A ‘Big Bad Bug’ threatens Lewis’s Humean metaphysics of chance (Lewis 1986a, p. XIV); his Principal Principle provides an intuitive link between and credence. Yet on the one hand, certain future developments are incompatible with true theory chance, but other such have a positive to occur. The combination these two claims leads inconsistent credences. I present solution Bug: chances relative li...

Power systems are shifted from conventional bulk generation toward renewable generation. This trend leads to the frequency security problem due decline of system inertia. On other hand, natural gas-fired units frequently scheduled provide operational flexibility their fast adjustment ability. The interdependence between power and gas is thus intensified. In this context, paper considers constra...

Well known classical backgammon is an ancient two-player game, at least a thousand years, older than chess according to some estimates. It is a game of luck and skill, because it involves dices. In this work we construct a new backgammon which we call No Chance Backgammon since it does not involve the dice. Instead of a random element, players choose their own numbers under certain constraints ...

In this paper, we introduce an approach for constructing uncertainty sets for robust optimization using new deviation measures for random variables termed the forward and backward deviations. These deviation measures capture distributional asymmetry and lead to better approximations of chance constraints. Using a linear decision rule, we also propose a tractable approximation approach for solvi...

We describe computational procedures to solve a wide-ranging class of stochastic programs with chance constraints where the random components of the problem are discretely distributed. Our procedures are based on a combination of Lagrangian relaxation and scenario decomposition, which we solve using a novel variant of Rockafellar and Wets’ progressive hedging algorithm. Experiments demonstrate ...

We investigate constrained optimal control problems for linear stochastic dynamical systems evolving in discrete time. We consider minimization of an expected value cost subject to probabilistic constraints. We study the convexity of a finite-horizon optimization problem in the case where the control policies are affine functions of the disturbance input. We propose an expectation-based method ...

We present a computationally-efficient approach for solving stochastic, multiperiod optimal power flow problems. The objective is to determine power schedules for controllable devices in a power network, such as generators, storage, and curtailable loads, which minimize expected short-term operating costs under various device and network constraints. These schedules are chosen in a multistage d...