![]() It can be applied in both production and load forecasting at various time scales. Machine learning algorithms can be exploited for different time horizons which correspond to different decision-making activities. Among all CI techniques, a data-driven method based on machine learning (ML) that includes neural networks, random forests, deep learning, evolutionary optimization, and fuzzy logic has the potential to contribute to the modelling and analysis of future energy applications. In addition, the forecasted increase in the number of electric vehicles represents an increase in the variability of the electrical loads.Ĭomputational intelligence (CI) provides a wide set of tools for effective designing, scheduling, and maintaining smart grids and microgrids. This transition has led to an increase in the penetration of renewable energy sources in the power grid, which poses new challenges. In particular, the transition of a microgrid from parallel operation to self-sustaining operation can place high demands on the control responses.The transition from low carbon to sustainable energy production is gaining momentum to support an increasing energy demand. Thus, the interaction of the individual components can be checked for compatibility and safety before the integration of new systems. Detailed models of the grid, the loads, the existing generators and the controllers are necessary for this task. Static and dynamic stability can be investigated in simulations. As a result, much functionality can be implemented, but it needs to be tuned to the specific island or micro grid environment. These have complex controller structures, making their behavior more accessible to system programming and less dependent on physical responses. Renewable energies are often connected via inverter-based systems. For this purpose, a selective adjustment of the consumption, an increase in the production capacities or a temporal shift of electrical energy through storage can be useful. #Micro grids how to#It is therefore important to develop strategies on how to align production and consumption in the best possible way. However, these renewable energies are dependent on variable resource availability hence their maximum production capacity is subject to natural fluctuations. Wind and solar power are independent of imported fuels and environmentally friendly, and therefore the logical choice for island and microgrids. Due to the unavoidable dependence on fuel price and delivery options, and the environmental impact, alternatives are being sought. Diesel generators are still frequently used for this task. One challenge of island grids and microgrids is to maintain the balance between production and consumption. Microgrids, in contrast, are designed to increase the security of supply in case the large network breaks down. Island grids are typically the result of geographical circumstances that render the connection to a large network costly or even impossible. Microgrids are similar, but also have the capability to connect synchronously to a large network. Island grids do not have a synchronous connection to a large network and therefore have to be able to provide all tasks necessary for long-lasting and safe operation on their own. Island grids are an electrical power supply task with a small number of power generating plants and consumers. ![]()
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