Optimal Energy Management for Grid Integration of Distributed Energy Resources
Access status:
Open Access
Type
ThesisThesis type
Doctor of PhilosophyAuthor/s
Yi, YuAbstract
The proliferation of distributed energy resources (DERs) encourages end-users to engage in energy delivery with the upper-level grid through aggregators. However, DERs, such as solar panels, exhibit intermittency owing to their reliance on weather conditions, rendering coordination ...
See moreThe proliferation of distributed energy resources (DERs) encourages end-users to engage in energy delivery with the upper-level grid through aggregators. However, DERs, such as solar panels, exhibit intermittency owing to their reliance on weather conditions, rendering coordination with other network devices challenging. Furthermore, the high penetration of solar panels and the charging of numerous EVs significantly influence the overall load profile. The uncertainty induced by DERs cannot be overlooked in power system operation and dispatch. The presence of uncertainties in the home energy management (HEM) model for customer-side and the AC OPF model for the network side poses challenges that cannot be effectively addressed through simple methods. These uncertainties introduce complex stochastic variables, rendering conventional solving approaches inadequate for addressing the stochastic nature of the problem. Furthermore, the AC OPF problem is compounded by nonlinear constraints, high-dimensional variable matrices, and the need to consider a wide time coverage, further exacerbating the difficulty in finding efficient and accurate solutions. This thesis aims to formulate an optimal energy management framework for distribution networks, considering the escalating integration of DERs. The exploration commences with Chapter 2, providing a concise review of this thesis, Chapter 3 undertakes a comprehensive exploration of existing literature. The subsequent chapters present and elaborate on four primary research works. Chapter 3 formulates a stochastic home energy management framework for smart homes, taking into account the dynamics of household DERs. Chapters 4 and 5 propose novel forecasting methods, utilizing distinct approaches to generate probabilistic forecasting results mirroring real-world scenarios. In Chapter 6, a bi-level framework under uncertainty is introduced for distribution networks, aiming to compute robust operating envelopes.
See less
See moreThe proliferation of distributed energy resources (DERs) encourages end-users to engage in energy delivery with the upper-level grid through aggregators. However, DERs, such as solar panels, exhibit intermittency owing to their reliance on weather conditions, rendering coordination with other network devices challenging. Furthermore, the high penetration of solar panels and the charging of numerous EVs significantly influence the overall load profile. The uncertainty induced by DERs cannot be overlooked in power system operation and dispatch. The presence of uncertainties in the home energy management (HEM) model for customer-side and the AC OPF model for the network side poses challenges that cannot be effectively addressed through simple methods. These uncertainties introduce complex stochastic variables, rendering conventional solving approaches inadequate for addressing the stochastic nature of the problem. Furthermore, the AC OPF problem is compounded by nonlinear constraints, high-dimensional variable matrices, and the need to consider a wide time coverage, further exacerbating the difficulty in finding efficient and accurate solutions. This thesis aims to formulate an optimal energy management framework for distribution networks, considering the escalating integration of DERs. The exploration commences with Chapter 2, providing a concise review of this thesis, Chapter 3 undertakes a comprehensive exploration of existing literature. The subsequent chapters present and elaborate on four primary research works. Chapter 3 formulates a stochastic home energy management framework for smart homes, taking into account the dynamics of household DERs. Chapters 4 and 5 propose novel forecasting methods, utilizing distinct approaches to generate probabilistic forecasting results mirroring real-world scenarios. In Chapter 6, a bi-level framework under uncertainty is introduced for distribution networks, aiming to compute robust operating envelopes.
See less
Date
2024Rights statement
The author retains copyright of this thesis. It may only be used for the purposes of research and study. It must not be used for any other purposes and may not be transmitted or shared with others without prior permission.Faculty/School
Faculty of Engineering, School of Electrical and Information EngineeringAwarding institution
The University of SydneyShare