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Forecasting Nodal Price Difference between Day-ahead and Real-time Electricity Markets using Long-Short Term Memory and Sequence-to-Sequence Networks

Ronit Das, Rui Bo, Haotian Chen, Waqas Ur Rehman, Donald Wunsch
2021 IEEE Access  
Digital Object Identifier 10.1109/ACCESS.2017.DOI Forecasting Nodal Price Difference between Day-ahead and Real-time Electricity Markets using Long-Short Term Memory and Sequence-to-Sequence Networks RONIT  ...  to forecast nodal price difference between day-ahead and real-time markets.  ... 
doi:10.1109/access.2021.3133499 fatcat:t3isdvr77nfwvjoxwqt2acc5my

Deep Learning for Day-Ahead Electricity Price Forecasting

CHI ZHANG, Ran Li, Heng Shi, Furong Li
2020 IET Smart Grid  
This study proposes a deep recurrent neural network (DRNN) method to forecast day-ahead electricity price in a deregulated electricity market to explore the complex dependence structure of the multivariate  ...  Accurate electricity price forecasting (EPF) could help the market participants to hedge against the price movements and maximise their profits.  ...  structure of long short-term memory (LSTM) unit.  ... 
doi:10.1049/iet-stg.2019.0258 fatcat:57nogjfq65fhbdgnpanpzgfeku

Comparison of Classical and Nonlinear Models for Short-Term Electricity Price Prediction [article]

Elaheh Fata, Igor Kadota, Ian Schneider
2018 arXiv   pre-print
As the "smart grid" grows, short-term price forecasts are becoming an important input to bidding and control algorithms for battery operators and demand response aggregators.  ...  Short-term forecasting of electricity prices is an important endeavor because it helps electric utilities control risk and because it influences competitive strategy for generators.  ...  Thank you to Hoon Cho for his help and comments on the research.  ... 
arXiv:1805.05431v1 fatcat:6mrmuzfcdrbk3lhzo5i2nwy6my

Day Ahead Price Forecasting Models in Thin Electricity Market [article]

Sayani Gupta, Puneet Chitkara
2020 arXiv   pre-print
Day Ahead Electricity Markets (DAMs) in India are thin but growing. Consistent price forecasts are important for their utilization in portfolio optimization models.  ...  These needs to be considered in forecasting models.  ...  INTRODUCTION Thin day ahead electricity markets (DAMs) are characterized by highly volatile prices and the complication in understanding price variations increases when infrastructure is fragile.  ... 
arXiv:2011.01423v1 fatcat:2eezp45gqnaflaljkfo2vsftem

Central- versus Self-Dispatch in Electricity Markets

Victor Ahlqvist, Par Holmberg, Thomas Tangerås
2018 Social Science Research Network  
The day-ahead market is sometimes called the spot market, as the day-ahead price is often used as a strike price to settle financial contracts and determine retail prices.  ...  Many plants have long ramping times, and thus prefer to schedule how much to produce ahead of delivery on the day-ahead market. This is where most of the physical trade takes place.  ...  All electricity markets are coordinated by a system operator in real-time, but central coordination ahead of delivery differs between markets.  ... 
doi:10.2139/ssrn.3302569 fatcat:j5gbbla6f5banp4oelet3djzay

Electricity price forecasting: A review of the state-of-the-art with a look into the future

Rafał Weron
2014 International Journal of Forecasting  
A variety of methods and ideas have been tried for electricity price forecasting (EPF) over the last 15 years, with varying degrees of success.  ...  This review article aims to explain the complexity of available solutions, their strengths and weaknesses, and the opportunities and threats that the forecasting tools offer or that may be encountered.  ...  Special thanks for feedback on earlier versions of the manuscript and computational assistance go to Katarzyna Maciejowska, Paweł-Maryniak and Jakub Nowotarski.  ... 
doi:10.1016/j.ijforecast.2014.08.008 fatcat:gn47m4pvybgt5mx7c37fxp4tye

Development of Neurofuzzy Architectures for Electricity Price Forecasting

Abeer Alshejari, Vassilis S. Kodogiannis, Stavros Leonidis
2020 Energies  
In this study, a prototype asymmetric-based neuro-fuzzy network (AGFINN) architecture has been implemented for short-term electricity prices forecasting for ISO New England market.  ...  Results related to the minimum and maximum electricity prices for ISO New England, emphasize the superiority of the proposed model over well-established learning-based models.  ...  Long-short term memory (LSTM) and convolutional neural network (CNN) based models have been applied and compared against ARIMA and single hidden-layer MLP models for the day-ahead price market in Belgium  ... 
doi:10.3390/en13051209 fatcat:pvt47nalq5eoxcotea4lt4ipmi

Allocation of power meters for online load distribution estimation in smart grids

Konstantinos Kouzelis, Iker Diaz De Cerio Mendaza, Birgitte Bak-Jensen, Jayakrishnan R. Pillai, Bishnu Prasad Bhattarai
2015 2015 IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA)  
These investments will transform distribution networks into "smarter" grids, which will facilitate flexible load/generation control in return for financial and reliability benefits to electricity consumers  ...  The theoretical idea of modernising the grid so as to cope with load and generation changes is indeed innovative; nevertheless, its implementation is, up to day, impaired by many practical difficulties  ...  Equivalently to modern regulating markets, local regulation is expected to be conducted via long term flexibility contracts and/or ultra short term intra day local markets.  ... 
doi:10.1109/isgt-asia.2015.7387010 fatcat:z47qbufvd5dfjpnqgtqc2kwjzi

