Accurate price forecasting is crucial for all market participants in electricity markets. This study presents a hybrid price forecasting framework based on a new data fusion algorithm. Owing to the complexity and distinct nature of the electricity price, a single forecast engine cannot capture all different patterns of the price signals. Hence, this study focuses on a hybrid forecasting method to extract the advantages of several forecasting engines. In the proposed method, artificial neural network, adaptive neuro-fuzzy inference system and autoregressive moving average are employed as primary forecast engines (agents) which provide three independent price forecasts. Then, a new data fusion algorithm, the modified ordered weighted average (modified OWA), is proposed to combine the three forecasts to generate a single unified price forecast. Hopefully, the fusion's output outperforms all the agents' forecasts. The author's proposed fusion algorithm, unlike conventional OWA, uses the feedback from the agents' error. The proposed framework is evaluated on the Spanish electricity market. The results confirm the ability of the proposed fusion framework to provide more accurate forecasts compared with the input agents forecasts. Results are also compared with some of the recent electricity price forecasting methods.

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Incumbent firms play a decisive role in the success of renewable support policies. Their investments in renewables as well as their operational strategies for their conventional CO 2 emitting technologies affect the transition to a sustainable energy system. We use a game theoretical framework to analyze incumbents' reactions to different renewable support policies, namely feed-in tariff (FIT), feed-in premium (FIP), and auction-based policies. We show that a regulator should choose a support scheme based on concerns about either market power or emission abatement: in FIP-based policies, the incumbent's strategic behavior leads to lower CO 2 emissions, but a higher market price compared to FIT-based policies. Furthermore, for FIP-based policies, the regulator might want to incentivize incumbents directly (to further reduce CO 2 emissions) or newcomers (to further reduce market power). Particularly in FIP-based auctions, incumbents have the incentive to obtain all auctioned capacity, which could lead to an unchanged market price despite the entrance of new capacity into the market.

In this paper, Kalman Fusion algorithm is applied to combine outputs of three forecasting engines which are used to predict electricity price signal of the Spanish electricity market. Employed engines which are Adaptive Neuro-fuzzy Inference System (ANFIS), Artificial Neural Networks (ANN) and Autoregressive Moving Average (ARMA), are all powerful and popular kinds of time series models. After applying these algorithms on the preprocessed data of the Spanish electricity market, outputs of the aforementioned models are fused by Kalman fusion algorithm in order to exploit the advantages of these forecasting engines simultaneously, as a result of which different patterns existing among price time series can be forecasted more accurately. In comparison with single forecasting methods utilized in this paper to forecast electricity price signal, results of the proposed model based on Kalman Fusion algorithm prove that this approach in effective to enhance accuracy of prediction.

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