Development of a supervisory controller for energy management problems

Abstract

Multi energy source systems, like hybrid electric vehicles in automotive industry, started to attract attention as a remedy for the greenhouse gas emission problem. Although their environmental performances are better than conventional technologies such as the case of gasoline vehicles versus hybrid electric vehicles in automotive industry, their operational management can be challenging due to their increased complexity. One of these challenges is the operational management of the energy flow among these multiple sources and sinks which in this context referred as the energy management problem. In this thesis, a supervisory controller is developed to operate at a residential environment with multiple energy sources. First, dynamic optimization techniques are applied to the available mathematical models of the multi-energy sources to create a non-causal optimal controller. Then, a set of implementable rules are extracted by analyzing the optimal trajectories resulted from the dynamic optimization to create a causal supervisory controller. Several simulations are conducted with Matlab/Simulink to validate the developed controller. The supervisory controller achieves not only a daily cost reduction between 6-7.5% compared to conventional energy infrastructure used in residential areas but also performs 2% better than heuristic control techniques available in the literature. Another simulation study is conducted, with different demand cycles, for verification of the controller. Although its performance reduces as expected, it still performs 1% better than heuristic control strategies. In the final part of this thesis, the formulation used in the residential problem which was originally adopted from an example in automotive industry, is generalized so that it can be used in all types of energy management problems. Finally, for exemplary purposes, a formulation for energy management problem in mobile devices is created by using the developed generic formulation.