4 research outputs found
Deep Reinforcement Learning with Graph ConvNets for Distribution Network Voltage Control
This paper proposes a model-free Volt-VAR control (VVC) algorithm via the
spatio-temporal graph ConvNet-based deep reinforcement learning (STGCN-DRL)
framework, whose goal is to control smart inverters in an unbalanced
distribution system. We first identify the graph shift operator (GSO) based on
the power flow equations. Then, we develop a spatio-temporal graph ConvNet
(STGCN), testing both recurrent graph ConvNets (RGCN) and convolutional graph
ConvNets (CGCN) architectures, aimed at capturing the spatiotemporal
correlation of voltage phasors. The STGCN layer performs the feature extraction
task for the policy function and the value function of the reinforcement
learning architecture, and then we utilize the proximal policy optimization
(PPO) to search the action spaces for an optimum policy function and to
approximate an optimum value function. We further utilize the low-pass property
of voltage graph signal to introduce an GCN architecture for the the policy
whose input is a decimated state vector, i.e. a partial observation. Case
studies on the unbalanced 123-bus systems validate the excellent performance of
the proposed method in mitigating instabilities and maintaining nodal voltage
profiles within a desirable range.Comment: This work has been submitted to the IEEE for possible publication.
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Interannual variability analysis of utility-scale solar photovoltaic and wind power resources in Arizona and New Mexico
Interannual variability (IAV) of wind and solar resources impacts multi-million-dollar power
system decisions related to operations, planning, and investments. Decisions pertaining to power
systems are usually made based on a limited number of years where the consequences of
interannual variability are often neglected or reduced. As the electric sector transitions to a
renewable energy future, larger shares of wind and solar will introduce more variability to the
operation of the power system. Consequently, we use the National Radiation Database (1998-
2015) as well as the WIND Toolkit (2007-2013) to create wind and solar photovoltaic power time
series using NREL’s System Advisor Model (SAM), and study the interannual variability of wind
and solar resources in Arizona and New Mexico. We define IAV as half of the ratio between the
range of capacity factor (CF) and the long-term mean in the time frame of interest, expressed as a
percentage. Results show a ±3-4% IAV of annual solar CF and ±7-8% IAV of annual wind CF.
IAV of annual wind CF is significantly impacted by geographical location, with larger IAV values
in the Great Plains and southern Rocky Mountains. IAV of seasonal wind CF is calculated on the
order of ±11-26%, whereas IAV of seasonal solar CF was found to range from ±6% to ±16%. In
both cases, IAV of wind and solar seasonal CF showed lower IAV values in summer and larger in
winter. Extreme ramping events in aggregated wind and solar power time series are also studied.
It is shown that extreme events can increase as much as 40% from year to year. Impacts on
generation, transmission, and storage investments as well as impacts on operating reserves,
transmission congestion, maintenance and seasonal storage scheduling are discussed