Abstract
In this paper, the optimal operation of a neighborhood of smart households in terms of minimizing the total energy procurement cost is analyzed. Each household may comprise several assets such as electric vehicles, controllable appliances, energy storage and distributed generation. Bi-directional power flow is considered both at household and neighborhood level. Apart from the distributed generation unit, technological options such as vehicle-to-home and vehicle-to-grid are available to provide energy to cover self-consumption needs and to inject excessive energy back to the grid, respectively. The energy transactions are priced based on the net-metering principles considering a dynamic pricing tariff scheme. Furthermore, in order to prevent power peaks that could be harmful for the transformer, a limit is imposed to the total power that may be drawn by the households. Finally, in order to resolve potential competitive behavior, especially during relatively low price periods, a simple strategy in order to promote the fair usage of distribution transformer capacity is proposed.

Abstract
In deregulated electricity markets, producers offer their energy to the day-ahead market. As the subsidies for renewable producers are becoming lower and lower, they have to adapt to market prices. This paper models the energy trading in the day-ahead market for wind power producers. Different strategies are proposed for this purpose: 1) several wind farms offering their energy separately to the day-ahead market; 2) the same strategy as in 1) but compensating the imbalance among different wind farms; and 3) a joint offer involving several wind farms through an external agent in order to minimize the imbalances between the offer and the final power generation. The strategies are modeled with stochastic mixed integer linear programming and Conditional Value at Risk is used to consider the risk assessment. The expected profit including risk aversion is maximized for each wind power producer and for the set of wind power producers in the case of a joint offer. A comparison of the different cases is described in detail in a case study and relevant conclusions are provided.

Abstract
Ever since energy sustainability is an emergent concern, Plug-in Electric Vehicles (PEVs) significantly affect the approaching smart grids. Indeed, Demand Response (DR) brings a positive effect on the uncertainties of renewable energy sources, improving market efficiency and enhancing system reliability. This paper proposes a multi-stage stochastic model of a PEV aggregation agent to participate in day-ahead and intraday electricity markets. The stochastic model reflects several uncertainties such as the behaviour of PEV owners, electricity market prices, and activated quantity of reserve by the system operator. For this purpose, appropriate scenarios are utilized to realize the uncertain feature of the problem. Furthermore, in the proposed model, the PEV aggregation agents can update their bids/offers by taking part in the intraday market. To this end, these aggregation agents take part in Demand Response exchange (DRX), markets designed in the intraday session by employing DR resources. The numerical results show that DR provides a perfect opportunity for PEV aggregation agents to increase the profit. In addition, the results reveal that the PEV aggregation agent not only can increase its profit by participating in the DRX market, but also can become an important player in the mentioned market.

Abstract
The torrefaction of biomass is a thermochemical process based on the de composition of hemicellulose, which is the dominant reaction, while the cellulose and lignin fractions remain almost unaffected. Torrefaction of biomass improves its physical properties like grindability, particle shape, size, and distribution, pelletability, and composition properties like moisture, carbon and hydrogen contents, and calorific value. The already higher energy density can be increased further by a pelletizing step after torrefaction. These improved properties make torrefied biomass particularly suitable for co-firing in power plants. Co-firing biomass with fossil fuels is one of the solutions to reduce the greenhouse gas emissions of existing power plants. Several studies on torrefaction of biomass for heat and power applications have been documented in the literature, which need to be reviewed and analyzed for further actions in the field, because significant gaps remain in the understanding of the biomass torrefaction process, which necessitate further study, mainly concerning the characterization of the torrefaction chemical reactions, investigation of equipment performance and design, and elucidation of supply chain impacts. This is the main objective of the present review study, which consists in three parts. The first part focuses on the mechanism of biomass torrefaction. It is followed by a review of biomass co-firing with coal. Finally, market opportunities for the process are discussed.

Abstract
In this paper an overloading prevention of a private customer power distribution transformer (PDT) in an island in Portugal through the means of a new smart electric vehicle (EV) charging scheduler is proposed. The aim of this paper is to assess the repercussion of the penetration of additional power to restore the full level of EV battery state of charge (SoC) on dielectric oil deterioration of the PDT of a private industry client. This will allow EVs to charge while their owners are at work at three different working shifts during the day. In addition, the system is part of an isolated electric grid in a Portuguese Island. A transformer thermal model is utilised in this paper to assess hot-spot temperature by having the information of the load ratio. The data used for the main inputs of the model are the private industry client daily load profile, PDT parameters, the characteristics of the factory and EV parameters. This paper demonstrates that the proposed solution allows avoiding the overloading of the PDT, thus mitigating the loss-of-life, while charging all the EVs plugged-in at the beginning of each working shift.

Abstract