Multistage Stochastic Unit Commitment Using Stochastic Dual Dynamic Integer Programming

Zou, Jikai; Ahmed, Shabbir; Sun, Xu Andy

doi:10.1109/tpwrs.2018.2880996

Cited by 99 publications

(58 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides the standard Benders cuts, the authors propose to use other families of cutting planes including Lagrangean cuts, integer cuts, and strengthened Benders cuts. Applications of SDDiP have been reported in Escudero et al (2020); Zou et al (2018); Lara et al (2019).…”

Section: Algorithms For Multistage Stochastic (Mixed-integer) Linear Programsmentioning

confidence: 99%

A Review of Stochastic Programming Methods for Optimization of Process Systems Under Uncertainty

Grossmann

2021

Front. Chem. Eng.

View full text Add to dashboard Cite

Uncertainties are widespread in the optimization of process systems, such as uncertainties in process technologies, prices, and customer demands. In this paper, we review the basic concepts and recent advances of a risk-neutral mathematical framework called “stochastic programming” and its applications in solving process systems engineering problems under uncertainty. This review intends to provide both a tutorial for beginners without prior experience and a high-level overview of the current state-of-the-art developments for experts in process systems engineering and stochastic programming. The mathematical formulations and algorithms for two-stage and multistage stochastic programming are reviewed with illustrative examples from process industries. The differences between stochastic programming under exogenous uncertainty and endogenous uncertainties are discussed. The concepts and several data-driven methods for generating scenario trees are also reviewed.

show abstract

Section: Algorithms For Multistage Stochastic (Mixed-integer) Linear Programsmentioning

confidence: 99%

A Review of Stochastic Programming Methods for Optimization of Process Systems Under Uncertainty

Grossmann

2021

Front. Chem. Eng.

View full text Add to dashboard Cite

show abstract

“…This attractive algorithmic strategy builds up a solution piece by piece, and always chooses the next piece that offers the most obvious and immediate benefit [46,47]. In many discrete problems, a greedy strategy does not in general produce an optimal solution, but the global optimal solution must be achieved in a reasonable number of steps for the continuous LP model (21), because LP is convex. Furthermore, the flowchart of greedy algorithm is presented in Fig.…”

Section: Greedy Algorithm For Identifying Redundant Constraintsmentioning

confidence: 99%

Fast identifying redundant security constraints in SCUC in the presence of uncertainties

Ding

Bai

et al. 2020

IET Generation Trans & Dist

View full text Add to dashboard Cite

Security constrained unit commitment (SCUC), with renewable resources integrated into power grid, is one core function in the day-ahead market. However, it confronts with critical challenges as the heavy complicated security constraints are considered. Usually, it is observed that lots of security constraints are redundant, which can be identified and eliminated, so as to reduce the computational complexity. In this study, the authors first lift the uncertain feasible region of SCUC into a high dimensional space. Furthermore, a fast identification method is proposed to relax the original feasible region, which thus can be solved by the classical greedy algorithm. In order to prevent the over-relaxation and find more redundant constraints, an efficient feasible-based bound tightening strategy is utilised to provide a tighter bound. Numerical results on large-scale test systems verify the effectiveness of the proposed method.

show abstract

“…Renewable generations have the uncertainty of power output; that is why the introduction of stochastic UC programming is becoming very necessary [48,49]. Most of the recent papers have considered two or multistage stochastic UC [50][51][52][53][54][55]. Some articles considered hybrid scholastic UC to manage uncertainty on the expected net load [56].…”

Section: Stochastic Uc Problemmentioning

confidence: 99%

Energy Storage System Analysis Review for Optimal Unit Commitment

et al. 2019

View full text Add to dashboard Cite

Energy storage systems (ESSs) are essential to ensure continuity of energy supply and maintain the reliability of modern power systems. Intermittency and uncertainty of renewable generations due to fluctuating weather conditions as well as uncertain behavior of load demand make ESSs an integral part of power system flexibility management. Typically, the load demand profile can be categorized into peak and off-peak periods, and adding power from renewable generations makes the load-generation dynamics more complicated. Therefore, the thermal generation (TG) units need to be turned on and off more frequently to meet the system load demand. In view of this, several research efforts have been directed towards analyzing the benefits of ESSs in solving optimal unit commitment (UC) problems, minimizing operating costs, and maximizing profits while ensuring supply reliability. In this paper, some recent research works and relevant UC models incorporating ESSs towards solving the abovementioned power system operational issues are reviewed and summarized to give prospective researchers a clear concept and tip-off on finding efficient solutions for future power system flexibility management. Conclusively, an example problem is simulated for the visualization of the formulation of UC problems with ESSs and solutions.

show abstract

Multistage Stochastic Unit Commitment Using Stochastic Dual Dynamic Integer Programming

Cited by 99 publications

References 28 publications

A Review of Stochastic Programming Methods for Optimization of Process Systems Under Uncertainty

A Review of Stochastic Programming Methods for Optimization of Process Systems Under Uncertainty

Fast identifying redundant security constraints in SCUC in the presence of uncertainties

Energy Storage System Analysis Review for Optimal Unit Commitment

Contact Info

Product

Resources

About