Internet of Predictable Things (IoPT) Framework to Increase Cyber-Physical System Resiliency

The liberalization process of the energy sector and global Organization of the Petroleum Exporting Countries (OPEC) crisis in the 1970s are two major drivers of the decentralization and decarbonization energy generation systems. Distributed energy systems, especially renewable energy sources (RES), have become more economically viable, and their market share has significantly increased in the last two decades. Wind and solar energy plants are the most prominent RES, which generates a fluctuating and weather dependent power output. Power systems are operated according to certain national and international norms where the voltage and frequency parameters should not exceed certain operational boundaries. Power networks are also designed to carry specific maximum power capacities. The power output characteristics of RES increase the vulnerability and uncertainty levels of power systems, which makes it challenging for the power systems operators to integrate higher amounts of RES into their control zones. Energy forecasting is one of the most promising methods which increases the operational capabilities of RES. Wind and solar forecasting algorithms have been used for two decades by various energy market players, such as utilities, RES plant operators, and power traders. Transmission and distribution system operators use energy forecasting algorithms to schedule their daily energy generation profiles, thus minimizing last-minute balancing power needs.