Abstract
Emerging technologies arising in modern power systems have propelled the presence of new agents to manipulate these facilities. Participation of plug-in electric vehicles (PIEVs) in the electricity market is one of the main issues in this environment. PIEV participation in market place can affect the agent's strategy. Therefore, this paper investigates two states of power system where individual aggregators participate in the power market on behalf of home-charged PIEVs and parking lots (PLs) separately, as well as the coordinated version of the problem. Several scenarios are developed for deriving specific characteristics of PIEVs in home levels and in PLs. An optimization model is built and solved using mixed integer linear programming. The results are produced to suggest the optimum procedure for an aggregator to whether take the authority of home-charging PIEVs and PLs individually or coordinately.

Abstract
The prospect of distributed generation investment planning (DGIP) is especially relevant in insular networks because of a number of reasons such as energy security, emissions and renewable integration targets. In this context, this paper presents a DGIP model that considers various DG types, including renewables. The planning process involves an economic analysis considering the costs of emissions, reliability and other relevant cost components. In addition, a comprehensive sensitivity analysis is carried out in order to investigate the effect of variability and uncertainty of model parameters on DG investment decisions. The ultimate goal is to identify the parameters that significantly influence the decision-making process and to quantify their degree of influence. The results show that uncertainty has a meaningful impact on DG investment decisions. In fact, the degree of influence varies from one parameter to another. However, in general, ignoring or inadequately considering uncertainty and variability in model parameters has a quantifiable cost. The analyses made in this paper can be very useful to identify the most relevant model parameters that need special attention in planning practices.

Abstract
Electricity price forecast is key information for successful operation of electricity market participants. However, the time series of electricity price has nonlinear, non-stationary and volatile behaviour and so its forecast method should have high learning capability to extract the complex input/output mapping function of electricity price. In this paper, a Combinatorial Neural Network (CNN) based forecasting engine is proposed to predict the future values of price data. The CNN-based forecasting engine is equipped with a new training mechanism for optimizing the weights of the CNN. This training mechanism is based on an efficient stochastic search method, which is a modified version of chemical reaction optimization algorithm, giving high learning ability to the CNN. The proposed price forecast strategy is tested on the real-world electricity markets of Pennsylvania-New Jersey-Maryland (PJM) and mainland Spain and its obtained results are extensively compared with the results obtained from several other forecast methods. These comparisons illustrate effectiveness of the proposed strategy.

Abstract
The cork industry presents itself as one of the most entrepreneurial in the Portuguese industrial sector, contributing significantly to the increase of exports. However, it is an industry in which the use of raw materials is maximised leaving a large volume of waste. The cork industry has tried to take advantage of these residues, mainly through direct energy recovery, despite the technical and safety difficulties presented by the use of such low density material, which complicates and hinders its transportation for industrial uses outside the area in which it is produced. The densification process opens new doors for such use and also for its storage, because it produces better results when compared with other more common products, such as wood sawdust or even forest and agricultural waste. Thus, cork pellets emerge as a safer and more easily transportable alternative for energy recovery from cork dust and other granulated types of cork waste, which offer the prospects for wider use. The results demonstrate that cork pellets have higher calorific value when compared with other biomass pellets; typically, approximately 20 MJ/kg with 3% volume of ashes, which is equivalent to that obtained from the combustion of pellets produced from combined forest and agricultural waste with a bulk density of 750 kg/m(3), which offers real advantages in terms of logistics.

Abstract
With the restructuring of the electricity sector in recent years, and the increased variability and uncertainty associated with electricity market prices, it has become necessary to develop forecasting tools with enhanced capabilities to support the decisions of market players in a competitive environment. Hence, this paper proposes a new hybrid evolutionary-adaptive methodology for electricity prices forecasting in the short-term, i.e., between 24 and 168 h ahead, successfully combining mutual information, wavelet transform, evolutionary particle swarm optimization, and the adaptive neuro-fuzzy inference system. In order to determine the accuracy, competence and proficiency of the proposed methodology, results from real-world case studies using real data are presented, together with a thorough comparison considering the results obtained with previously reported forecasting tools. Not only is the accuracy an important factor, but also the computational burden is relevant in a comparative study. The results show that it is possible to reduce the uncertainty associated with electricity market prices prediction without using any exogenous data, just the historical values, thus requiring just a few seconds of computation time.

Abstract
A control technique of multilevel converter topologies based on Direct Lyapunov Control method is presented in this paper for integration of Distributed Generation (DG) resources into the power grid. The compensation of instantaneous variations in the reference current components in ac-side and dc-voltage variations of cascaded capacitors in dc-side of the interfacing system are considered properly, which is the main contribution and novelty of this work in comparison with other control strategies. The proposed control technique provides the continuous injection of active power in fundamental frequency from DG sources to the grid. In addition, reactive power and harmonic current components of loads are provided with a fast dynamic response; thereby, achieving sinusoidal grid currents in phase with load voltages, while the required power from load side is more than the maximum capacity of interfaced converter, is possible. Simulation results confirm the effectiveness of the proposed control strategy in DG technology during dynamic and steady-state operating conditions.