Dynamic Data Driven Adaptive Simulation Framework for Automated Control in Microgrids
In this paper, we introduce a novel dynamic data driven adaptive simulation framework for the operation and control of microgrids (MGs) that significantly accelerates the real-time computation of the resource allocation, and controls decisions to optimize the operational cost, energy surety, as well...
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| Veröffentlicht in: | IEEE transactions on smart grid 2017-01, Vol.8 (1), p.209-218 |
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| container_title | IEEE transactions on smart grid |
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| creator | Thanos, Aristotelis E. Bastani, Mehrad Celik, Nurcin Chun-Hung Chen |
| description | In this paper, we introduce a novel dynamic data driven adaptive simulation framework for the operation and control of microgrids (MGs) that significantly accelerates the real-time computation of the resource allocation, and controls decisions to optimize the operational cost, energy surety, as well as emissions per MW. The proposed framework includes a database receiving input from electrical and environmental sensors, a fault detection algorithm that discovers liabilities and potential hazards within the MG, an agent-based simulation of the MG system, an optimal computing budget allocation-based control selection algorithm that uses the agent-based simulation to decide the best control design of the MG, and a multiobjective algorithm for optimizing the decisions of the MG given the best control design. For validating our framework, we use the structure of a realistic MG that is simulated using real-historical data. The experiments reveal that the proposed framework significantly reduces the computational burden of a considerably complex multiobjective problem. |
| doi_str_mv | 10.1109/TSG.2015.2464709 |
| format | Article |
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The proposed framework includes a database receiving input from electrical and environmental sensors, a fault detection algorithm that discovers liabilities and potential hazards within the MG, an agent-based simulation of the MG system, an optimal computing budget allocation-based control selection algorithm that uses the agent-based simulation to decide the best control design of the MG, and a multiobjective algorithm for optimizing the decisions of the MG given the best control design. For validating our framework, we use the structure of a realistic MG that is simulated using real-historical data. The experiments reveal that the proposed framework significantly reduces the computational burden of a considerably complex multiobjective problem.</description><subject>Adaptive control</subject><subject>Agent-based simulation</subject><subject>Algorithm design and analysis</subject><subject>Algorithms</subject><subject>Automatic control</subject><subject>autonomous control</subject><subject>Buildings</subject><subject>Computational modeling</subject><subject>Computer simulation</subject><subject>Control design</subject><subject>Data models</subject><subject>Decisions</subject><subject>Distributed generation</subject><subject>Fault detection</subject><subject>Liabilities</subject><subject>microgrids (MGs)</subject><subject>multiobjective optimization</subject><subject>Multiple objective analysis</subject><subject>Operating costs</subject><subject>Optimization</subject><subject>real-time simulation</subject><subject>Resource allocation</subject><subject>Sensors</subject><subject>Simulation</subject><issn>1949-3053</issn><issn>1949-3061</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kM9PwjAUxxujiQS5m3hp4nnY127teiQgaILxAJybbutMka2z6zT895ZAeJf3PXy_78cHoUcgUwAiX7ab1ZQSyKY05akg8gaNQKYyYYTD7VVn7B5N-n5PYjHGOJUjtFscW93YEi900Hjh7a9p8azSXYgKb2wzHHSwrsVLrxvz5_w3rp3HsyG4RgdT4blrg3cHbFv8YUvvvryt-gd0V-tDbyaXPka75et2_pasP1fv89k6KamEkMiiqEWVZwUTQmguKWMZ05xDTiRIUwmZFlDqoqgIq2SeF_HHEgTNBKGCipSN0fN5bufdz2D6oPZu8G1cqSDPOMuBchld5OyK5_W9N7XqvG20Pyog6sRPRX7qxE9d-MXI0zlijTFXu6BAKAX2D_sEaio</recordid><startdate>20170101</startdate><enddate>20170101</enddate><creator>Thanos, Aristotelis E.</creator><creator>Bastani, Mehrad</creator><creator>Celik, Nurcin</creator><creator>Chun-Hung Chen</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| source | IEEE Electronic Library (IEL) |
| subjects | Adaptive control Agent-based simulation Algorithm design and analysis Algorithms Automatic control autonomous control Buildings Computational modeling Computer simulation Control design Data models Decisions Distributed generation Fault detection Liabilities microgrids (MGs) multiobjective optimization Multiple objective analysis Operating costs Optimization real-time simulation Resource allocation Sensors Simulation |
| title | Dynamic Data Driven Adaptive Simulation Framework for Automated Control in Microgrids |
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