Uncertainty Modeling and Optimization in Smart Grid with Renewable Generation

• Author / Creator

  Shi, Zhichao

• Electricity is an essential part of our daily life which can be supplied by power systems with fossil fuels or renewable energy sources. Nowadays, traditional power systems are evolving towards new smart grid with the development of advanced information and communication technology. Compared with the general electrical grid, smart grid features a two-way flow of electricity and information as a distributed energy delivery network. With lots of benefits such as increasing system efficiency, robustness and reducing outage costs, smart grid has attracted worldwide attention in recent years both in industry and academia.

  Conventional electricity generation method mainly relies on the combustion of fossil fuels. However, considering the limited supply of fossil fuels and increasing environmental pollution, it is necessary and urgent to enhance the use of alternative energy sources. Hence, the clean renewable energy sources such as wind and solar have drew more public attention recently. Although renewable energy has many advantages, the main drawback is the intermittent and random characteristic. With the increasing integration of renewable generation in smart grid, many new technical challenges have also emerged in regard to the reliable operation of power systems, especially the uncertainty related problems. Therefore, it is imperative to handle the uncertainties in smart grid to achieve reliable and stable operation.

  The focus of this research is to study uncertainty modeling and related optimization problems in smart grid. There are various uncertain sources in smart grid such as renewable generation, load demand and electricity price, among which the uncertain renewable generation has attracted more attentions in recent years. In this work, we focus on the uncertainties caused by renewable generation such as wind power generation. In order to deal with the possible uncertainties in system operation, different approaches have been studied including the direct forecast methods and some mathematical modeling methods. Although point forecast methods have been widely studied for wind energy, the forecast errors cannot be fully eliminated which may bring significant influence in power system operational decision. In this thesis, probabilistic forecast is investigated, and a recurrent neural network (RNN) based interval prediction model is first proposed to forecast uncertain wind power which can generate intervals with a predefined confidence level. As the source of wind power, wind speed forecast is also investigated in a multiobjective interval prediction framework.

  Furthermore, microgrid is an important part of future smart grid, and microgrid energy management has been a popular topic for a long time. To capture the uncertainties of wind power in microgrid, mathematical modeling methods based on distributionally robust optimization (DRO) and robust optimization (RO) are investigated for energy management problems in this thesis. First, a distributionally robust chance-constrained energy management model is proposed for islanded microgrids, which considers the possible power imbalance due to uncertain wind power, and a novel moment based ambiguity set is designed. The chance constraint for power balance is processed with DRO technique and the problem is reformulated as a tractable second-order conic programming (SOCP) problem. The effectiveness of the proposed approach has been validated by case studies.

  Based on the research of microgrid energy management, the uncertainty modeling in transmission system is also studied in this thesis. Particularly, two-stage chance-constrained unit commitment (UC) problem and energy and reserve dispatch problem are studied with DRO method. The statistical-distance based ambiguity set is proposed to describe the uncertain distribution of wind power in such problems.

  To overcome the anticipativity of uncertainty in single-stage or two-stage models, multistage energy management problem is investigated for grid-connected microgrids which takes the non-anticipativity into account. The uncertainty modeling methods based on RO and DRO techniques are analyzed for wind power with interval uncertainty set or second-order conic representable ambiguity set, respectively. The effectiveness of the proposed multistage model is verified by case studies.

• Subjects / Keywords

  • Smart Grid
  • Uncertainty Modeling
  • Optimization

• Graduation date

  Spring 2020

• Type of Item

  Thesis

• Degree

  Doctor of Philosophy

• DOI

  https://doi.org/10.7939/r3-eygn-c123

• License

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