Computationally Efficient Identification of Power Flow Alternative Solutions with Application to Geomagnetic Disturbance Analysis

2020 ◽  
Author(s):  
Komal S Shetye ◽  
Thomas J Overbye ◽  
Adam B Birchfield ◽  
James D. Weber ◽  
Tracy L. Rolstad
Keyword(s):  
Power Flow ◽  
Geomagnetic Disturbance ◽  
Computationally Efficient ◽  
Disturbance Analysis ◽  
Alternative Solutions

scholarly journals Flow Allocation in Meshed AC-DC Electricity Grids

2020 ◽  
Author(s):  
Fabian Hofmann ◽  
Markus Schlott ◽  
Alexander Kies ◽  
Horst Stöcker
Keyword(s):  
Power Systems ◽  
Power Flow ◽  
Low Carbon ◽  
Computationally Efficient ◽  
Shift Parameter ◽  
Transmission Grid ◽  
High Voltage Direct Current ◽  
Transmission Expansion ◽  
Electricity Grids ◽  
Power Transfer Distribution Factors

In power systems, flow allocation (FA) methods allow to allocate usage and costs of the transmission grid to each single market participant. Based on predefined assumptions, the power flow is split into isolated generator specific or producer specific sub-flows. Two prominent FA methods, Marginal Participation (MP) and Equivalent Bilateral Exchanges (EBE), build upon the linearized power flow and thus on the Power Transfer Distribution Factors (PTDF). Despite their intuitive and computationally efficient concept, they are restricted to networks with \emph{passive} transmission elements only. As soon as a significant number of \emph{controllable} transmission elements, such as High-voltage direct current (HVDC) lines, operate in the system, they loose their applicability. This work reformulates the two methods in terms of Virtual Injection Patters (VIP) which allows to efficiently introduce a shift parameter $q$, tuning contributions of net sources and net sinks in the network. Major properties and differences of the methods are pointed out. Finally, it is shown how the MA and EBE algorithm can be applied to generic meshed AC-DC electricity grids: Introducing a \emph{pseudo-impedance} which reflects the operational state of controllable elements, allows to extend the PTDF matrix under the assumption of knowing the current system's flow. Basic properties from graph theory are used for solving the pseudo-impedance dependent on the position in the network. This directly enables \emph{e.g.} HVDC lines to be considered in the MP and EBE algorithm. The extended methods are applied to a low-carbon European network model (PyPSA-EUR) with a spatial resolution of N=181 and an 18\% transmission expansion. The allocations of VIP and MP, show that countries with high wind potentials profit most from the transmission grid expansion. Based on the average usage of transmission system expansion a method of distributing operational and capital expenditures is proposed. Further it is shown, how injections from renewable resources strongly drive country-to-country allocations and thus cross-border electricity flows.

scholarly journals Flow Allocation in Meshed AC-DC Electricity Grids

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1233 ◽  
Author(s):  
Fabian Hofmann ◽  
Markus Schlott ◽  
Alexander Kies ◽  
Horst Stöcker
Keyword(s):  
Power Systems ◽  
Power Flow ◽  
Low Carbon ◽  
Computationally Efficient ◽  
Shift Parameter ◽  
Transmission Grid ◽  
High Voltage Direct Current ◽  
Total Transmission ◽  
Transmission Expansion ◽  
Electricity Grids

In power systems, flow allocation (FA) methods enable to allocate the usage and costs of the transmission grid to each single market participant. Based on predefined assumptions, the power flow is split into isolated generator-specific or producer-specific sub-flows. Two prominent FA methods, Marginal Participation (MP) and Equivalent Bilateral Exchanges (EBEs), build upon the linearized power flow and thus on the Power Transfer Distribution Factors (PTDFs). Despite their intuitive and computationally efficient concepts, they are restricted to networks with passive transmission elements only. As soon as a significant number of controllable transmission elements, such as high-voltage direct current (HVDC) lines, operate in the system, they lose their applicability. This work reformulates the two methods in terms of Virtual Injection Patterns (VIPs), which allows one to efficiently introduce a shift parameter q to tune contributions of net sources and net sinks in the network. In this work, major properties and differences in the methods are pointed out, and it is shown how the MP and EBE algorithms can be applied to generic meshed AC-DC electricity grids: by introducing a pseudo-impedance ω ¯ , which reflects the operational state of controllable elements and allows one to extend the PTDF matrix under the assumption of knowing the current flow in the system. Basic properties from graph theory are used to solve for the pseudo-impedance in dependence of the position within the network. This directly enables, e.g., HVDC lines to be considered in the MP and EBE algorithms. The extended methods are applied to a low-carbon European network model (PyPSA-EUR) with a spatial resolution of 181 nodes and an 18% transmission expansion compared to today’s total transmission capacity volume. The allocations of MP and EBE show that countries with high wind potentials profit most from the transmission grid expansion. Based on the average usage of transmission system expansion, a method of distributing operational and capital expenditures is proposed. In addition, it is shown how injections from renewable resources strongly drive country-to-country allocations and thus cross-border electricity flows.

scholarly journals Adaptive leader election for control of tactical microgrids

2020 ◽  
pp. 154851292090478
Author(s):  
Robert S Jane ◽  
Steven Y Goldsmith ◽  
Gordon G Parker ◽  
Wayne W Weaver ◽  
Denise M Rizzo
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Communication Complexity ◽  
Leader Election ◽  
Electrical Network ◽  
Computationally Efficient ◽  
Mobile Vehicle ◽  
Optimal Power ◽  
Asset Inventory ◽  
Desirability Index

An adaptive leader election protocol (LEP) was developed to control both stationary and mobile generation assets (generators and vehicles), achieved using an energy management system (EMS). The LEP algorithm adapts to changes in both topology and the asset inventory using the longevity criterion (available fuel, future availability), used to compute a desirability index, for election of a leader. The leader then implemented an optimal power flow EMS to ensure sufficient and optimal power flow within the electrical network was maintained in the presence of a complex electrical load, regardless of the asset mix. Both the LEP and EMS algorithms were distributed to the generation assets. This capability supports stationary grid-tied, vehicle-to-grid, and mobile vehicle-to-vehicle-based applications. Simulated case studies illustrate that the adaptive LEP was resistive to deterministic events (maintenance, available fuel), which could yield an inoperable asset, compromising grid stability. The use of the adaptive LEP resulted in a communication complexity of at most [Formula: see text]; in contrast, a fully connected communication system requires [Formula: see text] communications, limiting the scalability of the network. The EMS was optimized, resulting in a computationally efficient and scalable optimal power flow algorithm that can be extended for more general stationary or mobile energy networks.

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