Computational intelligence: The key to making smart grids really smart

Around the world, in developed as well as developing countries, utilities are embracing smart grid technologies to improve the grid’s efficiency and incorporate renewable energy resources into the grid. The strategy is both practical and laudable because it will create the means to meet society’s future electricity demands in a way that also minimizes carbon emissions and reduces electricity’s environmental impacts.

The recognition of smart grid’s pivotal roles, along with many regional and national policies acknowledging its importance and often mandating its adoption, are inspiring others to move into the new smart grid era. The smart grid is inevitable now. We should all join in to help it advance and succeed.

Computational intelligence (CI) is one of the techniques utilities will use to make sure their smart grids are as intelligent as possible and delivering tangible, worthwhile benefits to utilities, users and the environment. A comparatively new era in computing, CI is already at the heart of many types of “smart” technologies and services in a variety of industries. In particular, CI enables us to interpret big data and create knowledge from it to make decisions in real-time for critical business functions. The purpose of this article is to discuss some of the exciting ways in which CI is being applied in smart grids.

Computational intelligence for smart grid optimization and planning

CI can be used throughout a utility to optimize operations and planning. Broadly speaking, it can be used in power generation, transmission, distribution and consumption applications to meet the Smart Grid’s safety, security, reliability, resilience and efficiency needs. Many examples are available to illustrate very practical applications.

For example, CI can be used to forecast the amount of renewable energy that might be injected into a grid during a particular time period. We can then use this information to determine how much power production is needed from traditional sources, such as coal.

We can use CI for both short-term and long-term load forecasting. This is particularly important, given that population growth is one of the main factors inspiring the industry to produce more power. Similarly, we can use CI to inform our demand management programs.

CI can be used to help utilities respond to outages caused by natural disasters, such as storms or downed trees, which impact high-voltage transmission lines. When incorporated in a smart grid, CI can detect asymmetric single-phase-to-ground or two-phase-to-ground faults or symmetric three-phase to-ground faults and determine where the faults have occurred in the transmission and distribution system. This is a significant attribute in any power system.

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