scispace - formally typeset
Search or ask a question
Topic

Stochastic programming

About: Stochastic programming is a research topic. Over the lifetime, 12343 publications have been published within this topic receiving 421049 citations.


Papers
More filters
Journal ArticleDOI
TL;DR: In this article, a stochastic programming approach for increasing resiliency of a distribution system exposed to an approaching wildfire is proposed, where the uncertainties associated with solar radiation, wind speed, and wind direction are taken into account.
Abstract: Natural disasters can cause significant damage to power grids. During summer, in countries with high temperatures, distribution systems passing through forested areas are prone to wildfires. This paper proposes a stochastic programming approach for increasing resiliency of a distribution system exposed to an approaching wildfire. Dynamic line rating of the overhead lines is considered in order to model the impact of the wildfire on conductor temperature and flowing current. The uncertainties associated with solar radiation, wind speed, and wind direction that affect the progression of the wildfire and the production of stochastic distributed generators are taken into account. A scenario reduction algorithm is applied to reduce the number of scenarios in a tractable size and subsequently the computational burden. The proposed model is transformed to a mixed-integer problem with quadratic constraints, which provides effective solution to the operation of a distribution system against an approaching wildfire. A modified IEEE 33-bus distribution system is used to illustrate the applicability of the proposed approach.

144 citations

Journal ArticleDOI
TL;DR: The paper describes the implementation of a new integrated tool for risk management in hydropower systems where operation scheduling and hedging by future contacts are integrated in one model and the resulting large stochastic dynamic optimization problem is solved.
Abstract: The paper describes the implementation of a new integrated tool for risk management in hydropower systems. Earlier practice in Scandinavia has been to separate operations scheduling and contract management. In the present approach operation scheduling and hedging by future contacts are integrated in one model. The risk level is controlled by setting revenue targets. Revenues below target are penalized; this implicitly defines a revenue utility function to reduce risk. The possibility of dynamically changing the future contract portfolio is now represented. The resulting large stochastic dynamic optimization problem is solved using a combination of stochastic dynamic programming and stochastic dual dynamic programming. Simulations for a test case show that the profit in the lower range is considerably improved with the new tool. The approach can be useful for hydropower companies that face price risks in addition to the inflow uncertainty, as is the case in a deregulated system.

144 citations

Journal ArticleDOI
TL;DR: A sample average approximation (SAA) method for stochastic programming problems with expected value constraints, for example, in portfolio selection with constraints on conditional value-at-risk (CVaR).

143 citations

Posted Content
TL;DR: An efficient stochastic dynamic programming model is introduced to optimally charge an electric vehicle while accounting for the uncertainty inherent to its use and it is shown that the randomness intrinsic to driving needs has a substantial impact on the charging strategy to be implemented.
Abstract: The combination of electric vehicles (EVs) and renewable energy is taking shape as a potential driver for a future free of fossil fuels. However, the efficient management of the EV fleet is not exempt from challenges. It calls for the involvement of all actors directly or indirectly related to the energy and transportation sectors, ranging from governments, automakers and transmission system operators, to the ultimate beneficiary of the change: the end-user. An EV is primarily to be used to satisfy driving needs, and accordingly charging policies must be designed primarily for this purpose. The charging models presented in the technical literature, however, overlook the stochastic nature of driving patterns. Here we introduce an efficient stochastic dynamic programming model to optimally charge an EV while accounting for the uncertainty inherent to its use. With this aim in mind, driving patterns are described by an inhomogeneous Markov model that is fitted using data collected from the utilization of an EV. We show that the randomness intrinsic to driving needs has a substantial impact on the charging strategy to be implemented.

143 citations

Journal ArticleDOI
TL;DR: In this paper, a spectral stochastic approach for the representation and propagation of uncertainties with an existing deterministic topology optimization technique is proposed, which combines the spectral-stochastic approach and the deterministic approach.

143 citations


Network Information
Related Topics (5)
Optimization problem
96.4K papers, 2.1M citations
86% related
Scheduling (computing)
78.6K papers, 1.3M citations
85% related
Optimal control
68K papers, 1.2M citations
84% related
Supply chain
84.1K papers, 1.7M citations
83% related
Markov chain
51.9K papers, 1.3M citations
79% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023175
2022423
2021526
2020598
2019578
2018532