Abstract
In this paper, an innovative bi-level model is developed for the interactions of the plug-in electric vehicle (PEV) parking lot (PL) with the upstream markets through an aggregator in the system. The preferences of the PEVs on using the PL are also considered in the study. The PEVs have a choice in participating in grid-to-vehicle or vehicle-to-grid modes with fixed or flexible amount of departed state of charge (SOC). The PL assigns various tariffs for different requirements of the PEVs. In this study, the mutual effect of the variation of PEV tariffs and the PL's behavior in the market is investigated. It is shown that the preferences of PEVs can significantly affect the PL's strategy. The changes of the equilibrium prices for PL and aggregator interaction are thoroughly analyzed in response to changes in PEV tariffs to illustrate their influence on their behavior.

Abstract
The inevitable need of new infrastructures for encouraging plug-in electric vehicles (PEVs) penetration into the system has drawn various attentions towards the required studies. In this regard, the PEVs parking lot (PL) has shown to have proper potentials. Other than providing a charging place for the PEVs, PLs can be considered as an integrated form of PEV batteries and act as storage in the system. However, the accuracy of PL's operation can be enhanced through detailed modeling of various affecting factors such as PEV's preferences on using the charging capability of the PL. In this study, a new model to add the PEVs' preferences in the PL's market participation problem is proposed. Various categories of PEVs are considering based on their arrival/departure pattern, duration of stay in the PL, and charging requirements. The results showed a considerable difference in PL's strategy in market participation, with and without considering PEVs' preferences.

Abstract
In this paper, a mixed-integer linear programing ( MILP) model for the traffic behavior of plug-in electric vehicles ( PEVs) in an urban environment is proposed. It is assumed that any environment can be categorized into different zones based on their urban functions ( e. g. industrial, residential, and commercial). Therefore, the interaction of PEVs that travel between these zones has to be modeled. Besides, it is assumed that in each zone a parking lot ( PL) and individual charging stations exist to provide the required state of charge ( SOC) for PEVs during their daily travel. As a result, the amount of power that these PEVs consume ( rather in PL or charging stations) and the amount of SOC that PEVs carry with them should be precisely computed. The proposed MILP model is applied on two zones urban area with residential and industrial districts and the numerical results prove the proficiency of the model.

Abstract
The increasing penetration of plug-in electric vehicles (PEVs) has encouraged solutions for facilitating their utilization. In this regard, PEV parking lots (PLs) have proved to be essential for future systems with high amounts of PEVs. However, operating a PL in order to maximize its profit while enforcing its own constraints generates conflicts between the PL and the distribution system operator (DSO). As a result, this paper proposes a model to solve the PL equilibria behavior. A bi-level problem including the operation of DSO and PL is solved using mathematical programming with equilibrium constraints (MPEC). The PL's behavior is defined by its hourly energy and reserve interaction with the market through the DSO. As the total amount of PL's interaction with the grid will affect the DSO's operational decision making, the proposed model finds the equilibrium point to maximize the benefit for PL and DSO.

Abstract
In a multienergy system, there are different types of dependencies among the energy carriers. Internal dependencies refer to possible changes in the energy source in the presence of energy converters and storage, and are managed by the system operator through the control strategies applied to the equipment. External dependencies (EDs) are due to the choice of the energy supply according to customer preferences when alternative solutions are available. This paper introduces a new model of EDs within a multigeneration representation based on energy hubs. EDs are addressed through a stochastic model in order to take into account the possible uncertainty in the customers' decisions. This model is then used to introduce carrier-based demand response (DR) in which the user participates in DR programs aimed at promoting the shifting among different energy sources by preserving the service provided to the end users. The results obtained from the new model in deterministic and stochastic cases indicate the appropriateness and usefulness of the proposed approach.

Abstract
The concept of Carrier-Based Demand Response (CBDR) programs in Smart Multi-Energy Systems (MES) is proposed in this paper. It is discussed that by establishing the bi-directional relation between multi-energy demand and MES through the penetration of multi-carrier device technologies, the opportunity of demand-side participation in system operation can be activated. In this paper, the external dependency caused by multi-carrier devices is employed as a demand response. The CBDR is introduced as the flexibility of end-use to change the conversion pattern of input carriers into required demand. As the CBDR program is influenced by energy carrier prices, upstream network obligations and also the customer's behavior, its uncertainty is effectively modeled in this paper. The results compare the difference between the stochastic and deterministic approaches to the problem and show the improved accuracy through the stochastic modeling. The role of those customers that are not taking part in CBDR program is also investigated.