{"id":1921,"date":"2019-03-22T20:01:50","date_gmt":"2019-03-22T20:01:50","guid":{"rendered":"https:\/\/beta.research.ece.ncsu.edu\/adac\/?page_id=1921"},"modified":"2019-08-23T19:17:06","modified_gmt":"2019-08-23T19:17:06","slug":"codems","status":"publish","type":"page","link":"https:\/\/research.ece.ncsu.edu\/adac\/codems\/","title":{"rendered":"Collaborative Distributed Energy Management Systems"},"content":{"rendered":"<p>[et_pb_section bb_built=&#8221;1&#8243; inner_width=&#8221;auto&#8221; inner_max_width=&#8221;960px&#8221;][et_pb_row][et_pb_column type=&#8221;4_4&#8243;][et_pb_post_title admin_label=&#8221;CODEMS&#8221; _builder_version=&#8221;3.21&#8243; meta=&#8221;off&#8221; author=&#8221;off&#8221; date=&#8221;off&#8221; \/][et_pb_video admin_label=&#8221;CoDEMS Video&#8221; _builder_version=&#8221;3.25.1&#8243; src=&#8221;https:\/\/www.youtube.com\/watch?v=gvZjdBHR2uE&#8221; box_shadow_horizontal_tablet=&#8221;0px&#8221; box_shadow_vertical_tablet=&#8221;0px&#8221; box_shadow_blur_tablet=&#8221;40px&#8221; box_shadow_spread_tablet=&#8221;0px&#8221; z_index_tablet=&#8221;500&#8243; \/][et_pb_text admin_label=&#8221;Personnel&#8221; _builder_version=&#8221;3.21&#8243;]<\/p>\n<h3>Personnel:<\/h3>\n<p>[\/et_pb_text][et_pb_text admin_label=&#8221;Personnel Text&#8221; _builder_version=&#8221;3.21&#8243;]<\/p>\n<p><span style=\"font-weight: 400;\">Mo-Yuen Chow (PI), Ziang Zhang, Wente Zeng, Navid Rahbari Asr, Yuan Zhang, Jie Duan, Alberto Castelo Becerra, and Zheyuan Cheng.<\/span><\/p>\n<p>[\/et_pb_text][et_pb_text admin_label=&#8221;Objective&#8221; _builder_version=&#8221;3.21&#8243;]<\/p>\n<h3>Objective:<\/h3>\n<p>[\/et_pb_text][et_pb_text admin_label=&#8221;Objective Text&#8221; _builder_version=&#8221;3.21&#8243;]<\/p>\n<p><span style=\"font-weight: 400;\">To enhance the microgrid\/smart grid\u2019s scalability, reliability, and resilience, this project aims to develop a <em><strong>collaborative<\/strong><\/em> and <em><strong>distributed<\/strong><\/em> energy management system (CoDEMS) that can determine <em><strong>globally optimal<\/strong><\/em> control commands without the need for a central coordinator.<\/span><\/p>\n<p>[\/et_pb_text][et_pb_text admin_label=&#8221;Funding&#8221; _builder_version=&#8221;3.21&#8243;]<\/p>\n<h3>Funding:<\/h3>\n<p>[\/et_pb_text][et_pb_image admin_label=&#8221;Funding Image&#8221; _builder_version=&#8221;3.21&#8243; src=&#8221;https:\/\/research.ece.ncsu.edu\/wp-content\/uploads\/sites\/3\/2019\/03\/Screen-Shot-2019-03-19-at-9.47.01-AM.png&#8221; max_width=&#8221;60%&#8221; align_last_edited=&#8221;on|desktop&#8221; align_tablet=&#8221;center&#8221; \/][et_pb_text admin_label=&#8221;Summary&#8221; _builder_version=&#8221;3.21&#8243;]<\/p>\n<h3>Summary:\u00a0<\/h3>\n<p>[\/et_pb_text][et_pb_text admin_label=&#8221;Summary text&#8221; _builder_version=&#8221;3.21&#8243;]<\/p>\n<p><span style=\"font-weight: 400;\">The core of CoDEMS is a rolling-horizon optimization that set both the day-ahead optimal schedule and 5-min re-dispatch commands. By leveraging the consensus-based distributed optimization technique, the above mentioned global optimization can be solved by DER on-site controllers. Current results indicates that the proposed distributed control framework delivers not only the same control functions as the centralized framework but also greater scalability, reliability, and resiliency.