(454b) Supervisory Predictive Control of Integrated Wind/Solar Energy Generation Systems | AIChE

(454b) Supervisory Predictive Control of Integrated Wind/Solar Energy Generation Systems


Chilin, D. - Presenter, Univ. of California, Los Angeles
Chen, X. - Presenter, Univ. of California, Los Angeles
Liu, J. - Presenter, University of California, Los Angeles

Alternative energy technologies, like wind-based and solar-based energy generation systems, are receiving national and worldwide attention owing to the rising rate of consumption of fossil fuels. In particular, drivers for solar/wind renewable energy systems are the environmental benefits (reduction of carbon emissions due to the use of renewable energy sources and the efficient use of fossil fuels), reduced investment risk, fuel diversification and energy autonomy, increased energy efficiency (less line losses) as well as potential increase of power quality and reliability and in certain cases, potential grid expansion deferral due to the possibility of generation close to demand. However, achieving such major renewable energy production goals requires addressing key fundamental challenges in the operation and reliability of intermittent (variable output) renewable resources like solar- and wind-based energy systems. With respect to previous results on control of wind and solar systems, most of the efforts have focused on stand-alone wind or solar systems. Specifically, there is a significant body of literature dealing with control of wind energy generation systems, while several contributions have been made to the control of solar-based energy generation systems. However, there are few works that have focused on the control of stand-alone hybrid wind-solar energy generation systems.

In this work, we present model predictive control methods needed for the optimal management and operation of integrated wind/solar energy systems coupled with a battery bank and dealing with load variability. Specifically, the primary control objective is to manipulate the operating conditions of the wind subsystem and of the solar subsystem to generate enough energy to satisfy the load demand. The second control objective is to optimize the operating conditions to improve the closed-loop performance, to deal with control actuator and/or state constraints and to maximize the life of the battery bank. We design a supervisory predictive control system to achieve the aforementioned control objectives in the context of a representative wind/solar energy generation system. First, two local control systems are designed for the wind subsystem and the solar subsystem, respectively. The two local control systems manipulate duty cycles of DC/DC converters. Based on the two local control systems, a supervisory controller is designed, using model predictive control theory, to optimize the operating set points of the two local control systems to improve the closed-loop performance and to satisfy operating constraints. The proposed control scheme can also take other useful information, such as weather forecast for wind variability and future load changes, into account to optimize the operating conditions.