical approach to obtain feasible OPF solutions with extremely good quality within a time framework that is compatible with the real-time operation in the current industry practice

We propose pouch motors, a new family of printable soft actuators integrated with computational design. The pouch motor consists of one or more inflatable gas-tight bladders made of sheet materials. This printable actuator is designed and fabricated in a planar fashion. It allows both easy prototyping and mass fabrication of affordable robotic systems. We provide theoretical models of the actuators compared with the experimental data. The measured maximum stroke and tension of the linear pouch motor are up to 28% and 100 N, respectively. The measured maximum range of motion and torque of the angular pouch motor are up to 80°and 0.2 N, respectively. We also develop an algorithm that automatically generates the patterns of the pouches and their fluidic channels. A custom-built fabrication machine streamlines the automated process from design to fabrication. We demonstrate a computer-generated life-sized hand that can hold a foam ball and perform gestures with 12 pouch motors, which can be fabricated in 15 min.

The growing uncertainty associated with the increasing penetration of wind and solar power generation has presented new challenges to the operation of large-scale electric power systems. Motivated by these challenges, we present a multistage adaptive robust optimization model for the most critical daily operational problem of power systems, namely the unit commitment (UC) problem, in the situation where nodal net electricity loads are uncertain. The proposed multistage robust UC model takes into account the time causality of the hourly unfolding of uncertainty in the power system operation process, which we show to be relevant when ramping capacities are limited and net loads present significant variability. To deal with large-scale systems, we explore the idea of simplified affine policies and develop a solution method based on constraint generation. Extensive computational experiments on the IEEE 118-bus test case and a real-world power system with 2736 buses demonstrate that the proposed algorithm is effective in handling large-scale power systems and that the proposed multistage robust UC model can significantly outperform the deterministic UC and existing two-stage robust UC models in both operational cost and system reliability.