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. © 2015 IEEE.

Abstract
This paper presents a novel multi-stage stochastic distributed generation investment planning model for making investment decisions under uncertainty. The problem, formulated from a coordinated system planning viewpoint, simultaneously minimizes the net present value of costs rated to losses, emission, operation, and maintenance, as well as the cost of unserved energy. The formulation is anchored on a two-period planning horizon, each having multiple stages. The first period is a short-term horizon in which robust decisions are pursued in the face of uncertainty; whereas, the second one spans over a medium to long-term horizon involving exploratory and/or flexible investment decisions. The operational variability and uncertainty introduced by intermittent generation sources, electricity demand, emission prices, demand growth, and others are accounted for via probabilistic and stochastic methods, respectively. Metrics such as cost of ignoring uncertainty and value of perfect information are used to clearly demonstrate the benefits of the proposed stochastic model. A real-life distribution network system is used as a case study and the results show the effectiveness of the proposed model.

Abstract
This paper deals with the radial distribution system reconfiguration problem in a multi-objective scope, aiming to determine the optimal configuration by means of minimization of active power losses and several reliability indices. A novel way to calculate these indices under a mixed-integer linear programming (MILP) approach is provided. Afterwards, an efficient implementation of the e-constraint method using lexicographic optimization is employed to solve the multi-objective optimization problem, which is formulated as a MILP problem. After the Pareto Efficient solution set is generated, a multi-attribute decision making procedure is used, namely the technique for order preference by similarity to ideal solution (TOPSIS) method, so that a decision maker (DM) can express preferences over the solutions and facilitate the final selection.

Abstract
The growing trend of variable energy source integration in power systems (especially at a distribution level) is leading to an increased need for flexibility in all levels of the energy flows in such systems: the supply, the network and the demand sides. This paper focuses on a viable flexibility option that can be provided by means of a dynamic network reconfiguration (DNR), an automatic changing of line statuses in response to operational conditions in the system. The ultimate aim is to assess the impacts of such flexibility on the utilization levels of variable power sources (mainly, solar and wind) integrated at a distribution level. To perform this analysis, a stochastic mixed integer linear programming (S-MILP) operational model is developed in this work. The objective of the optimization problem is to minimize the sum of the most relevant cost terms while meeting a number of model constraints. The proposed model dynamically finds an optimal configuration of an existing network system in accordance with the system's operational conditions. The operation scale in the current work is one day, but with the possibility of an hourly reconfiguration. The standard IEEE 41-bus system is employed to test the proposed model and perform the analysis. Numerical results generally show that DNR leads to a more efficient utilization of renewable type DGs integrated in the system, reduced costs and losses, and a substantially improved system performance especially the voltage profile in the system.

Abstract
This paper presents a comprehensive sensitivity analysis to identify the uncertain parameters which significantly influence the decision-making process in distributed generation (DG) investments and quantify their degree of influence. To perform the analysis, a DG investment planning model is formulated as a novel multistage and multiscenario optimization problem. Moreover, to ensure tractability and make use of exact solution methods, the entire problem is kept as a mixed-integer linear programming optimization. A real-world distribution network system is used to carry out the analysis. The results of the analysis generally show that uncertainty as well as operational variability of the considered parameters have meaningful impacts on investment decisions of DG. The degree of influence varies from one parameter to another. But, in general, ignoring or inadequately considering uncertainty and variability in model parameters has a quantifiable cost. Hence, the analysismade in this paper can be very useful to identify the most relevant model parameters that need special attention in planning practices.

Abstract
The distribution systems (DS) reconfiguration problem is formulated in this paper as a multi-objective mixed-integer linear programming (MILP) multi-period problem, enforcing that the obtained topology is radial in order to exploit several advantages those configurations offer. The effects of distributed generation (DG) and energy storage systems (ESS) are also investigated. To address the multi-objective problem, an improved implementation of the e-constraint method (AUGMECON-2) is used, providing an adequate representation of the Pareto set. The objective functions considered stand for the minimization of active power losses and the minimization of switching operations. The proposed methodology is tested using a real system based on the S. Miguel Island, Azores, Portugal. The potential uses of cloud-based engineering systems, both in terms of exploiting the enhanced decentralized computational opportunities they offer and of utilizing them in order to achieve communication and coordination between several entities that are engaged in DS, are thoroughly discussed.