Abstract
The growing adoption of photovoltaic systems as a result of government incentives and the cost-effectiveness of the technology will bring significant environmental benefits and help countries meeting their international commitments in terms of renewables share. Nevertheless, an unsuitable site location could compromise its production and lead to a poor integration. An optimal location of photovoltaic systems must account for factors such as land use restrictions, orography, environmental, climatic limitations, and proximity to infrastructure. A key aspect that needs to be further researched is the influence of the electric demand requirement and its spatial distribution on the enhancement of photovoltaic integration. This paper proposes a novel approach to define optimal sites for photovoltaic plants, connected to the medium-voltage level, using a geographic information system based multi-criteria decision making and spatial overlay with electric load. The main feature of this work is the use of high-resolution information to spatially characterize the demand and make a density analysis. The performance of the proposed method is assessed in the service area of an Ecuadorian power utility. Scenarios considering solar potential and the massive penetration of a new type of load are assessed to define the photovoltaic sites that enhance the integration of renewable sources in the case study.

Abstract
Domestic energy policies destined to foster the use of end-use electric technologies could cause rapid penetration of new residential loads and, consequently, this could cause a significant increase in the demand for electricity in urban areas. This paper presents a spatial-temporal growth model for estimating the adoption of new end-use electric technologies encouraged by energy-efficiency policies. The proposed method consists of three modules: temporal, spatial and grouping. The temporal module calculates by districts or census tracts of a city, the percentage of homes in which residents are prospective buyers of a new end-use electric technology. Then, the spatial module adjusts the calculations made by the temporal module, considering the spatial interactions among the inhabitants of the districts. Finally, the grouping module discovers the low-voltage transformer where the prospective buyers are connected. The results of the proposed model are a spatial database with information related to the percentage of homes in which residents are prospective buyers of a new end-use electric technology, as well as the number of prospective buyers connected to each low-voltage transformer. The results can visualize through thematic maps to identify the districts where the new technology will have faster adoption. The proposed method was employed to estimate the adoption of induction heating cookers in a medium-sized Ecuadorian city. The Ecuadorian government has developed a program of economic subsidies to encourage its population to use this electrical appliance. The results from this application are an important tool to estimate the spatial increase in electricity demand, decide important issues related to the planning of distributed resources, and develop demand-side management programs. Furthermore, the results can be used to evaluate and manage energy policies formulated to achieve environmental and energy goals.

Abstract
The photovoltaic solar potential in an urban sector and the effects produced by the electricity input into a low-voltage grid are determined, the analysis is performed for one year. First, the generation profiles are estimated, assuming the incorporation limits of typical silica panels and using photovoltaic (PV) tiles on roofs as an architectural alternative. Then, the consumer class demand is estimated. Production-demand matching is performed at the load point level to avoid impacts on the grid. A scenario incorporating a new load, induction heating cookers (IHCs) for all residential users, is posed, the use of which coincides with high-radiation hours. Finally, electrical storage is assumed to maximise the PV supply. A 16% coverage with silica PV panels, or 33% with PV tiles, would supply 46% or 39% of the consumption, respectively. With massive incorporation of IHCs and storage, the supply is increased to 73% and 59% of the consumption with silica panels and PV tiles, respectively. An annual consumption reduction of 16 Tn of liquefied petroleum gas is attained in the cases studied. Additionally, it is necessary to redirect the current subsidies for hydro dams and the overall energy sector towards promoting distributed microgeneration.

Abstract
In recent years, spatial load forecasting studies have helped to direct the expansion of the distribution systems in cities with rapid urban growth, providing maps that showing the spatial distribution of expected load. However, these maps do not allow to determine how load varies on the existing network elements. This information is important to define the reinforcements or the installation of new facilities in the electrical distribution network. In order to help planners in such decisions, a search method to allocate the loads resulting from spatial load forecasting studies is presented. This method treats each of these forecast loads as new load center to be connected to an existing distribution feeder. To find the path from a load center, the proposed method uses a list of its nearby feeders. Allocation depends on the path cost function, which is calculated based on the supply capability of the network elements. The proposal chooses the shortest path with sufficient capacity to supply the new load, i.e., it finds the path with minimal cost function for list of nearby feeders. The result is the final available capability of existing networks (after the allocation process) to supply the expected loads in the geographic area. The method is tested using the results of a spatial load forecast for a real distribution system in a medium-sized Brazilian city. In this test system, the load allocation influenced the number of network elements to be reinforced. The proposal was compared to commercial software, showing a configuration with smaller numbers of overload elements and a lower cost of expansion to the most overloaded feeders.

Abstract
The government's endorsement of renewable energy objectives and the requirement to use carbon-free energy sources to keep up with the growth in energy consumption have expanded the integration of solar photovoltaic (PV) systems in distribution networks. However, an excessive PV penetration may lead to operational threshold violations. PV system allocation that is optimal in terms of placement and sizing can enhance power quality and grid performance. We formulate the allocation of PV systems as a combinatorial mixed-integer nonlinear model to maximize the distribution network PV hosting capacity (PVHC). We chose three differential evolution (DE) mutation strategies, namely DE/rand/1/bin, DE/current.to.best/1/bin, and DE/rand/1/either.or, and the vortex search (VS) algorithm to solve that optimization problem. This study aims to identify the method that solves the PV allocation problem with higher quality. We performed manual parameter tuning to set both the population and iteration numbers for each algorithm. In addition, for the DE mutation strategies, we set the scale factor and crossover rate parameters. The results show that the VS provides the highest grid PVHC.