Abstract
Integration of emerging energy resources in distribution level reveals new opportunities for decision makers to coordinate various energy vectors under the concept of multi-energy system (MES). In this paper, the behavior of a multi-energy player (MEP) who can trade more than one energy carrier to enhance operational flexibility of MES has been investigated. MEP participates in retail and wholesale energy markets to maximize its profit. The strategic behavior of MEP in these two markets is modeled as two synchronized bi-level problems. The problem is linearized and solved through CPLEX 12 solver. Numerical results show the behavior of MEP as a prosumer in the electricity market to make a smother demand and price profile. Moreover, the results reveal a mutual effect of local and wholesale equilibrium prices by increasing the market share of MEP.

Abstract
This paper investigates the effects of Demand Response Programs (DRPs) on the behavior of electricity market players in the day-ahead energy market. To this end, an electricity market environment is proposed based on the multi-agent systems in order to model the strategic self-scheduling of each market player as an individual agent. In such oligopolistic environment, market interactions are considered by using a game theoretic model and the market transactions are cleared by means of a security constrained unit commitment problem. Different types of DRPs are also considered consisting of Time Of Use (TOU), Real Time Pricing (RTP), Critical Peak Pricing (CPP), and Emergency Demand Response Program (EDRP). The proposed model is applied on a modified IEEE six-bus test system. The numerical results indicate that different types of DRPs differently affect the oligopolistic behavior of market players that should be studied by the system operators before their implementation.

Abstract
This paper models the impacts of Demand Response Programs (DRPs) on the behavior of energy market participants in the electricity markets in the presence of renewable energies. In such oligopolistic environment, market interactions are considered by using a game theoretic model, and the market transactions are cleared by means of a Security Constraint Unit Commitment program (SCUC). One sample is considered from each main category of DRPs consisting of different types of time of use tariffs, real-time pricing, critical peak pricing from Price-Based Demand Response (PBDR), and different types of emergency demand response program tariffs from Incentive Based Demand Response (IBDR) in the presence of the wind farms. It is expected that the numerical results with the presence of renewable energy resources indicate that different types of these DRPs differently affect the oligopolistic behavior of market players that should be studied by the system operators before their implementation. Using Monte Carlo simulation method, several scenarios are generated to show the possible contingencies in Day-Ahead energy market. Then some scenario reduction methods are used for reduction the numbers of scenarios. Finally, a two-stage stochastic model is applied to solve this scheduling in a mixed-integer linear programming through GAMS. Consequently, the effect of demand response in the reduction of the operation cost is proved. The proposed approach is tested on a modified IEEE six-bus system.

Abstract
This paper proposes an oligopolistic model for a wind power producer (WPP) with a market power to compete with other Gencos and take part in day-ahead, intraday and balancing markets. In order to model the mentioned oligopoly markets from WPP's viewpoint, a bi-level optimization framework is proposed based on multi-agent system and incomplete information game theory. In this context, the WPP participates in the intraday market where demand response resources are incorporated, to update its day-ahead offers. The problem uncertainties, i.e., wind power and market prices, are considered using a multi-stage stochastic programming approach. Because of these uncertainties, a well-known risk measurement, CVaR, is considered for problem optimization. Several numerical studies are accomplished and various aspects of the problem are analyzed. According to the obtained results, the proposed WPP model reveals that the prices of day-ahead and balancing markets could be increased due to the market power of WPP.

Abstract
This paper aims to make a comparison between the logistics costs of buying Wood Pellets (WP) and Torrefied Biomass Pellets (TBP) produced in Portugal and exported to the major consumer markets of Northern Europe. The starting point is to determine the value of a shipload of WP and TBP delivered to a North European port and loaded in Aveiro, the main Portuguese WP expeditor port. Torrefaction implies higher energy and bulk density pellets, which contributes to increase the logistics costs associated with them. The loss of mass is greater than the loss of energy. These changes in bulk and energy densities are an advantage in terms of logistics: more tonnes per unit of volume and more energy per tonne will decrease the transportation cost per energy unit. The analysis carried out in this paper determines the energy in gigajoules (GJ) per tonne and all the comparisons are based on the cost per energy unit. This analysis is supported by real data collected in the Argus Biomass Markets report.

Abstract
Among renewable power sources, wind energy is the most promising technology; however, the inter-temporal uncertainty of this source makes impossible its massive integration. Forecasting of wind generation is a key factor for the economical operation of the power system. Thus, the error related to this process is typically modeled by means of a determined probability distribution to be later incorporated to the unit scheduling and load dispatch optimization procedures. In this paper, wind power forecasting error has been modeled by using Weibull and Levy alpha-stable probability distributions and incorporated to the economic dispatch problem in order to probabilistically describe power production and generating cost. The proposed methodology is illustrated by analyzing a case study composed by 13 conventional generators; the obtained results are compared with Monte Carlo Simulation approach for evaluating and testing the capabilities of the proposed model, observing reasonable accuracy on the estimated results.