Proceedings ArticleDOI
Forecasting Transmission Congestion Using Day- Ahead Shadow Prices
Guang Li,Chen-Ching Liu,Harold Salazar +2 more
- pp 1705-1709
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TLDR
In this article, a factor model is proposed to forecast shadow prices for a market based on locational marginal prices, which is a useful tool for market participants as well as market operators in a wholesale electricity market.Abstract:
Day-ahead shadow price forecasting is a useful tool for market participants as well as market operators in a wholesale electricity market. Shadow price forecasting is seen by market operators as an additional decision-making support tool for congestion management. Similarly, different market participants may use shadow price forecasting as a tool for strategy improvement in day-ahead or spot markets. This paper proposes a factor model to forecast shadow prices for a market based on locational marginal prices. The proposed approach handles time series using least-squares estimation. This method performs day-ahead shadow price forecasting and provides interpretable signals for different congestive conditionsread more
Citations
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Journal Article
Adaptive Control
TL;DR: In this paper, two major figures in adaptive control provide a wealth of material for researchers, practitioners, and students to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs.
Journal ArticleDOI
Transmission Congestion Relief Using Privately Owned Large-Scale Energy Storage Systems in a Competitive Electricity Market
TL;DR: In this article, a new real-time optimal dispatch (RTOD) algorithm is proposed that aims to generate revenue primarily by exploiting electricity price arbitrage opportunities in the day-ahead electricity market while optimally preparing the ESS to maximize its contribution to congestion relief as an ancillary service.
Journal ArticleDOI
Short-Term Congestion Forecasting in Wholesale Power Markets
TL;DR: In this paper, the authors proposed a new short-term forecasting algorithm for congestion, LMPs, and other power system variables based on the concept of system patterns - combinations of status flags for generating units and transmission lines.
Posted Content
Short-term congestion forecasting in wholesale power markets
TL;DR: In this paper, the authors proposed a new short-term forecasting algorithm for congestion, LMPs, and other power system variables based on the concept of system patterns, which combines of status flags for generating units and transmission lines.
Journal ArticleDOI
Resolving the forecasting problems of overshoot and volatility clustering using ANFIS coupling nonlinear heteroscedasticity with quantum tuning
TL;DR: An adaptive neuro-fuzzy inference system (ANFIS) coupling a nonlinear generalized autoregressive conditional heteroscedasticity (NGARCH) adapted by quantum minimization (QM) is introduced to resolve the drawbacks of the predicted outputs with big residuals around the inflection points of a data sequence and time-varying conditional variance in residual errors.
References
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Book
Adaptive Control
TL;DR: Benefiting from the feedback of users who are familiar with the first edition, the material has been reorganized and rewritten, giving a more balanced and teachable presentation of fundamentals and applications.
Journal Article
Adaptive Control
TL;DR: In this paper, two major figures in adaptive control provide a wealth of material for researchers, practitioners, and students to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs.
Journal ArticleDOI
ARIMA models to predict next-day electricity prices
TL;DR: In this article, a method to predict next-day electricity prices based on the ARIMA methodology is presented, which is used to analyze time series and have been mainly used for load forecasting, due to their accuracy and mathematical soundness.
Journal ArticleDOI
ARIMA Models to Predict Next-Day Electricity Prices
Journal ArticleDOI
Day-ahead electricity price forecasting using the wavelet transform and ARIMA models
TL;DR: A novel technique to forecast day-ahead electricity prices based on the wavelet transform and ARIMA models is proposed, where the historical and usually ill-behaved price series is decomposed using the wavelets to reconstruct the future behavior of the price series and therefore to forecast prices.