This paper presents a computation-efficient stochastic dynamic programming algorithm for solving energy storage price arbitrage considering variable charge and discharge efficiencies. We formulate the problem using model real-time prices as Markov process. Then we propose an analytical solution piecewise linear approximation of value-to-go function. Our achieves extreme computation performance ...

This paper proposes and tests an approximation of the solution of a class of piecewise deterministic control problems, typically used in the modeling of manufacturing ow processes. This approximation uses a stochastic programming approach on a suitably discretized and sampled system. The method proceeds through two stages: (i) the Hamilton-Jacobi-Bellman (HJB) dynamic programming equations for ...

This paper proposes a stochastic hydro unit commitment (SHUC) model for price-taker hydropower producer in liberalized market. The objective is to maximize the total revenue of producer, including immediate revenue, future (i.e., opportunity cost), and startup shutdown cost. market price uncertainty taken into account through scenario tree. solution challenging task due its non-convex high-dime...

where L(t) is a given matrix. Unlike the deterministic setting, when the state is a random variable one cannot directly use the variational optimality conditions since the adjoint equation λ̇ = −∂xH cannot describe all possible random evolutions of x(t). We must resort to dynamic programming. We next consider dynamic programming for stochastic systems through the derivation of the stochastic Ham...

in this study, a new stochastic model is proposed to deal with a multi-product, multi-period, multi-stage, multi-site production and transportation supply chain planning problem under demand uncertainty. a two-stage stochastic linear programming approach is used to maximize the expected profit. decisions such as the production amount, the inventory level of finished and semi-finished product, t...

Practical exploitation of optimal dual control (ODC) theory continues to be hindered by the difficulties involved in numerically solving the associated stochastic dynamic programming (SDPs) problems. In particular, high-dimensional hyper-states coupled with the nesting of optimizations and integrations within these SDP problems render their exact numerical solution computationally prohibitive. ...