Optimized Enhanced Control System for the Unibadan’s Virtual Power Plant Project Using Genetic Algorithm

Pertinent literature arguing on the need for the University of Ibadan Management to decentralize its electricity generation by increasing the penetration of renewable energy sources (RES) in solving its dire energy problems have necessitated similar calls to University Management in incorporating relevant and evolving technologies in its desired efforts to try alternative energy sources. Some of these calls have opined that envisaging a future increase in the amount of distributed generation (DG) schemes, University Management put in place modalities for the incorporation of a virtual power plant (VPP). Leading authors have suggested that despite the proliferation of these DG schemes, their visibility and control would remain a mirage if technologies that could aggregate these distributed energy resources (DERs) in allowing for their participation in displacing part of the University’s load demand are not put in place. As a follow up to such scholarly proposals for the need for a virtual power plant, leading authors have suggested and proposed different schemes from load discrimination to improved load scheduling in the envisaged electricity network of the University. A critical evaluation though of these literatures and upon expert evaluation of existing infrastructure (DG schemes and RES) via modeling, we have discovered that the implementation of the proposed VPP project would be hampered due to existing technical incompatibilities and poor design specifications. Realizing these fundamental problems, we are thus proposing as a follow up to earlier write ups the need for the incorporation of an optimized enhanced control system using genetic algorithm (GA). The ease of arriving at solutions faster by stochastic optimization techniques over classical optimization techniques cannot be overemphasized hinging on the ability of the former not to get stuck in a local optimum solution.