Forecasting Nodal Price Difference Between Day-Ahead and Real-Time Electricity Markets Using Long-Short Term Memory and Sequence-to-Sequence Networks

Ronit Das,Donald Wunsch,Rui Bo,Waqas Ur Rehman,Haotian Chen

doi:10.1109/access.2021.3133499

Ronit Das, Donald Wunsch + Show 3 more

Open Access

https://doi.org/10.1109/access.2021.3133499

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Journal: IEEE Access	Publication Date: Jan 1, 2022
Citations: 4	License type: CC BY 4.0

Affiliation: Missouri University of Science and Technology

Abstract

Price forecasting is at the center of decision making in electricity markets. Much research has been done in forecasting energy prices for a single market while little research has been reported on forecasting price difference between markets, which presents higher volatility and yet plays a critical role in applications such as virtual trading. To this end, this paper takes the first attempt at it and employs novel deep learning architecture with Bidirectional Long-Short Term Memory (LSTM) units and Sequence-to-Sequence (Seq2Seq) architecture to forecast nodal price difference between day-ahead and real-time markets. In addition to value prediction, these deep learning architectures are also used to develop classification models to predict the price difference bands/ranges. The proposed methods are tested using historical PJM market data, and evaluated using Root Mean Squared Error (RMSE) and other customized performance metrics. Case studies show that both deep learning methods outperform common methods including ARIMA, XGBoost and Support Vector Regression (SVR) methods. More importantly, the deep learning methods can capture the magnitude and timing of price difference spikes. Numerical results show the Seq2Seq model performs particularly well and demonstrates generalization capability to extended forecasting lead time.

Highlights

W ITH the development of competitive deregulated electricity markets, electricity price forecasting has become crucial for market participants: precise short-term forecast of electricity prices is decisive for generation companies (GENCOs) to develop optimal bidding strategies and participate in electricity markets, and thereby maximize their revenue
Built upon the foundational work in [19], this paper comprehensively reports the pioneering work on DA/RT price difference forecast using proposed state-of-theart models
This section gives a brief introduction of the different types of Recurrent Neural Nets(RNNs), Bidirectional Long-Short Term Memory (LSTM) and Seq2Seq network architectures, used in time series forecasting and explains why these neural network architectures work in order to make better forecasts

Summary

Introduction

W ITH the development of competitive deregulated electricity markets, electricity price forecasting has become crucial for market participants: precise short-term forecast of electricity prices is decisive for generation companies (GENCOs) to develop optimal bidding strategies and participate in electricity markets, and thereby maximize their revenue. Medium-term forecast of electricity prices is exploited by power producers to commit to favorable bilateral contracts. In the long-term, GENCOs leverage the electricity price forecasting to make investment decisions for maintenance planning and generation capacity planning. Author et al.: Preparation of Papers for IEEE TRANSACTIONS and JOURNALS short-term and medium-term forecast of electricity prices is utilized by large consumers and load serving entities (LSEs) to optimally bid in the electricity market and enter into favorable bilateral contracts [1]–[11]. The price volatility and spikes can be instigated by many factors, including load uncertainties, generation uncertainties, transmission network congestion, fluctuating fuel prices, market participants’ behavior, and weather conditions [2]

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