Abstract
In this paper, a mixed-integer linear programing (MIL?) model for the traffic behavior of plug-in electric vehicles (PEVs) in a multi energy system (MES) is proposed. It is assumed that two micro-MESs are covering two traffic zones with different consumption patterns. The difference between these two micro-MESs is not only the different multi energy demand (MED) they provide, but also different PEV traffic pattern that travel in these two micro-MESs. The PEVs traffic pattern and their behavior in using parking lot (PL) or charging station (CS) as their charging places is integrated in the MES operation model. The results demonstrate an improved strategy of the MES operator in using its components, such as combined heat and power (CHP) unit and auxiliary boiler (AB), in response to extra added load of the PEVs. The stochastic behavior of the PEVs is implemented in the model through various scenarios of arrival and departure. © 2016 IEEE.

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.

Abstract
Secondary Reserve Requirements (SRR) are usually estimated based upon unit failure rates, and demand and intermittent productions forecasting errors. These requirements are very often inputs to energy and reserve generation dispatch models. However, for the long term, the fact that renewable generation investments must also be computed, affects these requirements. This paper proposes a new Unit Commitment (UC) to represent the SRR in long-term electricity generation models as a function of the renewable investment decisions. Specifically, SRRs are computed as a function of the forecasting errors of renewable productions, and of the unavailability rates of the generation units, which are also outputs of the UC. The case studies show that, when SRRs are endogenous, investments in renewable generation can be lower than expected due to the additional reserve costs these technologies involve. © 2017 IEEE.