English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In this paper, we introduce a new framework to address the problem of voltageregulation in unbalanced distribution grids with deep photovoltaic penetration.In this framework, both real and reactive power setpoints are explicitlycontrolled at each solar panel smart inverter, and the objective is tosimultaneously minimize system-wide voltage deviation and maximize solar poweroutput. We formulate the problem as a Markov decision process with continuousaction spaces and use proximal policy optimization, a reinforcementlearning-based approach, to solve it, without the need for any forecast orexplicit knowledge of network topology or line parameters. By representing thesystem in a quasi-steady state manner, and by carefully formulating the Markovdecision process, we reduce the complexity of the problem and allow for fullydecentralized (communication-free) policies, all of which make the trainedpolicies much more practical and interpretable. Numerical simulations on a240-node unbalanced distribution grid, based on a real network in Midwest U.S.,are used to validate the proposed framework and reinforcement learningapproach.

Fully Decentralized Reinforcement Learning-based Control of Photovoltaics in Distribution Grids for Joint Provision of Real and Reactive Power