Abstract
In this paper, a mixed-integer linear programing (MILP) model for characterizing the traffic behavior of plug-in electric vehicles (PEVs) in an urban environment is proposed. The paper proposes the procedure to mathematically model the uncertain behavior of the PEVS. As the PEV's traffic pattern affects the potential of PEV parking lot (PL) in terms of available capacity and state of charge, the characterized PEVs are employed to provide the proper pricing schemes for the PEVs in PL. The results discuss the mutual effect of PEVs behavior on the profit of PL, as well as the effects of PL's tariffs on motivating PEVs to provide more flexibility for the PL. The results show that with a proper pricing scheme both parties (i.e., PEV owners and PL) can earn higher profits, while the charging requirements are met based on the preferences of the owners.

Abstract
The introduction of plug-in electric vehicles (PEVs) in the electrical system is bringing various challenges. One of the recent solutions to deploy the potentials of PEVs in the system is through PEV parking lots (PL). The PLs not only provide a place for the PEVs charging requirements, but also give the possibility of exploiting the vehicle-to-gird (V2G) potential of PEVs in a more efficient manner comparing to PEV aggregators. On the other hand, the V2G mode gives the reserve market possibility to the PL operator, which can be a source of income other than selling energy to the PEV owners. However, the willingness of participating in the V2G mode may be low due to exposing battery degradation and not sufficient departure state of charge (SOC). As a result, this study investigates the behavior of PL on deploying the V2G mode of PEVs in reserve market participation considering the PEVs' traffic behavior.

Abstract
In recent years, in addition to the traditional aspects concerning efficiency and profitability, the energy sector is facing new challenges given by environmental issues, security of supply, and the increasing role of the local demand. Therefore, the researchers have developed new decision-making frameworks enabling higher local integration of distributed energy resources (DER). In this context, new energy players appeared in the retail markets, increasing the level of competition on the demand side. In this paper, a multienergy player (MEP) is defined, which behaves as a DER aggregator between the wholesale energy market and a number of local energy systems (LES). The MEP and the LES have to find a long-term equilibrium in the multienergy retail market, in which they are interrelated through the price signals. To achieve this goal, in this paper the decision-making conflict between the market players is represented through a bilevel model, in which the decision variables of the MEP at the upper level are parameters for the decision-making problem at the lower level (for the individual LES). The problem is transformed into a mathematical program with equilibrium constraints by implementing duality theory, which is solved with the CPLEX 12 solver. The numerical results show the different MEP behavior in various conditions that impact on the total flexibility of the energy system.

Abstract
This paper addresses the location of parking lots (PLs) to be used for plug-in electric vehicles (PEVs) by using a probabilistic traffic model and taking into account the PL participation in electricity markets. The PLs are used both for grid-to-vehicle and vehicle-to-grid. The system includes private or public charging stations only used for PEV charging. The traffic model considers the partitioning of the territory into areas. The case study is based on traffic and market data referring to Italy.

Abstract
Plenty of literature exists about how to model liberalized electricity generation markets for the medium and long terms, contributing to the analyze and understanding of those markets, helping companies to plan cost-efficient shortterm market strategies and/or long-term generation capacity investments, and supporting regulators and policymakers in policy decisions and market designs. However, those models do not explicitly consider the impact on investment decisions, mix of technologies and wholesale market prices; of policy decisions but as an external passive input to the model. This paper reviews existing approaches to model policy decisions in such a context, and provides a theoretical modeling framework that explicitly considers the interaction of policymakers' decisions with the generation investment and operation, and customers' response in a liberalized power system. Such kind of model, based on bi-level optimization, contributes to the longterm assessment of some policy decisions in the electricity sector. © 2017 IEEE.

Abstract
The increasing penetration of Intermittent Generation (IG) is being accompanied by the revision of the needs of traditional regulation reserves, as well as the discussion of new flexibility products for system balancing. In the context of electricity generation models, it is of high relevance to adequately represent, not only the energy, but also the reserve dispatch constraints, by providing the models with the expected secondary reserve requirements (SRR), so as to output more realistic energy and reserve schedules and prices. This paper analyses the SRR published by the Spanish System Operator and uses several forecasting tools for determining its main explanatory variables. Results confirm that the SRR have remained almost constant during the years of significant IG growth (and even slightly decreasing in the most recent years), and that the best SRR estimation models found use the demand and its inter-hour variations as the main explanatory variables, but not wind productions as could be expected. © 2017 IEEE.