Abstract
Combining large penetration of wind and solar generation with Plug-in Electric Vehicles (PEVs) seems a promising solution for energy cost saving and emissions reduction. PEVs connected to the grid with smart charging strategies can be an effective way to integrate non-dispatchable renewable generation, smoothing the load curve, contributing to the system stability by providing regulation services, and moving unhealthy emissions away from city centers. This paper analyzes the combined penetration of PEVs, and wind and solar generation using a Unit Commitment model for the Spanish power system, providing some insight on how the penetration of these technologies affects relevant variables such as energy and reserve, thermal plants behavior (such as starts-up and shut-downs, technological energy share, generation costs or emissions) and systems costs. Results show that PEV increase total demand, but its optimal charging smooths the net demand (to be supplied by thermal units) and the final electricity prices. In addition, solar generation penetration leads to a larger net demand with more variability but with lower production costs than wind generation penetration, due to their different hourly profiles. Finally neither solar nor wind generation penetrations are totally profitable for the system with the assumptions made, since their investments costs do not compensate the production cost decrement, but grid parity is almost reached for both technologies. (C) 2016 The Authors. Published by Elsevier Ltd.

Abstract
Previous studies of market power within a regional system have considered multiple competing generation operators and the role of transmission constraints. However, these studies typically assume simplified structures in which each operator is restricted to a unique node in a transmission constrained network. Real systems typically have operators making decisions for units in several zones at once. Past studies also implicitly treated market power as a static concept for a given set of market rules and network configuration. We demonstrate that market power is dynamic, and can vary significantly with fuel prices and even with large-scale weather patterns. We also demonstrate the impact on region-wide market power of operators that manage units in multiple zones. We use the Electric Reliability Council of Texas (ERCOT) as an illustrative case study, and apply a conjectured supply function equilibrium (CSFE) approach that accounts for transmission constraints. We show that the companies with greater influence on the market price will depend on the relative prices of coal and natural gas. We also show that a weather event, such as a period without any wind, can have a substantial impact on market power.

Abstract
This paper proposes a linear programming problem to find the optimal planning and operation of aggregated distributed energy resources (DER), managed by an aggregator that participates in the day-ahead wholesale electricity market as a price-maker agent. The proposed model analyzes the impact of the size of the aggregated resources and gives the optimal planning and management of DER systems, and the corresponding energy transactions in the wholesale day-ahead market. The results suggest that when the aggregated resources are large enough, DER systems can achieve up to 32% extra economic benefits depending on the market share, compared with a business-as-usual approach (not implementing DER systems). © 2016 IEEE.

Abstract
A smart city is a sustainable and efficient urban centre that provides a high quality of life to its inhabitants through optimal management of its resources. Energy management is one of the most demanding issues within such urban centres owing to the complexity of the energy systems and their vital role. Therefore, significant attention and effort need to be dedicated to this problem. Modelling and simulation are the major tools commonly used to assess the technological and policy impacts of smart solutions, as well as to plan the best ways of shifting from current cities to smarter ones. This paper reviews energy-related work on planning and operation models within the smart city by classifying their scope into five main intervention areas: generation, storage, infrastructure, facilities, and transport. More-complex urban energy models integrating more than one intervention area are also reviewed, outlining their advantages and limitations, existing trends and challenges, and some relevant applications. Lastly, a methodology for developing an improved energy model in the smart-city context is proposed, along with some additional final recommendations.

Abstract
In order to improve their competitive performance, airline companies often adopt as a strategy to establish arrangement between two or more organizations agreeing to cooperate on a substantial level. This strategy is often known as airline alliances. A paradigm to analyze the collective intelligence behavior which emerges from a group, as a strategic alliance, is the flocking behavior. Inspired by the Cucker and Smale algorithm (C-S) we propose a new version of the flocking behavior algorithm applied to airline alliances. Our goal is to understand the link between strategic alliances and flocks. For this new approach, metrics were obtained for the parameters of C-S algorithm, namely position, velocity and influence, where the latter uses cooperative games. Besides, reinforcement learning mechanisms have been explored. Some relevant outputs for airline alliances as the permanence rate and the growth rate were computed for each of the five configurations in analysis.

Abstract