Abstract
The smart city seeks a highly interconnected, monitored and globally optimized environment to profit from the synergies among systems such as energy, transports or waste management. From an energy perspective, transport systems and facilities are among the bigger energy consumers inside cities. However, despite the research available on such systems, few works focus on their interactions and potential synergies to increase their efficiencies. This paper address this problem by assessing the benefits of the interconnection and joint management of different energy systems in a smart city context. This is done using a linear programming problem, modelling a district with residential loads, distributed energy resources (DER) and electric vehicles (EV), which are also connected to an electrical metro substation. This connection allows to store the metro regenerative braking energy into EVs' batteries to be used later for other trains or for the EVs themselves. The objective of the linear programming model is to find the optimal planning and operation of all the considered systems, achieving minimum energy costs. Therefore, the main contributions of this paper are the assessment of synergies of the interconnection of these systems and the detailed analysis of the impact of different EV penetration levels. Results show important economic benefits for the overall system (up to 30%) when the investments and its operation are globally optimized, especially reducing the metro energy costs. Also, analysing the energy transfers between metro-EV, it is evident that the metro takes advantages of the cheaper energy coming from the district (through the EVs), showing the existence of “opportunistic” synergies. Lastly, EV saturation points (where extra EVs represent more load but do not provide additional useful storage to the system) can be relatively small (200–300 EVs) when the energy transfer to the metro electrical substation is restricted, but it is also reduced by the presence of DER systems. © 2017 Elsevier Ltd

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
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
The continuous penetration of intermittent technologies is gradually reinforcing the technical and economic importance of electricity ancillary services, which are responsible for guaranteeing the reliability and security of the power systems. Generation companies', regulating entities, system operators and other institutions (such as researchers on these fields) are more and more concerned on using market models to forecast most relevant outcomes for particular markets (such as energy and reserves cleared quantities and prices), under different simulation scenarios (such as costs or demand) and under different markets structures (such as more competitive or more oligopolistic). This paper reviews most energy and reserve markets implementations (mainly focusing on reserve types and dispatching methods), and discusses different approaches to model them. A theoretical equilibrium model for energy and reserve markets is also proposed.

Abstract
Environmental policies could accelerate the replacement of Internal Combustion Engine Vehicles (ICEV) by Plug-in-Electric Vehicles (PEV) in many countries. However, in countries where these policies are still not implemented (for example those with no significant PEV subsidies like Spain), technical and economic criteria can also be relevant to assess the future PEV penetration. This work develops a new long-term expansion model that computes the share of PEV and ICEV based on economic criteria, including the impact of PEV on the electricity price and generation mix. The model minimizes the power and transport system costs (investment, operation and maintenance costs, etc.) considering electricity and private transportation needs. Results provide insight on the combined evolution of the renewable generation and of the PEV and ICEV future fleets in Spain, considering environmental constraints such as those imposed by the European Commission.

Abstract
Capacity generation expansion problems have traditionally been represented with low time resolution models due to their high computational cost, very often using blocks of hours with similar demand. However, the current transformation of the power system with the new generation and consumption technologies, the flexibility and reserve requirements, and the expected new behavioral consumption patterns, requires more complex and detailed models with higher time resolution to provide accurate investment decisions and allow for closer analyses. In particular, these challenges require chronological hourly models with constraints linking all the years of the planning horizon, compromising in most cases the computational feasibility. This paper presents a new approach to synthetize a reduced representative time period for capacity expansion problems, for being used in detailed chronological hourly models, while keeping them computationally feasible. The representative period is synthetized by selecting, with a genetic algorithm, those real days that minimizes the distance between the duration curves of a set of relevant variables (such as demand, renewable generation, ramps, etc.) computed for the original and for the representative periods. Results show that investments decisions with the representative period are very similar to those obtained with the full planning horizon, while computational times are strongly reduced.