Abstract
Optimal operation of power systems with high integration of renewable power sources has become difficult as a consequence of the random nature of some sources like wind energy and photovoltaic energy. Nowadays, this problem is solved using Monte Carlo Simulation (MCS) approach, which allows considering important statistical characteristics of wind and solar power production such as the correlation between consecutive observations, the diurnal profile of the forecasted power production, and the forecasting error. However, MCS method requires the analysis of a representative amount of trials, which is an intensive calculation task that increases considerably with the number of scenarios considered. In this paper, a model to the scheduling of power systems with significant renewable powergeneration based on scenario generation/reduction method, which establishes a proportional relationship between the number of scenarios and the computational time required to analyse them, is proposed. The methodology takes information from the analysis of each scenario separately to determine the probabilistic behaviour of each generator at each hour in the scheduling problem. Then, considering a determined significance level, the units to be committed are selected and the load dispatch is determined. The proposed technique was illustrated through a case study and the comparison with stochastic programming approach was carried out, concluding that the proposed methodology can provide an acceptable solution in a reduced computational time.

Abstract
The use of biomass as energy source becomes considered again as a possibility, after a period in which practically felt in disuse, especially in industrial environments. Nowadays is becoming again an alternative, mainly due to the increasing prices of fossil fuels, especially oil and natural gas, as well as by the growing environmental and sustainability concerns, since the biomass fuel is considered neutral in terms of CO2 emissions. The use of biomass as solid fuel presents logistic difficulties, which do not allow their straightforward dissemination in all industrial units that intend to change its fuel. However, counterbalanced by excellent results from the economical point of view, achieved due to the stability of biomass prices in Portugal, the application of biomass is envisaged in this paper as a sustainable alternative to fossil fuels for the Portuguese textile industry. Hence, a real case study is provided to discuss advances and developments in the field of biomass applications.

Abstract
Transformers are one of the more expensive pieces of equipment found in a distribution network. The transformer's role has not changed over the last decades. With simple construction and at the same time mechanically robust, they offer long term service that on average can reach half a century. Today, with the ongoing trend to supply a growing number of non-linear loads along with the notion of distributed generation (DG), a new challenge has arisen in terms of transformer sustainability, with one of the possible consequences being accelerated ageing. In this paper we carefully review the existing studies in the literature of the effect of loads and other key factors on oil-transformer ageing. The state-of-the-art is reviewed, each factor is analysed in detail, and in the end a smart transformer protection method is sought in order to monitor and protect it from upcoming challenges.

Abstract
This chapter aims to provide an overview of renewable energy systems (RESs) and the underlying issues related to the RES theme such as climate change and its mitigation. The types of RESs are also briefly discussed focusing on their characteristics and technological aspects. This is followed by the most important economic aspects as well as the challenges and opportunities of integrating RESs in power systems. This chapter also discusses the need for optimization tools adequately equipped to effectively capture the intrinsic characteristics of RESs and facilitate their optimal integration in power systems. All this leads to an efficient exploitation of their wide-range benefits while sufficiently minimizing their negative impacts. Finally, the chapter is summarized with some concluding remarks.

Abstract
Transmission congestion management plays a key role in deregulated energy markets. To correctly model and solve this problem, power system voltage and transient stability limits should be considered to avoid obtaining a vulnerable power system with low stability margins. Congestion management is modeled as a multi-objective optimization problem in this paper. The proposed scheme includes the cost of congestion management, voltage stability margin and transient stability margin as its multiple competing objectives. Moreover, a new effective Multi-objective Mathematical Programming (MMP) solution approach based on normalized normal constraint (NNC) method is presented to solve the multi-objective optimization problem of the congestion management, which can generate a well-distributed and efficient Pareto frontier. The proposed congestion management model and MMP solution approach are implemented on the New-England's test system and the obtained results are compared with the results of several other congestion management methods. These comparisons verify the superiority of the proposed approach.

Abstract
The increasing operational complexity of power systems considering the higher renewable energy penetration and changing load characteristics, together with the recent developments in the ICT field have led to more research and implementation efforts related to the activation of the demand side. In this manner, different direct load control (DLC) and indirect load control concepts have been developed and DLC strategies are considered as an effective tool for load serving entities (LSEs) with several real-world application examples. In this study, a new DLC strategy tailored for residential air-conditioners (ACs) participating in the day-ahead planning, based on offering energy credits to the enrolled end-users is proposed. The mentioned energy credits are then used by residential end-users to lower their energy procurement costs during peak-price periods. The strategy is formulated as a stochastic mixed-integer linear programming (MILP) model considering uncertainties related to weather conditions. The outcomes regarding the end-user comfort level and economic benefits are also analyzed.