Optimization based fuzzy resource allocation framework for smart grid

The integration of renewable energy resources with distributed and intermittent generation, diversity in operational scenarios, increased electrification, and mission critical energy demand has made the electric grid more vulnerable to imperceptible failures. Thus, resource allocation becomes a major area of research to allocate best power source to a sink and at the same time reduce the operating costs. Computational intelligence, optimization, and control play a vital role to overcome these challenges and make the grid smarter. This paper proposes a power flow control scheme using a framework of fuzzy logic (FL) and genetic algorithm (GA) to efficiently manage desired power flow levels within the smart grid. A fuzzy decision criteria is designed to choose a most suitable power source to deliver power to a certain demand. GA is used to choose a most suitable route from source to demand and optimize a cost function based on distance. Simulations show that the smart grid power flow can achieve the desired thresholds by incorporating the proposed approach even in the presence of unpredictable power fluctuations from renewable energy resources. This research provides an optimum power flow control framework to test even complex and practical electricity grids.