Microgrids Real-Time Pricing Based on Clustering Techniques

Liu, Hao; Mahmoudi, Nadali; Chen, Kui

doi:10.3390/en11061388

Cited by 15 publications

(6 citation statements)

References 24 publications

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“…However, several dynamic pricing modalities have been proposed for demand response such as Time-of-Use (TOU), Critical Peak Pricing (CPP), Real-Time Pricing (RTP), and Day-Ahead Pricing (DAP) [353]. For instance, the articles [354][355][356][357][358][359] have adopted the TOU as a price modality, whereas the References [360][361][362][363][364][365][366][367][368][369][370] have used the RTP scheme. Furthermore, CPP modality can be efficiently utilized, as reported in References [371][372][373][374][375][376][377], while the authors of References [378][379][380][381][382][383] have proposed a DAP as a pricing modality in a competitive electricity market.…”

Section: Dynamic Pricing Mechanismsmentioning

confidence: 99%

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A Comprehensive Review of Recent Advances in Smart Grids: A Sustainable Future with Renewable Energy Resources

et al. 2020

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The smart grid is an unprecedented opportunity to shift the current energy industry into a new era of a modernized network where the power generation, transmission, and distribution are intelligently, responsively, and cooperatively managed through a bi-directional automation system. Although the domains of smart grid applications and technologies vary in functions and forms, they generally share common potentials such as intelligent energy curtailment, efficient integration of Demand Response, Distributed Renewable Generation, and Energy Storage. This paper presents a comprehensive review categorically on the recent advances and previous research developments of the smart grid paradigm over the last two decades. The main intent of the study is to provide an application-focused survey where every category and sub-category herein are thoroughly and independently investigated. The preamble of the paper highlights the concept and the structure of the smart grids. The work presented intensively and extensively reviews the recent advances on the energy data management in smart grids, pricing modalities in a modernized power grid, and the predominant components of the smart grid. The paper thoroughly enumerates the recent advances in the area of network reliability. On the other hand, the reliance on smart cities on advanced communication infrastructure promotes more concerns regarding data integrity. Therefore, the paper dedicates a sub-section to highlight the challenges and the state-of-the-art of cybersecurity. Furthermore, highlighting the emerging developments in the pricing mechanisms concludes the review.

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Section: Dynamic Pricing Mechanismsmentioning

confidence: 99%

“…The proposed study considered the variations in social welfare under different market schemes. [358,359] May 2018, 2013 The authors have investigated the impact of the existence of DER on the TOU tariffs.…”

Section: Refmentioning

confidence: 99%

A Comprehensive Review of Recent Advances in Smart Grids: A Sustainable Future with Renewable Energy Resources

et al. 2020

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“…Furthermore, various uncertainties pertaining to the economic operation of MGs have been considered. These factors are based on the assumption that low-voltage DNs sell energy to the MGs at real-time pricing tariffs [18]. In addition to these studies, which have only focused on the economic operation of MGs, several works have explored the benefits of using MGs with the DN.…”

Section: Literature Review and Motivationmentioning

confidence: 99%

Optimal Operational Scheduling of Distribution Network with Microgrid via Bi-Level Optimization Model with Energy Band

Kim

2019

Applied Sciences

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An optimal operation of new distributed energy resources can significantly advance the performance of power systems, including distribution network (DN). However, increased penetration of renewable energy may negatively affect the system performance under certain conditions. From a system operator perspective, the tie-line control strategy may aid in overcoming various problems regarding increased renewable penetration. We propose a bi-level optimization model incorporating an energy band operation scheme to ensure cooperation between DN and microgrid (MG). The bi-level formulation for the cooperation problem consists of the cost minimization of the DN and profit maximization of the MG. The goal of the upper-level is to minimize the operating costs of the DN by accounting for feedback information, including the operating costs of the MG and energy band. The lower-level aims to maximize the MG profit, simultaneously satisfying the reliability and economic targets imposed in the scheduling requirements by the DN system operator. The bi-level optimization model is solved using an advanced method based on the modified non-dominated sorting genetic algorithm II. Based on simulation results using a typical MG and an actual power system, we demonstrate the applicability, effectiveness, and validity of the proposed bi-level optimization model.

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“…[7,8] utilized simple k-means clustering, while Refs. [9,10] employed fuzzy c-means and proposed fuzzy average k-means clustering. However, these studies still require some prior knowledge and information to precisely estimate the proper cluster number.…”

Section: Profile Extraction With Clusteringmentioning

confidence: 99%

“…Finally, the centroids are defined to be the mean of the shifted data instances belonging to each cluster. Mean-Shift algorithm shifts a data instance X l toward a higher density region with respect to the stationary data instances X n , ∀n = 1, 2, ..., N. The update function for ith iteration with a bandwidth, h, is described in Equation (10). The similarity measurement in the likelihood parts of the update function in Equation 10, specifically the exponential parts, is originally based on Euclidean distance.…”

Section: Mean-shift Clustering With Spcc Distancementioning

confidence: 99%

Load Profile Extraction by Mean-Shift Clustering with Sample Pearson Correlation Coefficient Distance

et al. 2018

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In this paper, a clustering method with proposed distance measurement to extract base load profiles from arbitrary data sets is studied. Recently, smart energy load metering devices are broadly deployed, and an immense volume of data is now collected. However, as this large amount of data has been explosively generated over such a short period of time, the collected data is hardly organized to be employed for study, applications, services, and systems. This paper provides a foundation method to extract base load profiles that can be utilized by power engineers, energy system operators, and researchers for deeper analysis and more advanced technologies. The base load profiles allow them to understand the patterns residing in the load data to discover the greater value. Up to this day, experts with domain knowledge often have done the base load profile realization manually. However, the volume of the data is growing too fast to handle it with the conventional approach. Accordingly, an automated yet precise method to recognize and extract the base power load profiles is studied in this paper. For base load profile extraction, this paper proposes Sample Pearson Correlation Coefficient (SPCC) distance measurement and applies it to Mean-Shift algorithm based nonparametric mode-seeking clustering. The superiority of SPCC distance over traditional Euclidean distance is validated by mathematical and numerical analysis.

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Microgrids Real-Time Pricing Based on Clustering Techniques

Cited by 15 publications

References 24 publications

A Comprehensive Review of Recent Advances in Smart Grids: A Sustainable Future with Renewable Energy Resources

A Comprehensive Review of Recent Advances in Smart Grids: A Sustainable Future with Renewable Energy Resources

Optimal Operational Scheduling of Distribution Network with Microgrid via Bi-Level Optimization Model with Energy Band

Load Profile Extraction by Mean-Shift Clustering with Sample Pearson Correlation Coefficient Distance

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