Search

Every country has objectives for climate change, and public policies are implemented to achieve those commitments. This paper aims to review the different public policies used to promote the integration of photovoltaic technology into smart grids, taking the case of Portugal as reference. An analysis of previous supporting policies is performed both in Portugal and some European countries; these policies consist of feed-in tariffs, feed-in premiums, green certificates, electricity compensation, direct capital subsidies, and tax credits. The policies currently in force in Portugal no longer aim to support the technology; instead, they intend to promote it. Energy communities, local markets, and solar auctions are examples of public policies that are currently being used, not only to promote PV power but also the development of microgrids. Finally, the Portuguese solar auctions of 2019 and 2020 are analyzed and compared. It is concluded that auctions are a very effective way of expanding the installed capacity of the PV technology in the country and have more weight on promoting the technology than other promoting policies currently being used. [ABSTRACT FROM AUTHOR]

Wind power has been increasing its participation in electricity markets in many countries around the world. Due to its economical and environmental benefits, wind power generation is one of the most powerful technologies to deal with global warming and climate change. However, as wind power grows, uncertainty in power supply increases due to wind intermittence. In this context, accurate wind power scenarios are needed to guide decision-making in power systems. In this paper, a novel methodology to generate realistic wind power scenarios for the long term is proposed. Unlike most of the literature that tackles this problem, this paper is focused on the generation of realistic wind power production scenarios in the long term. Moreover, spatial-temporal dependencies in multi-area markets have been considered. The results show that capturing the dependencies at the monthly level could improve the quality of scenarios at different time scales. In addition, an evaluation at different time scales is needed to select the best approach in terms of the distribution functions of the generated scenarios. To evaluate the proposed methodology, several tests have been made using real data of wind power generation for Spain, Portugal and France. [ABSTRACT FROM AUTHOR]