Abstract
The number of electric vehicles (EVs) has been gradually increasing over the last decades. In order to eliminate the concerns related to charging demand in power systems, the appropriate integration of EVs to the grid is of great importance. Electric vehicle parking lots (EVPLs) offer a crucial occasion to manage the charging process of EVs. Further, EVs are capable of either charging from the grid or supplying power to the grid due to the vehicle-to-grid (V2G) features. Through an agent, namely an aggregator, EVPLs can participate in the electricity market and a considerable amount of profit can be obtained in terms of EVPLs, EV owners, and aggregators by energy selling. However, EV owners may not be willing to participate in this structure due to the concerns related to their comforts. In this context, a model in which EVPLs can bid for energy selling to the grid through an aggregator is proposed in this study. Additionally, the comfort violation of EV owners is taken into account. In order to validate the effectiveness of the devised model, various case studies are also performed.

Abstract
This paper presents an AC optimal power flow (AC-OPF) model including flexible resources (FRs) to handle uncertain wind power generation (WPG). The FRs considered are thermal units with up/down re-dispatching capability, corrective topology control (CTC), and thyristor-controlled series capacitors (TCSC). WPG uncertainty has been modeled through a proposed interval-based robust approach, the goal of which is to maximize the variation range of WPG uncertainty in power systems while maintaining an adequate reliability level at a reasonable cost with the aid of FRs. However, utilization of FRs (especially CTC and TCSC devices) is limited due to the difficulty of their incorporation in the AC-OPF. The optimization framework of the full FR-augmented AC-OPF problem is a mixed-integer nonlinear programming (MINLP) in which the solution for large-scale systems is very hard to obtain. To solve this issue, this paper uses a two-stage decomposition algorithm to decompose the MINLP representation into a mixed-integer linear program (MILP) and a nonlinear program (NLP). Finally, the robust AC-OPF model with FRs is implemented and tested on a 6-bus and the IEEE 118-bus test systems to evaluate its efficiency and performance.

Abstract
Photovoltaic energy is one of the clean and efficient energies which has been developing quickly in the last years. As the penetration of solar plants is increasing in the electricity network, new problems have arisen in network operation. This paper models a high penetration factor of solar energy in the electricity network and investigates the impact of solar energy growth on both the generation schedule of different power plants and in the natural gas transmission network. Fuel management of gas power plants is modeled through simulation of the natural gas transmission network. To this end, an increase in the penetration of solar energy in the electricity network inevitably leads to a sudden increase in the output of gas fired units and a linear and integrated model with the electricity and the natural gas transmission networks has been presented to analyze both of them at the same time to better depict the impact of a high penetration of the solar energy in natural gas transmission grids. In this method, natural gas transmission network and Security Constrained Unit Commitment (SCUC) are presented in a single level program. Gas network constraints are linearized and added to the SCUC problem. The stress imposed on the gas network due to a sudden increase in the load of the electricity network is investigated. Conclusions are duly drawn.

Abstract
Distributed energy resources (DERs) change the supply-demand balance of power systems. To better manage these resources, they are operated as micro-grids (MGs) in both grid-connected and standalone modes. In the presence of MGs, the decision making framework in power systems is changing from the centralized structure into the decentralized one. In such framework, modeling the operation problem of MGs with the participation in energy and reserve markets in the presence of uncertainties is considered as an important challenge. Most of the studies use probability distribution functions (PDFs) to model the uncertain parameters. In this paper, the operation problem of a grid-connected MG is modeled while the MG operator (MGO) faces with uncertainties of renewable energy sources (RESs) without considering their PDFs. For this purpose, an information gap decision theory (IGDT)-based approach is employed to model the uncertain behavior of RESs as well as to control the risk level of the MGO on the optimal scheduling of DERs. To investigate the effectiveness of the model, a modified 15-bus low voltage MG us used as a test system. The results show that optimal decisions of the risk-averse MGO are different from those of a risky one. © 2019 IEEE.

Abstract
An increasing implementation of renewable sources and electric vehicles can be of help in reducing the total operational cost of a power system, affecting power system technical operation. To this end, a multi-objective optimisation method using Normalised Normal Constraint (NNC) is applied in this paper by which two competitive objectives are considered: Minimisation of Active Distribution System (ADS) operational cost and minimisation of ADS power losses. Meanwhile, the uncertain behaviour of wind, photovoltaic units, and arrival and departure time of electric vehicles are considered. The proposed model is a multi-objective problem which comprises two stochastic stages and is simulated under GAMS environment on a modified IEEE 18-bus test system. The results clearly represent the trade-off between economic and technical benefits of the considered ADS. Furthermore, the effect of electric vehicles charging and discharging tariffs on the operational cost of the system are shown.

Abstract
Together with the increasing population and prosperity levels of the growing countries, the economic and technological developments and modernized lifestyles of the end users have increased their electrical, heating and cooling energy demands extraordinarily. These services are generally provided to the end users independently, which leads to considerable reductions in the system efficiencies. For the purpose of providing a decrease in the demands of the end users and to transfer the energy carriers to the end users in an integrated and more efficient way, the concept of multi energy systems (MESs) has emerged. MESs could be developed at a district level, a city level and lastly at a country level. In this paper, a smart neighborhood MES model is proposed with the aim of achieving cost optimization by mixed integer linear programming (MILP) based formulation considering a time-of-use (TOU) tariff. In order to testify the effectiveness of the proposed optimization algorithm, two different case studies are conducted by taking into account flexible energy production capability of different energy carriers.