Abstract
The role of transportation in the overall emissions in light of the increasing environmental awareness has led to a rapid transition to the use of electric vehicles (EVs), especially in the last decade. The EVs have seminal advantages in terms of different point of views; however, they may pose vital challenges for the electric power system operation due to their stochastic characteristics as an electrical load. Several industrial and academic research studies have already been and are still conducted in this respect. Specifically, the development of combined technical and business-oriented operational models is extremely significant for sustainable penetration of EVs. In this study, an interoperability platform is proposed for EV charging service taking dual sides of the mentioned service as system operator and EV owner into account, being proposed as a new perspective in this area, also compared to industrial software platforms for EV charging service by service providers rather than power system operators. The developed software platforms are demonstrated and case study based analyses are conducted to present the applicability of the proposed concept. © 2019 IEEE.

Abstract
The residential sector is one of the sectors with the highest rates of electricity consumption worldwide. For years, many studies have been presented in order to minimize energy consumption at the residential level. The idea of such studies is that the residential customer (RC) is the interested party of their own consumption. Moreover, the algorithms that have been developed to predict and manage the energy consumption, also analyze the behavior of the loads, with the objective of minimizing the energy costs, with good safety, robustness, and comfort levels. In the context of the smart house (SH), one of the objectives of smart grids (SGs) is to enable the RC, with home energy management systems (HEM), to actively participate, allowing for higher reliability at different levels. In this work, a new model that simulates the behavior of an SH, considering heating, ventilation and air conditioning (HVAC) and sanitarian water heater (SWH) devices, is presented. For this purpose, the proposed model considers realistic physical parameters of the SH, together with customer comfort, in order to mitigate the RC disinterest. The proposed model considers the electric vehicle (EV), a battery-based energy storage system (ESS), a micro production unit, and different types of tariffs that the RC might choose, aiming to maximize the benefits, and temporarily shifting the proposed loads.

Abstract
The demand side, which was formerly considered as an inelastic part of the power system operation, has recently been evaluated as a source of flexibility to enhance the effectiveness and economy of power system operation. Several real world examples and literature studies exist to evaluate the contribution of demand side flexibility in power system operation in this manner. Accordingly, in this study, a smart thermostat controller for direct load control based demand response applications for controlling the thermostatically controllable loads by Thermostat Set-point Control Mechanism (TSCM) method is developed differently from the existing studies, where few of them have considered the real world applicability requirements of such concepts. The concept has been experimentally verified under different case studies. © 2019 IEEE.

Abstract
This paper proposes a novel method for solving the Optimal Power Flow (OPF) problem in conditions close to realtime. The linearized cost function of the generating units is used to this end. Besides, the presented linear model is solved using the Consensus Alternating Direction Method of Multipliers (C-ADMM) approach. This technique would provide the possibility of modeling the problem both in centralized and decentralized manners. The suggested method exploits the power flow results obtained from the previous iteration to considerably improve the rate of convergence. As the C-ADMM method uses an iterative technique, Lagrange multipliers, and the norm function, the rate of convergence highly depends upon assigning the initial conditions and the optimality gap. Thus, using the operating points of the previous instant due to being close to the operating point of the current instant would enhance the results. The proposed model has been implemented on two case studies including the Pennsylvania-New Jersey-Maryland (PJM) network to verify the results and the 9-bus system to evaluate the performance of the model for the daily operation. © 2019 IEEE.

Abstract
In power system static security analysis, it often requires to calculate continuous power flow from a certain load condition to a bifurcation point along a given direction, which is referred to as the maximum loadability problem. This paper proposes a convex optimization method for maximum loadability problem over meshed power grids based on the semidefinite relaxation approach. Because the objective is to maximize the load increasing distance, convex relaxation model is generally inexact, unlike the situation in cost-minimum optimal power flow problem. Inspired by the rank penalty method, this paper proposes an iterative procedure to retrieve the maximum loadability. The convex quadratic term representing the penalty on the rank of matrix variable is updated in each iteration based on the latest solution. In order to expedite convergence, generator reactive power is also included in the objective function, which has been reported in literature. Numeric tests on some small-scale systems validate its effectiveness. Any sparsity-exploration and acceleration techniques for semidefinite programming can improve the efficiency of the proposed approach. © 2019 IEEE.

Abstract
We present a novel fully distributed strategy for joint scheduling of consumption and trading within transactive energy networks. The aim is maximizing social welfare, which itself is redefined and adapted for peer-to-peer prosumer-based markets. In the proposed scheme, hourly energy values are calculated to coordinate the joint scheduling of consumption and trading, taking into consideration both preferences and needs of all network participants. Electricity market prices are scaled locally based on hourly energy values of each prosumer. This creates a system where energy consumption and trading are coordinated based on the value of energy use throughout the day, rather than only the market price. For each prosumer, scheduling is done by allocating load (consumption) and supply (trading) blocks, maximizing the energy value globally and locally within the network. The proposed strategy was tested using a case study of typical residential prosumers. It was shown that the proposed model could provide potential benefits for both prosumers and the grid, albeit with a user-centered, fully distributed management model which relies solely on local scheduling in transactive energy networks. © 2019 IEEE.