A Prediction Market Trading Strategy to Hedge Financial Risks of Wind Power Producers in Electricity Markets

IEEE Trans. Sustain. Energy

2022

Self Cite

This paper demonstrates how a binary prediction market is capable of achieving a probabilistic renewable energy forecast. In prediction markets, participants trade shares associated with the outcome of unknown future events (here, the renewable production, as a random variable), and the instantaneous price of shares represents the probability of the outcome. The focus of this study is to exploit this informational value of the prediction market price in renewable energy forecasting. To this end, in this paper three different methods of renewable probabilistic forecasting have been considered as the trading agents in a binary prediction market, the aggregated probability of the renewable output is elicited from the equilibrium price in this market and finally, the full cumulative distribution function of possible renewable output is extracted through regression analysis. The proposed method is applied to the test cases of three onshore wind farms in Australia. The simulation results suggest that the performance of the proposed method is superior to the individual models and forecasting is improved in terms of reduction in the electricity market imbalance costs.

Section: Evaluation Of the Forecasting Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction Markets for Probabilistic Forecasting of Renewable Energy Sources

Shamsi

IEEE Trans. Sustain. Energy

2022

Self Cite

“…Since then, only a few projects have explored this relatively novel idea. In [18], a blockchain prediction marketplace was employed to minimize the imbalance risks of wind generators in competitive electricity markets through the wisdom of the crowd. Here, the aggregated token-backed predictions from the decentralized crowd enable improved wind power forecasts to reduce imbalance exposures.…”

Section: B Decentralized Financementioning

confidence: 99%

Implementing Contract-for-Difference Arrangements for Hedging Electricity Price Risks of Renewable Generators on a Blockchain Marketplace

Alao

2023

IEEE Trans. Ind. Inf.

Self Cite

The dynamic nature of competitive electricity markets means that participants often resort to some form of derivative financial instrument. One such instrument is a Contract-for-Difference (CFD), usually available to renewable generators in certain electricity markets to enable them to hedge their price risk. Embracing CFD presents new risks such as counterparty credit, margining, third-party, legal, and process risks. Derivative instruments existing on blockchains have recently demonstrated potential as suitable hedging tools for minimizing the risks of renewable generators. The present manuscript applies this concept for the first time to hedge the price risk of renewable generators by implementing a novel decentralized finance instrument, an Ethereum blockchain marketplace governed by a smart contract to mediate between stakeholders mutually enrolled in bilateral CFD arrangements. The employed structure mitigates the underlying risks of traditional arrangements, underpinned by a suite of autonomous mechanisms.

“…The general idea of using prediction markets for renewables' forecasting and hedging has been proposed in [10]. In [12], authors have employed scalar prediction markets as a hedging tool against imbalance costs in the day-ahead electricity market. In this letter, we propose using a simple binary prediction market as a weather derivative to reduce the revenue uncertainty of renewable generators.…”

Section: Introductionmentioning

confidence: 99%

Using Binary Prediction Markets as Hedging Instruments: Strategies for Renewable Generators

Shamsi

IEEE Trans. Sustain. Energy

2022

Self Cite

Renewable energy sources face volumetric risk in their revenue streams, in any electricity sale structure, due to changeability associated with weather conditions. Weather/power derivatives are often employed to hedge against such financial risks. This letter proposes that a binary prediction market, if is adequately liquid, has the potential to resemble the function of such derivatives to improve the financial profile of renewable sources. The size and the price of shares (contracts) to be purchased by the renewable generator are determined analytically in the methodology of this paper. To this end, two different approaches have been considered: the indifference utility condition and the maximisation of utility function to reflect different risk preferences of investors.