Chance‐constrained programming approach to stochastic congestion management considering system uncertainties

Hojjat, Mehrdad; Javidi, Mohammad Hossein

doi:10.1049/iet-gtd.2014.0376

Cited by 24 publications

(13 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Chance constrained programming [11] is an important branch of stochastic programming, which was come up with by Charles and Cooper first. If there are random variables in constraint conditions, and the decision has to be made before the random variables are determined, the principle as follows can be considered: the decision that does not satisfy the constraint conditions is allowed to some extent.…”

Section: Chance Constraint Programmingmentioning

confidence: 99%

Economic benefits evaluating of wind power integration considering transmission congestion

Bie

Zhang

Deng

et al. 2016

2016 Clemson University Power Systems Conference (PSC)

View full text Add to dashboard Cite

In this paper, a model of evaluating the economic benefits brought by wind power integration considering transmission congestion is proposed, in which the Probabilistic Load Flow based on Cumulants Method (PLF-CM) is applied to calculate the probability distribution of section power flow. According to the case study, the transmission congestion is the main factor that limits the utilization of wind power, which also limits the economic benefits of wind power. Moreover, the different penetration of wind power and different integration places of wind farms also bring different benefits to the grid, which can provide some references of choosing appropriate wind power integrating places and deciding the best integrating wind power capacity for power grid plan-makers.

show abstract

Section: Chance Constraint Programmingmentioning

confidence: 99%

Economic benefits evaluating of wind power integration considering transmission congestion

Bie

Zhang

Deng

et al. 2016

2016 Clemson University Power Systems Conference (PSC)

View full text Add to dashboard Cite

show abstract

“…In [15], the problem is solved using an iterative gradient calculation and numerical integration, while [16] employs an iterative approach based on the cumulant method and Cornish-Fischer expansion. AC CC-OPF based on linearized equations has been used to find optimal redispatch schedules [17] and solve voltage-constrained OPF in distribution grids [18]. Modelling of the AC CC-OPF based on convex relaxations of the AC power flow equations have been applied in [19], [21].…”

Section: Introductionmentioning

confidence: 99%

Chance-Constrained AC Optimal Power Flow: Reformulations and Efficient Algorithms

Roald

Andersson

2018

IEEE Trans. Power Syst.

210

185

View full text Add to dashboard Cite

Higher levels of renewable electricity generation increase uncertainty in power system operation. To ensure secure system operation, new tools that account for this uncertainty are required. In this paper, we adopt a chance-constrained AC optimal power flow formulation, which guarantees that generation, power flows and voltages remain within their bounds with a pre-defined probability. We then discuss different chanceconstraint reformulations and solution approaches for the problem. We first describe an analytical reformulation based on partial linearization, which enables us to obtain a tractable representation of the optimization problem. We then provide an efficient algorithm based on an iterative solution scheme which alternates between solving a deterministic AC OPF problem and assessing the impact of uncertainty. The flexibility of the iterative scheme enables not only scalable implementations, but also alternative chance-constraint reformulations. In particular, we suggest two sample based reformulations that do not require any approximation or relaxation of the AC power flow equations.In a case study based on four different IEEE systems, we assess the performance of the method, and demonstrate scalability of the iterative scheme. We further show that the analytical reformulation accurately and efficiently enforces chance constraints in both in-and out-of-sample tests, and that the analytical reformulations outperforms the two alternative, sample-based chance constraint reformulations.

show abstract

“…In practice, system operators desire to utilise wind power as much as possible and to be able to request a portion of wind power at a certain probability. Chance-based constraints can be used to deal with the above issue and to describe the percentage of utilised wind power [21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Chance‐constrained maintenance scheduling for interdependent power and natural gas grids considering wind power uncertainty

Wang

et al. 2019

IET Generation, Transmission & Distribution

View full text Add to dashboard Cite

Chance‐constrained programming approach to stochastic congestion management considering system uncertainties

Cited by 24 publications

References 26 publications

Economic benefits evaluating of wind power integration considering transmission congestion

Economic benefits evaluating of wind power integration considering transmission congestion

Chance-Constrained AC Optimal Power Flow: Reformulations and Efficient Algorithms

Chance‐constrained maintenance scheduling for interdependent power and natural gas grids considering wind power uncertainty

Contact Info

Product

Resources

About