Prediction Markets as Forecasting and Hedging Instruments in the Renewable Energy Sector

Power systems are expected to face rapid growth in the integration of renewable energy sources, supported by the decarbonisation plans of most countries due to climate change concerns. Such transition to high renewable sources penetration level, along with the recent technological advances reducing their manufacturing costs, make them competitive with conventional power plants. Under these circumstances, the renewable sources will be treated similarly by the related electricity market regulations. Therefore, they are expected to contribute to mitigating the unavoidable consequences of the inherent weather-dependent volatility and uncertainty associated with their production outputs. This situation requires employing more accurate forecasting methods and hedging solutions to manage their business risk profiles. In this work, we have explored the potential of using prediction markets for these purposes. Prediction markets are a class of future markets for trading the outcome of future events and have proven to provide accurate forecasting signals reflected in the instantaneous market price, which represents the consensus forecast of the market participants. This approach, in comparison to the existing approaches in the literature, benefits from decentralised access to massive and diverse sources of data, and thanks to the advent of blockchain-hosted platforms, they can be even more flexible and accessible. The core idea presented in this work is to define the renewable energy output as the random variable to be traded and thus forecasted in the prediction market and use the informational value of the market price. In the first application, it is demonstrated how a set of binary prediction markets is capable of achieving a probabilistic renewable energy forecast. To this end, in this application, three different methods of renewable probabilistic forecasting have been considered as the trading agents in binary prediction markets, 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 second application examines how trading in prediction markets compliments the trading activities of renewable producers in the day-ahead electricity market and thus provides hedging against the imbalance costs. We have proposed that the wind power producers might participate in a prediction market to trade the future value of the wind power. This way taking an opposite position compared to the electricity market will offset the imbalance costs through payouts in the prediction market. Wind power is modeled as a stochastic variable, and an optimal trading strategy is developed where the trading volume in the prediction market is analytically derived and formulated by minimising the maximum possible loss, and the pricing of shares is determined via indifference utility condition. The third application generalises the above hedging idea by considering the fact that renewable energy sources experience volumetric (quantity) risk in their revenue streams due to changeability associated with weather conditions in any electricity sale structure. To this end, two different approaches have been considered: the indifference utility condition and the maximisation of utility function to reflect the different risk preferences of investors. The proposed solutions are applicable against poor weather conditions even in sale mechanisms with guaranteed tariffs such as power purchase agreements.
Finally, in the last piece of this work, we have looked at the potential of conditional prediction markets to act as decision support tools to identify effective policies for the promotion of renewable energy.