Abstract
It is expected that in the near future the number of Plug-in Electric Vehicles (PEV) could increase significantly due to their low pollution emissions, high fuel economy, and mitigation of security issues related to oil technical and economic management. Many works have dealt with the impact of PEV on the power and distribution grids, and on other particular aspects, but very few perform more general cost benefit analyses of their global impact. This paper does it in two steps. First, a hydro-thermal unit commitment for a full year simulation provides electricity and reserve prices for different charging strategies. Then, the model computes economic estimations for the costs of the charging infrastructure, specific PEV costs and main externalities (emissions, health benefits and energetic dependence). The model is intended to provide meaningful results on the global economic balance of PEV penetration, helping for example in feed-in tariffs, fuel taxes redesign or other regulatory analyses. © 2013 IEEE.

Abstract
The smart city is a sustainable and efficient urban center that provides high quality of life to its inhabitants with an optimal management of its resources, where clean and cost effective energy generation is a key issue. Under this setting, distributed generation can provide an adequate tool to deal with energy reliability and to successfully implement renewable sources; nevertheless, selection and scaling of energy systems, considering location, is not a trivial task. Frequently, the stakeholders analyze only one or two 'popular' generation systems, and then calculate the output and return of investment in a simplified and approximated approach. This practice could lead the stakeholder to an inadequate technology mix. To tackle this problem, this paper reviews and models most common energy sources for distributed generation in a smart city context. Then, a technical economic analysis is developed for 2 cases, a household and a district, considering not only renewable sources but also efficient non-renewable technologies. The results of the numerical analysis help to assess the more adequate generation systems for a given application, energetic demand, and geographical characteristics. A well-developed analysis is essential for a better understanding of the available technologies and their synergies; as a result, the investors can choose the appropriate solutions, maximizing overall benefits. © 2013 IEEE.

Abstract
Distributed generation (DG) represents an important resource to address relevant energy issues, such as reliability and sustainability, in the current and future smart cities. It is expected that distributed generation will gain considerable presence in the following years; however, the selection and sizing of the generation and storage systems is commonly done without an adequate level of detail. This simplified or approximated approach usually results in a suboptimal technology mix with an inadequate type of system and/or scale, which could compromise the economic feasibility of the DG project. To tackle this problem, stakeholders should consider many factors, including geographical characteristics (sun, wind...) energy costs, local regulation, and energetic demand patterns, apart from analysing different technologies. Considering as an example location the city of Madrid, Spain, this paper proposes a linear programming model to evaluate the most common distributed generation technologies, with and without storage systems and under different electricity pricing scenarios. As a result, not only the optimal sizing, but also the optimal operation scheduling of the aforementioned systems are found. Then, an economic feasibility analysis is developed, comparing the different technologies and defining the best option for a given scenario. Furthermore, this study helps to find important milestones, such as battery prices, that could make distributed generation more attractive. © 2013 WIT Press.

Abstract
Large deployment of distributed generation into distribution systems brings new challenges regarding the shift from the passive to the active control paradigm. These challenges have been extended to the field of dynamic equivalence. Developing effective reduced order models for active distribution network cells for dynamic and stability studies require a careful evaluation of the techniques that have been used in conventional power systems. Thus, a survey of the existing approaches is presented in this paper. Also a critical overview is provided regarding their application to active distribution network cells and microgrids. Technical requirements are identified and recommendations are provided.

Abstract

Abstract
This paper discusses static transmission expansion planning (STEP) in terms of minimizing the costs of investment and operations. We propose a transmission expansion model that divides into investment and operations problems. We use a binary particle swarm optimization algorithm (BPSO) to solve the investment problem and a DC optimal power flow (DCOPF) to solve the operations problem. We model uncertainty as stochastic demand at each node. A simulated case study numerically evaluates the efficiency of the proposed method. © 2013 IEEE.