Abstract
The idea behind the present work is the application of a Mixed Integer Linear Programming (MILP) model for the reconfiguration and performance analysis of weakly-meshed distribution networks with emphasis on the variability of demand and the integration of wind power generation at specific buses. The ultimate goal is the minimization of active power losses for the corresponding optimal topology in the short-term operation considering key distribution parameters and power factor constraints. A distribution system case study with twenty wind scenarios is considered to evaluate the impact of wind integration in distribution networks. The effectiveness of a simplified linearized model for the study of active power losses and the impacts of wind generation and periodic demand variation on node voltages, branch currents and grid reconfigurations under normal operation conditions is illustrated by the case study. Issues resulting from solving a 25-bus case study are presented and discussed.

Abstract
Because of various developments in communications and technologies, each residential consumer has been enabled to contribute in Demand Response Programs (DRPs), manage its electrical usage and reduce its cost by using a Household Energy Management (HEM) system. An operational HEM model is investigated to find the minimum consumer's cost in every DRP and to guarantee the end-user's satisfaction, as well as to ensure the practical constraints of every battery and residential appliance. The numerical studies show that the presented method considerably affects the operational patterns of the HEM system in each DRP. According to the obtained results, by employing the presented method the consumer's cost is decreased up to 40%. © 2017 IEEE.

Abstract
Integration of wind energy and other renewable energy resources in electrical systems create some challenges due to their uncertain and variable characteristics. In the quest for more flexibility of the electric systems, combination of these endogenous and renewable resources in accordance with strategies of Demand Response (DR) allows an increment/improvement of the demand potential, as well as a more secure, robust, sustainable and economically advantageous operation. This paper proposes a new model for integration of wind power and DR, thus optimizing supply and demand side operations through a price rule Time of Use (TOU), or incentive with Emergency DR Program (EDRP), as well as combining TOU and EDRP together. The problem is modelled using a stochastic Heuristic Multi-Objective Multi-Criteria Decision Making (HMM) method which aims to minimize operation costs and environmental emissions simultaneously, ensuring the security constraints through two-stage stochastic programming, considering various techno-economic indices such as load factor, electricity market prices, Energy Not Supplied (ENS) and Share Weighted Average Lerner Index (SWALI). Comprehensive numerical results indicate that the proposed model is entirely efficient in DR planning and power system operation.

Abstract
Nowadays, there is a global consensus that integrating renewable energy sources (RES) is highly needed to meet an increasing demand for electricity and reduce the overall carbon footprint of power production. Framed in this context, the coordination of RES integration with distributed energy storage systems (DESS), along with the network's switching capability and/or network reinforcement, is expected to significantly improve system flexibility, thereby increasing chances of accommodating large-scale RES power. This paper presents an innovative method to quantify the impacts of network switching and/or reinforcement as well as installing DESSs on the level of renewable power integrated in the system. To carry out this analysis, a dynamic and multi-objective stochastic mixed integer linear programming (S-MILP) model is developed, which jointly takes into account the optimal RES-based DGs and DESS integration in coordination with distribution network reinforcement and/or switching. A standard distribution network system is used as a case study. Numerical results show the capability of DESSs integration in dramatically increasing the level of renewable DGs integrated in the system. Although case-dependent, the impact of network switching on RES power integration is not significant.

Abstract
The increment in generation costs is one of the most important factors that characterizes the operation of insular power systems, and is related to the location of these systems and the type of fuel used to provide electricity. This situation motivates the integration of renewable generation at high rates, as well as energy storage systems (ESSs), to improve the utilization of these resources. In this paper, a new control strategy is presented for the day-ahead scheduling of insular power systems with a battery energy storage system. The method presented here incorporates the effects of the most relevant components such as thermal generators, wind power generation, power converter, charge controller and ESS, being integrated into the scheduling process of insular power systems as a new contribution to earlier studies. The results provided show a fuel saving of 2% and an improvement in the wind power use of 20%, which is significant.

Abstract
This paper presents a robust optimization problem of the flexible bidirectional power management of a smart distribution network and harmonic compensation of nonlinear loads using electric vehicles (EVs) equipped with bidirectional chargers. The base deterministic model of the proposed problem is as mixed-integer nonlinear programming (MINLP), having the objective function to minimize the economic and technical indices subject to harmonic load flow equations, EVs constraints, system operation and harmonic indices limits. This model is converted to a mixed-integer linear programming (MILP) model in the next step. In the proposed MILP model, the active, reactive and apparent loads, electrical energy, reactive power and harmonic current prices, as well as EVs characteristics, are considered uncertain parameters. Accordingly, two alternative robust optimization approaches have been implemented for the conditions of having both the probability distribution function or the bounded uncertainty in the proposed MILP problem model. The proposed model is tested on distribution networks to demonstrate its efficiency and performance. The results show that the MINLP model can be substituted by the proposed high-speed MILP model. In addition, the capacity of the injecting power of EVs is reduced in the worst case scenario with respect to the scenario that is used in the deterministic model, while the consumed power of loads and EVs and energy price increases in this scenario. Finally, the payment of EV owners is reduced by considering EVs power and harmonic control.