Immune Procedure for Optimal Scheduling of Complex Energy Systems

The management of complex energy systems where different power sources are active in a time varying scenario of costs and prices needs efficient optimization approaches. Usually the scheduling problem is is formulated as a Mixed Integer Linear Programming (MILP) to guarantee the convergence to the global optimum. The goal of this work is to propose and compare a hybrid technique based on Artificial Immune System (AIS) and linear programming versus the traditional MILP approach. Different energy scheduling problem cases are analyzed and results of the two procedures are compared both in terms of accuracy of results and convergence speed. The work shows that, on some technical cases, AIS can efficiently tackle the energy scheduling problem in a time varying scenario and that its performances can overcome those of MILP. The obtained results are very promising and make the use of immune based procedures available for real-time management of energy systems.