Estimating DLMP confidence intervals in distribution networks with AC power flow model and uncertain renewable generation

Wei, Wei; Shen, Ziqi; Wu, Lei; Li, Fangxing; Ding, Tao

doi:10.1049/iet-gtd.2019.0958

Cited by 18 publications

(9 citation statements)

References 63 publications

(89 reference statements)

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“…The contribution of DERs in distribution system operation via voltage support and loss reduction was rewarded by the DLMP. Wei et al [32] estimated the intervals of the DLMP and the confidence levels with the consideration of renewable generation. Papavasiliou [37] presented a detailed analysis of three approaches toward understanding the DLMP because the interpretation of the DLMP is critical for policymaking in the growth of future distribution markets.…”

Section: B Distribution Locational Marginal Pricementioning

confidence: 99%

DLMP of Competitive Markets in Active Distribution Networks: Models, Solutions, Applications, and Visions

Wang

Bai

et al. 2023

Proc. IEEE

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| Traditionally, the electric distribution system operates with uniform energy prices across all system nodes. However, as the adoption of distributed energy resources (DERs) propels a shift from passive to active distribution network (ADN) operation, a distribution-level electricity market has been proposed to manage new complexities efficiently. In addition, distribution locational marginal price (DLMP) has been established in the literature as the primary pricing mechanism. The DLMP inherits the LMP concept in the transmission-level wholesale market but incorporates characteristics of the distribution system, such as high R/X ratios and power losses, system imbalance, and voltage regulation needs. The DLMP provides a solution that can be essential for competitive market operation in future distribution systems. This article first provides an overview of the current distribution-level market architectures and their early implementations. Next, the general clearing model, model relaxations, and DLMP formulation are comprehensively reviewed.The state-of-the-art solution methods for distribution market Manuscript

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Section: B Distribution Locational Marginal Pricementioning

confidence: 99%

DLMP of Competitive Markets in Active Distribution Networks: Models, Solutions, Applications, and Visions

Wang

Bai

et al. 2023

Proc. IEEE

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“…A second‐order cone relaxation method is used in [85] to convexify the ACOPF model for the DLMP calculation, which is shown to reach the global optimality efficiently with a sequential optimization algorithm. The second‐order cone relaxation is also utilized in [86] together with the global polyhedral approximation to derive the DLMP considering the AC power flow constraints. In this study, the confidence level of the DLMP is also calculated to incorporate the uncertainty of renewable generation.…”

Section: Pricing In Distribution Networkmentioning

confidence: 99%

Extensions of the locational marginal price theory in evolving power systems: A review

Tan

Cheng

Liu

et al. 2022

IET Generation Trans & Dist

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The locational marginal price (LMP) theory is the foundation of nowadays electricity markets. With the rapid growth of intermittent renewable generation, new features are emerging in power systems, such as the uncertainty of power supply, the scarcity of flexibility, and the increase of distributed generation. In this situation, the electricity pricing scheme needs to be augmented with new features to meet requirements of incentive compatibility, cost causation-awareness, revenue adequacy, and transparency in a new market environment. Though a broad spectrum of literature has investigated extensions of the LMP addressing different challenges, a panorama of these extension directions is absent. To this end, this paper provides a systematic review of existing LMP extension research from four aspects, including pricing with non-convexity, pricing with multiple intervals, pricing with uncertainty, and pricing in distribution systems. These extension directions are derived from the impacts of the power system evolution on dispatch and pricing models. For each extension direction, the motivation and basic problem setup are introduced, followed by a critical survey of related research progress. Future directions and research gaps of the LMP extension are also indicated in this paper.

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“…In this context, confidence interval estimation, instead of the point estimation, is devoted to determining the trigger for anomaly detection to consider the uncertainty inherent in the detection course. Compared with the point estimation, the confidence interval estimation specifies instead a range within which the parameter is estimated to lie (DiCiccio and Efron, 1996; Efron, 1987), which has been widely employed to between-subject designs (Loftus and Masson, 1994), clinical research (Cho et al, 2020; Schober and Vetter, 2020), distribution locational marginal price (Wei et al, 2020), etc. To the best of the author’s knowledge, however, no investigation has been observed to investigate the anomaly detection of large span bridges in the context of confidence interval estimation of extreme value analytics.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic anomaly trend detection for cable-supported bridges using confidence interval estimation

Qian

Huang

et al. 2022

Advances in Structural Engineering

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To rate uncertainties within anomaly detection course for large span cable-supported bridges, a probabilistic approach is developed based on confidence interval estimation of extreme value analytics. First, raw signals from structural health monitoring system are pre-processed, including missing data imputation using moving time window mean imputation approach and thermal response separation through multi-resolution wavelet-based method. Then, an energy index is extracted from time domain signals to enhance robust of detection performance. A resampling-based method, namely the bootstrap, is adopted herein for confidence interval estimation. Four confidence levels are defined for the anomaly trend detection in this study, namely 95%, 80%, 50%, and 20%. Finally, the effectiveness of the proposed anomaly trend detection methodology is validated by using in-situ cable force measurements from the Nanjing Dashengguan Yangtze River Bridge. As a result, the four-level anomaly detection triggers are determined by using the confidence interval estimation based on cable force measurements in 2007, which are 58,671, 48,862, 42,499 and 39,035, respectively. Subsequently, three cases are presented, which are spike detection, overloading vehicle detection and snow disaster detection. Through the spike detection, it is verified that energy index is capable to tolerate signal spikes. Three overloading events are simulated to conduct overloading vehicle detections. As a result, the three overloading events are detected successfully associated with different confidences. Snow disaster is detected with a more than 80% confidence based on the field measurements during the snow storm time window.

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Estimating DLMP confidence intervals in distribution networks with AC power flow model and uncertain renewable generation

Cited by 18 publications

References 63 publications

DLMP of Competitive Markets in Active Distribution Networks: Models, Solutions, Applications, and Visions

DLMP of Competitive Markets in Active Distribution Networks: Models, Solutions, Applications, and Visions

Extensions of the locational marginal price theory in evolving power systems: A review

Probabilistic anomaly trend detection for cable-supported bridges using confidence interval estimation

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