Abstract
Cloud storage allows users to remotely store their data, giving access anywhere and to anyone with an Internet connection. The accessibility, lack of local data maintenance and absence of local storage hardware are the main advantages of this type of storage. The adoption of this type of storage is being driven by its accessibility. However, one of the main barriers to its widespread adoption is the sovereignty issues originated by lack of trust in storing private and sensitive information in such a medium. Recent attacks to cloud-based storage show that current solutions do not provide adequate levels of security and subsequently fail to protect users' privacy. Usually, users rely solely on the security supplied by the storage providers, which in the presence of a security breach will ultimate lead to data leakage. In this paper, we propose and implement a broker (ARGUS) that acts as a proxy to the existing public cloud infrastructures by performing all the necessary authentication, cryptography and erasure coding. ARGUS uses erasure code as a way to provide efficient redundancy (opposite to standard replication) while adding an extra layer to data protection in which data is broken into fragments, expanded and encoded with redundant data pieces that are stored across a set of different storage providers (public or private). The key characteristics of ARGUS are confidentiality, integrity and availability of data stored in public cloud systems.

Abstract

Abstract
The price evolution in electricity market with large share of renewables often exhibits a deep volatility, triggered by external factors such as wind and water availability, load level and also by business strategies of market agents. Consequently, in many real applications, the performance of electricity price is not appropriate. The goal of this article is to analyze the available market data and characterize circumstances that affect the evolution of prices, in order to allow the identification of states that promote price instability and to confirm that class segmentation allows increasing forecast performance. A regression technique (based on Artificial Neural Networks) was applied first to the whole set and then to each class individually. Performances results showed a clear advantage (above 20%) of the second approach when compared to the first one.

Abstract

Abstract
In the last decades, the weight of renewable energies sources (RES-E) in the electricity generation mix of most European countries has considerably increased, constituting an important contribution to the transition towards a low-carbon economy. Until very recently, RES-E were supported by favorable investment mechanisms specially designed to endorse investment in RES-E. More recently, as RES-E are becoming increasingly more competitive (especially wind and solar photovoltaic), RES-E are starting to be remunerated according to market mechanisms. This has generated a lively debate on the economic pros and cons of dispatching RES-E in the market. This paper contributes to this debate by developing a game theoretical model in the context of which we analyze how the inclusion of RES-E in the electricity wholesale market affects equilibrium outcomes under demand and supply uncertainty. Then, we examine how the inclusion of RES-E in the electricity wholesale market impacts firms' incentives to invest in conventional energy sources, characterizing the optimal investment under demand and supply uncertainty. We find that, when RES-E capacity and asymmetry in firms' marginal production costs are sufficiently high, RES-E producers may strategically reduce the market price, in order to evict the less efficient conventional source in that period. Although, in the short-run, this strategy may actually favor energy consumers (since prices are lower), the expectations of inactivity periods (regardless of whether they arise for strategic or market reasons) may negatively affect investment in back-up capacity, possibly leading to an increase in future prices (since less back-up capacity is available). Finally, we provide an analytical characterization of optimal investment levels in conventional energy sources under demand and supply uncertainty.

Abstract