Abstract
The power system worldwide is going through a revolutionary transformation due to the integration with various distributed components, including advanced metering infrastructure, communication infrastructure, distributed energy resources, and electric vehicles, to improve the reliability, energy efficiency, management, and security of the future power system. These components are becoming more tightly integrated with IoT. They are expected to generate a vast amount of data to support various applications in the smart grid, such as distributed energy management, generation forecasting, grid health monitoring, fault detection, home energy management, etc. With these new components and information, artificial intelligence techniques can be applied to automate and further improve the performance of the smart grid. In this paper, we provide a comprehensive review of the state-of-the-art artificial intelligence techniques to support various applications in a distributed smart grid. In particular, we discuss how artificial techniques are applied to support the integration of renewable energy resources, the integration of energy storage systems, demand response, management of the grid and home energy, and security. As the smart grid involves various actors, such as energy produces, markets, and consumers, we also discuss how artificial intelligence and market liberalization can potentially help to increase the overall social welfare of the grid. Finally, we provide further research challenges for large-scale integration and orchestration of automated distributed devices to realize a truly smart grid.
Highlights
Increasing population worldwide demands more and more facilities, which in turn mandates the energy service providers to escalate their generation
Smart energy markets fascinated with artificial intelligence (AI) techniques can make it easier to design good policy incentives and allow consumers/utility to make decisions about their consumption/generation in an efficient way that contributes to the reduction of CO2 emissions
The reliability of the power system increased by reducing peaks and cost-saving for smart homes using renewable energy sources (RES) is achieved in [61] when genetic algorithm (GA), binary particle swarm optimization (PSO), and Cuckoo search algorithms are embedded in the HEM system
Summary
Increasing population worldwide demands more and more facilities, which in turn mandates the energy service providers to escalate their generation. To cope with global warming due to increasing CO2 emission from the traditional power system, governments around the world are encouraging renewable electric energy sources. With advances in information communication technology (ICT) connected with consumer data, it can transform the electric power grid with high penetration of distributed generations in power systems [3]. Smart energy markets fascinated with artificial intelligence (AI) techniques can make it easier to design good policy incentives and allow consumers/utility to make decisions about their consumption/generation in an efficient way that contributes to the reduction of CO2 emissions. Restricted Boltzmann Machine (CRBM)) for forecasting building energy consumption [29]
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