(515b) A Distributed Predictive Control Framework for Smart Grid Development

While the traditional electrical grid has been successful, in recent years there have been numerous calls for the development of the so-called "smart electrical grid" by expanding the traditional electrical grid with distributed, medium-scale renewables-based energy generation systems and digital technologies, for example, communications, computing, sensing and automation, to better meet the increasing energy demand and environmental regulations. Incorporated with two-way communication networks, digital devices and distributed optimization and control systems, the "smart grid" is expected to be more reliable, more secure, more energy efficient and more environmentally friendly. One important feature of the smart grid is its capability of integrating distributed energy resources and generation, for example, renewable energy resources, into the electrical
grid. Renewable energy resources, like wind and solar-based energy generation systems, are receiving national and worldwide attention owing to the rising rate of consumption of fossil fuels. In addition to the environmental benefits, solar and wind renewable energy generation systems also have a reduced investment risk and an increased energy efficiency. However, integrating renewable energy generation systems with the electrical grid requires addressing key fundamental challenges, for example, variable output, in the
operation of intermittent renewable resources like solar- and wind-based energy generation systems.

In this work, we propose a conceptual distributed control framework for electrical grid integrated with distributed renewable energy generation systems in order to enable the development of the so-called "smart electrical grid". First, we introduce the key elements and their interactions in the proposed control architecture and discuss the design of the distributed control systems which are able to coordinate their actions to account for optimization considerations on the system operation. Subsequently, we focus on a specific
wind/solar energy generation system connected to a reverse osmosis desalination system and the electrical grid and design two supervisory predictive controllers via model predictive control to operate the integrated system taking into account short-term and long-term optimal maintenance and operation considerations, respectively. Simulations are carried out to illustrate the applicability and effectiveness of the proposed approach.