Advanced Stochastic Programming for Smart Grid Operation under Uncertainties

  • Author / Creator
    Wang, Yue
  • Smart grid, typically regarded as the next generation of electrical power grid, can bring numerous benefits for electric utilities and customers with substantial economic and ecological benefits. However, uncertainties caused by the distributed power generation from renewable energy sources, household appliance power consumption under demand response programs, and electric vehicle charging demand under random usage and traffic patterns, bring us new challenges to ensure the efficiency and reliability of smart grid operation. These uncertainties usually lead to the risk of generation shortage or unusual peak power demands, and the consequences will be disastrous if a failure happens.

    In literature, aggregated household electrical consumption is widely used as the input of optimization problems in distribution systems. Yet, as pointed out by recent research works, bottom-up probabilistic residential electrical load models can better characterize the random operating conditions of appliances by considering the uncertain human behaviour. However, the dimensions of the optimization problems become enormous if we analyze electrical appliances based on a bottom-up probabilistic model in distribution systems. The optimization problems are further complicated if the randomness of renewable power generation and electric vehicle usage in smart grid is considered. Therefore, there is an urgent need to develop more efficient optimization algorithms for smart grid operation under uncertainties.

    This research focuses on the development of advanced stochastic programming algorithms for smart grid operation under uncertainties. In particular, a two-stage stochastic programming problem is formulated to address the random usage patterns of appliances, for which the distribution system operation cost is minimized in the first stage, by considering various distribution system operation constraints. The scheduling of shiftable appliances is optimized in the second stage, by considering the random usage patterns of non-shiftable appliances. To reduce the computational complexity caused by a large number of home appliances in distribution systems, scenario reduction technique is applied to reduce the number of possible scenarios while still retaining the essential features of the original scenario set. Further, a parallel decomposition method is developed for large-scale stochastic programming in a distribution system with renewable energy sources and energy storage units. By leveraging nested decomposition, the problem can be converted into independent sub-problems with a series of time periods. The reformulated problem is fully parallel for speed-up in execution. Dealing with the uncertainties associated with vehicle-to-grid applications in smart grid with renewable generation, we propose a bottom-up approach to analyze customers' uncertain driving mode. By implementing decentralized processing, the computational complexity can be significantly reduced. Moreover, real-time simulation considering uncertainties plays an important role in smart grid operation. We developed a stochastic programming problem with Lyapunov optimization technique to minimize distribution system expenses. The effectiveness of the proposed methods is validated by simulation results.

  • Subjects / Keywords
  • Graduation date
    Spring 2021
  • Type of Item
  • Degree
    Doctor of Philosophy
  • DOI
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.