Large-scale decentralized unit commitment

Feizollahi, Mohammad Javad; Costley, Mitch; Ahmed, Shabbir; Grijalva, Santiago

doi:10.1016/j.ijepes.2015.04.009

Cited by 96 publications

(73 citation statements)

References 35 publications

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“…In the particular domain of mixed‐integer problems such as SMIP problems, there has been renewed interest in the use of augmented Lagrangian approaches (Burachik et al., ; Feizollahi et al., ; Geißler et al., ). The augmented Lagrangian relaxation of

ζ^{S I P}

that relaxes the NAC in Equation is

ζ_{ρ}^{L R +} false(ω false) : = \min_{x, y, z} \sum_{s \in S} L_{s} false(x_{s}, y_{s}, z, ω_{s} false) + ψ_{ρ}^{s} false(x_{s} - z false)

s.t.

…”

Section: Technical Backgroundmentioning

confidence: 99%

Combining penalty‐based and Gauss–Seidel methods for solving stochastic mixed‐integer problems

Oliveira

Christiansen

Dandurand

et al. 2018

Int Trans Operational Res

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In this paper, we propose a novel decomposition approach (named PBGS) for stochastic mixed‐integer programming (SMIP) problems, which is inspired by the combination of penalty‐based Lagrangian and block Gauss–Seidel methods. The PBGS method is developed such that the inherent decomposable structure that SMIP problems present can be exploited in a computationally efficient manner. The performance of the proposed method is compared with the progressive hedging (PH) method, which also can be viewed as a Lagrangian‐based method for obtaining solutions for SMIP problems. Numerical experiments performed using instances from the literature illustrate the efficiency of the proposed method in terms of computational performance and solution quality.

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ζ^{S I P}

that relaxes the NAC in Equation is

ζ_{ρ}^{L R +} false(ω false) : = \min_{x, y, z} \sum_{s \in S} L_{s} false(x_{s}, y_{s}, z, ω_{s} false) + ψ_{ρ}^{s} false(x_{s} - z false)

s.t.

…”

Section: Technical Backgroundmentioning

confidence: 99%

Combining penalty‐based and Gauss–Seidel methods for solving stochastic mixed‐integer problems

Oliveira

Christiansen

Dandurand

et al. 2018

Int Trans Operational Res

View full text Add to dashboard Cite

show abstract

Section: Assessment and Modelling Of Primary Frequency Response Requimentioning

confidence: 99%

“…In this case, primary frequency response (PFR) is activated in order to confine the variation of system frequency and recover it to a normal range within a pre-set timeframe. Many publications, even recent ones, on UC research do not consider a PFR constraint [24,[32][33][34] or only consider a total PFR requirement instead of linking the PFR provision rate requirement with system inertia in UC optimisation [31]. Considering the high penetration level of non-synchronous RES which can result in a low-inertia system [35] and the increment of the largest generation loss due to the construction of nuclear plant in the future, both PFR provision rate and full requirement are expected to have a substantial increase (however, the requirements for PFR could potentially be reduced if nuclear plants are incentivised to operate in part-loaded mode).…”

Section: Assessment and Modelling Of Primary Frequency Response Requimentioning

confidence: 99%

System-Level Operational and Adequacy Impact Assessment of Photovoltaic and Distributed Energy Storage, with Consideration of Inertial Constraints, Dynamic Reserve and Interconnection Flexibility

et al. 2017

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Abstract:The growing penetration of solar photovoltaic (PV) systems requires a fundamental understanding of its impact at a system-level. Furthermore, distributed energy storage (DES) technologies, such as batteries, are attracting great interest owing to their ability to provide support to systems with large-scale renewable generation, such as PV. In this light, the system-level impacts of PV and DES are assessed from both operational and adequacy perspectives. Different control strategies for DES are proposed, namely: (1) centralised, to support system operation in the presence of increasing requirements on system ramping and frequency control; and (2) decentralised, to maximise the harnessing of solar energy from individual households while storing electricity generated by PV panels to provide system capacity on request. The operational impacts are assessed by deploying a multi-service unit commitment model with consideration of inertial constraints, dynamic reserve allocation, and interconnection flexibility, while the impacts on adequacy of supply are analysed by assessing the capacity credit of PV and DES through different metrics. The models developed are then applied to different future scenarios for the Great Britain power system, whereby an electricity demand increase due to electrification is also considered. The numerical results highlight the importance of interconnectors to provide flexibility. On the other hand, provision of reserves, as opposed to energy arbitrage, from DES that are integrated into system operation is seen as the most effective contribution to improve system performance, which in turn also decreases the role of interconnectors. DES can also contribute to providing system capacity, but to an extent that is limited by their individual and aggregated energy availability under different control strategies.

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“…[49] and [50] introduce a more flexible, distributed scheme that avoids the hierarchical structure of TRIANA. Both decompose the resulting large scale MILP for coordinating thermo-electrical heating units, thereby facilitating the solution process.…”

Section: Conceptsmentioning

confidence: 99%