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Cross Hedging Using Prediction Error Weather Derivatives for Loss of Solar Output Prediction Errors in Electricity Market

  • Takuji MatsumotoEmail author
  • Yuji Yamada
Article
  • 35 Downloads

Abstract

Predicting future solar conditions is important for electricity industries with solar power generators to quote a day-ahead sales contract in the electricity market. If a prediction error exists, the market-monitoring agent has to prepare another power generation resource to immediately compensate for the shortage, resulting in an additional cost. In this context, a penalty may be required depending on the size of the prediction error, which may lead to a significant loss for solar power producers. Because the main source of such losses is from prediction errors of solar conditions, they can instead effectively utilize a derivative contract based on solar prediction errors. The objective of this work is to provide such a derivative contract, namely, a prediction error weather derivative. First, defining a certain loss function, we measure the hedge effect of the derivative on solar radiation prediction error, thereby verifying that the existing hedging method for wind power can also be applied to solar power generation with periodic trends. By introducing the temperature derivative on the absolute prediction error, we also propose a cross-hedging method, where we demonstrate not only a further variance reduction effect when used with solar radiation derivatives, but also a certain hedge effect obtained even when only the temperature derivative is used. For temperature derivative pricing and optimal contract volume estimation, we propose a method using a tensor-product spline function that simultaneously incorporates the smoothing conditions of both the direction of intraday time trend and seasonal trend, and consequently verify its effectiveness.

Keywords

Cross hedge Non-parametric regression Minimum variance hedge Prediction errors Solar power energy Weather derivatives 

JEL Classification

G19 

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Copyright information

© Springer Japan KK, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Graduate School of Business SciencesUniversity of TsukubaTokyoJapan
  2. 2.Faculty of Business SciencesUniversity of TsukubaTokyoJapan

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