Abstract
The increasing use of renewable energy sources and distributed generation brought deep changes in power systems, namely with the operation of competitive electricity markets. With the eminent implementation of micro grids and smart grids, new business models able to cope with the new opportunities are being developed. Virtual Power Players are a new type of player, which allows aggregating a diversity of entities, e.g. generation, storage, electric vehicles, and consumers, to facilitate their participation in the electricity markets and to provide a set of new services promoting generation and consumption efficiency, while improving players' benefits. In order to achieve this objective, it is necessary to define tariff structures that benefit or penalize agents according to their behavior. In this paper a method for determining the tariff structures has been proposed, optimized for different load regimes. Daily dynamic tariff structures were defined and proposed, on an hourly basis, 24 hours day-ahead from the characterization of the typical load profile, the value of the electricity market price and considering the renewable energy production.

Abstract

Abstract
The problem of joint estimation of parameters and state of continuous time systems using discrete time observations is addressed. The plant parameters are assumed to be modeled by a Wiener process. The a priori probability density function (pdf) of an extended state that comprises the plant state variables and the parameters is propagated in time using an approximate solution of the Fokker-Planck equation that relies on Trotter's formula for semigroup decomposition. The a posteriori (i. e., given the observations) pdf is then computed at the observation instants using Bayes law.

Abstract
In this work we study a way to explore and extract more information from data sets with a hierarchical tree structure. We propose that any statistical study on this type of data should be made by group, after clustering. In this sense, the most adequate approach is to use the Mahalanobis-Wasserstein distance as a measure of similarity between the cases, to carry out clustering or unsupervised classification. This methodology allows for the clustering of cases, as well as the identification of their profiles, based on the distribution of all the variables that characterises each subject associated with each case. An application to a set of teenagers' interviews regarding their habits of communication is described. The interviewees answered several questions about the kind of contacts they had on their phone, Facebook, email or messenger as well as the frequency of communication between them. The results indicate that the methodology is adequate to cluster this kind of data sets, since it allows us to identify and characterise different profiles from the data. We compare the results obtained with this methodology with the ones obtained using the entire database, and we conclude that they may lead to different findings.

Abstract
Electric vehicles (EVs) are to play an important role in electricity markets, since their energy storage capability can be beneficial to power systems operation. Electric vehicle aggregators will consequently develop adequate offering strategies to participate in energy and reserve markets, accounting for the market rules and operational capabilities of EVs aggregators (e.g., fleet of EVs). In this paper, we propose an offering strategy model for an EV aggregator to participate in the frequency-controlled normal operation reserve service (FCR-N) in Eastern Denmark. The aim is to maximize the expected revenue of the aggregator, accounting for potential penalties for missing the provision of both upward and downward reserves. The methodology has been modeled and tested under the scope of the PARKER project, which considers a case study based on real data from a small fleet of electric vehicles. An important conclusion relates to the availability patterns of the EVs that significantly changes the strategical participation of the EV aggregator in the service.

Abstract
The coordination of various energy vectors under the concept of multi-energy system (MES) has introduced new sources of operational flexibility to system managers. In this paper, the behavior of multi-energy players (MEP) who can trade with more than one energy carrier to maximize their profits and mitigate their operational risks has been investigated. The MES is represented based on a multilayer structure, namely the energy market, MEP, the local energy system (LES), and multi-energy demand. In such environment, an MEP aggregates LES and participates in the wholesale electricity market, simultaneously to maximize its profit. The decision-making conflict of the MEP with other energy players for the aggregation of LES and participation in the electricity market is modeled based on a bilevel approach. Numerical results show the behavior of the MEP as a prosumer in the electricity market to produce smoother demand and price profiles. Results reveal a mutual effect of local and wholesale equilibrium prices by increasing the share of the MEP.