Stochastic unit commitment problem

Shiina, Takayuki; Birge, John R.

doi:10.1111/j.1475-3995.2004.00437.x

Cited by 87 publications

(46 citation statements)

References 12 publications

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“…Many popular algorithms for stochastic unit commitment (SUC) problems are based on decomposition techniques. They can be divided into three groups: Benders decomposition [9], Progressive Hedging [10], and Lagrangian relaxation [11] or Dantzig-Wolfe decomposition [12]. All three approaches are applicable to two-stage or multi-stage models and can be used to decompose the problem by stages, scenarios, or generation units.…”

Section: Introductionmentioning

confidence: 99%

The value of stochastic programming in day-ahead and intra-day generation unit commitment

Schulze

McKinnon

2016

Energy

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The recent expansion of renewable energy supplies has prompted the development of a variety of efficient stochastic optimization models and solution techniques for hydro-thermal scheduling. However, little has been published about the added value of stochastic models over deterministic ones. In the context of day-ahead and intra-day unit commitment under wind uncertainty, we compare two-stage and multi-stage stochastic models to deterministic ones and quantify their added value. We present a modification of the WILMAR scenario generation technique designed to match the properties of the errors in our wind forecasts, and show that this is needed to make the stochastic approach worthwhile. Our evaluation is done in a rolling horizon fashion over the course of two years, using a 2020 central scheduling model based on the British power system, with transmission constraints and a detailed model of pump storage operation and system-wide reserve and response provision. We show that in day-ahead scheduling the stochastic approach saves 0.3% of generation costs compared to the best deterministic approach, but the savings are less in intra-day scheduling.

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Section: Introductionmentioning

confidence: 99%

The value of stochastic programming in day-ahead and intra-day generation unit commitment

Schulze

McKinnon

2016

Energy

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“…Alternative relaxations were subsequently presented by Carpentier et al [20] and Nowak and Römisch [21]. Shiina and Birge [22] presented an alternative decomposition approach for solving the stochastic unit commitment problem using column decomposition. In Papavasiliou et al [14] the authors present a dual decomposition algorithm for solving the problem that is also used in this paper.…”

Section: Introductionmentioning

confidence: 99%

A stochastic unit commitment model for integrating renewable supply and demand response

Papavasiliou

Oren

2012

2012 IEEE Power and Energy Society General Meeting

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Abstract-We present a stochastic unit commitment model for assessing the reserve requirements resulting from the large-scale integration of renewable energy sources and deferrable demand in power systems. We present three alternative demand response paradigms for assessing the benefits of demand flexibility in absorbing the uncertainty and variability associated with renewable supply: centralized co-optimization of generation and demand by the system operator, demand bids and coupling renewable resources with deferrable loads.We present simulation results for a model of the Western Interconnection.

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“…Representative integer programming and combinatorial optimization problems include the unit commitment problem , Shiina and Birge (2004) Shiina and Watanabe (2004)) and the power generation planning problem (Shiina and Birge (2003)). Discussions of the application of stochastic programming methods to the electricity industry can be found in works by Ruszczyński and Shapiro (2008) and by Shapiro, Dentcheva and Ruszczyński (2009).…”

Section: Introductionmentioning

confidence: 99%

Design of distribution network using power flow controller

Shiina

2018

JAMDSM

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We consider the design of distribution network using power flow controller. In power delivery systems, the use of dispersed generation and security control to improve network utilization requires the optimal use of system control devices. The installation of loop controller allows the distribution system to operate in a loop configuration, achieving effective management of voltage and power flow. In the investment planning process, it is important to identify the optimal location and installed capacity of the equipment such that all operational constraints are satisfied. This paper presents a method for identifying the optimal location and capacity with the minimum installation cost. Our novel approach uses an economic model that considers the fixed costs. A slope scaling procedure is presented, and its efficiency is demonstrated using numerical experiments.

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Stochastic unit commitment problem

Cited by 87 publications

References 12 publications

The value of stochastic programming in day-ahead and intra-day generation unit commitment

The value of stochastic programming in day-ahead and intra-day generation unit commitment

A stochastic unit commitment model for integrating renewable supply and demand response

Design of distribution network using power flow controller

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