<\/span><\/p>\n<p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row][et_pb_column type=&#8221;4_4&#8243;][et_pb_text admin_label=&#8221;Timeline&#8221; _builder_version=&#8221;3.21&#8243;]<\/p>\n<h3>Timeline:<\/h3>\n<p>[\/et_pb_text][et_pb_image admin_label=&#8221;Timeline Image&#8221; _builder_version=&#8221;3.21&#8243; src=&#8221;https:\/\/research.ece.ncsu.edu\/wp-content\/uploads\/sites\/3\/2019\/03\/Screen-Shot-2019-03-19-at-9.48.50-AM.png&#8221; align_last_edited=&#8221;on|desktop&#8221; align_tablet=&#8221;center&#8221; \/][et_pb_text admin_label=&#8221;Results&#8221; _builder_version=&#8221;3.21&#8243;]<\/p>\n<h3>Results:\u00a0<\/h3>\n<p>[\/et_pb_text][et_pb_text admin_label=&#8221;Results text&#8221; _builder_version=&#8221;3.21&#8243;]<\/p>\n<p><span style=\"font-weight: 400;\">The CoDEMS algorithm is successfully developed and its intellectual merits, approaches, results, and contributions are well documented in 36 journal and 64 conference publications. In addition to publications, the CoDEMS is also implemented in the ADAC lab and demonstrated on the FREEDM GEH testbed as shown in Figure 1 and 2 respectively.<\/span><\/p>\n<p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row][et_pb_column type=&#8221;1_2&#8243;][et_pb_text admin_label=&#8221;CODEMS Lab Implementation Heading&#8221; _builder_version=&#8221;3.21&#8243;]<\/p>\n<h3>CODEMS Lab Implementation:<\/h3>\n<p>[\/et_pb_text][et_pb_image admin_label=&#8221;CoDEMS Lab Implementation&#8221; _builder_version=&#8221;3.21&#8243; src=&#8221;https:\/\/research.ece.ncsu.edu\/wp-content\/uploads\/sites\/3\/2019\/03\/physical_system.png&#8221; align_last_edited=&#8221;on|desktop&#8221; align_tablet=&#8221;center&#8221; \/][\/et_pb_column][et_pb_column type=&#8221;1_2&#8243;][et_pb_text admin_label=&#8221;CODEMS GEH Implementation Heading&#8221; _builder_version=&#8221;3.21&#8243;]<\/p>\n<h3>CODEMS GEH Implementation:<\/h3>\n<p>[\/et_pb_text][et_pb_image admin_label=&#8221;CODEMS GEH Implementation&#8221; _builder_version=&#8221;3.21&#8243; src=&#8221;https:\/\/research.ece.ncsu.edu\/wp-content\/uploads\/sites\/3\/2019\/03\/IMG_1808.jpg&#8221; align_last_edited=&#8221;on|desktop&#8221; align_tablet=&#8221;center&#8221; \/][\/et_pb_column][\/et_pb_row][et_pb_row][et_pb_column type=&#8221;4_4&#8243;][et_pb_text admin_label=&#8221;Impacts&#8221; _builder_version=&#8221;3.21&#8243;]<\/p>\n<h3>Impacts:<\/h3>\n<p>[\/et_pb_text][et_pb_text admin_label=&#8221;Impacts text&#8221; _builder_version=&#8221;3.21&#8243;]<\/p>\n<p><span style=\"font-weight: 400;\">The proposed CoDEMS distributed control framework is a key enabling technology for integrating the ever-growing DERs. As the distributed controllers maximize the utilization of these DERs and, therefore, reduce the system operation costs, investors are motivated to deploy more residential-scale renewable energy resources and DERs. As a result, the proposed technology breaks the bottleneck of the DER integration and take the U.S. one step further towards the zero-carbon energy future. Furthermore, the proposed collaborative distributed framework fosters the resilience of the distribution grid. The system leverages the controller redundancy to sustain the critical system functionalities under abnormal operating conditions, e.g. climate disasters, device failures, and misbehaving agents.