Keith Moffat

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2022-225

September 9, 2022

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2022/EECS-2022-225.pdf

Traditionally, utility distribution companies have treated electricity distribution networks as passive loads, assuming that the possibility of network constraint violations cannot be managed by controlling distributed energy resources (DERs) such as electric vehicle charging stations. The emergence of edge computing has made it possible for DER management systems (DERMS) to control the power injected/extracted by DERs in real time. By actively managing DERs when necessary, DERMS both increase interconnection availability and reduce the need for infrastructure upgrades. This dissertation presents several tools that can either make power-injection decisions for a DERMS or that support the system’s decision-making. These tools include Unsupervised Impedance and Topology Estimation, Multiple Model Adaptive Power System State Estimation, Voltage Phasor Control, Linearized Output Projected Gradient Descent Feedback Optimization, and Nullspace-Based Power Flow Linearization. The focus of the work is to develop theory which can explain when and how to use these tools on real distribution networks. A large portion of this dissertation develops the theory behind Nullspace-Based Power Flow Linearization, a novel interpretation of power flow linearization that explicitly considers constant-voltage buses, phasor angle-reference ambiguity, and power balance. This dissertation also includes a comment that was submitted in August 2022 on CPUC Proceeding R2207005. With Proceeding R2207005, the California Public Utilities Commission is investigating new rules and price structures for utilities which will change the future of DER management in California.

Advisors: Claire Tomlin and Alexandra von Meier

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BibTeX citation:

@phdthesis{Moffat:EECS-2022-225,
    Author= {Moffat, Keith},
    Title= {Learning, Control and Optimization for Electricity Distribution Networks},
    School= {EECS Department, University of California, Berkeley},
    Year= {2022},
    Month= {Sep},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2022/EECS-2022-225.html},
    Number= {UCB/EECS-2022-225},
    Abstract= {Traditionally, utility distribution companies have treated electricity distribution networks as passive loads, assuming that the possibility of network constraint violations cannot be managed by controlling distributed energy resources (DERs) such as electric vehicle charging stations. The emergence of edge computing has made it possible for DER management systems (DERMS) to control the power injected/extracted by DERs in real time. By actively managing DERs when necessary, DERMS both increase interconnection availability and reduce the need for infrastructure upgrades. This dissertation presents several tools that can either make power-injection decisions for a DERMS or that support the system’s decision-making. These tools include Unsupervised Impedance and Topology Estimation, Multiple Model Adaptive Power System State Estimation, Voltage Phasor Control, Linearized Output Projected Gradient Descent Feedback Optimization, and Nullspace-Based Power Flow Linearization. The focus of the work is to develop theory which can explain when and how to use these tools on real distribution networks. A large portion of this dissertation develops the theory behind Nullspace-Based Power Flow Linearization, a novel interpretation of power flow linearization that explicitly considers constant-voltage buses, phasor angle-reference ambiguity, and power balance. This dissertation also includes a comment that was submitted in August 2022 on CPUC Proceeding R2207005. With Proceeding R2207005, the California Public Utilities Commission is investigating new rules and price structures for utilities which will change the future of DER management in California.},
}

EndNote citation:

%0 Thesis
%A Moffat, Keith 
%T Learning, Control and Optimization for Electricity Distribution Networks
%I EECS Department, University of California, Berkeley
%D 2022
%8 September 9
%@ UCB/EECS-2022-225
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2022/EECS-2022-225.html
%F Moffat:EECS-2022-225