Abstract
Renewable energies as a solution for environmental issues have always been a key research area due to Demand Response Programs (DRPs). However, the intermittent nature of such energies may cause economic and technological challenges for Independent System Operators (ISOs) besides DRPs, since the acceptable effective solution may exceed the requirement of further investigations. Although, previous studies emphasized employing Demand Response and Renewable Energies in power systems, each problem was investigated independently, and there have been few studies which have investigated these problems simultaneously. In these recent studies, authors neither analyzed these problems simultaneously nor discussed which scientific and practical aspects of demand response and renewable energy injection were employed. Motivated by this requirement, this research has focused on a comprehensive review of recent research of these cases to provide a comprehensive reference for future works.

Abstract
In the last decade, the level of variable renewable energy sources (RESs) integrated in distribution network systems have been continuously growing. This adds more uncertainty to the system, which also faces all traditional sources of uncertainty and those pertaining to other emerging technologies such as demand response and electric vehicles. As a result, distribution system operators are finding it increasingly difficult to maintain an optimal daily operation of such systems. Such challenges/limitations are expected to be alleviated when distribution systems undergo the transformation process to smart grids, equipped with appropriate technologies such as energy storage systems (ESSs) and switchable capacitor banks (SCBs). These technologies offer more flexibility in the system, allowing effective management of the uncertainty in RESs. This paper presents a stochastic mixed integer linear programming (SMILP) model, aiming to optimally operate distribution network systems, featuring variable renewables, and minimizing the impact of RES uncertainty on the system's overall performance via ESSs and SCBs. A standard 41-bus distribution system is employed to show the effectiveness of the proposed S-MILP model. Simulation results indicate that strategically placed ESSs and SCBs can substantially alleviate the negative impact of RES uncertainty in the considered system.

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
Power system reliability faces serious challenges when supply shortage occurs because of unexpected generation or transmission line outages especially during extreme weather conditions. Alternative to conventional approaches that solicit aids from the generation side, operators can now leverage the demand-side resources through a variety of electricity market mechanisms to balance the active power and enhance system reliability. The benefits of the demand response (DR) have long been recognized in many works and empirical cases. Systematic analyses, however, have never been addressed to assess the values of the market-based DR for supporting system reliability. In this paper, a case study on the performance of DR in PJM Interconnections during the 2014 North American Polar Vortex is provided to highlight the significant contributions to improving system reliability and maintaining grid stability from DR programs. The unique merits in technical, economic and environmental aspects exhibited by DR during this extreme event verse conventional system-reliability-improving approaches are also demonstrated accordingly. Furthermore, we reveal the difference of DR programs driven by various existing market mechanisms after describing the fundamental DR functions. Values of various DR programs are also highlighted. At last, challenges and opportunities facing China on the design and implementation of DR programs during the transform from the monopoly scheme to an open electricity market during the power industry restructuring in recent years are also discussed.

Abstract
In this paper an innovative, hierarchical, four-level optimization framework is proposed to model the optimal participation of Electric Vehicle Aggregators (EVAs) in day-ahead and real-time energy and regulation markets in conjunction with the optimal real-time charging management of the EV fleet. The scope of the framework is to incorporate in an integrated approach all the conjugated optimization tasks that an EVA should follow for cost-efficient and reliable market participation as well as for successful electric vehicles smart charging in real-time that will satisfy the EV owners. The PJM market design is modeled and a case study with 1000 PHEVs, represented by an EVA, is presented for one operating day. In the base case the EVA participates in the regulation market by following the dynamic regulation control signal. The base case is compared with one where the EVA follows the traditional regulation control signal and one where the EV fleet behaves in real-time differently than initially expected. The presentation of the main functionalities of the framework highlight its ability to model and evaluate the performance of the EV charging process in second-by-second time resolution in a market environment.