(359c) Optimization-Based Power Management of a Hybrid Energy System

Future power generation systems will rely on utilization of renewable and non-renewable energy resources. These systems will vary in size and have a possibility of operating in autonomous or grid-connected configurations. Despite all the improvements in renewable energy technologies, the stochastic nature of renewable energy sources (RES) represents an obstacle in their utilization.

This study deals with the development of an optimization-based power management tool for a 10kW hybrid energy system built in Lambton College, Sarnia, ON. The system consists of photovoltaic (PV) arrays, wind turbine, electrolyzer, hydrogen storage tanks, and fuel cell.

In our previous work we developed a comprehensive dynamic model for each component and integrated the system. A hierarchical control strategy, consisting of a supervisory controller and a set of local controllers was designed. A supervisory, decision-based controller ensured power balance between the renewable energy sources and the load demand.¹

In this study, we investigate the effectiveness of an on-line optimization framework as a supervisory controller. The optimization strategy incorporates weather and demand forecast information to determine the optimal set-points which are passed to a set of low level controllers. The hybrid system is designed and modeled in a modular approach for all components. We compare the use of the previously developed decision based supervisory power controller that neglects the future evolution of the weather and the load demand with the predictive optimization framework.  The advantage of the dynamic optimization frameworks is that economic objectives and operational limits can be handled directly by the optimizer in a systematic manner. We demonstrate that incorporating forecasts in the control strategy can result in significant cost and energy savings through proof-of-concept simulation studies.

1. M. Trifkovic, M. Sheikhzadeh, K. Nigim, P. Daoutidis. Hybrid energy system modeling and control, submitted to IEEE Transactions on Control Systems Technology.