Abstract
The concept of SG (Smart Grids) encompasses a set of technologies that raise the intelligence of the electrical networks, such as smart meters or instruments of communication, sensing and auto-correction of networks. Nevertheless, the cost is still an important obstacle for the transformation of the current electricity system into a smarter one. Regulation can have an important role in setting up a favorable framework that fosters investments. However, the novelty with SG is the disembodied character of the technology, which may change the incentives of the regulated network companies to invest, affecting the effectiveness of the regulatory instruments ("cost plus" or "price cap"). This paper demonstrates that the solution to this "Smart" paradox requires strong incentive regulation mechanisms able to stimulate the adoption of SG technologies. Moreover, the regulation should not jeopardize conventional investments that are unable to be substituted by SG. Thus, a combination of performance regulation and efficiency obligations may be necessary.

Abstract
This paper extends the symmetric/constrained fuzzy power flow models by including the potential correlations between nodal injections. Therefore, the extension of the model allows the specification of fuzzy generation and load values and of potential correlations between nodal injections. The enhanced version of the symmetric/constrained fuzzy power flow model is applied to the 30-bus IEEE test system. The results prove the importance of the inclusion of data correlations in the analysis of transmission system adequacy. Copyright (c) 2015 John Wiley & Sons, Ltd.

Abstract
In restructured power systems, the adequacy of the transmission network may be defined as the ability to meet reasonable demands by transmission of electricity (as stated by the Directive 2009/72/EC). The symmetric/constrained fuzzy power flow (CFPF) was recently proposed as a suitable tool to quantify that adequacy. In this paper, the use of the symmetric fuzzy power flow/CFPF is extended to support the decision process of investment in network components to accomplish a specific adequacy criteria. A technique based on dual variables, obtained from the linear formulation of the CFPF, is used. The importance of the duality information concerning the adequacy indices is explained. The proposed methodology is applied on IEEE 14 bus reliability test system to demonstrate its applicability. Copyright © 2017 John Wiley & Sons, Ltd.

Abstract
The symmetric/constrained fuzzy power flow (SFPF/CFPF) models are suitable to quantify the adequacy of transmission network in satisfying “reasonable demands for the transmission of electricity” as defined, in the European Directive 2009/72/EC. In this paper, SFPF/CFPF is mainly used to identify the basic repressions (inappropriate definition of reasonable demands) of fuzzy data (generation or load) when the adequacy of a transmission system is assessed. This situation arises essentially in cases where the network configuration does not fully support the requested power specifications. It means that these requests are inadequately formulated and lead to the creation of artificial repression in the results (artificial inadequacy of the transmission system). In this article, it is intended to show how these cases can be identified. For this purpose, the SFPF will be used, which does not consider branch limits. With this study, it is also shown how the reference power flows of the system are obtained in order to identify the congested branches.

Abstract
In this paper, the IEEE 14 bus test system is used in order to perform adequacy assessment of a transmission system when large scale integration of electric vehicles is considered at distribution levels. In this framework, the symmetric/constrained fuzzy power flow (SFPF/CFPF) was proposed. The SFPF/CFPF models are suitable to quantify the adequacy of transmission network to satisfy "reasonable demands for the transmission of electricity" as defined, for instance, in the European Directive 2009/72/EC. In this framework, electric vehicles of different types will be treated as fuzzy loads configuring part of the "reasonable demands". With this study, it is also intended to show how to evaluate the amount of EVs that can be safely accommodated to the grid meeting a certain adequacy level.

Abstract
A hybrid population-based metaheuristic, Hybrid Canonical Differential Evolutionary Particle Swarm Optimization (hC-DEEPSO), is applied to solve Security Constrained Optimal Power Flow (SCOPF) problems. Despite the inherent difficulties of tackling these real-world problems, they must be solved several times a day taking into account operation and security conditions. A combination of the C-DEEPSO metaheuristic coupled with a multipoint search operator is proposed to better exploit the search space in the vicinity of the best solution found so far by the current population in the first stages of the search process. A simple diversity mechanism is also applied to avoid premature convergence and to escape from local optima. A experimental design is devised to fine-tune the parameters of the proposed algorithm for each instance of the SCOPF problem. The effectiveness of the proposed hC-DEEPSO is tested on the IEEE 57-bus, IEEE 118-bus and IEEE 300-bus standard systems. The numerical results obtained by hC-DEEPSO are compared with other evolutionary methods reported in the literature to prove the potential and capability of the proposed hC-DEEPSO for solving the SCOPF at acceptable economical and technical levels.