Chance-constrained dynamic programming with application to risk-aware robotic space exploration
Existing approaches to constrained dynamic programming are limited to formulations where
the constraints share the same additive structure of the objective function (that is, they can …
the constraints share the same additive structure of the objective function (that is, they can …
Chance-constrained optimal power flow: Risk-aware network control under uncertainty
When uncontrollable resources fluctuate, optimal power flow (OPF), routinely used by the
electric power industry to redispatch hourly controllable generation (coal, gas, and hydro …
electric power industry to redispatch hourly controllable generation (coal, gas, and hydro …
AC Optimal Power Flow in Power Systems with Renewable Energy Integration: A Review of Formulations and Case Studies
The primary goal of a power system is to provide consumers with reliable access to power at
the most economical cost. Under the declining costs of renewable energy sources, the …
the most economical cost. Under the declining costs of renewable energy sources, the …
Optimal power flow in the presence of wind power using modified cuckoo search
This study proposes a solution to the optimal power flow (OPF) in power systems
incorporating wind power. The wind generated electricity's cost modelling is presented …
incorporating wind power. The wind generated electricity's cost modelling is presented …
Joint chance-constrained dynamic programming
This paper presents a novel joint chance-constrained dynamic programming algorithm,
which explicitly bounds the probability of failure to satisfy given state constraints. Existing …
which explicitly bounds the probability of failure to satisfy given state constraints. Existing …
Risk-limiting power grid control with an arma-based prediction model
This paper is concerned with the risk-limiting operation of electric power grids with stochastic
uncertainties due to, for example, demand and integration of renewable generation. The …
uncertainties due to, for example, demand and integration of renewable generation. The …
Robust modeling of probabilistic uncertainty in smart grids: Data ambiguous chance constrained optimum power flow
Future Grids will integrate time-intermittent renewables and demand response whose
fluctuating outputs will create perturbations requiring probabilistic measures of resilience …
fluctuating outputs will create perturbations requiring probabilistic measures of resilience …
Risk-limiting, market-based power dispatch and pricing
The purpose of this work is to enable risk-limiting electricity dispatch through a market
mechanism. There has been a solid body of work on centralized risk-limiting dispatch, which …
mechanism. There has been a solid body of work on centralized risk-limiting dispatch, which …
Convex optimal uncertainty quantification: Algorithms and a case study in energy storage placement for power grids
How does one evaluate the performance of a stochastic system in the absence of a perfect
model (ie probability distribution)? We address this question under the framework of optimal …
model (ie probability distribution)? We address this question under the framework of optimal …
A mean-variance optimization approach to the development of portfolios of renewable generation in transmission-constrained systems
We propose a general modeling framework that, under a set of assumptions, allows the
representation of problems involving the construction of portfolios of renewable generators …
representation of problems involving the construction of portfolios of renewable generators …