The global fleet of electric vehicles (EV) has been rising in recent years, and public policies can play an important role in this scene. The objective of this work is to evaluate the impact of public policies in the diffusion of EVs in Brazil, based on Beck’s adaptation for the Bass diffusion model. This modification on the Bass model allows the estimation of EV diffusion, taking into account the direct and indirect economic influence of the main EV incentive instruments used worldwide. In addition, this work conducts a forecast of the total passenger cars in Brazil through a regression model, considering macroeconomic and social indicators. The results indicate that EV high prices may still be the major barrier for EV diffusion in Brazil over the studied horizon, keeping them inaccessible to the majority of the population. Therefore, policies aimed at subsidizing EVs may be more effective in stimulating EV sales.

Traditionally, electric power systems are subject to uncertainties related to equipment availability, topological changes, faults, disturbances, behaviour of load, etc. In particular, the dissemination of distributed generation (DG), especially those based on renewable sources, has introduced new challenges to power systems, adding further randomness to the management of this segment. In this context, stochastic analysis could support planners and operators in a more appropriate manner than traditional deterministic analysis, since the former is able to properly model the power system uncertainties. The objective of this work is to present recent achievements of one of the most important techniques for stochastic analysis, the Monte Carlo Method (MCM), to study the technical and operational aspects of electric networks with DG. Besides covering the DG topic itself, this paper also addresses emerging themes related to smart grids and new technologies, such as electric vehicles, storage, demand response, and electrothermal hybrid systems. This review encompasses more than 90 recent articles, arranged according to the MCM application and the type of analysis of power systems. The majority of the papers reviewed apply the MCM within stochastic optimization, indicating a possible trend.

This work simulated the economic viability of electric vehicles (EVs) and renewable energy charging integration at a university campus. Homer energy simulation software was used to determine the optimal solution for the power loading, energy dispatch, and economic feasibility of each electricity source. Three scenarios were considered: case 1 (C1) is the baseline that provided the electric parameters considering a grid without renewable energy integration; case 2 (C2) is the addition of solar and wind systems supplying power to the grid and EVs; case 3 (C3) is the same conditions as C2, but EVs operate on vehicle‐to‐grid (V2G) scheme supplying energy to grid. Economic output solutions were classified according to the lowest net present value (NPV). The V2G mechanism considered on the C3 offered conditions that decreased the costs of operation of the system, estimating the lowest NPV among the options analyzed. In C2, the simulations recorded higher costs due to the system operation associated with extra local load to power the EVs. In both C2 and C3 scenarios, EVs aided the renewable energy sources' penetration on the system; however, the operation of EVs on a V2G mechanism contributed to the highest rate of renewable energy penetration.

Brazil’s status as a rapidly developing country is visible in its need for more energy, including electricity. While the current electricity generation mix is primarily hydropower based, high-quality dam sites are diminishing and diversification to other sources is likely. We combined life-cycle data for electricity production with scenarios developed using the IAEA’s MESSAGE model to examine environmental impacts of future electricity generation under a baseline case and four side cases, using a Monte-Carlo approach to incorporate uncertainty in power plant performance and LCA impacts. Our results show that, under the cost-optimal base case scenario, Brazil’s GHGs from electricity (excluding hydroelectric reservoir emissions) rise 370% by 2040 relative to 2010, with the carbon intensity per MWh rising 100%. This rise would make Brazil’s carbon emissions targets difficult to meet without demand-side programs. Our results show a future electricity mix dominated by environmental tradeoffs in the use of large-scale renewables, questioning the use tropical hydropower and highlighting the need for additional work to assess and include ecosystem and social impacts, where information is currently sparse.