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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.

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.

In prediction markets participants bet on the outcome of uncertain future events and the instantaneous price of such shares then represents an accurate forecasting signal. This paper demonstrates how conditional prediction markets can be employed as decision support tools to identify effective policies for the promotion of renewable energy. In this model, the policy maker's objective (e.g. achievement of renewable generation targets, social welfare, carbon emission level, etc.) is defined as the settlement metric of the prediction market; participants speculate on whether this metric will be achieved, conditional on each of available policy alternatives being implemented. For instance, one prediction market could ask will the installed capacity of renewable generation in a specific region exceed 6 gw by the end of the year 2023, conditional on implementing a feed-in-tariff policy ?; another prediction market may ask will the installed capacity of renewable generation in a specific region exceed 6 gw until the end of the year 2023 conditional on implementing renewable portfolio standards policy? The policy with the highest market price can be interpreted as the option believed by the market participants as having the best prospects for acheiving the installed capacity target of 6 gw. We have simulated the evolution of prices within such a prediction market, where an automated market maker aggregates the trades and settles the market. By using the proposed approach, a case study evaluating two renewable support schemes (comparing feed-in-tariff and renewable portfolio standards) in achieving the renewable generation target is analysed.