Abstract
In recent years, the level of variable Renewable Energy Sources (vRESs) integrated in power systems has been increasing steadily. This is driven by a multitude of global and local concerns related to energy security and dependence, climate change, etc. The integration of such energy sources is expected to continue growing in the coming years. Despite their multifaceted benefits, variable energy sources introduce technical challenges mainly because of their intermittent nature, particularly at distribution levels. The flexibility of existing distribution systems should be significantly enhanced to partially reduce the side effects of vRESs. One way to do this is using a dynamic network reconfiguration. Framed in this context, this work presents an optimization problem to investigate the impacts of grid reconfiguration on the level of integration and utilization of vRES power in the system. The developed combinatorial model is solved using a genetic algorithm. A standard IEEE 33-node distribution system is employed in the analysis. Simulation results show the capability of network switching in supporting large-scale integration of vRESs in the system while alleviating their side effects. Moreover, the simultaneous consideration of vRES integration and network reconfiguration lead to a better voltage profile, reduced costs and losses in the system.

Abstract
This paper deals with the distribution network reconfiguration problem in a multi-objective scope, aiming to determine the optimal radial configuration by means of minimizing the active power losses and a set of commonly used reliability indices formulated with reference to the number of customers. The indices are developed in a way consistent with a mixed-integer linear programming (MILP) approach. A key contribution of the paper is the efficient implementation of the epsilon-constraint method using lexicographic optimization in order to solve the multi-objective optimization problem. After the Pareto efficient solution set is generated, the resulting configurations are evaluated using a backward/forward sweep load-flow algorithm to verify that the solutions obtained are both non-dominated and feasible. Since the epsilon-constraint method generates the Pareto front but does not incorporate decision maker (DM) preferences, a multi-attribute decision making procedure, namely, the technique for order preference by similarity to ideal solution (TOPSIS) method, is used in order to rank the obtained solutions according to the DM preferences, facilitating the final selection. The applicability of the proposed method is assessed on a classical test system and on a practical distribution system.

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
The prospect of distributed generation investment planning (DGIP) is especially relevant in insular networks because of a number of reasons such as energy security, emissions and renewable integration targets. In this context, this paper presents a DGIP model that considers various DG types, including renewables. The planning process involves an economic analysis considering the costs of emissions, reliability and other relevant cost components. In addition, a comprehensive sensitivity analysis is carried out in order to investigate the effect of variability and uncertainty of model parameters on DG investment decisions. The ultimate goal is to identify the parameters that significantly influence the decision-making process and to quantify their degree of influence. The results show that uncertainty has a meaningful impact on DG investment decisions. In fact, the degree of influence varies from one parameter to another. However, in general, ignoring or inadequately considering uncertainty and variability in model parameters has a quantifiable cost. The analyses made in this paper can be very useful to identify the most relevant model parameters that need special attention in planning practices.

Abstract
Distribution Systems (DS) are usually structured as weakly-meshed but the majority of them operate with a radial topology, mainly in order to accommodate the protection coordination. Obtaining the optimal radial configuration under several criteria has been an active research topic for more than two decades. Because of the computational burden and the non-linearity of the problem, the majority of the proposed methods and techniques, single or multi-objective, use various meta-heuristics. The DS reconfiguration problem, respecting the radiality constraints, is formulated in this paper as a multi-objective Mixed-Integer Linear Programming (MILP) problem. An adequate representation of the Pareto set is produced using an improved implementation of the epsilon-constrained method. The objective is to determine the optimal radial configuration during several time intervals, minimizing the active power losses and the cost emerging from the switching operations. The proposed methodology is tested using a 16-node sample system.

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.