Abstract
The pellet market in Portugal is a developing market where the main producers of wood pellets try to convince the small-scale buyer that wood pellets are a good alternative as a fuel because energy from them is cost-effective in long-term use. For this work, data about production, production capacity and consumption in Portugal were collected. Consumption levels are low since the majority of sales are exports. The different points of pellet production in Portugal have been located, and a price analysis has been made. To obtain data on the production capacity of the plants and get referrals from the market, the database Bioraise has been used in order to complement the information raised through prospecting and inquiries. Although it is noted that the pellet industry in Portugal is a sector in the initial stage of its development, the potential for growth is high, since internal consumption is low compared to the level of exports to other countries in which the pellet industry is booming. We conclude that the pellet industry is an important alternative in the Portuguese energy market due to the competitive price of this fuel compared to traditional fuels.

Abstract
In this paper, a mixed-integer linear programing (MIL?) model for the traffic behavior of plug-in electric vehicles (PEVs) in a multi energy system (MES) is proposed. It is assumed that two micro-MESs are covering two traffic zones with different consumption patterns. The difference between these two micro-MESs is not only the different multi energy demand (MED) they provide, but also different PEV traffic pattern that travel in these two micro-MESs. The PEVs traffic pattern and their behavior in using parking lot (PL) or charging station (CS) as their charging places is integrated in the MES operation model. The results demonstrate an improved strategy of the MES operator in using its components, such as combined heat and power (CHP) unit and auxiliary boiler (AB), in response to extra added load of the PEVs. The stochastic behavior of the PEVs is implemented in the model through various scenarios of arrival and departure. © 2016 IEEE.

Abstract
A practical electric distribution system is a nonlinear network, which is generally governed by a large number of differential and algebraic equations (DAE). For instance, the ordinary differential equations are defined by the dynamics of the generators (e.g., small-scale hydro generation units) and the loads, as well as distributed generation (DG) units and their controllers. Algebraic equalities are described by the distribution network current balance equations and internal static behaviors of passive devices. In this paper, a rigorous approach is used to convert a semi-explicit DAE system into an explicit ordinary differential equations (ODE) system. In this way, it is possible to apply the robust and well consolidated implicit integration methods to solve the semi-explicit DAE systems as for implicit ODE system. Two applications of the methods are presented. In the first example, the procedure solves the classical Robertson's problem, while in the second case study the dynamic behavior of a simple distribution system with a generator, an induction motor and an admittance load is reported. The results indicate that the proposed procedure is able to reach exactly the same results obtained by applying the classical solution procedure, without any further assumption about the nature of the results obtained.

Abstract
In this work, the self-scheduling problem of a power producer in smart grids is addressed using a stochastic programming approach. Different uncertainties are considered as price uncertainties, forced-outage of the unit as well as generation reallocation. The conditional value-at-risk index is used for modeling of risk. The markets considered in this study are bilateral contracts, day-ahead and ancillary services, including spinning reserve and regulation, and spot market decisions, while an incomplete competitive market is considered. In this sense, an innovative method for bilateral contracts is proposed to increase the profit of the market without ignoring any regulatory rules. The Monte Carlo method is implemented together with a reduction scenario process to generate scenarios.

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.

Abstract
In this paper, an optimal scheduling of thermal and wind power plants is presented by using a stochastic programming approach to cover the uncertainties of the forecasted generation of wind farms. Uncertainties related to wind forecast error, consequently wind generation outage power and also system load demand are modeled through scenario generation. Then, with regard to day-ahead and real-time energy markets and taking into account the relevant constraints, the thermal unit commitment problem is solved considering wind energy injection into the system. Besides, in order to assess impacts of Demand Response (DR) on the problem, a load reduction demand response model has been applied in the base model. In this approach, self and cross elasticity is used for modeling the customers' behavior modeling. The results indicate that the DR Programs (DRPs) improves the market efficiency especially in peak hours when the thermal Gencos become critical suppliers and the combination of DRPs and wind farm can be so efficient.