Robust optimization vs. stochastic programming incorporating risk measures for unit commitment with uncertain variable renewable generation

Kazemzadeh, Narges; Ryan, Sarah M.; Hamzeei, Mahdi

doi:10.1007/s12667-017-0265-5

Cited by 29 publications

(21 citation statements)

References 52 publications

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“…Their results show that the SP formulation leads to comparatively less robust results with higher computational cost. Kazemzadeh et al [29] focused on two UC formulations involving risk: a risk-averse SP formulation and an ARO formulation. They built two types of uncertainty sets for ARO, one based on ranges and another one that includes probabilities of scenarios together with ranges.…”

Section: Comparison Between Risk-averse Methodologies: Sp Vs Aromentioning

confidence: 99%

Risk-Averse Stochastic Programming vs. Adaptive Robust Optimization: A Virtual Power Plant Application

Conejo

Giraldi

Maître

et al. 2022

INFORMS Journal on Computing

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This paper compares risk-averse optimization methods to address the self-scheduling and market involvement of a virtual power plant (VPP). The decision-making problem of the VPP involves uncertainty in the wind speed and electricity price forecast. We focus on two methods: risk-averse two-stage stochastic programming (SP) and two-stage adaptive robust optimization (ARO). We investigate both methods concerning formulations, uncertainty and risk, decomposition algorithms, and their computational performance. To quantify the risk in SP, we use the conditional value at risk (CVaR) because it can resemble a worst-case measure, which naturally links to ARO. We use two efficient implementations of the decomposition algorithms for SP and ARO; we assess (1) the operational results regarding first-stage decision variables, estimate of expected profit, and estimate of the CVaR of the profit and (2) their performance taking into consideration different sample sizes and risk management parameters. The results show that similar first-stage solutions are obtained depending on the risk parameterizations used in each formulation. Computationally, we identified three cases: (1) SP with a sample of 500 elements is competitive with ARO; (2) SP performance degrades comparing to the first case and ARO fails to converge in four out of five risk parameters; (3) SP fails to converge, whereas ARO converges in three out of five risk parameters. Overall, these performance cases depend on the combined effect of deterministic and uncertain data and risk parameters. Summary of Contribution: The work presented in this manuscript is at the intersection of operations research and computer science, which are intrinsically related with the scope and mission of IJOC. From the operations research perspective, two methodologies for optimization under uncertainty are studied: risk-averse stochastic programming and adaptive robust optimization. These methodologies are illustrated using an energy scheduling problem. The study includes a comparison from the point of view of uncertainty modeling, formulations, decomposition methods, and analysis of solutions. From the computer science perspective, a careful implementation of decomposition methods using parallelization techniques and a sample average approximation methodology was done . A detailed comparison of the computational performance of both methods is performed. Finally, the conclusions allow establishing links between two alternative methodologies in operations research: stochastic programming and robust optimization.

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Section: Comparison Between Risk-averse Methodologies: Sp Vs Aromentioning

confidence: 99%

Risk-Averse Stochastic Programming vs. Adaptive Robust Optimization: A Virtual Power Plant Application

Conejo

Giraldi

Maître

et al. 2022

INFORMS Journal on Computing

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“…Many distinctive formulations had been proposed to approach UC and lots of answer methodologies had been developed [1]- [10]. Over time those components and techniques have advanced and overcome their antecesors based on priority list and dynamic programming [1], as new approaches of the past years the Kullback-Leibler Divergence [2] or Distributionally Robust Optimization [8] have been presented in order to satisfy modern-day maximum performance.…”

Section: Uc Models Under Uncertainity and Solutions Algorithmsmentioning

confidence: 99%

An Overview of Application of Optimization Models Under Uncertainty to the Unit Commitment Problem

Zambrano¹

2022

Preprint

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Optimization models have been broadly used withinside the energy industry as useful decision-making systems for scheduling and dispatching electric powered energy resources; this is applied in a system called unit commitment (UC).Unit Commitment seeks the maximum price adequate generator commitment scheme for an electric powered energy device to satisfy a certain demand, at the same time as fulfilling the operational constraints on transmission models and computational resources. Taking into account risk variability in those processes and checking out their comparative overall performance for a single or multi-stage energy model as a function of monetary performance in addition to the risk related to the commitment decisions.Stochastic programming and Robust optimization are by a vast majority the most widely studied methodologies for UC under net load uncertainty. These techniques will be discussed in this paper.

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“…Both methodologies are different in the way in which they deal with uncertain parameters. In the stochastic programming approach, the uncertain parameters are captured by a discrete number of probabilistic scenarios, whereas in the robust optimization approach, their value ranges are defined by a continuous set [95]. Additionally, they have the ability to deal with errors while the algorithm is running.…”

Section: Ems Based On Stochastic and Robust Programmingmentioning

confidence: 99%

Towards Optimal Management in Microgrids: An Overview

et al. 2021

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A microgrid is a set of decentralized loads and electricity sources, mainly renewable. It can operate connected to and synchronized with a traditional wide-area synchronous grid, i.e., a macrogrid, but can also be disconnected to operate in “island mode” or “isolated mode”. When this microgrid is able to manage its own resources and loads through the use of smart meters, smart appliances, control systems, and the like, it is referred to as a smart grid. Therefore, the management and the distribution of the energy inside the microgrid is an important issue, especially when operating in isolated mode. This work presents an overview of the different solutions that have been tested during the last few years to manage microgrids. The review shows the variety of mature and tested solutions for managing microgrids with different configurations and under several approaches.

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Robust optimization vs. stochastic programming incorporating risk measures for unit commitment with uncertain variable renewable generation

Cited by 29 publications

References 52 publications

Risk-Averse Stochastic Programming vs. Adaptive Robust Optimization: A Virtual Power Plant Application

Risk-Averse Stochastic Programming vs. Adaptive Robust Optimization: A Virtual Power Plant Application

An Overview of Application of Optimization Models Under Uncertainty to the Unit Commitment Problem

Towards Optimal Management in Microgrids: An Overview

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