Toward scalable stochastic unit commitment

Cheung, Kwok W.; Gade, Dinakar; Silva-Monroy, Cesar A.; Ryan, Sarah M.; Watson, Jean‐Paul; Wets, Roger J.-B.; Woodruff, David L.

doi:10.1007/s12667-015-0148-6

Cited by 55 publications

(20 citation statements)

References 23 publications

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“…While we do not examine multi-stage stochastic unit commitment models in the companion paper [4], we note that our scenarios are tree-structured and consequently can be effectively used in that context.…”

Section: Resultsmentioning

confidence: 99%

“…Our experiments proceed in the context of publicly available data from the Independent System Operator of New England (ISO-NE). The resulting scenarios are then used in the companion paper [4] to rigorously test the scalability of a stochastic unit commitment solver. These scenarios and the resulting test cases are publicly available, filling a critical need for researchers investigating the scalability of stochastic unit commitment solvers.…”

Section: Introductionmentioning

confidence: 99%

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Toward scalable stochastic unit commitment. Part 1: load scenario generation

et al. 2015

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Unit commitment decisions made in the day-ahead market and during subsequent reliability assessments are critically based on forecasts of load. Tra-ditional, deterministic unit commitment is based on point or expectation-based load forecasts. In contrast, stochastic unit commitment relies on multiple load sce-narios, with associated probabilities, that in aggregate capture the range of likely load time-series. The shift from point-based to scenario-based forecasting necessi-tates a shift in forecasting technologies, to provide accurate inputs to stochastic unit commitment. In this paper, we discuss a novel scenario generation method-ology for load forecasting in stochastic unit commitment, with application to real data associated with the Independent System Operator for New England (ISO-NE). The accuracy of the expected scenario generated using our methodology is consistent with that of point forecasting methods. The resulting sets of realistic scenarios serve as input to rigorously test the scalability of stochastic unit com-mitment solvers, as described in the companion paper. The scenarios generated by our method are available as an online supplement to this paper, as part of a novel, publicly available large-scale stochastic unit commitment benchmark. Abstract Unit commitment decisions made in the day-ahead market and during subsequent reliability assessments are critically based on forecasts of load. Traditional, deterministic unit commitment is based on point or expectation-based load forecasts. In contrast, stochastic unit commitment relies on multiple load scenarios, with associated probabilities, that in aggregate capture the range of likely load time-series. The shift from point-based to scenario-based forecasting necessitates a shift in forecasting technologies, to provide accurate inputs to stochastic unit commitment. In this paper, we discuss a novel scenario generation methodology for load forecasting in stochastic unit commitment, with application to real data associated with the Independent System Operator for New England (ISO-NE). The accuracy of the expected scenario generated using our methodology is consistent with that of point forecasting methods. The resulting sets of realistic scenarios serve as input to rigorously test the scalability of stochastic unit commitment solvers, as described in the companion paper. The scenarios generated Disciplines Industrial Engineering | Systems Engineering

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Toward scalable stochastic unit commitment. Part 1: load scenario generation

et al. 2015

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“…Scenario decomposition approaches based on Rockafellar and Wets' progressive hedging algorithm have been proposed in [24], [25] to solve two-stage Stochastic Mixed-Integer Linear Problems (SMILP) with uncertainty at the operational level. In [25] the investment model is static and the decisions can only be made at the beginning of the period.…”

Section: A Decomposition Techniquesmentioning

confidence: 99%

“…Progressive Hedging (PH) is another popular decomposition technique applied to SMILP problems; however, it does not guarantee convergence to an optimal solution while intermediate solutions may be infeasible. In addition, PH has been largely limited to two-stage cases [24], [25], [26]. Recently, a method to compute lower bounds in the Progressive Hedging Algorithm for multi-stage mixed integer programs has been proposed in [27].…”

Section: A Decomposition Techniquesmentioning

confidence: 99%

Planning With Multiple Transmission and Storage Investment Options Under Uncertainty: A Nested Decomposition Approach

Falugi

Konstantelos

Štrbac

2018

IEEE Trans. Power Syst.

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Abstract-Achieving the ambitious climate change mitigation objectives set by governments worldwide is bound to lead to unprecedented amounts of network investment to accommodate low-carbon sources of energy. Beyond investing in conventional transmission lines, new technologies such as energy storage can improve operational flexibility and assist with the costeffective integration of renewables. Given the long lifetime of these network assets and their substantial capital cost, it is imperative to decide on their deployment on a long-term costbenefit basis. However, such an analysis can result in large-scale Mixed Integer Linear Programming (MILP) problems which contain many thousands of continuous and binary variables. Complexity is severely exacerbated by the need to accommodate multiple candidate assets and consider a wide range of exogenous system development scenarios that may occur. In this manuscript we propose a novel, efficient and highly-generalizable framework for solving large-scale planning problems under uncertainty by using a temporal decomposition scheme based on the principles of Nested Benders. The challenges that arise due to the presence of non-sequential investment state equations and sub-problem non-convexity are highlighted and tackled. The substantial computational gains of the proposed method are demonstrated via a case study on the IEEE 118 bus test system that involve planning of multiple transmission and storage assets under longterm uncertainty. The proposed method is shown to substantially outperform the current state-of-the-art. All the parameters involved in the optimization model can be represented by the vector ρ containing B n,g Bus-to-generation incidence matrix. I n,ℓ Bus-to-line incidence matrix. S n,s Bus-to-storage incidence matrix.

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“…Moreover, the introduction of variable energy resources (e.g., wind and solar) leads to circumstances in which predictions from deterministic models are subject to significant errors. In order to accommodate such challenges, there have been recommendations that UC models should be solved using either the stochastic or the robust UC formulations (see, e.g., [15] and [16], respectively). In either case, the speed with which large scale deterministic UC models can be solved becomes important.…”

Section: Introductionmentioning

confidence: 99%

A State Transition MIP Formulation for the Unit Commitment Problem

Atakan

Lulli

Sen

2018

IEEE Trans. Power Syst.

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Abstract-In this paper, we present the state-transition formulation for the unit commitment (UC) problem. This formulation uses new decision variables that capture the state transitions of the generators, instead of their on/off statuses. We show that this new approach produces a formulation which naturally includes valid inequalities, commonly used to strengthen other formulations. We demonstrate the performance of the statetransition formulation and observe that it leads to improved solution times especially in longer time-horizon instances. As an important consequence, the new formulation allows us to solve realistic instances in less than 12 minutes on an ordinary desktop PC, leading to a speed-up of a factor of almost two, in comparison to the nearest contender. Finally, we demonstrate the value of considering longer planning horizons in UC problems.

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Toward scalable stochastic unit commitment

Cited by 55 publications

References 23 publications

Toward scalable stochastic unit commitment. Part 1: load scenario generation

Toward scalable stochastic unit commitment. Part 1: load scenario generation

Planning With Multiple Transmission and Storage Investment Options Under Uncertainty: A Nested Decomposition Approach

A State Transition MIP Formulation for the Unit Commitment Problem

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