Deregulated Wholesale Electricity Prices in Italy

Bruno Bosco, Lucia Parisio, Matteo M. Pelagatti
2006 Social Science Research Network  
In this paper we analyze the time series of daily mean prices generated in the Italian electricity market, which started to operate as a Pool in April 2004.  ...  The objective is to characterize the high degree of autocorrelation and multiple seasonalities in the electricity prices.  ...  We would like to thank the University of Milan-Bicocca for financing the research with a FRA/2003 (ex 60%) grant and MURST for a PRIN 2004 grant.  ... 
doi:10.2139/ssrn.888736 fatcat:ygrssodvxrdrrnzbeu25uggpii

Chance-Constrained Outage Scheduling using a Machine Learning Proxy [article]

Gal Dalal, Elad Gilboa, Shie Mannor, Louis Wehenkel
2018 arXiv   pre-print
To tackle tractability issues arising in large networks, we use machine learning to build a proxy for predicting outcomes of power system operation processes in this context.  ...  On the IEEE-RTS79 and IEEE-RTS96 networks, our solution obtains cheaper and more reliable plans than other candidates.  ...  When performing his decision, the short-term planner is exposed to y s , which carries the realizations of the day-ahead generation and load forecasts.  ... 
arXiv:1801.00500v1 fatcat:xid4ljfilzdsjf3kbn2vsc5uyu

Review of Low Voltage Load Forecasting: Methods, Applications, and Recommendations [article]

Stephen Haben, Siddharth Arora, Georgios Giasemidis, Marcus Voss, Danica Vukadinovic Greetham
2021 arXiv   pre-print
Applications on low voltage, local networks, such as community energy markets and smart storage will facilitate decarbonisation, but they will require advanced control and management.  ...  Reliable forecasting will be a necessary component of many of these systems to anticipate key features and uncertainties.  ...  Long Short-term Memory Kong et al.  ... 
arXiv:2106.00006v2 fatcat:rb2yrt4tsjap3jhrb7dz76dg2e

A regulatory framework for an evolving electricity sector: Highlights of the MIT utility of the future study

Ignacio J. P�rez-Arriaga, Jesse D. Jenkins, Carlos Batlle
2017 Economics of Energy & Environmental Policy  
avoid conflicts of interest, and improvements to electricity markets.  ...  , market, and policy reform designed to enable the efficient evolution of the power sector over the next decade and beyond.  ...  producing intraday price signals that reflect the operation of the power system between day-ahead markets and the time of electricity delivery.  ... 
doi:10.5547/2160-5890.6.1.iper fatcat:om7xttfgybahrmbskairjb6xji

Deregulated Wholesale Electricity Prices in Italy: An Empirical Analysis

Bruno Paolo Bosco, Lucia P. Parisio, Matteo M. Pelagatti
2007 International Advances in Economic Research  
In this paper we analyze the time series of daily average prices generated in the Italian electricity market, which started to operate as a Pool in April 2004.  ...  The objective is to characterize the high degree of autocorrelation and multiple seasonalities in the electricity prices.  ...  We would like to thank the University of Milan-Bicocca for financing the research with a FRA/2003 (ex 60%) grant and MURST for a PRIN 2004 grant.  ... 
doi:10.1007/s11294-007-9105-z fatcat:rqsae5eiybbv3cp447j5iyk6va

Gis-Based Simulation Studies for Power Systems Education [chapter]

Ralph D. Badinelli, Virgilio Centeno, Boonyarit Intiyot
2010 Operation and Control of Electric Energy Processing Systems  
a long time and that can take on many scenarios due to randomness in system parameters such as loads and market prices.  ...  Termed the Virginia Tech Electricity Grid and Market Simulator (VTEGMS), the main component of this package makes use of 2 object-oriented programming as a means to maximize the robustness and flexibility  ...  /Control option Capacity constraint Demand constraint Day-ahead dispatch Day-ahead commitments Day-ahead commitments Real-time dispatch Imbalance commitments Demand forecast Ancillary service  ... 
doi:10.1002/9780470602782.ch5 fatcat:gyjmerdmffcfzn4ttqqts6wcti

Machine Learning-Driven Virtual Bidding with Electricity Market Efficiency Analysis [article]

Yinglun Li, Nanpeng Yu, Wei Wang
2021 arXiv   pre-print
A recurrent neural network-based Locational Marginal Price (LMP) spread forecast model is developed by leveraging the inter-hour dependencies of the market clearing algorithm.  ...  This paper develops a machine learning-driven portfolio optimization framework for virtual bidding in electricity markets considering both risk constraint and price sensitivity.  ...  patterns between day-ahead and real-time markets.  ... 
arXiv:2104.02754v1 fatcat:katpzlf3dzhjdazvl4h4vmn6bm
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