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The vulnerability of electric potential generation from water resources has been evidenced due to uncertainties of climatic origin and energy planning. Changes in rainfall regimes are multifactorial phenomena difficult to model. Rainfall is the main natural way of restoring the reservoir levels of hydroelectric power plants. The reduction in rainfall levels may increase in the coming years, resulting in a probable change of the energy matrix configuration of the state of Minas Gerais (MG). This could reduce the share of renewable sources increasing dependence on the importance of energy from fossil fuels, which are the immediate primary sources of energy available. Prior examinations have portrayed the depletion of hydroelectricity in Brazil, or its macro-regions, however this work tries to supply a more focused look at the effects on the electricity and social sectors of the state of Minas Gerais. The study developed in this paper aims to investigate the hydrological potential alteration impacts and consequences on the energy matrix of the state, the greenhouse gas emissions and the social reflexes of these changes due to scenarios motivated by climate change. The investigation intends to show the importance of the water-energy-emissions nexus for energy planning and decision-making, in order to evaluate the best options for dealing with adverse and potentially destabilizing conditions. The results of the analyses show that while hydropower demand was 5.24 Mtoe in 2015, in 2030 the supply of hydropower will be 5.81 Mtoe and 4.72 Mtoe, for the conservative and severe scenarios, respectively. In addition, the effect of climate change may require an increase in the share of non-renewable sources in the energy matrix, which could increase CO 2 e emissions from 4.2% to 7.4% by 2030, compared to the levels in 2016. • Reduction in rainfall levels will change the energy matrix configuration of MG state. • Population growth will boost demand for energy. • It is shown the importance of the water-energy-emissions nexus for energy planning and decision-making. • The energy matrix may considerably increase the CO 2 e emissions by 2030, compared with 2016 levels in the MG state. [ABSTRACT FROM AUTHOR]

Abstract: In much of the world, there is increasing demand for electricity to serve rural communities, isolated from the existing grids and typified by low-density electrical consumption. Because these non-urban consumer markets require rather high implementation investments (as well as high operation and maintenance costs), new technological and policy options are required to meet the needs of these markets. These consumers typically use energy in daytime peak hours of electricity, typically for lighting, television, and communication—as well as for a variety of cultural habits such as hot water that impose high demands on the utility''s power distribution and generation system. This has been the case in Brazil, making it necessary to identify decentralized generation technologies to meet the potential markets, typically serving rural and poorer areas. The government itself provided the impetus with the passage of the Brazilian “Universalization Law” that mandated supplying electricity access for the entire population by the year 2010. This law allows the use of both the distribution grid and renewable energy off-grid technologies. In response, Brazil''s largest state utility, Energetic Company of Minas Gerais (CEMIG) has aggressively implemented the use of decentralized photovoltaic systems to supplement the conventional power grid to satisfy the “universalization” targets. This paper provides a summary of the status and the future prospects of solar photovoltaic Energy in Brasil, within the context of the “universal electricity supply” policy. The focus here is to highlight the successes and the issues experienced to date in the State of Minas Gerais. This includes examining the methods implemented to ensure system reliability for the consumers, as well as the standards established under the Agéncia Nacional de Energy Elétrica (ANEEL), the national regulator electrical agency that ensures compliance with the federal regulations. [Copyright &y& Elsevier]

In recent years, renewable and sustainable energy sources have attracted the attention of various investors and stakeholders, such as energy sector agents and even consumers. It is perplexing to observe and anticipate the required levels of photovoltaic generation, which are inherent tasks for such rapid insertion into the electric grid. This distributed/renewable generation must be integrated in a coordinated way such that there is no negative impact on the electric performance of the grid, increasing in the complexity of energy management. In this article, a methodology for photovoltaic generation forecasting is addressed for a horizon of one week ahead, using a new approach based on an artificial neural network (ANN) ensemble. Two main questions will be explored with this approach: how to select the ANNs, and how to combine them in the ensemble. The design of experiments (DOE) approach is applied to the photovoltaic time series factors and ANN factors. Then, a cluster analysis is performed to select the best networks. From this point on, a mixture (MDE) is employed to determine the ideal weights for the ensemble formation. The methodology is detailed throughout the paper and, based on the combination of forecasts, the photovoltaic generation was estimated for a specific panel set located in the state of Minas Gerais, Brazil, reaching the value of 4.7% for the weekly mean absolute percentage error. The versatility of the proposed method allowed the change of the number of factors to be used in the experimental arrangement, the forecast model, and the desired forecast horizon, and consequently enhancing the forecasting determination. • An ANN ensemble is created for medium-term photovoltaic generation forecasting. • ANNs are selected by using a new design of experiments approach. • The ANN ensemble is built by using a new mixture design of experiments approach. • We predicted the photovoltaic generation for a panel set located in Brazil. [ABSTRACT FROM AUTHOR]