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Distribution Grid planning by self-designing with reinforce..:

, In: CIRED Porto Workshop 2022: E-mobility and power distribution systems,

Pohl, O. ; Rose, M. ; Frye, T. - p. None , 2022

Link: https://doi.org/10.1049/icp.2022.0860

RT 
T1 <cTitle>CIRED Porto Workshop 2022: E-mobility and power distribution systems</cTitle>: T1 <Title>Distribution Grid planning by self-designing with reinforcement learning</Title>

UL https://suche.suub.uni-bremen.de/peid=ieee-9841749&Exemplar=1&LAN=DE
				
A1 Pohl, O.
A1 Rose, M.
A1 Frye, T.
YR 2022
K1 air pollution control
K1 electric vehicles
K1 load flow
K1 optimisation
K1 power cables
K1 power distribution economics
K1 power distribution planning
K1 power engineering computing
K1 power generation planning
K1 power grids
K1 reinforcement learning
K1 renewable energy sources
K1 distribution grid planning
K1 decentral energy resources
K1 energy transition
K1 greenhouse gas emissions reduction
K1 amplifying electrification
K1 electrical grid
K1 central energy generation principles
K1 static load profiles
K1 fully automated pipeline
K1 reinforcement learning scheme
K1 electrical graph
K1 cable reinforcement
K1 cable route planner
K1 load flow calculations
K1 grid data
K1 grid violations
K1 grid action sequences
K1 Germany
K1 decentralization
K1 cable construction cos
K1 optimization
K1 EV penetration
K1 grid action costs
K1 EV concentrations
SP None
LK http://dx.doi.org/https://doi.org/10.1049/icp.2022.0860
DO https://doi.org/10.1049/icp.2022.0860
SF ELIB - SuUB Bremen

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