Planning, Management and Control in Large-Scale Systems: Enabling the Integration of Intermittent Energy Sources

Carnegie Mellon University, Pittsburgh PA

Investigators

Abstract

Intellectual Merit: A high penetration of renewable energy sources in the power system is a major component of the vision for a clean and sustainable electric power generation. However, the main target in terms of renewable generation is currently on wind generation. The intermittency of these sources as well as the fact that the locations of high availability of the primary energy source wind do not coincide with the locations of the load center are major challenges which have to be resolved. For a successful integration of wind generation, we must find a way to balance this intermittency effectively and to transmit the power to the loads without the need for environmentally unfriendly backup generation and a substantial extension of the transmission system. In this project, these challenges are addressed by proposing a new distributed predictive control algorithm for the coordination of the intermittent energy sources with storage, demand control and backup generation. The proposed predictive control allows for an optimal utilization of the available capacities with the objective to minimize the overall use of backup generation but also the changes in its output as well as minimizing the required demand control. The control is carried out in a distributed way taking into account that control of the power system is shared among several entities. In addition, the question is asked how much storage and backup generation capacity and load flexibility is needed for a reliable operation of the power system with a given level of intermittent renewable power generation penetration and vice versa. This is a fundamental question for the planning of the future power system. A systematic tool based on stochastic programming is proposed to answer this question and to give an estimate for the feasibility of the intended intermittent generation penetration. Broader Impacts: The resulting problem size and the involved computation effort or the shared control structure so far often prevented an application of predictive control to large-scale systems. The features of the proposed distributed predictive control are low computational effort, fast convergence and no parameter tuning which make this control algorithm applicable to large-scale systems and superior to other distributed predictive control methods. Even though the distributed predictive control method is proposed for power systems, the mathematical framework is applicable to many other large-scale problems opening the door for predictive control to other large scale systems. The results of this project will be included in the content of existing courses providing access to research results for students.