The use of renewable energy sources has increased year‐on‐year. Thus, there is an increasing rate of small generating units connected directly to distribution networks and micro‐grids close to consumers. At the same time, these micro‐sources must provide stability and reliability of electrical energy to the power network to which they are connected. In the technical literature, several studies have been done to ensure power systems with traditional generating sources to operate in a stable and reliable way, but there is an issue regarding generation uncertainty when a distribution system has many micro‐sources. This is because of the uncertainty of primary sources, for example, wind and radiation intensity, and could result in intermittent generation. In this study, stability and reliability of voltage in a power system with distributed generation is analysed using simulation techniques. In the proposed method in this study voltage security analysis is jointly considered with probability laws. Moreover reliability theory is also considered in the proposed voltage collapse analysis methodology. The responsibility of generator in the voltage collapse process, the probabilistic risk of voltage collapse of each operating point and the probability of enlarging the system load as a function of different operating points are the outcome of the methodology, and it is validated by using the IEEE34 test feeder.

With the current worsening of climate change-associated risks, the transition to low-carbon energy sources has become a global priority. In this context, the advances in the implementation of smart grids, which, in addition to greater efficiency and resilience, also allow greater penetration of renewable distributed energy resources, are becoming increasingly important. However, the necessary investments will be colossal. Many specialists see the process of opening up the electric energy markets as essential to boosting these new technologies. Greater decentralization of the decision-making process can potentially promote greater scalability. However, not all liberalization reforms have led to good results. Several researchers have been evaluating experiences in different countries. Brazil, a country with continental dimensions and peculiar characteristics, already counts with a mostly renewable electric energy generation mix. In recent decades, however, it has become increasingly dependent on fossil fuel sources. Brazil has been conducting a process of opening the electric energy market since the 1990s. This process has faced a series of barriers. This article presents a critical bibliographic review of the Brazilian Power System history and its ongoing opening process, its possible successes and errors, as well as its perspectives and challenges.

Power system blackouts harm economic activities and worsen the customers' welfare. Smart grids' self-healing capacity is an important feature for future power systems and it should also include the ability to manage the distributed energy sources to ensure electricity supply for a long time. This is required because the blackout duration is unknown and buck power system blackstart is a complex task. This paper proposes a methodology for microgrid management and control to maximize the duration of electricity supply in emergency situations. In order to accomplish this goal, some management options are considered as intentional load shedding, dispatch of expensive fossil fuel sources, system reconfiguration and so on. The proposed methodology is tested with the IEEE 34 node test-system to investigate its feasibility.

This paper proposes a novel battery wear model for microgrid (MG) energy management applications. This model is based on a popular battery wear model originally proposed for vehicle-to-grid (V2G) applications. The presented model can be easily parameterized to fit the cycle life data of almost any battery and yields the wear cost of a charge/discharge event as function of the variation in the state of charge (SoC) of the battery. This wear model is incorporated into an energy management algorithm to optimize the operation of a grid-connected MG using a day-ahead planning strategy. To test the model, four simulated scenarios are considered in and MG composed of a diesel generator (DG), a photovoltaic (PV) system, a residential load and, naturally, a battery storage system (battery energy storage system (BESS)). The inclusion of the battery wear model leads the optimizer to be more selective about battery usage, cycling the battery at SoC levels that minimized battery wear, effectively prolonging its lifespan.