<\/span><\/p>\n<p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row][et_pb_column type=&#8221;4_4&#8243;][et_pb_text admin_label=&#8221;Key Reference Heading&#8221; _builder_version=&#8221;3.21&#8243;]<\/p>\n<h3>Key References:<\/h3>\n<p>[\/et_pb_text][et_pb_text admin_label=&#8221;Key References  text&#8221; _builder_version=&#8221;3.21&#8243;]<\/p>\n<p><span style=\"font-weight: 400;\">[1] Z. Cheng, J. Duan, and M. Chow, \u201cTo Centralize or to Distribute: That Is the Question: A Comparison of Advanced Microgrid Management Systems,\u201d IEEE Industrial Electronics Magazine, vol. 12, no. 1, pp. 6\u201324, Mar. 2018.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">[2] J. Duan and M. Chow, \u201cA Resilient Consensus-Based Distributed Energy Management Algorithm against Data Integrity Attacks,\u201d IEEE Transactions on Smart Grid, pp. 1\u20131, 2018.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">[3] Y. Zhang, N. Rahbari-Asr, J. Duan, and M. Chow, \u201cDay-Ahead Smart Grid Cooperative Distributed Energy Scheduling With Renewable and Storage Integration,\u201d IEEE Transactions on Sustainable Energy, vol. 7, no. 4, pp. 1739\u20131748, Oct. 2016.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">[4] N. Rahbari-Asr, U. Ojha, Z. Zhang, and M. Chow, \u201cIncremental Welfare Consensus Algorithm for Cooperative Distributed Generation\/Demand Response in Smart Grid,\u201d IEEE Transactions on Smart Grid, vol. 5, no. 6, pp. 2836\u20132845, Nov. 2014.<\/span><\/p>\n<p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"<p><div class=\"et_pb_row et_pb_row_0 et_pb_row_empty\">\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t<\/div> Personnel: Mo-Yuen Chow (PI), Ziang Zhang, Wente Zeng, Navid Rahbari Asr, Yuan Zhang, Jie Duan, Alberto Castelo Becerra, and Zheyuan Cheng. Objective: To enhance the microgrid\/smart grid\u2019s scalability, reliability, and resilience, this project aims to develop a collaborative and distributed energy management system (CoDEMS) that can determine globally optimal control commands without the need [&hellip;]<\/p>\n","protected":false},"author":7,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":"","_wp_rev_ctl_limit":""},"class_list":["post-1921","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/research.ece.ncsu.edu\/adac\/wp-json\/wp\/v2\/pages\/1921","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/research.ece.ncsu.edu\/adac\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/research.ece.ncsu.edu\/adac\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/research.ece.ncsu.edu\/adac\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/research.ece.ncsu.edu\/adac\/wp-json\/wp\/v2\/comments?post=1921"}],"version-history":[{"count":5,"href":"https:\/\/research.ece.ncsu.edu\/adac\/wp-json\/wp\/v2\/pages\/1921\/revisions"}],"predecessor-version":[{"id":1983,"href":"https:\/\/research.ece.ncsu.edu\/adac\/wp-json\/wp\/v2\/pages\/1921\/revisions\/1983"}],"wp:attachment":[{"href":"https:\/\/research.ece.ncsu.edu\/adac\/wp-json\/wp\/v2\/media?parent=1921"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}