The inevitability and successive implementation of the elements of the European Union (EU) energy policy and the freedom of achieving the goals left in this regard for the member states should translate into actions taking the specificity of local markets into account, in order to carry out liberalization processes in a harmonious manner. In 2016, the European Commission published a package of guidance documents "Clean Energy for All Europeans" in the perspective of 2030, also known as the Winter Package. The recommendations contained in some of the documents assume the continuation of integration of markets in the national and regional dimension, setting ambitious targets in the field of decarbonization, the increase of energy efficiency and the increase of Renewable Energy Sources (RES) share in the energy balance of EU countries. The short time to carry out a thorough reconstruction of the energy-generating sector forces to seek solutions that are in line with the European Community recommendations and, at the same time, do not constitute an excessive burden for the national economy and legal order. One of the activities is to use the potential of micro-networks of local communities striving for energy independence based on their own energy sources and to create regulations enabling the neighborly exchange of energy. This mechanism works in the form of pilot projects in many locations around the world (Sonnen Group; Power 54 Ledger). The paper presents the concept of functional and analytical assumptions for an exemplary structure of neighboring prosumers along with the presentation of simulation results based on real generation and consumption profiles and the presentation of investment profitability indicators for the proposed functional model.
Currently, the privileged position of wind energy producers is being weakened by their enforced participation in the market on equal terms. This requires accurate production forecasting. The main aim of this study is to comparatively examine the wind generation forecasts in Poland and Portugal, as well as to verify their influence on the day-ahead market prices. The statistical analysis revealed significant deviations of the forecasted and actual wind production in both countries, which referred to the corresponding spot and balancing prices caused considerable financial losses by the wind energy suppliers. In this paper, the influence of the wind generation forecasts on the spot prices has been examined through developed the auto-regressive moving average (ARMA), ARMA with exogenous inputs (ARMAX) and non-linear auto-regressive neural network (NAR), NAR with exogenous inputs (NARX)artificial neural network (ANN) models. The results have shown that the usability of the information of forecasted wind generation is not unequivocal in models developed for spot prices in Poland, mainly because of the randomness and volatility of recorded wind generation forecasts. However, in the case of Portugal, the forecasted wind generation occurred to be a valuable input in spot prices models, which results in an improvement in the models’ accuracy.
The resultant photovoltaic installation powers significantly affect the process of cluster coordination in terms of balancing, which is associated with the need for the most accurate forecast of photovoltaic generation. This article describes the application of similarity analysis in order to use commonly available meteorological data for predicting generation level from photovoltaic sources on the example of several selected installations and their corresponding real production profiles.
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