Abstract
This paper presents an analysis of the distribution system harmonic behavior when a photovoltaic (PV) system is connected at the low-voltage network. Through harmonic power flow simulations in the Open Distribution System Simulator (OpenDSS), harmonic impact at the point common coupling is studied. To reduce the harmonic analysis, simulations are carried out using a simplified equivalent for utility and customer. In order to model a more realistic distribution system, it is necessary to calibrate the harmonic sources before connecting the PV system. The harmonic state estimation method implemented in combination with the simulator is used to calibrate the utility harmonic voltage sources. After calibration, the harmonic levels at medium-voltage are verified. Results for a case study using quality measurements performed by Ecuadorian CENTROSUR electric utility show that the simulated harmonic levels are similar to those measured. It also shows that a PV system does not produce power quality problems.

Abstract
The long-term distribution planning should include an understanding of consumer behavior and needs to develop strategic expansion alternatives that meet the future demand. The magnitude of growth along with the place where and when it will be developed are determined by the spatial load forecasting. Thus, this paper proposes a spatial-temporal load forecasting method to recognize and predict development patterns using historical dynamics and determine the development of consumers and electric load in small areas. An artificial neural network is integrated to a cellular automaton method to establish transition rules, based on land-use preferences, neighborhood states, spatial constraints, and a stochastic disturbance. The main feature is the incorporation of temporality, as well as taking advantage of geospatial-temporal data analytics to calibrate and validate a holistic and integral framework. Validation consists of measuring the spatial error pattern during the training and testing phase. The performance of the method is assessed in the service area of an Ecuadorian power utility. The knowledge extraction from large-scale data, evaluating the sensitivity of parameters and spatial resolution was carried out in reasonable times. It is concluded that adequate normalization and use of temporality in the spatial factors improve the error in the spatial-temporal load forecasting.

Abstract
The trend for renewable energies has motivated residential consumers around the world to have a rapid penetration in the installation of rooftop solar photovoltaic systems. For this reason, power utility companies must plan the inclusion of rooftop solar photovoltaic systems in their distribution grid. The proposed method projects the quantity and location of these systems. The method is divided into 3 modules: temporal, spatial, and potential modules. In the case of the temporal module, it uses census data by dividing the area into districts, and also, it calculates the number of residential customers, which can be converted into rooftop solar photovoltaic systems. On the other hand, the spatial module adjusts the temporal module based on the interaction and spatial influence of neighbours for each district. Finally, the potential module calculates their energy potential according to the geographical location of the districts and evaluates it with the forecast number of customers from the spatial module. The performance of the method is assessed in the service area of an Ecuadorian power utility. The results show that in Cuenca the greatest influence on adoption is given by two variables, the number of heads of households with permanent employment and the district's electrical power. The customers and energy results produced represent for each scenario only the 7% and 9% of the energy demanded, this concentration is shown through thematic maps that allow identifying the districts that have rapid adoption of solar panels. The results are important tools for the planning of the distribution company, the company will have the areas of highest rooftop solar photovoltaic systems penetration to evaluate its distribution system and maintain its reliability levels.

Abstract
The growth of subtransmission network aims at satisfying load growth, maintaining a contingency level, and providing a high quality and reliable electricity service. Utilities direct the investments to reinforce this system and thus a meshed network with multiple-point feeding to the transmission system arises. At this point, an efficient alternative to achieve these objectives is to carry out a diagnosis of the network architecture and, taking advantage of the switching capability, to plan the switching of the subtransmission lines. An optimal subtransmission switching approach is proposed based on constrained multi-objective optimization that deals with energy losses and reliability, in addition to using information on the characteristics of loads and generation. A simulation-based optimization framework is constructed using the non-dominated genetic classification algorithm NSGA-II in the optimization phase and reliability assessment during simulation phase. As a result, a set of non-dominated solutions approximating the Pareto front is obtained, which allows the planner to make decisions based on its priorities and needs. The performance of the proposal is assessed with a real subtransmission system of an Ecuadorian power utility. This approach to the operational planning of a meshed subtransmission network constitutes a powerful decision-making tool that could be adopted by distribution utilities.

Abstract
Planning the expansion and the new topology of distribution networks requires knowing the location and characterization of the load as well as its future growth. Spatial load forecasting is a key tool in this task, providing high spatial resolution and adequate temporal granularity. Nowadays, with the penetration of distributed energy resources, multiple microgrid connection strategies, and implementation of self-healing and protection schemes, it is necessary to identify load blocks to plan the new active network architecture. Based on spatial load forecasting information, this paper proposes a graph partitioning technique to create load clusters in the distribution feeders. A weighted graph is constructed by means of a minimum spanning tree that allows to consider adjacency relations. The results of the simulation, carried out in a real distribution network, have demonstrated the effectiveness of the proposed method.

Abstract
The location of a new substation is a key factor in the expansion of electrical distribution systems. This location is strategic from the point of view of the costs associated with energy supply; therefore, a holistic and integral planning of sub-transmission and primary distribution subsystems requires the development of suitable optimization methods to support the decision process. Although future electric load growth is a critical factor to define capacity and location of new substations, other technical, environmental, soil characteristics, risk, social, and administrative criteria that influence the final location are also crucial. A multicriteria decision analysis based on geographic information system is proposed in this paper to combine those criteria taking into account decision makers' preferences and physical restrictions on land use. The main contributions of this paper are the identification of the criteria and the analysis of service areas in existing substations to impose constraints on the problem. A spatial heat map that facilitates the visual interpretation of the spatial relations of the criteria is produced based on a suitability score. The proposed method was evaluated in the service area of an Ecuadorian distribution energy utility. It was found that the two more important criteria are the electric load density and the distance to subtransmission network with weights of 44% and 23%, respectively. The proposed analysis is able to identify ideal locations for new substations, which can be used by the planner to find the best long-term network expansion alternative.