Electricity market price-spike classification in the smart grid

Datta, Aditi R.; Datta, Sohom

doi:10.1109/isgt.2016.7781161

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…Gerçek zamanlı enerji fiyat modelleri için talep tarafı yönetim üzerine algoritma geliştirilen bir başka çalışmada gerçek zamanlı fiyatlandırma modellerinin kullanılabilirliği üzerine araştırma yapılmıştır (Song ve Qu, 2014). Bir başka çalışmada akıllı sayaçtan alınan bilgilerin enerji piyasasından alınan veriler ile önceden yapılan fiyat tahminlerini yapay sinir ağı modelleri oluşturarak düzeltmeye çalışmışlardır (Datta ve Datta, 2016).…”

Section: Rtpunclassified

A Study on Dynamic Energy Pricing in Smart Grids

Yalçinöz

Kaygusuz

2022

BBD

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Üreticiler ve tüketiciler arasında karşılıklı haberleşmenin sağlandığı akıllı şebekelerde (SG) talep tarafı yük yönetimi (DSLM), yük tahmini ve bunlarla bağlantılı olan dinamik enerji fiyatlandırması günümüzde ve gelecekte büyük önem arz etmektedir. Dinamik enerji fiyatlandırması ile elde edilen sinyallerin sunucular aracılığıyla yayınlanması talep tarafı yönetim ve enerji piyasası için çok önemli bir başlıktır. Dinamik enerji fiyatlandırması tabanlı çalışmaların genelinde dağıtık üretim koşullarına rağmen enerji dengesi talep tarafı yönetim ile korunmaktadır. Enerji fiyat sinyallerinin dinamik yapıda olmasıyla ve yük tahmini ile dağıtık üretimden kaynaklanan belirsizliklere yanıt verilmektedir. Bu çalışmada talep yanıtı (DR) yöntemleri ve akıllı fiyatlandırma planları olan kullanım süresi (ToU), kritik tepe fiyatlandırması (CPP) ve gerçek zamanlı fiyatlandırma (RTP) ayrıntılı olarak incelenmiştir. Akıllı şebekelerde yapılan çalışmalar, avantajları, dezavantajları incelenmiş ve akıllı fiyat planları karşılaştırılmıştır.

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Section: Rtpunclassified

A Study on Dynamic Energy Pricing in Smart Grids

Yalçinöz

Kaygusuz

2022

BBD

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“…The USA played an important role in grid modernization and is known as a "world leader" in the development and installation of SGs as several US companies provide useful technological solutions, consequently increasing their SG exports [2]. However, in 2009, China surpassed the USA and became the largest market in the world in SG investment [47]. Table III lists the countrywise clean energy investments.…”

Section: Worldwide Top Renewable Energy Investorsmentioning

confidence: 99%

Statistical Energy Information and Analysis of Pakistan Economic Corridor Based on Strengths, Availabilities, and Future Roadmap

et al. 2020

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In Pakistan, the performance of conventional electrical grids is inefficient, resulting in severe energy crises. To overcome the alarming challenges persisting in the energy grids, Pakistan must focus on system protection, grid reliability, distribution and transmission, and power quality. The inefficiencies in grid protection and management signify an overall problematic energy scenario. The solutions to these problems include the improvement of domestic, commercial, and industrial demand-side management and the reduction in distribution network losses. A smart grid (SG) is a critical requirement as it can overcome the shortcomings of the existing grid owing to its promising features, enhanced consumer empowerment, utmost security; efficient and optimized energy flow; and demand-supply management. Thus, the SG is essential to overcome the energy crisis in Pakistan and achieve the standards of other developed nations in the energy sector. This study aims to highlight the significant prospects of SGs within Pakistan with the key objectives of its availability requirements. We compare the energy scenario in Pakistan with that of other countries and recommend various aspects that require improvement through SG implementation. Additionally, we discuss the incorporation of renewable energy resources and present a market analysis regarding SGs to illustrate the SG scenario and its implementation in Pakistan. Moreover, we analyze and evaluate detailed taxonomies of energy generation, energy projects, renewable energy assessment, power market trends in Pakistan, and the basic requirements of SGs. Furthermore, a critical analysis of the energy sector in Pakistan is elaborated, which describes the possibilities, requirements, and strengths pertaining to the transformation of the modern electric grid with respect to the China-Pakistan Economic Corridor. Thus, we believe that our work is more versatile in improving the energy system of Pakistan for the implementation of the SG.

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“…In [10], extreme learning machine is used to detect spikes because such a model could speed up the initial convergence process by randomly choosing the input weights and provide the sufficient generalization performance at extremely fast learning speed. Authors in [11] have stated that NBC and random forest classifier can detect spikes with high accuracy; while the authors in [12] have reported that a copula-based model can be used to effectively detect spikes. In order to leverage different classifying models, the authors in [13] have employed a compound classifier by combining three individual models, which are relevance vector machine, bagged decision trees and probabilistic neural network.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Feature Based Approach Incorporating Variable Thresholds for Detecting Price Spikes in the National Electricity Market of Australia

Muttaqi

Agalgaonkar

et al. 2021

IEEE Access

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Detecting electricity price spikes is crucial as it helps market participants to gain confidence and formulate appropriate strategy to maximize their benefits. In this paper, a multi-feature based approach with the incorporation of variable thresholds is developed to detect electricity price spikes in the national electricity market of Australia. The variable thresholds, which are determined using a weighted sliding window average and an adjusted standard deviation, help to segregate spikes from normal price variations. Also, significant features are extracted from the market after thoroughly analyzing the underlying causes resulting into the price spikes. These features are employed as inputs to a support vector machine to classify electricity prices as spikes or non-spikes. A case study is conducted using a dataset acquired from the state of New South Wales, Australia. The results show that the proposed method can successfully detect the price spikes with high accuracy and confidence.

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Electricity market price-spike classification in the smart grid

Cited by 7 publications

References 12 publications

A Study on Dynamic Energy Pricing in Smart Grids

A Study on Dynamic Energy Pricing in Smart Grids

Statistical Energy Information and Analysis of Pakistan Economic Corridor Based on Strengths, Availabilities, and Future Roadmap

A Multi-Feature Based Approach Incorporating Variable Thresholds for Detecting Price Spikes in the National Electricity Market of Australia

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