Robustly optimal operation and planning of distributed energy resources in active distribution networks

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Embargoed until 2019-07-01
Copyright: Zhang, Cuo
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Abstract
Distributed energy resources (DERs) with nature of flexible allocation bring significant technical and economic benefits to modern power systems. DERs include distributed generation (DG) units, energy storage (ES) systems and flexible loads which can be controlled by demand response methods. For distribution networks, high penetration of DG with advanced ES technologies and demand response strategies helps them evolve from conventional passive systems to active ones, i.e. active distribution networks (ADNs). ADNs control a combination of DERs (generators, controllable loads and storage devices), providing distribution system operators with possibility of managing electricity flows flexibly and optimally. Renewable distributed generators such as wind turbines and solar photovoltaics generate eco-friendly, low-cost and sustainable energy to ADNs. However, renewable energy is intermittent, volatile and non-dispatchable. Together with varying loads, the renewable energy brings high uncertainty to the DER operation and planning in ADNs, impacting power quality, voltage regulation and economic benefits. Considering the uncertainty, this thesis focuses on ADN operating robustness and develops a series of methodologies to achieve robustly optimal operation and planning of DERs which is immune to the uncertainty. In terms of ADN energy management systems, this thesis proposes robust multi-stage methods where multiple devices, such as micro-turbines, ES units, combined cooling, heat and power plants, and demand response strategies are coordinated efficiently. These strategies aim to optimize the economic benefits while guaranteeing robust operation against the uncertainty. For ADN voltage/VAR control (VVC), this thesis proposes robustly optimal coordination of centralized and local control hierarchies. The mutual impacts between centralized and local VVC are fully addressed. Robust and hierarchically-coordinated VVC can achieve significantly efficient power loss reduction and voltage fluctuation mitigation under uncertain operating conditions. In terms of DG planning in ADNs, novel sensitivity-based and probability-weighted robust approaches are developed to perform optimal DG investments and operating robustness in a long-term planning horizon. To address the uncertainty impacts, different optimization approaches are developed in this thesis and applied in the proposed operation and planning meth-odologies. The proposed methods have been successfully demonstrated and compared with existing works. Simulation results have verified their efficiency and superiority over the compared approaches.
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Author(s)
Zhang, Cuo
Supervisor(s)
Dong, ZhaoYang
Xu, Yan
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Publication Year
2018
Resource Type
Thesis
Degree Type
PhD Doctorate
UNSW Faculty
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