Risk-Limiting Unit Commitment in Smart Grid With Intelligent Periphery

Peng, Chaoyi; Hou, Yunhe; Yu, Nanpeng; Wang, Weisheng

doi:10.1109/tpwrs.2017.2672939

Cited by 23 publications

(20 citation statements)

References 27 publications

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“…As a result, the objective values of the overall model, for the actually realised scenario, should subtract the cost of the overlapped time periods. In contrast, the dispatch and recourse stages in [17,24] are in series and the objective values are additive in terms of the cost of various stages.…”

Section: Key Features Of Proposed Rl Ucmentioning

confidence: 99%

“…The risk factor in the last constraint in (14) is computationally difficult to process as the probability is not explicitly accessible in the scenario tree. Inspired by the work in [17,24], we apply the formulation of conditional value-at-risk (CVaR) as the risk factor. Let…”

Section: Risk Factor As Conditional Value-at-riskmentioning

confidence: 99%

“…The constraint (17) can be converted into s set of piece-wise linear constraints, as the loss function is a linear combination of loss shedding. The proof for the validity of CVaR constraint as the RL constraint can be referred to [17,24].…”

Section: Risk Factor As Conditional Value-at-riskmentioning

confidence: 99%

See 2 more Smart Citations

Improving wind power utilisation under stormy weather condition by risk‐limiting unit commitment

Cai

Qin

Hou

2018

IET Renewable Power Generation

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Power grid resilience has been a major concern as extreme weather events become more frequent recently. Particularly, wind power generation has constituted a significant body of generation portfolio worldwide. Under stormy weather conditions, wind power generation would be curtailed due to the over-speed protection of wind turbines. Subsequently, the sharply reduced generation resources would significantly endanger the reliability of power supply. In this study, the authors propose to apply the risk-limiting methodology to improve the efficient utilisation of wind power prior to reluctant wind curtailment on the advent of wind stormy events. The proposed method integrates three programs, namely, day-ahead unit commitment (UC), hourly-ahead UC, and real-time load shedding into one single model along with risk-limiting constraints. The first program provides the baseline of hourly dispatch; whereas, the latter two programs serve as recourse means while wind stormy event unfolds. The proposed model is cast into a multi-stage mixed-integer linear programming model and solved by commercial solvers. Illustrative examples demonstrate that the proposed method can reduce the total dispatch cost over the time-horizon including the wind stormy event, by postponing the timing of proactively starting quick-start generating units and shedding loads, in contrast to conventional two-stage stochastic dispatch methods.

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Section: Key Features Of Proposed Rl Ucmentioning

confidence: 99%

Section: Risk Factor As Conditional Value-at-riskmentioning

confidence: 99%

See 1 more Smart Citation

Improving wind power utilisation under stormy weather condition by risk‐limiting unit commitment

Cai

Qin

Hou

2018

IET Renewable Power Generation

Self Cite

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“…Risk limiting dispatch, with flexible uncertainty handling characteristic, is an effective way to deal with RES uncertainty [16]. Existing studies [17]- [19] however incorporate the risk related constraints in a coupled way, while distributed schemes require decoupling of risk constraints. Thus, further work should be done to integrate risk limitations into the distributed optimization method in order to provide a distributed risk-limiting load restoration scheme.…”

mentioning

confidence: 99%

“…However, the computation efficiency is challenged when the number of scenarios is large. Another scenario-based method, the conditional value-at-risk (CVaR) method, was employed in unit com-mitment [17], electricity market problems [19] and voltage control [20] to deal with all kinds of uncertainties. The CVaR method has the risk-tail representation ability and fast calculation characteristic even when the number of scenarios is large.…”

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confidence: 99%

Distributed Risk-Limiting Load Restoration for Wind Power Penetrated Bulk System

Zhao

Wang

et al. 2020

IEEE Trans. Power Syst.

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Stochastic investment model for custom Voltage‐Sag‐Preventing service considering service price and punitive damages of contract

Zhou

et al. 2019

IEEJ Transactions Elec Engng

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Custom power quality (PQ) plays a significant part in improving PQ in distribution networks and hedging economic losses for power‐quality‐sensitive electricity users. In an electricity market with multiple power companies competing with each other, for a power company, providing custom PQ with proper service prices becomes a key element to maximize its total economic profit and improve competitiveness. In this work, with a focus on the voltage sag (VS) problem, a stochastic investment model for installing voltage‐sag‐preventing equipment is proposed. First, basic cost–benefit models are established from the perspectives of power companies and electricity users. Then, considering uncertainties in providing power with an expected number of voltage sags resulting from equipment failure and so on, proper pricing mechanisms and punitive damages of service contracts are analyzed accordingly. With each mechanism, the stochastic investment decision‐making model is formulated with the goal of maximizing a power company's economic profit during the life cycle of voltage‐sag‐preventing equipment. The influences of different factors on the economic benefits of the investment are analyzed in a case study, including the compensation coefficient, investment cost, and failure uncertainties, providing a reference for decision‐making for investment in and possible pricing of custom VS services. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Risk-Limiting Unit Commitment in Smart Grid With Intelligent Periphery

Cited by 23 publications

References 27 publications

Improving wind power utilisation under stormy weather condition by risk‐limiting unit commitment

Improving wind power utilisation under stormy weather condition by risk‐limiting unit commitment

Distributed Risk-Limiting Load Restoration for Wind Power Penetrated Bulk System

Stochastic investment model for custom Voltage‐Sag‐Preventing service considering service price and punitive damages of contract

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