Applied AI and Machine Learning for Next-Generation Power Distribution<\/strong> Applied AI and Machine Learning for Next-Generation Power Distribution SCADS combines realistic testbeds with learning-based control. CPSyNet generates full cyber-physical distribution feeders, and an open-source Gym wrapper lets reinforcement-learning agents interact with OpenDSS in real time. Using this platform, we (i) pre-train a soft-actor-critic agent with OPF solutions for near-optimal battery-storage dispatch, (ii) run a […]<\/p>\n","protected":false},"author":44146,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"categories":[],"tags":[],"university_category":[],"location":[],"organization":[],"_links":{"self":[{"href":"https:\/\/labs.wsu.edu\/scads\/wp-json\/wp\/v2\/pages\/1602"}],"collection":[{"href":"https:\/\/labs.wsu.edu\/scads\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/labs.wsu.edu\/scads\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/labs.wsu.edu\/scads\/wp-json\/wp\/v2\/users\/44146"}],"replies":[{"embeddable":true,"href":"https:\/\/labs.wsu.edu\/scads\/wp-json\/wp\/v2\/comments?post=1602"}],"version-history":[{"count":11,"href":"https:\/\/labs.wsu.edu\/scads\/wp-json\/wp\/v2\/pages\/1602\/revisions"}],"predecessor-version":[{"id":1856,"href":"https:\/\/labs.wsu.edu\/scads\/wp-json\/wp\/v2\/pages\/1602\/revisions\/1856"}],"wp:attachment":[{"href":"https:\/\/labs.wsu.edu\/scads\/wp-json\/wp\/v2\/media?parent=1602"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/labs.wsu.edu\/scads\/wp-json\/wp\/v2\/categories?post=1602"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/labs.wsu.edu\/scads\/wp-json\/wp\/v2\/tags?post=1602"},{"taxonomy":"wsuwp_university_category","embeddable":true,"href":"https:\/\/labs.wsu.edu\/scads\/wp-json\/wp\/v2\/university_category?post=1602"},{"taxonomy":"wsuwp_university_location","embeddable":true,"href":"https:\/\/labs.wsu.edu\/scads\/wp-json\/wp\/v2\/location?post=1602"},{"taxonomy":"wsuwp_university_org","embeddable":true,"href":"https:\/\/labs.wsu.edu\/scads\/wp-json\/wp\/v2\/organization?post=1602"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
SCADS combines realistic testbeds with learning-based control. CPSyNet<\/em> generates full cyber-physical distribution feeders, and an open-source Gym wrapper lets reinforcement-learning agents interact with OpenDSS in real time. Using this platform, we (i) pre-train a soft-actor-critic agent with OPF solutions for near-optimal battery-storage dispatch, (ii) run a cluster-level MPC that coordinates PV inverters and EV chargers for voltage control, and (iii) deploy an adaptive model that disaggregates behind-the-meter PV from net smart-meter data. Together these tools advance reliable, data-driven operation in DER-rich grids.<\/p>\n\n\n\n
\n\n\n\nPapers<\/h2>\n\n\n\n
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CPSyNet: A Tool for Generating Customized Cyber-Power Synthetic Network for Distribution Systems with Distributed Energy Resources<\/a><\/strong>, IET Smart Grid<\/em> 5 (6): 463\u2013477 (2022).
Abstract<\/a> | Paper<\/a><\/li>\n\n\n\n
MPC-Based Decentralized Voltage Control in Power Distribution Systems with EV and PV Coordination<\/a><\/strong>, IEEE Transactions on Smart Grid<\/em> 13 (4): 2908\u20132919 (2022).
Abstract<\/a> | Paper<\/a><\/li>\n\n\n\n
An Adaptive Machine Learning Framework for Behind-the-Meter Load\/PV Disaggregation<\/a><\/strong>, IEEE Transactions on Industrial Informatics<\/em> 17 (10): 7060\u20137069 (2021).
Abstract<\/a> | Paper<\/a><\/li>\n\n\n\n
An Open-source Environment for Reinforcement Learning in Power Distribution Systems<\/a><\/strong>, IEEE Power & Energy Society General Meeting (PESGM 2022)<\/em>.
Abstract<\/a> | Paper<\/a><\/li>\n\n\n\n
Reinforcement Learning for Battery Energy Storage Dispatch Augmented with Model-based Optimizer<\/a><\/strong>, IEEE SmartGridComm 2021<\/em>.
Abstract<\/a> | Paper<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"